diff --git a/.agents/skills/alert-manager/SKILL.md b/.agents/skills/alert-manager/SKILL.md deleted file mode 100644 index 3cece47ca5..0000000000 --- a/.agents/skills/alert-manager/SKILL.md +++ /dev/null @@ -1,337 +0,0 @@ ---- -name: alert-manager -description: 'Configure SEO alerts for ranking drops, traffic changes, technical issues, competitor movements. SEO预警/排名监控' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when setting up monitoring alerts for rankings, traffic, backlinks, technical issues, or AI visibility changes." -argument-hint: " [metric]" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "low" - tags: - - seo - - geo - - seo-alerts - - ranking-alerts - - traffic-monitoring - - competitor-alerts - - automated-monitoring - - anomaly-detection - - SEO预警 - - SEOアラート - - SEO알림 - - alertas-seo - triggers: - # EN-formal - - "set up SEO alerts" - - "monitor rankings" - - "ranking notifications" - - "traffic alerts" - - "competitor alerts" - - "automated monitoring" - # EN-casual - - "notify me when rankings drop" - - "alert me if rankings drop" - - "notify me of traffic changes" - - "watch competitor changes" - - "watch my keywords for changes" - - "alert me about changes" - # EN-question - - "how to monitor my rankings" - - "how to set up SEO alerts" - # ZH-pro - - "SEO预警" - - "排名监控" - - "流量报警" - - "竞品变动提醒" - # ZH-casual - - "排名掉了提醒我" - - "流量异常" - - "有变化通知我" - # JA - - "SEOアラート" - - "ランキング監視" - # KO - - "SEO 알림" - - "순위 모니터링" - # ES - - "alertas SEO" - - "monitoreo de rankings" - # PT - - "alertas de SEO" - # Misspellings - - "SEO allerts" ---- - -# Alert Manager - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This monitoring skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -Sets up proactive monitoring alerts for critical SEO and GEO metrics. Triggers notifications when rankings drop, traffic changes significantly, technical issues occur, or competitors make moves. - -**System role**: Monitoring layer skill. It turns performance changes into deltas, alerts, and next actions. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs time-aware change detection, escalation, or stakeholder-ready visibility. - -- Setting up SEO monitoring systems -- Creating ranking drop alerts -- Monitoring technical SEO health -- Tracking competitor movements -- Alerting on content performance changes -- Monitoring GEO/AI visibility changes -- Setting up brand mention alerts - -## What This Skill Does - -1. **Alert Configuration**: Sets up custom alert thresholds -2. **Multi-Metric Monitoring**: Tracks rankings, traffic, technical issues -3. **Threshold Management**: Defines when alerts trigger -4. **Priority Classification**: Categorizes alerts by severity -5. **Notification Setup**: Configures how alerts are delivered -6. **Alert Response Plans**: Creates action plans for each alert type -7. **Alert History**: Tracks alert patterns over time - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Set Up Alerts - -``` -Set up SEO monitoring alerts for [domain] -``` - -``` -Create ranking drop alerts for my top 20 keywords -``` - -### Configure Specific Alerts - -``` -Alert me when [specific condition] -``` - -``` -Set up competitor monitoring for [competitor domains] -``` - -### Review Alert System - -``` -Review and optimize my current SEO alerts -``` - -## Skill Contract - -**Expected output**: a delta summary, alert/report output, and a short handoff summary ready for `memory/monitoring/`. - -- **Reads**: current metrics, previous baselines, alert thresholds, and reporting context from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing monitoring deliverable plus a reusable summary that can be stored under `memory/monitoring/`. -- **Promotes**: significant changes, confirmed anomalies, and follow-up actions to `memory/open-loops.md` and `memory/decisions.md`. -- **Next handoff**: use the `Next Best Skill` below when a change needs action. - -## Data Sources - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~SEO tool + ~~search console + ~~web crawler connected:** -Automatically monitor real-time metric feeds for ranking changes via ~~SEO tool API, indexing and coverage alerts from ~~search console, and technical health alerts from ~~web crawler. Set up automated threshold-based alerts with notification delivery. - -**With manual data only:** -Ask the user to provide: -1. Current baseline metrics for alert thresholds (rankings, traffic, backlinks) -2. Critical keywords or pages to monitor -3. Alert priority levels and notification preferences -4. Historical data to understand normal fluctuation ranges -5. Manual reporting on metric changes when they check their tools - -Proceed with the alert configuration using provided parameters. User will need to manually check metrics and report changes for alert triggers. - -## Instructions - -When a user requests alert setup: - -1. **Define Alert Categories** - - ```markdown - ## SEO Alert System Configuration - - **Domain**: [domain] - **Configured Date**: [date] - - ### Alert Categories - - | Category | Description | Typical Urgency | - |----------|-------------|-----------------| - | Ranking Alerts | Keyword position changes | Medium-High | - | Traffic Alerts | Organic traffic fluctuations | High | - | Technical Alerts | Site health issues | Critical | - | Backlink Alerts | Link profile changes | Medium | - | Competitor Alerts | Competitor movements | Low-Medium | - | GEO Alerts | AI visibility changes | Medium | - | Brand Alerts | Brand mentions and reputation | Medium | - ``` - -2. **Configure Alert Rules by Category** - - For each relevant category (Rankings, Traffic, Technical, Backlinks, Competitors, GEO/AI, Brand), define alert name, trigger condition, threshold, and priority level. - - > **Reference**: See [references/alert-configuration-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/alert-manager/references/alert-configuration-templates.md) for complete alert tables, threshold examples, and response plan templates for all 7 categories. - -3. **Define Alert Response Plans** - - Map each priority level (Critical, High, Medium, Low) to a response time and immediate action steps. - -4. **Set Up Alert Delivery** - - Configure notification channels (Email, SMS, Slack), recipient routing by role, suppression rules (duplicate cooldown, maintenance windows), and escalation paths. - -5. **Create Alert Summary** - - ```markdown - # SEO Alert System Summary - - **Domain**: [domain] - **Configured**: [date] - **Total Active Alerts**: [X] - - ## Alert Count by Category - - | Category | Critical | High | Medium | Low | Total | - |----------|----------|------|--------|-----|-------| - | Rankings | [X] | [X] | [X] | [X] | [X] | - | Traffic | [X] | [X] | [X] | [X] | [X] | - | Technical | [X] | [X] | [X] | [X] | [X] | - | Backlinks | [X] | [X] | [X] | [X] | [X] | - | Competitors | [X] | [X] | [X] | [X] | [X] | - | GEO | [X] | [X] | [X] | [X] | [X] | - | **Total** | **[X]** | **[X]** | **[X]** | **[X]** | **[X]** | - - ## Quick Reference - - ### If You Get a Critical Alert - - 1. Don't panic - 2. Check alert details - 3. Follow response plan - 4. Document actions taken - 5. Update stakeholders - - ### Weekly Alert Review Checklist - - - [ ] Review all alerts triggered - - [ ] Identify patterns - - [ ] Adjust thresholds if needed - - [ ] Update response plans - - [ ] Clean up false positives - ``` - -## Validation Checkpoints - -### Input Validation -- [ ] Alert thresholds are based on realistic baseline data -- [ ] Critical keywords and pages clearly identified -- [ ] Response plans defined for each alert priority level -- [ ] Notification channels configured with appropriate recipients - -### Output Validation -- [ ] Every metric cites its data source and collection date -- [ ] Alert thresholds account for normal metric fluctuations -- [ ] Response plans are specific and time-bound -- [ ] Source of each alert trigger clearly stated (~~SEO tool API alert, ~~search console notification, ~~web crawler alert, or manual user check) - -## Example - -**User**: "Set up ranking drop alerts for my top keywords" - -**Output**: - -```markdown -## Ranking Alert Configuration - -### Critical Keywords (Immediate Alert) - -| Keyword | Current | Alert If | Priority | -|---------|---------|----------|----------| -| best project management software | 2 | Drops to 5+ | 🔴 Critical | -| project management tools | 4 | Drops to 8+ | 🔴 Critical | -| team collaboration software | 1 | Any drop | 🔴 Critical | - -### Important Keywords (Same-Day Alert) - -| Keyword | Current | Alert If | Priority | -|---------|---------|----------|----------| -| agile project management | 7 | Drops out of top 10 | 🔴 High | -| kanban software | 9 | Drops out of top 10 | 🔴 High | - -### Alert Response Plan - -**If Critical Keyword Drops**: -1. Check if page is still indexed (site:url) -2. Look for algorithm update announcements -3. Analyze what changed in SERP -4. Review competitor ranking changes -5. Check for technical issues on page -6. Create recovery action plan within 24 hours - -**Notification**: Email + Slack to SEO team immediately -``` - -## Tips for Success - -1. **Start simple** - Don't create too many alerts initially -2. **Tune thresholds** - Adjust based on normal fluctuations -3. **Avoid alert fatigue** - Too many alerts = ignored alerts -4. **Document response plans** - Know what to do when alerts fire -5. **Review regularly** - Alerts need maintenance as your SEO matures -6. **Include positive alerts** - Track wins, not just problems - -## Alert Threshold Quick Reference - -| Metric | Warning | Critical | Frequency | -|--------|---------|----------|-----------| -| Organic traffic | -15% WoW | -30% WoW | Daily | -| Keyword positions | >3 position drop | >5 position drop | Daily | -| Pages indexed | -5% change | -20% change | Weekly | -| Crawl errors | >10 new/day | >50 new/day | Daily | -| Core Web Vitals | "Needs Improvement" | "Poor" | Weekly | -| Backlinks lost | >5% in 1 week | >15% in 1 week | Weekly | -| AI citation loss | Any key query | >20% queries | Weekly | -| Security issues | Any detected | Any detected | Daily | - -> **Reference**: See [references/alert-threshold-guide.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/alert-manager/references/alert-threshold-guide.md) for baseline establishment, threshold setting methodology, fatigue prevention, escalation paths, and response playbooks. - - -### Save Results - -After delivering monitoring data or reports to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/monitoring/YYYY-MM-DD-.md` containing: -- One-line headline finding or status change -- Top 3-5 actionable items -- Open loops or anomalies requiring follow-up -- Source data references - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - -## Reference Materials - -- [Alert Threshold Guide](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/alert-manager/references/alert-threshold-guide.md) — Recommended thresholds by metric, fatigue prevention strategies, and escalation path templates - -## Next Best Skill - -- **Primary**: [rank-tracker](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/rank-tracker/SKILL.md) — pair alerts with a baseline measurement workflow. diff --git a/.agents/skills/alert-manager/references/alert-configuration-templates.md b/.agents/skills/alert-manager/references/alert-configuration-templates.md deleted file mode 100644 index c962aed99a..0000000000 --- a/.agents/skills/alert-manager/references/alert-configuration-templates.md +++ /dev/null @@ -1,293 +0,0 @@ -# Alert Configuration Templates - -Detailed alert configuration templates for each alert category. Use these templates when setting up a new alert system for a domain. - ---- - -## Ranking Alerts - -### Position Drop Alerts - -| Alert Name | Condition | Threshold | Priority | Action | -|------------|-----------|-----------|----------|--------| -| Critical Drop | Any top 3 keyword drops 5+ positions | Position change >=5 | Critical | Immediate investigation | -| Major Drop | Top 10 keyword drops out of top 10 | Position >10 | High | Same-day review | -| Moderate Drop | Any keyword drops 10+ positions | Position change >=10 | Medium | Weekly review | -| Competitor Overtake | Competitor passes you for key term | Comp position < yours | Medium | Analysis needed | - -### Position Improvement Alerts - -| Alert Name | Condition | Threshold | Priority | -|------------|-----------|-----------|----------| -| New Top 3 | Keyword enters top 3 | Position <=3 | Positive | -| Page 1 Entry | Keyword enters top 10 | Position <=10 | Positive | -| Significant Climb | Keyword improves 10+ positions | Change >=+10 | Positive | - -### SERP Feature Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Snippet Lost | Lost featured snippet ownership | High | -| Snippet Won | Won new featured snippet | Positive | -| AI Overview Change | Appeared/disappeared in AI Overview | Medium | - -### Keywords to Monitor - -| Keyword | Current Rank | Alert Threshold | Priority | -|---------|--------------|-----------------|----------| -| [keyword 1] | [X] | Drop >=3 | Critical | -| [keyword 2] | [X] | Drop >=5 | High | -| [keyword 3] | [X] | Drop >=10 | Medium | - ---- - -## Traffic Alerts - -### Traffic Decline Alerts - -| Alert Name | Condition | Threshold | Priority | -|------------|-----------|-----------|----------| -| Traffic Crash | Day-over-day decline | >=50% drop | Critical | -| Significant Drop | Week-over-week decline | >=30% drop | High | -| Moderate Decline | Month-over-month decline | >=20% drop | Medium | -| Trend Warning | 3 consecutive weeks decline | Any decline | Medium | - -### Traffic Anomaly Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Traffic Spike | Unusual increase | Investigate | -| Zero Traffic | Page receiving 0 visits | High | -| Bot Traffic | Unusual traffic pattern | Medium | - -### Page-Level Alerts - -| Page Type | Alert Condition | Priority | -|-----------|-----------------|----------| -| Homepage | Any 20%+ decline | Critical | -| Top 10 pages | Any 30%+ decline | High | -| Conversion pages | Any 25%+ decline | High | -| Blog posts | Any 40%+ decline | Medium | - -### Conversion Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Conversion Drop | Organic conversions down 30%+ | Critical | -| CVR Decline | Conversion rate drops 20%+ | High | - ---- - -## Technical SEO Alerts - -### Critical Technical Alerts - -| Alert Name | Condition | Priority | Response Time | -|------------|-----------|----------|---------------| -| Site Down | HTTP 5xx errors | Critical | Immediate | -| SSL Expiry | Certificate expiring in 14 days | Critical | Same day | -| Robots.txt Block | Important pages blocked | Critical | Same day | -| Index Dropped | Pages dropping from index | Critical | Same day | - -### Crawl & Index Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Crawl Errors Spike | Errors increase 50%+ | High | -| New 404 Pages | 404 errors on important pages | Medium | -| Redirect Chains | 3+ redirect hops detected | Medium | -| Duplicate Content | New duplicates detected | Medium | -| Index Coverage Drop | Indexed pages decline 10%+ | High | - -### Performance Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Core Web Vitals Fail | CWV drops to "Poor" | High | -| Page Speed Drop | Load time increases 50%+ | Medium | -| Mobile Issues | Mobile usability errors | High | - -### Security Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Security Issue | GSC security warning | Critical | -| Manual Action | Google manual action | Critical | -| Malware Detected | Site flagged for malware | Critical | - ---- - -## Backlink Alerts - -### Link Loss Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| High-Value Link Lost | DA 70+ link removed | High | -| Multiple Links Lost | 10+ links lost in a day | Medium | -| Referring Domain Lost | Lost entire domain's links | Medium | - -### Link Gain Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| High-Value Link | New DA 70+ link | Positive | -| Suspicious Links | Many low-quality links | Review | -| Negative SEO | Spam link attack pattern | High | - -### Link Profile Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Toxic Score Increase | Toxic score up 20%+ | High | -| Anchor Over-Optimization | Exact match anchors >30% | Medium | - ---- - -## Competitor Monitoring Alerts - -### Ranking Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Competitor Overtake | Competitor passes you | Medium | -| Competitor Top 3 | Competitor enters top 3 on key term | Medium | -| Competitor Content | Competitor publishes on your topic | Info | - -### Activity Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| New Backlinks | Competitor gains high-DA link | Info | -| Content Update | Competitor updates ranking content | Info | -| New Content | Competitor publishes new content | Info | - -### Competitors to Monitor - -| Competitor | Domain | Monitor Keywords | Alert Priority | -|------------|--------|------------------|----------------| -| [Competitor 1] | [domain] | [X] keywords | High | -| [Competitor 2] | [domain] | [X] keywords | Medium | -| [Competitor 3] | [domain] | [X] keywords | Low | - ---- - -## GEO (AI Visibility) Alerts - -### AI Citation Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Citation Lost | Lost AI Overview citation | Medium | -| Citation Won | New AI Overview citation | Positive | -| Citation Position Drop | Dropped from 1st to 3rd+ source | Medium | -| New AI Overview | AI Overview appears for tracked keyword | Info | - -### GEO Trend Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Citation Rate Drop | AI citation rate drops 20%+ | High | -| GEO Competitor | Competitor cited where you're not | Medium | - ---- - -## Brand Monitoring Alerts - -### Mention Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Brand Mention | New brand mention online | Info | -| Negative Mention | Negative sentiment mention | High | -| Review Alert | New review on key platforms | Medium | -| Unlinked Mention | Brand mention without link | Opportunity | - -### Reputation Alerts - -| Alert Name | Condition | Priority | -|------------|-----------|----------| -| Review Rating Drop | Average rating drops | High | -| Negative Press | Negative news article | High | -| Competitor Comparison | Named in competitor comparison | Medium | - ---- - -## Alert Response Plans - -### Critical Alert Response - -**Response Time**: Immediate (within 1 hour) - -| Alert Type | Immediate Actions | -|------------|-------------------| -| Site Down | 1. Check server status 2. Contact hosting 3. Check DNS | -| Traffic Crash | 1. Check for algorithm update 2. Review GSC errors 3. Check competitors | -| Manual Action | 1. Review GSC message 2. Identify issue 3. Begin remediation | -| Critical Rank Drop | 1. Check if page indexed 2. Review SERP 3. Analyze competitors | - -### High Priority Response - -**Response Time**: Same day - -| Alert Type | Actions | -|------------|---------| -| Major Rank Drops | Analyze cause, create recovery plan | -| Traffic Decline | Investigate source, check technical issues | -| Backlink Loss | Attempt recovery outreach | -| CWV Failure | Diagnose and fix performance issues | - -### Medium Priority Response - -**Response Time**: Within 48 hours - -| Alert Type | Actions | -|------------|---------| -| Moderate Rank Changes | Monitor trend, plan content updates | -| Competitor Movement | Analyze competitor changes | -| New 404s | Set up redirects, update internal links | - -### Low Priority - -**Response Time**: Weekly review - -| Alert Type | Actions | -|------------|---------| -| Positive Changes | Document wins, understand cause | -| Info Alerts | Log for trend analysis | - ---- - -## Alert Notification Setup - -### Notification Channels - -| Priority | Channels | Frequency | -|----------|----------|-----------| -| Critical | Email + SMS + Slack | Immediate | -| High | Email + Slack | Immediate | -| Medium | Email + Slack | Daily digest | -| Low | Email | Weekly digest | - -### Alert Recipients - -| Role | Critical | High | Medium | Low | -|------|----------|------|--------|-----| -| SEO Manager | Yes | Yes | Yes | Yes | -| Dev Team | Yes | Yes (tech only) | No | No | -| Marketing Lead | Yes | Yes | No | No | -| Executive | Yes | No | No | No | - -### Alert Suppression - -- Suppress duplicate alerts for 24 hours -- Don't alert on known issues (maintenance windows) -- Batch low-priority alerts into digests - -### Alert Escalation - -| If No Response In | Escalate To | -|-------------------|-------------| -| 1 hour (Critical) | SEO Manager -> Director | -| 4 hours (High) | Team Lead -> Manager | -| 24 hours (Medium) | Team -> Lead | diff --git a/.agents/skills/alert-manager/references/alert-threshold-guide.md b/.agents/skills/alert-manager/references/alert-threshold-guide.md deleted file mode 100644 index 9b4099872e..0000000000 --- a/.agents/skills/alert-manager/references/alert-threshold-guide.md +++ /dev/null @@ -1,377 +0,0 @@ -# Alert Threshold Guide - -Complete reference for configuring SEO/GEO alert thresholds. Covers baseline establishment, threshold setting methodology, tuning process, alert routing configuration, notification channel setup, and response playbooks for each alert type. - ---- - -## 1. Baseline Establishment Process - -Before setting any alert thresholds, you must establish a baseline that represents normal metric behavior for your site. Without a baseline, you will either set thresholds too tight (causing alert fatigue) or too loose (missing real problems). - -### Baseline Collection Timeline - -| Metric Category | Minimum Baseline Period | Ideal Baseline Period | Why | -|----------------|------------------------|----------------------|-----| -| Organic traffic | 4 weeks | 8-12 weeks | Accounts for weekly cycles and monthly patterns | -| Keyword rankings | 2-4 weeks | 4-8 weeks | Rankings fluctuate daily; need to establish normal range | -| Backlink metrics | 4 weeks | 8 weeks | Link acquisition is lumpy; need to see natural cadence | -| Technical metrics | 2 weeks | 4 weeks | Most technical metrics are relatively stable | -| Core Web Vitals | 4 weeks (28-day rolling) | 8 weeks | CrUX data is 28-day rolling average | -| AI citations | 4 weeks | 8 weeks | AI answer composition changes frequently | - -### Baseline Data Collection Steps - -| Step | Action | Output | -|------|--------|--------| -| 1 | Record daily metric values for the baseline period | Raw data spreadsheet | -| 2 | Calculate mean (average) for each metric | Central tendency | -| 3 | Calculate standard deviation for each metric | Normal variation range | -| 4 | Identify outliers (values > 2 standard deviations from mean) | Anomaly list | -| 5 | Remove known outliers (holidays, outages, one-time events) | Clean baseline | -| 6 | Recalculate mean and standard deviation on clean data | Final baseline values | -| 7 | Document seasonal patterns if baseline covers enough time | Seasonal adjustment notes | - -### Baseline Metrics to Record - -| Metric | Daily | Weekly | Monthly | -|--------|-------|--------|---------| -| Organic sessions | Record | Calculate WoW % change | Calculate MoM % change | -| Keyword positions (top 20) | Record | Calculate average movement | Calculate net position change | -| Keywords in top 10 | Record | Calculate weekly count | Calculate monthly trend | -| Crawl errors | Record | Calculate weekly new errors | Calculate monthly trend | -| New backlinks | N/A | Record weekly count | Calculate monthly velocity | -| Lost backlinks | N/A | Record weekly count | Calculate monthly velocity | -| Core Web Vitals | N/A | Record from CrUX | Calculate monthly trend | -| AI citations | N/A | Record weekly count | Calculate monthly trend | -| Pages indexed | N/A | Record weekly count | Calculate monthly change | -| Server response time | Record | Calculate weekly average | Calculate monthly average | - ---- - -## 2. Threshold Setting Methodology - -### The Standard Deviation Method - -For most metrics, set thresholds based on standard deviations from your baseline mean. - -| Threshold Level | Formula | Meaning | -|----------------|---------|---------| -| **Info** | Mean +/- 1 standard deviation | Normal fluctuation range; log but do not alert | -| **Warning** | Mean +/- 1.5 standard deviations | Unusual but not necessarily problematic | -| **Critical** | Mean +/- 2 standard deviations | Statistically significant anomaly; investigate | -| **Emergency** | Mean +/- 3 standard deviations | Extreme anomaly; immediate action required | - -**Example calculation:** - -``` -Metric: Daily organic sessions -Baseline mean: 10,000 sessions/day -Standard deviation: 800 sessions/day - -Info range: 8,200 - 11,800 (normal) -Warning: < 8,800 or > 11,200 -Critical: < 8,400 or > 11,600 -Emergency: < 7,600 or > 12,400 -``` - -### The Percentage Method - -For metrics where standard deviation is not practical, use percentage-based thresholds. - -| Metric | Warning Threshold | Critical Threshold | Comparison Period | -|--------|------------------|-------------------|-------------------| -| Organic traffic | -15% vs. comparison | -30% vs. comparison | Week over week | -| Keyword positions | >3 position average drop | >5 position average drop | Week over week | -| Pages indexed | -5% change | -20% change | Week over week | -| Referring domains | -5% loss | -15% loss | Month over month | -| Crawl error rate | >2x baseline rate | >5x baseline rate | Day over day | -| Conversion rate | -20% drop | -40% drop | Week over week | - -### The Absolute Value Method - -For binary or count-based metrics, use absolute thresholds. - -| Metric | Warning Threshold | Critical Threshold | -|--------|------------------|-------------------| -| New crawl errors | >10 new errors/day | >50 new errors/day | -| Server 5xx errors | Any occurrence | >5 occurrences/hour | -| Security issues | N/A | Any detection | -| Manual penalties | N/A | Any notification | -| SSL certificate expiry | <30 days to expiry | <7 days to expiry | -| Robots.txt changes | Any unexpected change | Key pages blocked | - ---- - -## 3. Threshold Configuration by Metric Category - -### Traffic Thresholds - -| Metric | Comparison | Warning | Critical | Emergency | -|--------|-----------|---------|----------|-----------| -| Total organic sessions | WoW | -15% | -30% | -50% | -| Total organic sessions | DoD | -25% (weekday) | -40% | Site appears down | -| Non-brand sessions | WoW | -20% | -35% | -50% | -| Organic conversions | WoW | -20% | -40% | -60% | -| Organic revenue | WoW | -15% | -30% | -50% | -| Bounce rate | WoW | +10pp | +20pp | +30pp | -| Page-level traffic (top 10 pages) | WoW | -25% | -40% | -60% | - -**Note:** Day-over-day traffic thresholds need day-of-week adjustment. Monday traffic typically differs from Saturday traffic. Compare Monday to Monday, not Monday to Sunday. - -### Ranking Thresholds - -| Metric | Scope | Warning | Critical | -|--------|-------|---------|----------| -| Position change (Tier 1 keywords) | Individual keyword | Drop >= 3 | Drop >= 5 | -| Position change (Tier 2 keywords) | Individual keyword | Drop >= 5 | Drop >= 10 | -| Position change (Tier 3 keywords) | Individual keyword | Drop >= 10 | Drop off page 3 | -| Average position (all keywords) | Aggregate | +2.0 (worsening) | +5.0 (worsening) | -| Keywords in top 10 | Count | -10% of count | -20% of count | -| Keywords in top 3 | Count | Any decrease | -3 or more | -| Brand keyword position | Individual | Any drop from #1 | Drops below #3 | -| Featured snippet lost | Individual | Any loss | Loss of 3+ snippets | - -### Technical Thresholds - -| Metric | Warning | Critical | Emergency | -|--------|---------|----------|-----------| -| New 4xx errors | >5/day | >20/day | >100/day | -| New 5xx errors | >1/day | >5/day | >20/day | -| Crawl rate change | -30% vs. baseline | -60% vs. baseline | Near-zero crawl | -| Index coverage drop | -5% | -15% | -30% | -| Average server response time | >500ms | >1000ms | >2000ms | -| LCP (mobile) | Moves to "Needs Improvement" | Moves to "Poor" | >6s | -| CLS | >0.1 | >0.25 | >0.5 | -| INP | >200ms | >500ms | >1000ms | -| Robots.txt change | Any unexpected edit | Pages blocked | Entire site blocked | -| Sitemap errors | New errors | Sitemap inaccessible | Sitemap returning 5xx | - -### Backlink Thresholds - -| Metric | Warning | Critical | -|--------|---------|----------| -| Referring domains lost (weekly) | >5% of total | >15% of total | -| High-authority link lost (DR 60+) | Any loss | Loss of 3+ in one week | -| Toxic link spike | >10 new toxic links/week | >50 new toxic links/week | -| Anchor text over-optimization | Exact match reaches 20% | Exact match reaches 30% | -| Negative SEO pattern | Unusual link velocity from low-DR sites | Massive spam link spike | - -### GEO / AI Visibility Thresholds - -| Metric | Warning | Critical | -|--------|---------|----------| -| AI citation rate | Drops 10+ percentage points | Drops below 10% | -| Key query citation lost | Any Tier 1 query | 3+ Tier 1 queries | -| Citation position degradation | Average position worsens by 2+ | Dropped from citations entirely | -| Competitor gains citation you lost | 1 instance | Pattern across queries | - ---- - -## 4. Alert Routing Configuration - -### Routing Matrix - -| Alert Category | P0 (Emergency) | P1 (Urgent) | P2 (Important) | P3 (Monitor) | -|---------------|----------------|-------------|----------------|--------------| -| **Traffic** | SEO Lead + Eng Manager + VP | SEO Lead + Marketing Mgr | SEO Team | Weekly digest | -| **Rankings** | SEO Lead + Content Lead | SEO Team | SEO Team | Weekly digest | -| **Technical** | SEO Lead + Eng Lead + DevOps | SEO Lead + Eng Team | SEO Team + Eng | Weekly digest | -| **Backlinks** | SEO Lead | SEO Team | SEO Team | Weekly digest | -| **Competitor** | N/A | SEO Lead | SEO Team | Weekly digest | -| **GEO/AI** | SEO Lead + Content Lead | SEO Team | SEO Team | Weekly digest | -| **Security** | SEO Lead + Eng Manager + VP + Legal | All above | N/A | N/A | - -### Role-Based Alert Filtering - -| Role | Receives | Does Not Receive | -|------|---------|-----------------| -| SEO Lead | All P0, P1, P2 alerts | P3 (weekly digest only) | -| SEO Analyst | P1, P2 in their area | P0 (escalation only), other areas | -| Content Lead | P0-P1 ranking + GEO alerts | Technical alerts, backlink alerts | -| Engineering Lead | P0-P1 technical alerts | Ranking, content, backlink alerts | -| Marketing VP | P0 only | P1-P3 (receives weekly summary) | -| DevOps | P0 technical + security | All non-infrastructure alerts | - ---- - -## 5. Notification Channel Setup - -### Channel Selection by Priority - -| Priority | Primary Channel | Secondary Channel | Escalation Channel | -|----------|----------------|-------------------|-------------------| -| P0 | SMS + Phone call | Slack (#seo-emergencies) | PagerDuty / on-call rotation | -| P1 | Slack (#seo-alerts) | Email | SMS (if not acknowledged in 4h) | -| P2 | Email | Slack (#seo-daily) | Auto-escalate to P1 after 1 week | -| P3 | Weekly digest email | Dashboard | Auto-escalate to P2 after 1 month | - -### Notification Content Requirements - -Every alert notification should include: - -| Field | Required | Example | -|-------|----------|---------| -| Alert name | Yes | "Critical Ranking Drop" | -| Priority level | Yes | "P0 — Emergency" | -| Metric affected | Yes | "Position for 'project management software'" | -| Current value | Yes | "Position 12" | -| Previous value | Yes | "Position 3 (yesterday)" | -| Threshold breached | Yes | "Dropped >5 positions" | -| Timestamp | Yes | "2025-01-15 09:00 UTC" | -| Affected URL | Yes (if applicable) | "yoursite.com/blog/pm-guide" | -| Quick action link | Yes | Link to relevant tool/dashboard | -| Suggested first step | Recommended | "Check if page is still indexed: site:yoursite.com/blog/pm-guide" | - -### Notification Suppression Rules - -| Rule | Configuration | Reason | -|------|-------------|--------| -| Duplicate cooldown | Do not re-alert on same metric for 24 hours | Prevent alert storms | -| Maintenance window | Suppress non-security alerts during scheduled maintenance | Avoid known-cause alerts | -| Weekend adjustment | Increase traffic thresholds by 20% on weekends | Weekend traffic naturally lower | -| Holiday adjustment | Suppress traffic alerts on major holidays | Known seasonal impact | -| Recovery auto-close | Auto-close alert if metric returns to normal within 48h | Reduce stale alerts | -| Batch related alerts | Group multiple ranking drops into single "Ranking Alert" | Reduce notification volume | - ---- - -## 6. Threshold Tuning Guide - -### When to Tune Thresholds - -| Signal | Action | -|--------|--------| -| Too many false positives (>30% of alerts are noise) | Widen thresholds by 0.5 standard deviations | -| Missed a real problem | Tighten the specific threshold that should have caught it | -| Seasonal change approaching | Adjust baselines for known seasonal patterns | -| Major site change (redesign, migration) | Re-establish baseline from scratch (2-4 week observation) | -| New competitor enters market | Add competitor monitoring, adjust ranking sensitivity | -| After algorithm update | Let metrics stabilize for 2-4 weeks, then recalibrate | - -### Monthly Threshold Review Checklist - -| Check | Action | -|-------|--------| -| Review all alerts fired in the past month | Count true positives vs. false positives | -| Calculate false positive rate | If >30%, thresholds are too tight | -| Check for missed events | If a real issue was not alerted, threshold is too loose | -| Review metric baselines | Recalculate mean and standard deviation with latest data | -| Adjust seasonal baselines | Incorporate seasonal patterns from year-over-year data | -| Update keyword tiers | Promote/demote keywords based on current business priority | -| Verify notification routing | Confirm all recipients are still in the correct roles | -| Test alert delivery | Send a test alert through each channel to verify delivery | - -### Threshold Evolution Over Time - -| Site Maturity | Threshold Approach | Rationale | -|-------------|-------------------|-----------| -| New site (0-6 months) | Wide thresholds, few alerts | Metrics are volatile; avoid noise | -| Growing (6-18 months) | Moderate thresholds, expand coverage | Enough data for meaningful baselines | -| Established (18+ months) | Tight thresholds, comprehensive | Stable baselines, can detect subtle changes | -| Post-migration | Reset to wide, re-tighten over 4-8 weeks | Old baselines are invalid | - ---- - -## 7. Playbook Templates by Alert Type - -### Playbook: Organic Traffic Emergency (P0) - -**Trigger:** Organic traffic drops >50% day-over-day - -| Step | Time | Action | Tool | -|------|------|--------|------| -| 1 | 0 min | Verify site is accessible from multiple locations | Manual browser check, uptime monitor | -| 2 | 5 min | Check Google Search Status Dashboard for outages | Google Status Dashboard | -| 3 | 10 min | Check Search Console for manual actions or security issues | ~~search console | -| 4 | 15 min | Check robots.txt for accidental blocking | Direct URL check | -| 5 | 20 min | Check for noindex tags added to key pages | Crawl or manual page inspection | -| 6 | 30 min | Review recent deployments or CMS changes | Deploy log, git history | -| 7 | 45 min | Check server logs for unusual patterns | Server access logs | -| 8 | 60 min | If unresolved, escalate to Engineering Manager | Slack/phone | - -### Playbook: Security Alert (P0) - -**Trigger:** Google Search Console security issue or manual action - -| Step | Time | Action | -|------|------|--------| -| 1 | 0 min | Read the exact message in Search Console | -| 2 | 5 min | Notify Engineering Manager and VP Marketing | -| 3 | 15 min | Scan site for malware or injected content | -| 4 | 30 min | If compromised: take affected pages offline, rotate all credentials | -| 5 | 1 hour | Identify attack vector and patch vulnerability | -| 6 | 2 hours | Clean all affected pages, submit for re-review | -| 7 | 24 hours | Verify resolution in Search Console | -| 8 | 1 week | Post-incident review and security hardening | - -### Playbook: Algorithm Update Impact (P1-P2) - -**Trigger:** Confirmed Google algorithm update + ranking/traffic changes - -| Step | Time | Action | -|------|------|--------| -| 1 | Day 0 | Confirm update via Google Search Status Dashboard or official channels | -| 2 | Day 0 | Document pre-update baseline metrics (rankings, traffic, visibility) | -| 3 | Day 1-3 | Monitor daily — do not make changes while update is rolling out | -| 4 | Day 7 | First analysis: which pages/keywords improved, which declined | -| 5 | Day 7 | Analyze pattern: content quality? link profile? technical? YMYL? | -| 6 | Day 14 | Develop action plan based on analysis | -| 7 | Day 14-60 | Implement improvements (content quality, E-E-A-T signals, technical fixes) | -| 8 | Next update | Re-evaluate impact after next core update | - -### Playbook: Backlink Attack / Negative SEO (P1) - -**Trigger:** Unusual spike in low-quality backlinks (>100 new links from spam domains in one week) - -| Step | Time | Action | -|------|------|--------| -| 1 | Day 0 | Verify the spike in ~~link database | -| 2 | Day 0 | Identify the pattern (same anchor text? same link network? same country?) | -| 3 | Day 1 | Export all new toxic links | -| 4 | Day 1 | Create disavow file with identified spam domains | -| 5 | Day 2 | Upload disavow to Google Search Console | -| 6 | Day 2 | Document the attack pattern for future reference | -| 7 | Day 7 | Re-check for continued spam link activity | -| 8 | Day 14 | Verify disavow processed, monitor rankings for impact | - -### Playbook: Core Web Vitals Degradation (P2) - -**Trigger:** Any CWV metric moves from "Good" to "Needs Improvement" or "Poor" - -| Step | Time | Action | -|------|------|--------| -| 1 | Day 0 | Identify which metric degraded and which page groups are affected | -| 2 | Day 1 | Run PageSpeed Insights on representative pages | -| 3 | Day 1 | Check recent deployments for potential cause (new scripts, images, layout changes) | -| 4 | Day 2 | Create engineering ticket with diagnosis and fix recommendations | -| 5 | Day 3-14 | Engineering implements fix | -| 6 | Day 14 | Verify improvement in lab data (PageSpeed Insights) | -| 7 | Day 42 | Verify improvement in field data (CrUX — 28-day rolling window) | - ---- - -## 8. Alert System Maintenance - -### Quarterly System Review - -| Task | Frequency | Owner | -|------|-----------|-------| -| Recalculate all baselines with latest data | Quarterly | SEO Lead | -| Review and update keyword tier assignments | Quarterly | SEO Team | -| Audit notification routing (team changes, role changes) | Quarterly | SEO Lead | -| Test all notification channels (SMS, Slack, email) | Quarterly | SEO Lead | -| Review alert response times (are SLAs being met?) | Quarterly | SEO Lead | -| Archive resolved alerts older than 90 days | Quarterly | SEO Analyst | -| Update playbooks based on lessons learned | Quarterly | SEO Team | - -### Alert Effectiveness Metrics - -Track these metrics about your alerting system itself: - -| Metric | Target | Meaning | -|--------|--------|---------| -| False positive rate | <30% | % of alerts that were not actionable | -| Mean time to acknowledge (MTTA) | P0: <15min, P1: <4h | Time from alert to first human response | -| Mean time to resolve (MTTR) | P0: <2h, P1: <24h | Time from alert to resolution | -| Missed incident rate | 0% | Real problems that were not alerted | -| Alert volume per week | Manageable for team size | If overwhelming, thresholds need tuning | diff --git a/.agents/skills/animation-vocabulary/SKILL.md b/.agents/skills/animation-vocabulary/SKILL.md deleted file mode 100644 index cd0af50454..0000000000 --- a/.agents/skills/animation-vocabulary/SKILL.md +++ /dev/null @@ -1,173 +0,0 @@ ---- -name: animation-vocabulary -description: Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one. ---- - -# Animation Vocabulary - -Turn a vague description of a motion or effect into the precise term, so the user knows what to ask for. - -## Quick Start - -The user describes an effect loosely. You return the matching term(s) in this format: - -``` -**Stagger** — Animate several items one after another with a small delay between each, creating a cascade. -``` - -If several terms could fit, list the best match first, then 1–2 alternates with a one-line note on how they differ. - -## Instructions - -1. **Read for intent, not keywords.** Users describe what they *see* or *feel* ("springy", "slides off", "draws itself in"), not the technical name. Map the sensation to the glossary below. -2. **Quote the glossary verbatim.** Its descriptions are authoritative — use them as-is, don't paraphrase. -3. **Disambiguate close terms.** When two compete (*Clip-path* vs *Mask*, *Pop in* vs *Bounce*, *Shared element transition* vs *Layout animation*), contrast them so the user can pick. -4. **When nothing matches exactly,** name the closest term and say plainly it's an approximation, or describe the effect in the glossary's vocabulary (e.g. "that's a *stagger* of *scale-in* entrances"). -5. **Stay within this glossary.** If a term genuinely isn't here, say so rather than inventing one — though you may explain the concept using these words. -6. **Keep it tight.** A naming question wants a name, not an essay. Lead with the term; expand only if asked. - -## Examples - -**Example 1 — feel-based** -User: "What's it called when a popover seems to grow out of the button you clicked instead of from its middle?" -Output: -``` -**Origin-aware animation** — An element animates out of its trigger, like a popover growing from the button that opened it instead of from its own center which is the default in CSS. -``` - -**Example 2 — disambiguation** -User: "The thing where one image turns into another image." -Output: -``` -**Morph** — One shape smoothly turns into another shape, e.g. Dynamic Island. - -Close alternates: -- **Crossfade** — if they simply fade over each other in the same spot. -- **Shared element transition** — if an element travels and transforms from one position into another. -``` - -**Example 3 — physics feel** -User: "That iOS scroll where it resists and snaps back when you pull too far." -Output: -``` -**Rubber-banding** — Resistance and snap-back when you drag past a boundary (the iOS overscroll feel). -``` - -## Glossary - -_A curated snapshot mirroring the project's `/vocabulary` page; keep the two in sync when either changes._ - -### Entrances & Exits — how elements appear and disappear -- **Fade in / Fade out** — Element appears or disappears by changing opacity. -- **Slide in** — Element enters by sliding in from off-screen (left, right, top, or bottom). -- **Scale in** — Element grows from smaller to full size as it appears, often paired with a fade. -- **Pop in** — Element appears with a slight overshoot, like it bounces into place. -- **Reveal** — Content is uncovered gradually, often by animating a clip-path or mask. -- **Enter / Exit** — The animation an element plays when it's added to or removed from the screen. - -### Sequencing & Timing — coordinating multiple elements or moments -- **Keyframes** — Defined points in an animation (0%, 50%, 100%) that the browser fills the gaps between. -- **Interpolation / Tween** — Generating all the in-between frames between a start and end value, so motion is continuous. -- **Stagger** — Animate several items one after another with a small delay between each, creating a cascade. -- **Orchestration** — Deliberately timing multiple animations so they feel like one coordinated motion. -- **Delay** — Time before an animation starts. -- **Duration** — How long an animation takes. -- **Fill mode** — Whether an element keeps its first or last frame's styles before the animation starts or after it ends (e.g. forwards). -- **Stepped animation** — An animation that is divided into discrete steps, like a countdown timer. - -### Movement & Transforms — changing an element's position, size, or angle -- **Translate** — Move an element along the X or Y axis. -- **Scale** — Make an element bigger or smaller. -- **Rotate** — Spin an element around a point. -- **Skew** — Slant an element along the X or Y axis, shearing it out of its rectangular shape. -- **3D tilt / Flip** — Rotate in 3D space (rotateX / rotateY) to add depth. -- **Perspective** — How strong the 3D effect looks — a lower value exaggerates depth, like the viewer is closer. -- **Transform origin** — The anchor point a scale or rotation grows or spins from. -- **Origin-aware animation** — An element animates out of its trigger, like a popover growing from the button that opened it instead of from its own center which is the default in CSS. - -### Transitions Between States — connecting one state, view, or element to another -- **Crossfade** — One element fades out as another fades in, in the same spot. -- **Continuity transition** — A change that keeps the user oriented by visually connecting before and after. For example, making the same rectangle bigger and smaller. -- **Morph** — One shape smoothly turns into another shape, e.g. Dynamic Island. -- **Shared element transition** — An element travels and transforms from one position into another, like a thumbnail expanding into a card. -- **Layout animation** — When an element's size or position changes, it animates to the new spot instead of snapping. -- **Accordion / Collapse** — A section smoothly expands and collapses its height to show or hide content. -- **Direction-aware transition** — Content slides one way going forward and the opposite way going back, so navigation has a sense of direction. - -### Scroll — motion tied to scrolling or navigating between views -- **Scroll reveal** — Elements fade or slide into place as they enter the viewport. -- **Scroll-driven animation** — An animation whose progress is tied directly to scroll position. -- **Parallax** — Background and foreground move at different speeds while scrolling, creating depth. -- **Page transition** — An animation that plays when navigating from one page or route to another. -- **View transition** — The browser morphs between two states or pages, connecting shared elements. - -### Feedback & Interaction — responding to the user's actions -- **Hover effect** — Visual change when the cursor moves over an element. -- **Press / Tap feedback** — A subtle scale-down when an element is clicked, so it feels physical. -- **Hold to confirm** — A progress effect that fills up while the user holds a button. -- **Drag** — Moving an element by grabbing it, often with momentum when released. -- **Drag to reorder** — Dragging items in a list to rearrange them, while the others shift to make room. -- **Swipe to dismiss** — Dragging an element off-screen to close it, like a drawer or toast. -- **Rubber-banding** — Resistance and snap-back when you drag past a boundary (the iOS overscroll feel). -- **Shake / Wiggle** — A quick side-to-side jitter signaling an error or rejected input. -- **Ripple** — A circle expanding from the point of a tap, confirming the press. - -### Easing — how speed changes over an animation -- **Easing** — The rate at which an animation speeds up or slows down. -- **Ease-out** — Starts fast, ends slow. The default for most UI and anything responding to the user. -- **Ease-in** — Starts slow, ends fast. Usually avoided; can feel sluggish. -- **Ease-in-out** — Slow, fast, slow. Good for elements already on screen moving from A to B. -- **Linear** — Constant speed. Avoid for UI; reserve for spinners or marquees. -- **Cubic-bezier** — A custom easing curve you define for precise control. -- **Asymmetric easing** — A curve that accelerates and decelerates at different rates. Feels more alive than a symmetric one. - -### Spring Animations — physics-based motion as an alternative to fixed-duration easing -- **Spring** — Motion driven by physics (tension, mass, damping) rather than a set duration. -- **Stiffness / Tension** — How strongly the spring pulls toward its target. Higher feels snappier. -- **Damping** — How quickly a spring settles. Lower damping means more bounce and oscillation. -- **Mass** — How heavy the animated element feels. More mass makes it slower and more sluggish. -- **Bounce** — A spring that overshoots and settles, adding playfulness. -- **Perceptual duration** — How long a spring feels finished, even though it keeps micro-settling underneath. -- **Momentum** — Motion that carries velocity, especially after a drag or interruption. -- **Velocity** — How fast and in which direction an element is moving. A spring carries it into the next animation when interrupted, so a flicked element keeps its speed. -- **Interruptible animation** — An animation that can be smoothly redirected mid-flight instead of finishing first. - -### Looping & Ambient Motion — animations that run on their own -- **Marquee** — Text or content that scrolls continuously in a loop. -- **Loop** — An animation that repeats, a set number of times or infinitely. -- **Alternate (yoyo)** — A loop that plays forward then reverses each iteration, instead of jumping back to the start. -- **Orbit** — An element circling around another in a continuous path. -- **Pulse** — A gentle repeating scale or opacity change to draw attention. -- **Float** — A gentle, continuous up-and-down drift that makes a static element feel alive and weightless. -- **Idle animation** — Subtle motion that plays while an element is just sitting there, waiting to be interacted with. - -### Polish & Effects — the small touches that separate good from great -- **Blur** — A blur filter used to soften an element or mask tiny imperfections. -- **Clip-path** — Clipping an element to a shape, used for reveals, masks, and before/after sliders. -- **Mask** — Hiding or revealing parts of an element using a shape or gradient — like clip-path, but with soft, fadeable edges. -- **Before / after slider** — A draggable divider that wipes between two overlaid images to compare them. -- **Line drawing** — An SVG path that draws itself in, like an invisible pen tracing it. -- **Text morph** — Text that animates character by character when it changes, drawing attention to the new value. -- **Skeleton / Shimmer** — A placeholder with a moving sheen shown while content loads. -- **Number ticker** — Digits rolling or counting up to a value. -- **Tabular numbers** — Fixed-width digits so numbers don't shift around as they change. Essential for tickers, timers, and counters. -- **Typewriter** — Text appearing one character at a time, as if being typed. - -### Performance — what keeps motion smooth instead of stuttering -- **Frame rate (FPS)** — Frames drawn per second. 60fps is the baseline for smooth motion; 120fps on newer displays. -- **Jank** — Visible stutter when the browser drops frames because it can't keep up with the animation. -- **Dropped frame** — A frame the browser missed its deadline to draw, causing a tiny hitch in motion. -- **Compositing** — Letting the GPU move or fade an element on its own layer without redoing layout or paint. -- **will-change** — A CSS hint that an element is about to animate, so the browser can promote it to its own layer ahead of time. -- **Layout thrashing** — Animating properties like width, height, top, or left that force the browser to recalculate layout every frame, causing jank. - -### Principles to Know — concepts that guide when and how to animate -- **Purposeful animation** — Motion should serve a function — orient, give feedback, show relationships — not just decorate. -- **Anticipation** — A small wind-up in the opposite direction before a move, hinting at what's about to happen. -- **Follow-through** — Parts of an element keep moving and settle slightly after the main motion stops, adding weight. -- **Squash & stretch** — Deforming an element as it moves to convey weight, speed, and flexibility. -- **Perceived performance** — The right animation makes an interface feel faster, even when it isn't. -- **Frequency of use** — The more often a user sees an animation, the shorter and subtler it should be. -- **Spatial consistency** — Animating so an element keeps its identity and position across states, so users never lose track of where things went. -- **Hardware acceleration** — Animating transform and opacity lets the GPU keep motion smooth. -- **Reduced motion** — Respecting the user's prefers-reduced-motion setting by toning down or removing motion. diff --git a/.agents/skills/apple-design/SKILL.md b/.agents/skills/apple-design/SKILL.md deleted file mode 100644 index 66f56807cb..0000000000 --- a/.agents/skills/apple-design/SKILL.md +++ /dev/null @@ -1,282 +0,0 @@ ---- -name: apple-design -description: Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces. ---- - -# Apple Design - -How Apple builds interfaces that stop feeling like a computer and start feeling like an extension of you. This knowledge comes from Apple's WWDC design talks — chiefly *Designing Fluid Interfaces* (WWDC 2018) — distilled and translated into the web platform (CSS, Pointer Events, `requestAnimationFrame`, spring libraries like Motion/Framer Motion). - -The through-line: **an interface feels alive when motion starts from the current on-screen value, inherits the user's velocity, projects momentum forward, and can be grabbed and reversed at any instant.** Springs are the tool that makes all of this natural, because they are inherently interruptible and velocity-aware. - -## The Core Idea - -> "When we align the interface to the way we think and move, something magical happens — it stops feeling like a computer and starts feeling like a seamless extension of us." - -An interface is fluid when it behaves like the physical world: things respond instantly, move continuously, carry momentum, resist at boundaries, and can be redirected mid-motion. Everything below is a way to get closer to that. - -Apple frames design as serving four human needs: **safety/predictability, understanding, achievement, and joy.** Every rule here serves one of them. - -## 1. Response — kill latency - -The moment lag appears, the feeling of directness "falls off a cliff." Response is the foundation everything else is built on. - -- **Respond on pointer-down, not on release.** Highlight a button the instant it's pressed. Waiting for `click`/touch-up to show feedback feels dead. -- **Be vigilant about every latency.** Audit debounces, artificial timers, transition waits, and the ~300ms tap delay. Anything on the input path that isn't essential is a regression. -- **Feedback must be continuous *during* the interaction, not just at the end.** For a drag, slider, or drawer, update the UI 1:1 with the pointer the whole way through — never animate only when the gesture completes. - -```css -/* Feedback lives on the press, and it's instant */ -.button:active { - transform: scale(0.97); - transition: transform 100ms ease-out; -} -``` - -## 2. Direct manipulation — 1:1 tracking - -> "Touch and content should move together." - -When the user drags something, it must stay glued to the finger — and respect the offset from *where they grabbed it*. Snapping to the element's center on grab breaks the illusion immediately. - -- Use Pointer Events with `setPointerCapture` so tracking continues even when the pointer leaves the element's bounds. -- Track a short **velocity/position history** (last few `pointermove` events), not just the current point — you'll need velocity at release. - -```js -el.addEventListener('pointerdown', (e) => { - el.setPointerCapture(e.pointerId); - const grabOffset = e.clientY - el.getBoundingClientRect().top; // respect where they grabbed - // ...track position + timestamp history for velocity -}); -``` - -## 3. Interruptibility — the single most important principle - -> "The thought and the gesture happen in parallel." - -Every animation must be interruptible and redirectable at any moment. A user must be able to grab a moving element mid-flight and reverse it without waiting for the animation to finish. A closing modal the user grabs again should follow the finger — not finish closing first, then reopen. - -- **Never lock out input during a transition.** -- **Always animate from the *presentation* (current) value, never the target value.** On interrupt, read the element's live on-screen transform and start the new animation from there. Starting from the logical/target value causes a visible jump. -- **Avoid CSS transitions and `@keyframes` for anything gesture-driven** — they can't be smoothly grabbed and reversed mid-flight. Springs animate from the current value by default, which is exactly what interruption needs. -- **When a gesture reverses, blend velocity — don't hard-cut it.** Replacing one animation with another at a reversal creates a velocity discontinuity, a "brick wall." Spring libraries that carry velocity through a re-target avoid it. (This is what iOS's *additive animations* do natively; on the web, choose a spring library that re-targets from the current velocity.) -- **Decompose 2D motion into independent X and Y springs.** A single spring on a 2D distance desyncs when X and Y have different velocities. - -## 4. Behavior over animation — use springs - -> "Think of animation as a conversation between you and the object, not something prescribed by the interface." - -A pre-scripted, fixed-duration animation can't respond to new input. A spring can — new input just changes the target, and the motion stays continuous. Reach for springs for anything a user can touch. - -Apple deliberately replaced the physics triplet (mass/stiffness/damping) with two designer-friendly parameters. Think in these: - -- **Damping ratio** — controls overshoot. `1.0` = critically damped, no bounce, smooth settle. `< 1.0` = overshoots and oscillates. Lower = bouncier. -- **Response** — how quickly the value reaches the target, in seconds. Lower = snappier. **This is not "duration"** — a spring has no fixed duration; its settle time emerges from the parameters. - -**Defaults:** -- Start most UI at **damping `1.0`** (critically damped) — graceful and non-distracting. -- Add bounce (**damping ~`0.8`**) **only when the gesture itself carried momentum** (a flick, a throw, a drag release). Overshoot on a menu that just faded in feels wrong; overshoot on a card you flicked feels right. - -**Concrete values Apple ships:** - -| Interaction | Damping | Response | -| --- | --- | --- | -| Move / reposition (e.g. PiP) | `1.0` | `0.4` | -| Rotation | `0.8` | `0.4` | -| Drawer / sheet | `0.8` | `0.3` | - -**Web mapping (Motion / Framer Motion):** the `bounce` + `duration` spring API maps closely to Apple's damping + response. A safe house style is `damping: 1.0` springs everywhere by default; reserve bounce for momentum-driven, physical interactions. - -```js -import { animate } from 'motion'; - -// Critically damped default (no overshoot) -animate(el, { y: 0 }, { type: 'spring', bounce: 0, duration: 0.4 }); - -// Momentum interaction — a little bounce, only because a flick preceded it -animate(el, { y: target }, { type: 'spring', bounce: 0.2, duration: 0.4 }); -``` - -## 5. Velocity handoff — the seam between drag and animation - -When a gesture ends, the animation must **continue at the finger's exact velocity**, so there's no visible seam between dragging and animating. This is the detail that most separates "fluid" from "fine." - -Pass the pointer's release velocity as the spring's initial velocity. Some spring APIs want **relative** velocity — normalize it by the remaining distance to the target: - -``` -relativeVelocity = gestureVelocity / (targetValue − currentValue) -``` - -Example: element at `y=50`, target `y=150` (100px to go), finger moving 50px/s → initial spring velocity = `50 / 100 = 0.5`. Framer Motion / Motion take absolute px/s velocity directly (`velocity` option), so you usually hand it the raw value. - -## 6. Momentum projection — animate to where the gesture is *going* - -> "Take a small input and make a big output." - -Don't snap to the nearest boundary from the *release point*. Use velocity to **project the resting position** — exactly like scroll deceleration — then snap to the target nearest that projected point. This is what makes a flick feel like it throws the element. - -Apple's exact projection function (from the *Designing Fluid Interfaces* sample code): - -```js -// decelerationRate ≈ 0.998 for normal scroll feel; 0.99 for snappier -function project(initialVelocity /* px/s */, decelerationRate = 0.998) { - return (initialVelocity / 1000) * decelerationRate / (1 - decelerationRate); -} - -const projectedEndpoint = currentPosition + project(releaseVelocity); -const target = nearestSnapPoint(projectedEndpoint); // choose target from the projection -animateSpringTo(target, { velocity: releaseVelocity }); // then hand off velocity (§5) -``` - -Note: the physics-textbook `v²/(2·decel)` is *not* what Apple ships — use the exponential-decay form above. This is the standard behavior in good bottom-sheets and carousels (Vaul, Embla). - -## 7. Spatial consistency — symmetric paths, anchored origins - -> "If something disappears one way, we expect it to emerge from where it came." - -- **Enter and exit along the same path.** A panel that slides in from the right must dismiss to the right. In-from-right / out-the-bottom feels disconnected and confusing. -- **Anchor interactions to their source.** A menu, popover, or sheet should originate from the element that triggered it — set `transform-origin` to the trigger, so the spatial relationship between button and content is obvious. (This is the same origin-awareness point as popovers scaling from their trigger, not their center.) -- **Mirror the easing on reversible transitions** so the outbound path matches the return path (use inverse cubic-bézier control points for the two directions). - -## 8. Hint in the direction of the gesture - -Humans predict a final state from a trajectory. Intermediate motion should telegraph where things are going — Control Center modules "grow up and out toward your finger." Make the in-between frames point at the outcome, not just interpolate blindly to it. - -## 9. Rubber-banding — soft boundaries - -At an edge, resist progressively instead of stopping hard. A hard stop reads as "frozen"; continuous resistance reads as "responsive, but there's nothing more here." Apply damping that increases the further past the boundary the user drags. - -```js -// The further past the bound, the less the element follows — real things slow before they stop -function rubberband(overshoot, dimension, constant = 0.55) { - return (overshoot * dimension * constant) / (dimension + constant * Math.abs(overshoot)); -} -``` - -## 10. Gesture design details (the "feel" checklist) - -- **Tap:** highlight on touch-*down* (instant), commit on touch-*up*. Add ~10px of hysteresis/hit padding around the target, and allow cancel-by-dragging-away and back. -- **Drag/swipe:** require a small movement threshold (hysteresis, ~10px) before committing to a direction, then track 1:1. -- **Detect all plausible gestures in parallel from the first move**, then confidently cancel the losers once intent is clear. Avoid recognizers that only report a *final* state (`swipeleft`-type events) — they throw away the continuous tracking you need for feedback. -- **Minimize disambiguation delays.** Double-tap detection unavoidably delays single taps; only pay that cost where double-tap truly exists. - -## 11. Frame-level smoothness - -Smoothness is about *what's in the frames*, not just the frame rate. - -- Keep the per-frame positional change below the perception threshold to avoid strobing. -- For very fast motion, a subtle **motion blur / stretch** encodes speed and reads better than a hard sharp streak. -- `requestAnimationFrame` is the web's display-synced clock (Apple uses `CADisplayLink`). Animate only compositor-friendly properties — `transform` and `opacity` — and hint with `will-change` where motion is imminent. - -## 12. Materials & depth — translucency conveys hierarchy - -Apple uses translucent materials as a floating functional layer that brings structure without stealing focus. On the web, approximate with `backdrop-filter`. - -- **Build nav/toolbars/sheets as translucent layers** (`backdrop-filter: blur()` + a semi-transparent background) with content scrolling underneath — not opaque bars that consume a fixed strip. -- **Material weight encodes hierarchy:** darker/heavier materials separate structural regions (sidebars); lighter materials draw attention to interactive elements (buttons). **Never stack a light translucent surface on another** — legibility collapses. -- **Bigger surfaces should read as thicker:** stronger blur + a deeper shadow than small chips. Consider context-aware shadow — heavier over busy/text content for separation, lighter over plain backgrounds. -- **Dim to focus, separate to keep flow.** A modal task pairs the surface with a dimming scrim and pushes the background back/down. A parallel, non-blocking panel uses translucency and offset *without* a scrim so the flow isn't broken. For stacked sheets, progressively dim and push back each parent layer. -- **Vibrancy keeps text legible over changing backgrounds.** Over blurred/translucent surfaces, don't use flat gray text — use higher-contrast, slightly heavier weight, and a small letter-spacing bump. Put color on a solid layer, not the translucent foreground. -- **Scroll edge effects, not hard dividers.** Instead of a 1px border under a sticky header, fade a small blur/gradient mask where content meets floating chrome — only where floating UI actually overlaps content. -- **Materialize, don't just fade.** For glass/blur surfaces, animate blur radius and scale together on enter/exit, so the surface reads as a real material arriving rather than a plain opacity fade. - -```css -.toolbar { - background: rgba(255, 255, 255, 0.6); - backdrop-filter: blur(20px) saturate(180%); - border-top: 1px solid rgba(255, 255, 255, 0.4); /* bright top edge = light catching the material */ -} -``` - -## 13. Multimodal feedback — motion + sound + haptics - -Three rules for combining senses (from *Designing Audio-Haptic Experiences*): - -1. **Causality** — it must be obvious what caused the feedback. Trigger it on the actual causal event (the toggle flipping, the item snapping home), and match its character to the action's physicality. -2. **Harmony** — the visual, the sound, and the haptic must fire on the **same frame**. Latency between them destroys the illusion. Don't let a CSS transition lag the audio/haptic (Vibration API). -3. **Utility** — add feedback only where it earns its place. Reserve haptics/sound for meaningful moments (success, error, commit, snap). Over-feedback trains users to ignore all of it. - -## 14. Reduced motion & accessibility - -Reduced motion doesn't mean *no* feedback — it means a gentler, non-vestibular equivalent. Respond to three independent signals and bake them into your components: - -- **`prefers-reduced-motion: reduce`** — replace slides/springs/parallax with short opacity **cross-fades or static transitions**. Drop elastic/overshoot. Keep opacity/color changes that aid comprehension. -- **`prefers-reduced-transparency: reduce`** — make translucent surfaces frostier/solid: raise background opacity, drop the blur. -- **`prefers-contrast: more`** — near-solid backgrounds with a defined, contrasting border. - -Also: avoid full-viewport moving backgrounds, slow looping oscillations (near 0.2 Hz / one cycle per 5s), and abrupt brightness jumps (ease dark↔light theme changes). Make large moving objects semi-transparent while they travel, and fade big surfaces out during a large reposition and back in once settled. - -```css -@media (prefers-reduced-motion: reduce) { - .sheet { transition: opacity 200ms ease; transform: none !important; } -} -@media (prefers-reduced-transparency: reduce) { - .toolbar { background: white; backdrop-filter: none; } -} -``` - -## 15. Typography — optical sizing, tracking, leading - -Apple designs type to change shape with size; the same discipline applies on the web. (From *The Details of UI Typography*, WWDC 2020.) - -- **Tracking (letter-spacing) is size-specific — never one value for all sizes.** Large display text wants *negative* tracking (letters read too far apart as they grow); small text wants slightly *positive* tracking for legibility. A fixed `letter-spacing` is wrong somewhere. Tighten headings, leave body near `0`. -- **Leading (line-height) tracks size inversely.** Tight on large headings, looser on body copy. Increase it for scripts with tall ascenders/descenders; tighten it for dense, information-heavy UI. -- **Build hierarchy from weight + size + leading as a set,** not size alone. Emphasize with weight — it adds presence without taking more space. -- **Respect the user's text-size setting** (Dynamic Type). Scale layout *with* the text — spacing in `rem`/`em`, not fixed px — so a larger font doesn't break the layout. -- **Default to the platform's system font** before a custom face; it already ships optical sizing, tracking tables, and legibility tuning. Override only with a reason. - -```css -:root { font: 100%/1.5 system-ui, sans-serif; } /* body: system font, comfortable leading */ - -.display { - font-size: clamp(2rem, 5vw, 4rem); - line-height: 1.05; /* tight leading for large text */ - letter-spacing: -0.02em; /* negative tracking as it grows */ - font-optical-sizing: auto; -} -``` - -## 16. Design foundations — the eight principles - -The motion and craft above serve Apple's eight design principles (*Principles of Great Design*, WWDC 2026). Use these as the names you reason with: - -1. **Purpose.** Make with intention; decide what *not* to build. Every feature asks for the user's time, attention, and trust — spend that budget only where it pays off. -2. **Agency.** Keep people in control: offer choices, don't force a single path. Back it with forgiveness — easy undo for slips, a confirmation dialog only for genuinely destructive, irreversible actions (use sparingly; overusing it trains people to click through). -3. **Responsibility.** Act in the user's interest. Privacy: ask at the right moment, only for what's needed, transparently. Safety: anticipate misuse and harm — especially with AI (an allergy-aware recipe app must not suggest a harmful ingredient). Add previews, confirmations, disclaimers; cut a feature whose risk outweighs its value. -4. **Familiarity.** Build on what people already know. Use metaphors that are neither too literal nor too abstract (a trash can means delete), and honor their physics. Be consistent: things that look the same must behave the same and live in the same place (close is always top-left on macOS) so people can predict what happens next. Only break a familiar pattern if you can prove it's better — then test it, don't assume. -5. **Flexibility.** Design for different contexts, devices, and the full range of abilities. Adapt to the platform (iPhone = quick touch; desktop = deep workflows with precise pointer control) and to the situation. Design inclusively (age, language, expertise, accessibility). When no single layout fits everyone, let people personalize — rearrange controls, hide what they don't use. -6. **Simplicity — not minimalism.** Strip the unnecessary so the core purpose shines; burying everything in one place looks minimal but isn't simple. Be concise (plain language, no jargon, fewer steps) and clear (use hierarchy — order, spacing, contrast — so the most important thing is the most obvious). Every element earns its place; sometimes *adding* context simplifies (a video scrubber that shows time remaining). Show the common path first, advanced options one level deeper. -7. **Craft.** Uncompromising attention to detail builds trust. Beautiful typography, colors that adapt to light/dark, clear iconography, and responsive animations that give immediate, natural feedback. Nothing is random — every spacing, timing, and alignment value is a deliberate choice you can defend. Jittery scroll, misaligned icons, and layouts that break on rotation read as carelessness. Craft needs iteration and longevity — keep evolving the design as features and hardware change. -8. **Delight.** The result of getting the other seven right, not confetti tacked on top. Decide the emotion you want people to feel (calm, confident, excited) and reinforce it in every decision. - -Tactical rules that serve these: - -- **Feedback comes in four kinds:** status, completion, warning, error. Confirm meaningful actions, expose ongoing status, warn before problems, validate inline (not on submit). -- **Wayfinding.** Every screen should answer: Where am I? Where can I go? What's there? How do I get out? Never trap the user. -- **Grouping & mapping.** Proximity implies relationship; place a control near what it affects and arrange controls to mirror what they change. If you need a label to explain a control, the mapping is weak. -- **Direct, specific labels beat safe generic ones.** Name nav items for their contents ("Progress", "Library"), not vague umbrellas ("Home"). Specificity creates predictability. - -## 17. Process - -- **Prototype interactively — an interactive demo is worth "a million static designs."** You discover the interface by building and playing with it; a working prototype also sets a concrete bar that prevents a mediocre final implementation. -- **Design interaction and visuals together.** "You shouldn't be able to tell where one ends and the other begins." Motion is not a layer added after the pixels. -- **Test with real people in real context**, and review motion with fresh eyes — play it in slow motion / frame-by-frame to catch what's invisible at full speed. - -## Quick Reference - -| Need | Technique | Concrete value | -| --- | --- | --- | -| Default UI spring | Critically damped, no overshoot | `damping 1.0`, `response 0.3–0.4` | -| Momentum / flick spring | Under-damped, slight bounce | `damping ~0.8`, `response 0.3–0.4` | -| Gesture → spring velocity | Hand off release velocity | `gestureVelocity / (target − current)` if normalized | -| Flick landing point | Project momentum | `current + (v/1000)·d/(1−d)`, `d ≈ 0.998` | -| Interrupt cleanly | Start from presentation (live) value | read the on-screen transform | -| Avoid reversal "brick wall" | Carry velocity through re-target | spring that blends velocity | -| Reversible transition | Mirror the easing curve | inverse cubic-bézier | -| Decide reverse vs. commit | Use velocity **sign**, not position | at release | -| 1:1 drag | Pointer Events + capture | respect the grab offset | -| Feedback | On pointer-down, continuous | never only at the end | -| Boundary | Rubber-band, don't hard-stop | progressive resistance | -| Translucent chrome | `backdrop-filter` layer | content scrolls under | -| Type tracking | Size-specific, never fixed | tighten large text (`-0.02em`), body near `0` | -| Reduced motion | Cross-fade, not slide/spring | `@media (prefers-reduced-motion)` | diff --git a/.agents/skills/backlink-analyzer/SKILL.md b/.agents/skills/backlink-analyzer/SKILL.md deleted file mode 100644 index ce0a2e7977..0000000000 --- a/.agents/skills/backlink-analyzer/SKILL.md +++ /dev/null @@ -1,301 +0,0 @@ ---- -name: backlink-analyzer -description: 'Analyze backlink profiles: link authority, toxic links, building opportunities, competitor link gaps. 外链分析/反向链接' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when analyzing backlink profiles, link quality, toxic links, referring domains, or anchor text distribution." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "low" - tags: - - seo - - backlinks - - link-building - - link-profile - - toxic-links - - off-page-seo - - link-audit - - referring-domains - - disavow - - ahrefs-alternative - - 外链分析 - - 被リンク - - 백링크 - - backlinks-seo - triggers: - # EN-formal - - "analyze backlinks" - - "check link profile" - - "find toxic links" - - "link building opportunities" - - "link profile analysis" - - "backlink audit" - - "link quality" - # EN-casual - - "who links to me" - - "I have spammy links" - - "how do I get more backlinks" - - "how do I get more links" - - "disavow links" - - "link building outreach" - - "disavow file" - # EN-question - - "how to build backlinks" - - "how to find toxic backlinks" - # ZH-pro - - "外链分析" - - "反向链接" - - "有毒链接" - - "链接建设" - # ZH-casual - - "外链怎么做" - - "有垃圾外链" - - "谁链接到我" - - "友链" - - "互换友链" - - "外链建设" - # JA - - "被リンク分析" - - "バックリンク" - - "リンク構築" - # KO - - "백링크 분석" - - "링크 빌딩" - - "누가 내 사이트 링크해?" - - "백링크 어떻게 늘려?" - # ES - - "análisis de backlinks" - - "enlaces entrantes" - # PT - - "análise de backlinks" - # Misspellings - - "backlink anaylsis" - - "backlnk analysis" ---- - -# Backlink Analyzer - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This monitoring skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -Analyzes, monitors, and optimizes backlink profiles. Identifies link quality, discovers opportunities, and tracks competitor link building activities. - -**System role**: Monitoring layer skill. It turns performance changes into deltas, alerts, and next actions. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs time-aware change detection, escalation, or stakeholder-ready visibility. - -- Auditing your current backlink profile -- Identifying toxic or harmful links -- Discovering link building opportunities -- Analyzing competitor backlink strategies -- Monitoring new and lost links -- Evaluating link quality for outreach -- Preparing for link disavow - -## What This Skill Does - -1. **Profile Analysis**: Comprehensive backlink profile overview -2. **Quality Assessment**: Evaluates link authority and relevance -3. **Toxic Link Detection**: Identifies harmful links -4. **Competitor Analysis**: Compares link profiles across competitors -5. **Opportunity Discovery**: Finds link building prospects -6. **Trend Monitoring**: Tracks link acquisition over time -7. **Disavow Guidance**: Helps create disavow files - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Analyze Your Profile - -``` -Analyze backlink profile for [domain] -``` - -### Find Opportunities - -``` -Find link building opportunities by analyzing [competitor domains] -``` - -### Detect Issues - -``` -Check for toxic backlinks on [domain] -``` - -### Compare Profiles - -``` -Compare backlink profiles: [your domain] vs [competitor domains] -``` - -## Skill Contract - -**Expected output**: a delta summary, alert/report output, and a short handoff summary ready for `memory/monitoring/`. - -- **Reads**: current metrics, previous baselines, alert thresholds, and reporting context from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing monitoring deliverable plus a reusable summary that can be stored under `memory/monitoring/`. -- **Promotes**: significant changes, confirmed anomalies, and follow-up actions to `memory/open-loops.md` and `memory/decisions.md`. -- **Next handoff**: use the `Next Best Skill` below when a change needs action. - -## Data Sources - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~link database + ~~SEO tool connected:** -Automatically pull comprehensive backlink profiles including referring domains, anchor text distribution, link quality metrics (DA/DR), link velocity, and toxic link detection from ~~link database. Competitor backlink data from ~~SEO tool for gap analysis. - -**With manual data only:** -Ask the user to provide: -1. Backlink export CSV (with source domains, anchor text, link type) -2. Referring domains list with authority metrics -3. Competitor domains for comparison -4. Recent link gains/losses if tracking changes -5. Any known toxic or spammy links - -Proceed with the full analysis using provided data. Note in the output which metrics are from automated collection vs. user-provided data. - -## Instructions - -When a user requests backlink analysis: - -1. **Generate Profile Overview** -- Key metrics (total backlinks, referring domains, DA/DR, dofollow ratio), link velocity (30d/90d/year), authority distribution chart, profile health score. - -2. **Analyze Link Quality** -- Top quality backlinks table, link type distribution, anchor text analysis (brand/exact/partial/URL/generic), geographic distribution. - -3. **Identify Toxic Links** -- Toxic score, risk indicators by type (spam, PBN, link farms, irrelevant), high-risk links to review, disavow recommendations (domain-level and URL-level). - -4. **Compare Against Competitors** -- Profile comparison table (referring domains, DA/DR, velocity, avg link DA), unique referring domains, link intersection analysis, competitor content attracting most links. - -5. **Find Link Building Opportunities** -- Link intersection prospects, broken link opportunities, unlinked mentions, resource page opportunities, guest post prospects, priority matrix (effort vs impact). - -6. **Track Link Changes** -- New and lost links for last 30 days with DA, type, anchor, dates. Net change and links to recover. - -7. **Generate Backlink Report** -- Executive summary, strengths, concerns, opportunities, competitive position, recommended actions (immediate/short-term/long-term), KPIs to track. - - > **Reference**: See [references/analysis-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/backlink-analyzer/references/analysis-templates.md) for complete output templates for all 7 steps above. - -### CITE Item Mapping - -When running `domain-authority-auditor` after this analysis, the following data feeds directly into CITE scoring: - -| Backlink Metric | CITE Item | Dimension | -|----------------|-----------|-----------| -| Referring domains count | C01 (Referring Domain Volume) | Citation | -| Authority distribution (DA breakdown) | C02 (Referring Domains Quality) | Citation | -| Link velocity | C04 (Link Velocity) | Citation | -| Geographic distribution | C10 (Link Source Diversity) | Citation | -| Dofollow/Nofollow ratio | T02 (Dofollow Ratio Normality) | Trust | -| Toxic link analysis | T01 (Link Profile Naturalness), T03 (Link-Traffic Coherence) | Trust | -| Competitive link intersection | T05 (Profile Uniqueness) | Trust | - -## Validation Checkpoints - -### Input Validation -- [ ] Target domain backlink data is complete and current -- [ ] Competitor domains specified for comparison analysis -- [ ] Backlink data includes necessary fields (source domain, anchor text, link type) -- [ ] Authority metrics available (DA/DR or equivalent) - -### Output Validation -- [ ] Every metric cites its data source and collection date -- [ ] Toxic link assessments include risk justification -- [ ] Link opportunity recommendations are specific and actionable -- [ ] Source of each data point clearly stated (~~link database data, ~~SEO tool data, user-provided, or estimated) - -## Example - -**User**: "Find link building opportunities by analyzing HubSpot, Salesforce, and Mailchimp" - -**Output**: - -```markdown -## Link Intersection Analysis - -### Sites linking to 2+ competitors (not you) - -| Domain | DA | HubSpot | Salesforce | Mailchimp | Opportunity | -|--------|-----|---------|------------|-----------|-------------| -| g2.com | 91 | ✅ | ✅ | ✅ | Get listed/reviewed | -| capterra.com | 89 | ✅ | ✅ | ✅ | Submit for review | -| entrepreneur.com | 92 | ✅ | ✅ | ❌ | Pitch guest post | -| techcrunch.com | 94 | ✅ | ❌ | ✅ | PR/news pitch | - -### Top 5 Immediate Opportunities - -1. **G2.com** (DA 91) - All competitors listed - - Action: Create detailed G2 profile - - Effort: Low - - Impact: High authority + referral traffic - -2. **Entrepreneur.com** (DA 92) - 2 competitors have links - - Action: Pitch contributed article - - Effort: High - - Impact: High authority + brand exposure - -3. **MarketingProfs** (DA 75) - All competitors featured - - Action: Apply for expert contribution - - Effort: Medium - - Impact: Relevant audience + quality link - -### Estimated Impact - -If you acquire links from top 10 opportunities: -- New referring domains: +10 -- Average DA of new links: 82 -- Estimated ranking impact: +2-5 positions for competitive keywords -``` - -## Tips for Success - -1. **Quality over quantity** - One DA 80 link beats ten DA 20 links -2. **Monitor regularly** - Catch lost links and toxic links early -3. **Study competitors** - Learn from their link building success -4. **Diversify your profile** - Mix of link types and anchors -5. **Disavow carefully** - Only disavow clearly toxic links - -## Link Quality and Strategy Reference - -> **Reference**: See [references/link-quality-rubric.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/backlink-analyzer/references/link-quality-rubric.md) for the complete link quality scoring matrix (6 weighted factors), toxic link identification criteria, link profile health benchmarks, and disavow file guidance. - -> **Reference**: See [references/outreach-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/backlink-analyzer/references/outreach-templates.md) for email outreach frameworks, subject line formulas, response rate benchmarks, follow-up sequences, and templates for each link building strategy. - - -### Save Results - -After delivering monitoring data or reports to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/monitoring/YYYY-MM-DD-.md` containing: -- One-line headline finding or status change -- Top 3-5 actionable items -- Open loops or anomalies requiring follow-up -- Source data references - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - - -**Gate check recommended**: If toxic link ratio exceeds 15%, recommend running domain-authority-auditor to assess overall domain trust impact. - -## Reference Materials - -- [Link Quality Rubric](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/backlink-analyzer/references/link-quality-rubric.md) — Quality scoring matrix with weighted factors and toxic link identification criteria -- [Outreach Templates](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/backlink-analyzer/references/outreach-templates.md) — Email frameworks, subject line formulas, and response rate benchmarks - -## Next Best Skill - -- **Primary**: [domain-authority-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/domain-authority-auditor/SKILL.md) — translate link findings into a domain-level trust view. diff --git a/.agents/skills/backlink-analyzer/references/analysis-templates.md b/.agents/skills/backlink-analyzer/references/analysis-templates.md deleted file mode 100644 index d1809dc291..0000000000 --- a/.agents/skills/backlink-analyzer/references/analysis-templates.md +++ /dev/null @@ -1,354 +0,0 @@ -# Backlink Analysis Output Templates - -Detailed output templates for each step of the backlink analysis workflow. Use these templates when generating analysis deliverables. - ---- - -## 1. Profile Overview Template - -```markdown -## Backlink Profile Overview - -**Domain**: [domain] -**Analysis Date**: [date] - -### Key Metrics - -| Metric | Value | Industry Avg | Status | -|--------|-------|--------------|--------| -| Total Backlinks | [X] | [Y] | [Above/Below avg] | -| Referring Domains | [X] | [Y] | [status] | -| Domain Authority | [X] | [Y] | [status] | -| Domain Rating | [X] | [Y] | [status] | -| Dofollow Links | [X] ([Y]%) | [Z]% | [status] | -| Nofollow Links | [X] ([Y]%) | [Z]% | [status] | - -### Link Velocity - -| Period | New Links | Lost Links | Net Change | -|--------|-----------|------------|------------| -| Last 30 days | [X] | [Y] | [+/-Z] | -| Last 90 days | [X] | [Y] | [+/-Z] | -| Last year | [X] | [Y] | [+/-Z] | - -### Authority Distribution - -``` -DA 80-100: [X]% -DA 60-79: [X]% -DA 40-59: [X]% -DA 20-39: [X]% -DA 0-19: [X]% -``` - -**Profile Health Score**: [X]/100 -``` - ---- - -## 2. Link Quality Analysis Template - -```markdown -## Link Quality Analysis - -### Top Quality Backlinks - -| Source Domain | DA | Link Type | Anchor | Target Page | -|---------------|-----|-----------|--------|-------------| -| [domain 1] | [DA] | Editorial | [anchor] | [page] | -| [domain 2] | [DA] | Guest Post | [anchor] | [page] | -| [domain 3] | [DA] | Resource | [anchor] | [page] | - -### Link Type Distribution - -| Type | Count | Percentage | Assessment | -|------|-------|------------|------------| -| Editorial | [X] | [Y]% | High quality | -| Guest posts | [X] | [Y]% | Good | -| Resource pages | [X] | [Y]% | Good | -| Directory | [X] | [Y]% | Moderate | -| Forum/Comments | [X] | [Y]% | Low quality | -| Sponsored/Paid | [X] | [Y]% | Risky | - -### Anchor Text Analysis - -| Anchor Type | Count | Percentage | Status | -|-------------|-------|------------|--------| -| Brand name | [X] | [Y]% | Natural | -| Exact match | [X] | [Y]% | [Warning if >30%] | -| Partial match | [X] | [Y]% | Natural | -| URL/Naked | [X] | [Y]% | Natural | -| Generic | [X] | [Y]% | Natural | - -**Top Anchor Texts**: -1. "[anchor 1]" - [X] links -2. "[anchor 2]" - [X] links -3. "[anchor 3]" - [X] links - -### Geographic Distribution - -| Country | Links | Percentage | -|---------|-------|------------| -| [Country 1] | [X] | [Y]% | -| [Country 2] | [X] | [Y]% | -| [Country 3] | [X] | [Y]% | -``` - ---- - -## 3. Toxic Link Analysis Template - -```markdown -## Toxic Link Analysis - -### Risk Summary - -**Toxic Score**: [X]/100 -**High Risk Links**: [X] -**Medium Risk Links**: [X] -**Action Required**: [Yes/No] - -### Toxic Link Indicators - -| Risk Type | Count | Examples | -|-----------|-------|----------| -| Spammy domains | [X] | [domains] | -| Link farms | [X] | [domains] | -| PBN suspected | [X] | [domains] | -| Irrelevant sites | [X] | [domains] | -| Foreign language spam | [X] | [domains] | -| Penalized domains | [X] | [domains] | - -### High-Risk Links to Review - -| Source Domain | Risk Score | Issue | Recommendation | -|---------------|------------|-------|----------------| -| [domain 1] | 95/100 | Link farm | Disavow | -| [domain 2] | 85/100 | Spam site | Disavow | -| [domain 3] | 72/100 | PBN | Investigate | - -### Disavow Recommendations - -**Domains to disavow** ([X] total): -``` -domain:[spam-site-1.com] -domain:[spam-site-2.com] -domain:[link-farm.com] -``` - -**Individual URLs to disavow** ([X] total): -``` -[specific-url-1] -[specific-url-2] -``` -``` - ---- - -## 4. Competitive Backlink Analysis Template - -```markdown -## Competitive Backlink Analysis - -### Profile Comparison - -| Metric | You | Competitor 1 | Competitor 2 | Competitor 3 | -|--------|-----|--------------|--------------|--------------| -| Referring Domains | [X] | [X] | [X] | [X] | -| Domain Authority | [X] | [X] | [X] | [X] | -| Domain Rating | [X] | [X] | [X] | [X] | -| Link Velocity (30d) | [X] | [X] | [X] | [X] | -| Avg Link DA | [X] | [X] | [X] | [X] | - -### Unique Referring Domains - -**Links only you have**: [X] domains -**Links competitors share**: [X] domains -**Links competitors have, you don't**: [X] domains -- Opportunity - -### Link Intersection Analysis - -**Sites linking to competitors but not you**: - -| Domain | DA | Links to Comp 1 | Comp 2 | Comp 3 | Opportunity | -|--------|-----|-----------------|--------|--------|-------------| -| [domain 1] | [DA] | Yes | Yes | Yes | High - All competitors | -| [domain 2] | [DA] | Yes | Yes | No | High - 2 competitors | -| [domain 3] | [DA] | Yes | No | No | Medium - 1 competitor | - -### Content Getting Most Links (Competitor Analysis) - -| Competitor | Content | Backlinks | Content Type | -|------------|---------|-----------|--------------| -| [Comp 1] | [Title/URL] | [X] | [Type] | -| [Comp 2] | [Title/URL] | [X] | [Type] | -| [Comp 3] | [Title/URL] | [X] | [Type] | - -**Insight**: [What content types attract most links in this niche] -``` - ---- - -## 5. Link Building Opportunities Template - -```markdown -## Link Building Opportunities - -### High-Priority Opportunities - -#### 1. Link Intersection Prospects - -Sites linking to multiple competitors but not you: - -| Domain | DA | Why Link | Contact Approach | -|--------|-----|----------|------------------| -| [domain 1] | [DA] | [resource page about X] | Suggest your resource | -| [domain 2] | [DA] | [links to similar tools] | Pitch your tool | -| [domain 3] | [DA] | [industry roundup] | Request inclusion | - -#### 2. Broken Link Opportunities - -| Source Page | Broken Link | Suggested Replacement | -|-------------|-------------|----------------------| -| [URL] | [broken URL] | [your relevant page] | - -#### 3. Unlinked Mentions - -| Site | Mention | Your Page to Link | -|------|---------|-------------------| -| [domain] | Mentioned your brand | [homepage] | -| [domain] | Referenced your data | [research page] | - -#### 4. Resource Page Opportunities - -| Resource Page | Topic | Your Relevant Content | -|---------------|-------|----------------------| -| [URL] | [topic] | [your content] | - -#### 5. Guest Post Prospects - -| Site | DA | Topic Fit | Contact | -|------|-----|-----------|---------| -| [domain] | [DA] | [relevance] | [contact info/page] | - -### Link Building Priority Matrix - -| Opportunity Type | Effort | Impact | Priority | -|------------------|--------|--------|----------| -| Link intersection | Medium | High | Highest | -| Broken links | Low | Medium | High | -| Unlinked mentions | Low | Medium | High | -| Resource pages | Medium | High | High | -| Guest posts | High | High | Medium | -``` - ---- - -## 6. Link Change Tracking Template - -```markdown -## Link Change Tracking - -### New Links (Last 30 Days) - -| Source | DA | Type | Anchor | Date | -|--------|-----|------|--------|------| -| [domain 1] | [DA] | [type] | [anchor] | [date] | - -**Total new links**: [X] -**Average DA of new links**: [X] -**Best new link**: [domain] (DA [X]) - -### Lost Links (Last 30 Days) - -| Source | DA | Reason | Action | -|--------|-----|--------|--------| -| [domain 1] | [DA] | Page removed | Reach out | -| [domain 2] | [DA] | Link removed | Investigate | - -**Total lost links**: [X] -**Net change**: [+/-X] - -### Links to Recover - -| Lost Link | Value | Recovery Strategy | -|-----------|-------|-------------------| -| [domain 1] | High | Contact webmaster | -| [domain 2] | High | Update content they linked to | -``` - ---- - -## 7. Backlink Report Template - -```markdown -# Backlink Analysis Report - -**Domain**: [domain] -**Report Date**: [date] -**Period Analyzed**: [period] - -## Executive Summary - -Your backlink profile is [healthy/needs attention/concerning]. - -**Key Stats**: -- Referring domains: [X] ([+/-Y] vs last month) -- Average link authority: [X] DA -- Link velocity: [X] new links/month -- Toxic link percentage: [X]% - -## Profile Strengths - -1. [Strength 1] -2. [Strength 2] -3. [Strength 3] - -## Areas of Concern - -1. [Concern 1] -2. [Concern 2] - -## Opportunities Identified - -| Opportunity | Potential Links | Effort | Priority | -|-------------|-----------------|--------|----------| -| Link intersection | [X] sites | Medium | High | -| Broken links | [X] sites | Low | High | -| Resource pages | [X] sites | Medium | Medium | - -## Competitive Position - -Your referring domains rank #[X] among [Y] competitors. - -| Rank | Domain | Referring Domains | -|------|--------|-------------------| -| 1 | [domain] | [X] | -| 2 | [domain] | [X] | -| 3 | [domain] | [X] | - -## Recommended Actions - -### Immediate (This Week) -- [ ] Disavow [X] toxic links identified -- [ ] Reach out to [X] unlinked mentions - -### Short-term (This Month) -- [ ] Pursue [X] link intersection opportunities -- [ ] Fix [X] broken link opportunities -- [ ] Recover [X] recently lost links - -### Long-term (This Quarter) -- [ ] Create linkable asset targeting [topic] -- [ ] Launch guest posting campaign -- [ ] Build [X] resource page links - -## KPIs to Track - -| Metric | Current | 3-Month Target | -|--------|---------|----------------| -| Referring domains | [X] | [Y] | -| Average DA of new links | [X] | [Y] | -| Link velocity | [X]/mo | [Y]/mo | -| Toxic link % | [X]% | <5% | -``` diff --git a/.agents/skills/backlink-analyzer/references/link-quality-rubric.md b/.agents/skills/backlink-analyzer/references/link-quality-rubric.md deleted file mode 100644 index 03e801a492..0000000000 --- a/.agents/skills/backlink-analyzer/references/link-quality-rubric.md +++ /dev/null @@ -1,292 +0,0 @@ -# Link Quality Rubric - -Comprehensive reference for evaluating backlink quality. Use this rubric to assess individual links, audit entire link profiles, perform competitive link gap analysis, and prepare disavow files. - ---- - -## 1. Individual Link Quality Evaluation - -### Scoring Methodology - -Evaluate each link across six factors. Multiply score (1-5) by factor weight to produce a weighted score. Sum all weighted scores for a final Link Quality Score (LQS). - -**Rating Scale:** -- **LQS 4.0-5.0**: Premium link — high authority, topically relevant, editorial placement -- **LQS 2.5-3.9**: Acceptable link — provides value, typical of healthy profiles -- **LQS 1.0-2.4**: Low quality — minimal value, review for potential risk - -### Factor 1: Domain Authority (25% weight) - -| Score | DR / DA Range | Characteristics | Examples | -|-------|-------------|-----------------|---------| -| 5 | DR 70+ | Major publication, established authority | NYTimes, Forbes, BBC, major university sites | -| 4 | DR 50-69 | Strong domain, recognized in industry | Industry publications, large blogs, government sites | -| 3 | DR 30-49 | Moderate authority, established site | Mid-tier blogs, regional publications, niche authorities | -| 2 | DR 15-29 | Low authority, newer or smaller site | Small blogs, newer companies, personal sites | -| 1 | DR <15 | Very low authority | New sites, abandoned sites, thin content sites | - -**Notes:** -- DR/DA is a proxy, not the sole indicator. A DR 30 site that is highly relevant to your niche may be more valuable than a DR 70 site in an unrelated field. -- Check if the domain's authority is organic (earned over time) or inflated (bought links, PBN). - -### Factor 2: Topical Relevance (25% weight) - -| Score | Relevance Level | Description | -|-------|----------------|-------------| -| 5 | Exact match | Same niche, same subtopic. A link from a CRM review site to your CRM product. | -| 4 | Closely related | Same industry, adjacent topic. A marketing blog linking to your email tool. | -| 3 | Broadly related | Same general field. A business blog linking to your SaaS product. | -| 2 | Tangentially related | Loose connection. A general news site mentioning your product in a tech roundup. | -| 1 | Unrelated | No topical connection. A cooking blog linking to your B2B software. | - -**How to assess relevance:** -1. Read the linking page content. Is it about your topic? -2. Check the linking site's overall focus. Is it in your industry? -3. Look at the surrounding content. Does the link make editorial sense? -4. Check the site's other outbound links. Are they topically coherent? - -### Factor 3: Traffic to Linking Page (15% weight) - -| Score | Estimated Monthly Traffic | Characteristics | -|-------|--------------------------|-----------------| -| 5 | 10,000+ visits/month | High-traffic page, likely drives referral traffic | -| 4 | 1,000-9,999 visits/month | Solid traffic, some referral value | -| 3 | 100-999 visits/month | Moderate traffic, primarily SEO value | -| 2 | 10-99 visits/month | Low traffic, SEO value only | -| 1 | <10 visits/month | No meaningful traffic, minimal value | - -**Why traffic matters:** -- Links from pages with real traffic are more likely to be genuine editorial placements. -- Google likely weights links from pages that receive traffic more highly. -- Referral traffic from the link provides direct business value beyond SEO. - -### Factor 4: Link Position (15% weight) - -| Score | Position | Description | -|-------|----------|-------------| -| 5 | In-content, editorial | Naturally placed within the article body as a citation or resource | -| 4 | In-content, contextual | Within the body text but in a "resources" or "further reading" section | -| 3 | Author bio or about section | Part of a contributor's bio or about page | -| 2 | Sidebar or dedicated links section | Widget, blogroll, or sidebar placement | -| 1 | Footer, sitewide, or hidden | Footer link, sitewide template link, or visually obscured | - -**Key principle:** Editorial in-content links carry the most weight because they represent a genuine endorsement. Footer and sitewide links are devalued by search engines. - -### Factor 5: Anchor Text (10% weight) - -| Score | Anchor Type | Example (for a CRM product) | -|-------|------------|----------------------------| -| 5 | Descriptive, natural | "this customer relationship management platform" | -| 4 | Partial match, natural | "CRM tools for small businesses" | -| 3 | Brand name | "Acme CRM" | -| 2 | Naked URL | "https://acmecrm.com" | -| 1 | Generic | "click here", "read more", "this website" | - -**Important nuance:** A natural link profile has a MIX of all anchor types. Too many exact-match anchors (score 5) can signal manipulation. The ideal distribution is: -- Brand anchors: 30-40% -- Naked URLs: 15-25% -- Generic anchors: 10-20% -- Descriptive/partial match: 15-25% -- Exact match: 5-15% - -### Factor 6: Follow Status (10% weight) - -| Score | Status | Description | -|-------|--------|-------------| -| 5 | Dofollow, editorial | Standard followed link from editorial content | -| 4 | Dofollow, non-editorial | Followed link from directory, profile, or user-generated content | -| 3 | Sponsored (rel="sponsored") | Properly disclosed sponsored/paid link | -| 2 | UGC (rel="ugc") | User-generated content link (forums, comments) | -| 1 | Nofollow (rel="nofollow") | Explicitly nofollowed link | - -**Notes:** -- Google treats nofollow as a "hint" rather than a directive since 2019. -- Nofollow links from high-authority sites (e.g., Wikipedia) still provide brand value and referral traffic. -- A healthy profile naturally includes a mix of followed and nofollowed links. Typical ratio: 60-80% dofollow, 20-40% nofollow. - ---- - -## 2. Example Link Profile Assessments - -### Example A: Strong Link Profile - -| Characteristic | Value | Assessment | -|---------------|-------|-----------| -| Total referring domains | 1,200 | Healthy for a mid-size SaaS company | -| Dofollow ratio | 72% | Natural distribution | -| Average linking domain DR | 38 | Solid average authority | -| Top anchor: brand name | 35% | Natural brand dominance | -| Exact match anchors | 8% | Within safe range | -| Topical relevance (sampled) | 75% related | Strong relevance signal | -| Link velocity | +25/month net | Steady organic growth | -| Toxic link estimate | 3% | Below 5% threshold — healthy | - -**Verdict:** Healthy profile with natural link distribution. Continue current strategy. - -### Example B: At-Risk Link Profile - -| Characteristic | Value | Assessment | -|---------------|-------|-----------| -| Total referring domains | 800 | Adequate but thin for competitive niche | -| Dofollow ratio | 92% | Suspiciously high — may indicate link manipulation | -| Average linking domain DR | 18 | Low average authority | -| Top anchor: exact match keyword | 42% | Over-optimized — risk of penalty | -| Exact match anchors | 42% | Far above safe threshold (>15%) | -| Topical relevance (sampled) | 30% related | Many irrelevant links | -| Link velocity | +80/month net | Unnaturally high — investigate | -| Toxic link estimate | 18% | Above 10% threshold — action needed | - -**Verdict:** Profile shows signs of manipulation. Immediate actions needed: disavow toxic links, diversify anchor text, slow down link acquisition pace. - -### Example C: New Site Link Profile - -| Characteristic | Value | Assessment | -|---------------|-------|-----------| -| Total referring domains | 45 | Expected for a 6-month-old site | -| Dofollow ratio | 65% | Natural | -| Average linking domain DR | 28 | Reasonable for early-stage outreach | -| Top anchor: brand name | 40% | Healthy | -| Exact match anchors | 5% | Conservative and safe | -| Topical relevance (sampled) | 80% related | Well-targeted outreach | -| Link velocity | +8/month net | Appropriate for new site | -| Toxic link estimate | 1% | Clean profile | - -**Verdict:** Healthy foundation. Focus on scaling link acquisition while maintaining quality standards. - ---- - -## 3. Competitive Link Gap Analysis Methodology - -### Step-by-Step Process - -**Step 1: Identify competitors** -Select 3-5 direct competitors who rank for your target keywords. - -**Step 2: Pull referring domain data** -Export the full referring domain list for each competitor from ~~link database. - -**Step 3: Create intersection matrix** - -| Referring Domain | You | Comp 1 | Comp 2 | Comp 3 | Overlap Count | -|-----------------|-----|--------|--------|--------|---------------| -| example-a.com | No | Yes | Yes | Yes | 3 | -| example-b.com | No | Yes | Yes | No | 2 | -| example-c.com | No | Yes | No | No | 1 | -| example-d.com | Yes | Yes | Yes | Yes | 3 (already have) | - -**Step 4: Prioritize opportunities** - -| Priority | Criteria | Rationale | -|----------|---------|-----------| -| Highest | Links to 3+ competitors, DR 50+, relevant | If all competitors have it, it is likely linkable | -| High | Links to 2+ competitors, DR 30+, relevant | Strong signal of willingness to link in niche | -| Medium | Links to 1 competitor, DR 50+, relevant | May be less accessible but high value | -| Lower | Links to 1 competitor, DR <30, or low relevance | Diminishing returns | - -**Step 5: Analyze link context** -For each high-priority opportunity, visit the actual linking page to understand: -- Why did they link to your competitor? (resource page, mention, guest post, etc.) -- What content on your site could replace or complement that link? -- What outreach angle would work? (broken link, better resource, relationship) - -**Step 6: Create outreach plan** -Build a prioritized list with contact information, outreach angle, and template selection. - ---- - -## 4. Disavow File Format Guide - -### When to Disavow - -Only disavow links when you have clear evidence of risk. Unnecessary disavow can hurt your rankings. - -| Situation | Disavow? | Reasoning | -|-----------|----------|-----------| -| Obvious PBN links | Yes | Clear manipulation signal | -| Paid links you cannot get removed | Yes | After attempting removal | -| Spam attack (negative SEO) | Yes | Protect from third-party manipulation | -| Low-quality directory links | Maybe | Only if pattern is excessive | -| Foreign language spam | Yes | If clearly unnatural | -| Low-DA sites with real content | No | Low quality is not toxic | -| Nofollow links from any source | No | Already nofollowed; no risk | - -### Disavow File Format - -The disavow file is a plain text file (.txt) uploaded to Google Search Console. - -``` -# Disavow file for example.com -# Generated: [date] -# Reason: Toxic link cleanup - -# Individual URLs to disavow -https://spam-site.com/page-with-link -https://another-spam.com/toxic-page - -# Entire domains to disavow (use for sites with multiple toxic links) -domain:link-farm-example.com -domain:pbn-network-site.com -domain:spam-directory.net -``` - -### Disavow File Best Practices - -| Practice | Why | -|----------|-----| -| Comment every entry or group | Future auditors need to understand why | -| Use `domain:` for sites with multiple bad links | More thorough than individual URLs | -| Use individual URLs when only one page is toxic | Avoid disavowing good links from the same domain | -| Keep a changelog | Track what was added and when | -| Review quarterly | Remove entries if domains have been cleaned up | -| Never disavow your own domain | Common mistake that causes severe damage | -| Back up before uploading | Keep previous version in case of errors | - -### Disavow Review Workflow - -| Step | Action | Tool | -|------|--------|------| -| 1 | Export full backlink profile | ~~link database | -| 2 | Filter for known toxic patterns | Spam score, DR <10, foreign spam | -| 3 | Manual review of flagged links | Visit each flagged domain | -| 4 | Attempt removal via email first | Contact webmasters | -| 5 | Wait 2 weeks for removal responses | Track outreach results | -| 6 | Add non-removed toxic links to disavow | Format as .txt file | -| 7 | Upload to Google Search Console | Disavow Links tool | -| 8 | Document all actions and dates | Internal records | -| 9 | Re-check in 4-6 weeks | Verify processing | - ---- - -## 5. Link Profile Health Benchmarks - -### Healthy Profile Indicators - -| Metric | Healthy Range | Warning Sign | Critical | -|--------|-------------|--------------|----------| -| Dofollow ratio | 60-80% | >90% | >95% | -| Exact match anchor % | <15% | 15-25% | >25% | -| Brand anchor % | 25-45% | <15% | <5% | -| Toxic link % | <5% | 5-10% | >10% | -| Referring domain growth | Positive, steady | Flat | Declining | -| Average linking DR | 25+ | 15-25 | <15 | -| Link diversity (unique domains / total links) | >0.3 | 0.1-0.3 | <0.1 | -| Topical relevance (sampled) | >60% | 40-60% | <40% | - -### Industry-Specific Benchmarks - -Authority expectations vary significantly by industry vertical. - -| Industry | Typical DR Range (Top 10 Sites) | Typical Referring Domains | Link Difficulty | -|----------|-------------------------------|--------------------------|----------------| -| Finance / Insurance | DR 60-90 | 5,000-50,000+ | Very High | -| Health / Medical | DR 50-85 | 3,000-30,000+ | Very High | -| Technology / SaaS | DR 40-80 | 1,000-20,000+ | High | -| E-commerce (general) | DR 35-75 | 500-15,000+ | High | -| Legal | DR 40-70 | 1,000-10,000+ | High | -| Education | DR 50-90 | 2,000-25,000+ | Medium-High | -| Local services | DR 15-45 | 50-500 | Medium | -| B2B niche | DR 25-60 | 200-5,000+ | Medium | -| Blog / Content site | DR 20-70 | 100-10,000+ | Medium | -| New startup | DR 5-25 | 10-200 | Starting point | - -_Note: These are general ranges. Actual requirements depend on your specific keyword competition._ diff --git a/.agents/skills/backlink-analyzer/references/outreach-templates.md b/.agents/skills/backlink-analyzer/references/outreach-templates.md deleted file mode 100644 index c6c25cf4b9..0000000000 --- a/.agents/skills/backlink-analyzer/references/outreach-templates.md +++ /dev/null @@ -1,578 +0,0 @@ -# Outreach Templates - -Copy-ready outreach email templates for each link building strategy. Includes subject line variations, follow-up sequences, personalization tips, and response handling scripts. - ---- - -## General Outreach Principles - -Before using any template, follow these rules: - -| Principle | Why It Matters | -|-----------|---------------| -| **Personalize every email** | Generic emails get deleted. Reference specific content. | -| **Lead with value** | Explain what is in it for them before making your ask. | -| **Keep it short** | 100-150 words maximum for initial outreach. | -| **One clear ask** | Do not combine multiple requests in one email. | -| **Professional sender address** | Use name@yourdomain.com, not a free email provider. | -| **No attachments on first email** | Attachments trigger spam filters and reduce trust. | -| **Follow up (once or twice)** | Most responses come from follow-ups, not initial emails. | -| **Track everything** | Log outreach in a spreadsheet or CRM for accountability. | - ---- - -## 1. Broken Link Building Templates - -### Template 1A: Broken Link — Direct Approach - -**Subject:** Found a broken link on your [topic] page - -**Body:** - -Hi [Name], - -I was reading your article on [specific article title] — great resource on [brief compliment about content]. - -I noticed the link to [description of broken link resource] in the [section name] section seems to be broken (returns a 404). - -I recently published a guide on [your topic] that covers similar ground: [Your URL] - -It might work as a replacement if you are updating the page. Either way, wanted to give you a heads-up about the broken link. - -Best, -[Your Name] - ---- - -### Template 1B: Broken Link — Roundup Page - -**Subject:** Quick heads-up about [their page title] - -**Body:** - -Hi [Name], - -Your [topic] resource page is one of my go-to references — really well curated. - -I noticed [X] links on the page are no longer working: -- [Broken URL 1] — returns 404 -- [Broken URL 2] — domain expired - -If you are updating the page, I have a [content type] that covers [topic]: [Your URL] - -Happy to suggest other replacement resources too if that would help. - -Thanks for maintaining such a useful list, -[Your Name] - ---- - -### Follow-Up (5-7 days after initial email) - -**Subject:** Re: [original subject] - -**Body:** - -Hi [Name], - -Just bumping this up in case it got buried. I spotted a broken link on your [page title] and thought you would want to know. - -No worries if the page is staying as-is — just wanted to make sure you saw it. - -[Your Name] - ---- - -## 2. Guest Post Pitching Templates - -### Template 2A: Guest Post — Topic Pitch - -**Subject:** Guest post idea: [proposed title] - -**Body:** - -Hi [Name], - -I have been following [their site name] for a while — your recent piece on [specific article] was especially [specific compliment]. - -I would love to contribute a guest post on [proposed topic]. Here is what I have in mind: - -**Title:** [Proposed title] -**Angle:** [1-2 sentences describing the unique angle] -**Why your readers would care:** [1 sentence on value to their audience] - -I have written for [1-2 relevant publications] previously. Here are a couple of samples: -- [Sample URL 1] -- [Sample URL 2] - -Would this be a fit for [their site name]? - -Best, -[Your Name] -[Your title / credentials] - ---- - -### Template 2B: Guest Post — Expertise Pitch - -**Subject:** [Your expertise area] contributor for [their site] - -**Body:** - -Hi [Name], - -I am [Your Name], a [your role/expertise] with [X years] experience in [field]. I noticed [their site] covers [topic area] extensively and thought I could contribute something useful. - -Three topic ideas that might work for your audience: - -1. [Title idea 1] — [one-line description] -2. [Title idea 2] — [one-line description] -3. [Title idea 3] — [one-line description] - -Happy to adjust angles or pitch different ideas. I can also share writing samples if helpful. - -Thanks, -[Your Name] - ---- - -### Follow-Up (7-10 days after initial email) - -**Subject:** Re: [original subject] - -**Body:** - -Hi [Name], - -Checking back on my guest post pitch. I know editorial calendars fill up fast, so no worries if the timing does not work. - -If any of the topics I suggested could work down the road, happy to keep in touch for a future slot. - -[Your Name] - ---- - -## 3. Resource Page Outreach Templates - -### Template 3A: Resource Page — Direct Suggestion - -**Subject:** Resource for your [topic] page - -**Body:** - -Hi [Name], - -I found your [topic] resource page while researching [related topic] — it is a really well-organized collection. - -I thought [your resource title] might be a good addition to the [specific section] section. It covers [brief description of what it covers and why it is useful]. - -Here is the link: [Your URL] - -No pressure at all — just thought it might be useful for your readers. - -Best, -[Your Name] - ---- - -### Template 3B: Resource Page — Value-Add Approach - -**Subject:** A few resources for your [topic] page - -**Body:** - -Hi [Name], - -I have been using your [topic] resource page as a reference and really appreciate the curation work. - -I wanted to suggest a few resources you might consider adding (not all mine): - -1. [External resource title] — [URL] — Great for [reason] -2. [Your resource title] — [Your URL] — Covers [topic] -3. [Another external resource] — [URL] — Useful for [reason] - -I included a couple of third-party resources alongside mine since they genuinely complement your page. - -Thanks for maintaining such a useful list, -[Your Name] - ---- - -## 4. Unlinked Mention Outreach Templates - -### Template 4A: Unlinked Brand Mention - -**Subject:** Thanks for mentioning [brand name] - -**Body:** - -Hi [Name], - -Just came across your article [article title] — thanks for mentioning [your brand/product]. Really appreciate the kind words about [specific thing they said]. - -Quick request: would you be able to add a link to [Your URL] where you mention us? It would help your readers find us directly and help us out with attribution. - -Totally understand if it is not possible. Either way, glad you found [product/service] useful. - -Best, -[Your Name] - ---- - -### Template 4B: Unlinked Data/Research Mention - -**Subject:** Thanks for citing our [research/data] - -**Body:** - -Hi [Name], - -I noticed you referenced our [study/statistic/data point] in your article [article title]. Thanks for citing our research. - -If you are able to add a link to the original source, it would help your readers verify the data and access the full [study/report]: [Your URL] - -We actually just updated it with [new data point] that might be relevant to your piece as well. - -Thanks, -[Your Name] - ---- - -### Follow-Up (5-7 days) - -**Subject:** Re: [original subject] - -**Body:** - -Hi [Name], - -Just a quick follow-up on adding a link for the mention of [brand/research] in your article. I know updating published content can be low priority, so no rush. - -If it helps, the exact URL is: [Your URL] - -Thanks again for the mention, -[Your Name] - ---- - -## 5. Digital PR / Data-Driven Outreach Templates - -### Template 5A: Original Research Pitch - -**Subject:** New data: [key finding headline] - -**Body:** - -Hi [Name], - -We just published [study/survey/analysis] that found [most surprising or newsworthy finding]. - -Key findings: -- [Finding 1 — the headline stat] -- [Finding 2 — supporting data] -- [Finding 3 — counterintuitive insight] - -Full report: [Your URL] - -Given your coverage of [their beat/topic], I thought this might be worth a mention. Happy to provide additional data, quotes, or custom analysis for your audience. - -[Your Name] -[Your title] - ---- - -### Template 5B: Expert Commentary Pitch - -**Subject:** Expert source for your [topic] coverage - -**Body:** - -Hi [Name], - -I saw you are covering [topic/trend] and thought I could be a useful source. - -I am [Your Name], [your credentials — brief]. I have [specific experience relevant to their coverage]. - -A couple of angles I could provide commentary on: -- [Angle 1] -- [Angle 2] - -Happy to jump on a quick call or provide written quotes. Available on short notice. - -[Your Name] -[Your title, company] -[Phone number for urgent requests] - ---- - -## 6. HARO / Source Request Templates - -### Template 6A: HARO Response — Expert Source - -**Subject:** [HARO] Re: [original query title] - -**Body:** - -Hi [Journalist Name], - -Re: your query on [topic]. - -**Source:** [Your Name], [Title] at [Company] -**Credentials:** [1-2 sentences on relevant expertise] - -**Response:** - -[2-3 paragraphs directly answering their query. Be specific, quotable, and concise. Lead with the most valuable insight.] - -**Key quote:** "[One highly quotable sentence they can use directly]" - -Happy to elaborate or provide additional data. Available at [email] or [phone]. - -[Your Name] - ---- - -### Template 6B: HARO Response — Data Source - -**Subject:** [HARO] Re: [original query title] — with data - -**Body:** - -Hi [Journalist Name], - -For your piece on [topic], here is data from our [study/platform/analysis]: - -- [Statistic 1]: [data point with context] -- [Statistic 2]: [data point with context] -- [Statistic 3]: [data point with context] - -Source: [Your URL for attribution] - -**Expert quote from [Your Name], [Title]:** -"[Quotable insight interpreting the data]" - -Full dataset available if needed for additional analysis. - -[Your Name] -[Contact info] - ---- - -## 7. Skyscraper Technique Templates - -### Template 7A: Skyscraper — Better Resource - -**Subject:** Updated resource on [topic] - -**Body:** - -Hi [Name], - -I noticed you linked to [competitor's article title] in your post on [their article title]. - -We just published an updated version of that topic: [Your URL] - -It includes [specific improvements over the original]: -- [Improvement 1 — e.g., "2025 data instead of 2022"] -- [Improvement 2 — e.g., "interactive calculator included"] -- [Improvement 3 — e.g., "covers 3 additional sub-topics"] - -If you think it would be a better resource for your readers, I would appreciate you considering swapping the link. No worries either way. - -Best, -[Your Name] - ---- - -## 8. Relationship Building Templates - -### Template 8A: Initial Relationship — No Ask - -**Subject:** Loved your piece on [topic] - -**Body:** - -Hi [Name], - -Just read your article on [specific article title] and wanted to say it was genuinely one of the best pieces I have read on [topic]. - -The section on [specific section] was especially useful — I actually shared it with my team and we are implementing [specific takeaway]. - -No ask here, just wanted to let you know your work is making an impact. - -[Your Name] - ---- - -### Template 8B: Follow-Up After Relationship Building (2-4 weeks later) - -**Subject:** [New topic] — thought of your readers - -**Body:** - -Hi [Name], - -Following up on our exchange a few weeks back. I just published something your audience might find useful: [Your URL] - -It covers [brief description] with a focus on [specific angle]. - -Would love to hear your thoughts if you get a chance to read it. - -Best, -[Your Name] - ---- - -## 9. Follow-Up Sequences - -### Standard Follow-Up Timeline - -| Email | Timing | Purpose | Tone | -|-------|--------|---------|------| -| Initial outreach | Day 0 | Make the ask | Professional, value-forward | -| Follow-up 1 | Day 5-7 | Gentle reminder | Casual, brief | -| Follow-up 2 | Day 14 | Final attempt | Short, no-pressure close | -| Stop | After follow-up 2 | Do not send more | Move on | - -### Follow-Up 2 Template (Final Attempt) - -**Subject:** Re: [original subject] - -**Body:** - -Hi [Name], - -Last follow-up on this — I know you are busy. - -[One-sentence reminder of what you asked] - -If the timing is not right, no worries at all. Feel free to bookmark [Your URL] for whenever it might be useful. - -Best, -[Your Name] - ---- - -## 10. Response Handling Scripts - -### Positive Response — They Will Add the Link - -**Reply:** - -That is great, [Name] — really appreciate it. - -The exact URL is: [Your URL] -Preferred anchor text (just a suggestion): [anchor text] - -Let me know if you need anything else. Happy to return the favor anytime. - -Best, -[Your Name] - ---- - -### Positive Response — They Want a Guest Post - -**Reply:** - -Wonderful, I would love to contribute. Here is what I am thinking: - -**Title:** [Proposed title] -**Outline:** -1. [Section 1] -2. [Section 2] -3. [Section 3] - -**Estimated length:** [word count] -**Delivery timeline:** [date] - -Do you have any editorial guidelines or specific requirements I should follow? - -Best, -[Your Name] - ---- - -### Neutral Response — They Are Interested but Noncommittal - -**Reply:** - -Totally understand, [Name]. No rush at all. - -I will keep [Your URL] updated with [latest data/content], so it will be here whenever the timing works. - -Feel free to reach out if I can ever be a source or contributor for [their site]. - -Best, -[Your Name] - ---- - -### Negative Response — They Decline - -**Reply:** - -Completely understand, [Name]. Thanks for letting me know. - -If anything changes or if I can ever be helpful as a source, do not hesitate to reach out. - -Best, -[Your Name] - -_Note: Never argue, push back, or send additional follow-ups after a decline. Keep the relationship positive for potential future opportunities._ - ---- - -### No Response — After Full Sequence - -Do not send further emails. Add to a "re-engage in 3-6 months" list. When you re-engage, use a completely new angle or piece of content. Never reference the fact that they did not respond previously. - ---- - -## 11. Personalization Tips - -### Research Checklist Before Sending - -| Check | Where to Find | How to Use | -|-------|-------------|-----------| -| Their name (correct spelling) | About page, LinkedIn, byline | Use in greeting and body | -| Recent article they wrote | Their blog, Google "[name] + [site]" | Reference in opening line | -| Their social media | Twitter/X, LinkedIn | Mention a recent post or shared interest | -| Their publication's audience | About page, media kit | Tailor your value proposition | -| Content they have linked to before | ~~link database, their recent articles | Match the type of content they prefer | -| How they prefer to be contacted | Website contact page, social bio | Some prefer Twitter DMs over email | - -### Personalization Levels - -| Level | Effort | Response Rate Impact | When to Use | -|-------|--------|---------------------|-------------| -| **Zero** (template only) | Minimal | Baseline (lowest) | Never recommended | -| **Basic** (name + site name) | Low | +20-30% | Bulk outreach to lower-priority targets | -| **Moderate** (+ specific article reference) | Medium | +50-80% | Standard outreach to mid-tier targets | -| **Deep** (+ shared interest, social reference, mutual connection) | High | +100-200% | High-priority targets (DR 60+ sites, key relationships) | - ---- - -## 12. Subject Line Variations by Strategy - -### High-Performing Subject Line Patterns - -| Strategy | Subject Lines (pick one) | -|----------|------------------------| -| **Broken link** | "Found a broken link on your [topic] page" / "Quick heads-up about [page title]" / "Broken resource on [their site]" | -| **Guest post** | "Guest post idea: [title]" / "[Topic] contributor for [their site]" / "Content pitch for [their site]" | -| **Resource page** | "Resource for your [topic] page" / "Suggestion for your [topic] list" / "Addition for [page title]?" | -| **Unlinked mention** | "Thanks for mentioning [brand]" / "Re: your mention of [brand/data]" / "Quick favor re: [their article]" | -| **Digital PR** | "New data: [headline stat]" / "Exclusive: [finding]" / "[Topic] research for your coverage" | -| **Skyscraper** | "Updated resource on [topic]" / "Better version of [competitor content]" / "Thought you'd want to see this" | - -### Subject Lines to Avoid - -| Avoid | Why | -|-------|-----| -| "Link exchange opportunity" | Screams spam and violates Google guidelines | -| "SEO partnership" | Immediately signals manipulative intent | -| ALL CAPS or excessive punctuation | Triggers spam filters, looks unprofessional | -| "I'd love a backlink" | Too direct; leads with your need, not their value | -| Misleading subjects ("Re:" when no prior conversation) | Damages trust immediately | -| Long subjects (>60 characters) | Gets truncated in inbox, lower open rates | diff --git a/.agents/skills/improve-codebase-architecture/DEEPENING.md b/.agents/skills/codebase-design/DEEPENING.md similarity index 80% rename from .agents/skills/improve-codebase-architecture/DEEPENING.md rename to .agents/skills/codebase-design/DEEPENING.md index ecaf5d7dcf..cd94075cfd 100644 --- a/.agents/skills/improve-codebase-architecture/DEEPENING.md +++ b/.agents/skills/codebase-design/DEEPENING.md @@ -1,6 +1,6 @@ # Deepening -How to deepen a cluster of shallow modules safely, given its dependencies. Assumes the vocabulary in [LANGUAGE.md](LANGUAGE.md) — **module**, **interface**, **seam**, **adapter**. +How to deepen a cluster of shallow modules safely, given its dependencies. Assumes the vocabulary in [SKILL.md](SKILL.md): **module**, **interface**, **seam**, **adapter**. ## Dependency categories @@ -8,7 +8,7 @@ When assessing a candidate for deepening, classify its dependencies. The categor ### 1. In-process -Pure computation, in-memory state, no I/O. Always deepenable — merge the modules and test through the new interface directly. No adapter needed. +Pure computation, in-memory state, no I/O. Always deepenable: merge the modules and test through the new interface directly. No adapter needed. ### 2. Local-substitutable @@ -31,7 +31,7 @@ Third-party services (Stripe, Twilio, etc.) you don't control. The deepened modu ## Testing strategy: replace, don't layer -- Old unit tests on shallow modules become waste once tests at the deepened module's interface exist — delete them. +- Old unit tests on shallow modules become waste once tests at the deepened module's interface exist; delete them. - Write new tests at the deepened module's interface. The **interface is the test surface**. - Tests assert on observable outcomes through the interface, not internal state. -- Tests should survive internal refactors — they describe behaviour, not implementation. If a test has to change when the implementation changes, it's testing past the interface. +- Tests should survive internal refactors, since they describe behaviour, not implementation. If a test has to change when the implementation changes, it's testing past the interface. diff --git a/.agents/skills/improve-codebase-architecture/INTERFACE-DESIGN.md b/.agents/skills/codebase-design/DESIGN-IT-TWICE.md similarity index 56% rename from .agents/skills/improve-codebase-architecture/INTERFACE-DESIGN.md rename to .agents/skills/codebase-design/DESIGN-IT-TWICE.md index 3197723a0d..7edc861a31 100644 --- a/.agents/skills/improve-codebase-architecture/INTERFACE-DESIGN.md +++ b/.agents/skills/codebase-design/DESIGN-IT-TWICE.md @@ -1,8 +1,8 @@ -# Interface Design +# Design It Twice -When the user wants to explore alternative interfaces for a chosen deepening candidate, use this parallel sub-agent pattern. Based on "Design It Twice" (Ousterhout) — your first idea is unlikely to be the best. +When the user wants to explore alternative interfaces for a chosen deepening candidate, use this parallel sub-agent pattern. Based on "Design It Twice" (Ousterhout): your first idea is unlikely to be the best. -Uses the vocabulary in [LANGUAGE.md](LANGUAGE.md) — **module**, **interface**, **seam**, **adapter**, **leverage**. +Uses the vocabulary in [SKILL.md](SKILL.md): **module**, **interface**, **seam**, **adapter**, **leverage**. ## Process @@ -12,33 +12,33 @@ Before spawning sub-agents, write a user-facing explanation of the problem space - The constraints any new interface would need to satisfy - The dependencies it would rely on, and which category they fall into (see [DEEPENING.md](DEEPENING.md)) -- A rough illustrative code sketch to ground the constraints — not a proposal, just a way to make the constraints concrete +- A rough illustrative code sketch to ground the constraints, not a proposal, just a way to make the constraints concrete Show this to the user, then immediately proceed to Step 2. The user reads and thinks while the sub-agents work in parallel. ### 2. Spawn sub-agents -Spawn 3+ sub-agents in parallel using the Agent tool. Each must produce a **radically different** interface for the deepened module. +Spawn 3+ sub-agents in parallel. Each must produce a **radically different** interface for the deepened module. Prompt each sub-agent with a separate technical brief (file paths, coupling details, dependency category from [DEEPENING.md](DEEPENING.md), what sits behind the seam). The brief is independent of the user-facing problem-space explanation in Step 1. Give each agent a different design constraint: -- Agent 1: "Minimize the interface — aim for 1–3 entry points max. Maximise leverage per entry point." -- Agent 2: "Maximise flexibility — support many use cases and extension." -- Agent 3: "Optimise for the most common caller — make the default case trivial." +- Agent 1: "Minimize the interface: aim for 1–3 entry points max. Maximise leverage per entry point." +- Agent 2: "Maximise flexibility: support many use cases and extension." +- Agent 3: "Optimise for the most common caller: make the default case trivial." - Agent 4 (if applicable): "Design around ports & adapters for cross-seam dependencies." -Include both [LANGUAGE.md](LANGUAGE.md) vocabulary and CONTEXT.md vocabulary in the brief so each sub-agent names things consistently with the architecture language and the project's domain language. +Include both [SKILL.md](SKILL.md) vocabulary and CONTEXT.md vocabulary in the brief so each sub-agent names things consistently with the architecture language and the project's domain language. Each sub-agent outputs: -1. Interface (types, methods, params — plus invariants, ordering, error modes) +1. Interface (types, methods, params, plus invariants, ordering, error modes) 2. Usage example showing how callers use it 3. What the implementation hides behind the seam 4. Dependency strategy and adapters (see [DEEPENING.md](DEEPENING.md)) -5. Trade-offs — where leverage is high, where it's thin +5. Trade-offs: where leverage is high, where it's thin ### 3. Present and compare Present designs sequentially so the user can absorb each one, then compare them in prose. Contrast by **depth** (leverage at the interface), **locality** (where change concentrates), and **seam placement**. -After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated — the user wants a strong read, not a menu. +After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated: the user wants a strong read, not a menu. diff --git a/.agents/skills/codebase-design/SKILL.md b/.agents/skills/codebase-design/SKILL.md new file mode 100644 index 0000000000..3f63c8146d --- /dev/null +++ b/.agents/skills/codebase-design/SKILL.md @@ -0,0 +1,114 @@ +--- +name: codebase-design +description: Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary. +--- + +# Codebase Design + +Design **deep modules**: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface. Use this language and these principles wherever code is being designed or restructured. The aim is leverage for callers, locality for maintainers, and testability for everyone. + +## Glossary + +Use these terms exactly: don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point. + +**Module**: anything with an interface and an implementation. Deliberately scale-agnostic: a function, class, package, or tier-spanning slice. _Avoid_: unit, component, service. + +**Interface**: everything a caller must know to use the module correctly: the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. _Avoid_: API, signature (too narrow, they refer only to the type-level surface). + +**Implementation**: what's inside a module, its body of code. Distinct from **Adapter**: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise. + +**Depth**: leverage at the interface. The amount of behaviour a caller (or test) can exercise per unit of interface they have to learn. A module is **deep** when a large amount of behaviour sits behind a small interface, **shallow** when the interface is nearly as complex as the implementation. + +**Seam** _(Michael Feathers)_: a place where you can alter behaviour without editing in that place; the *location* at which a module's interface lives. Where to put the seam is its own design decision, distinct from what goes behind it. _Avoid_: boundary (overloaded with DDD's bounded context). + +**Adapter**: a concrete thing that satisfies an interface at a seam. Describes *role* (what slot it fills), not substance (what's inside). + +**Leverage**: what callers get from depth. More capability per unit of interface they learn. One implementation pays back across N call sites and M tests. + +**Locality**: what maintainers get from depth. Change, bugs, knowledge, and verification concentrate in one place rather than spreading across callers. Fix once, fixed everywhere. + +## Deep vs shallow + +**Deep module** = small interface + lots of implementation: + +``` +┌─────────────────────┐ +│ Small Interface │ ← Few methods, simple params +├─────────────────────┤ +│ │ +│ Deep Implementation│ ← Complex logic hidden +│ │ +└─────────────────────┘ +``` + +**Shallow module** = large interface + little implementation (avoid): + +``` +┌─────────────────────────────────┐ +│ Large Interface │ ← Many methods, complex params +├─────────────────────────────────┤ +│ Thin Implementation │ ← Just passes through +└─────────────────────────────────┘ +``` + +When designing an interface, ask: + +- Can I reduce the number of methods? +- Can I simplify the parameters? +- Can I hide more complexity inside? + +## Principles + +- **Depth is a property of the interface, not the implementation.** A deep module can be internally composed of small, mockable, swappable parts; they just aren't part of the interface. A module can have **internal seams** (private to its implementation, used by its own tests) as well as the **external seam** at its interface. +- **The deletion test.** Imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep. +- **The interface is the test surface.** Callers and tests cross the same seam. If you want to test *past* the interface, the module is probably the wrong shape. +- **One adapter means a hypothetical seam. Two adapters means a real one.** Don't introduce a seam unless something actually varies across it. + +## Designing for testability + +Good interfaces make testing natural: + +1. **Accept dependencies, don't create them.** + + ```typescript + // Testable + function processOrder(order, paymentGateway) {} + + // Hard to test + function processOrder(order) { + const gateway = new StripeGateway(); + } + ``` + +2. **Return results, don't produce side effects.** + + ```typescript + // Testable + function calculateDiscount(cart): Discount {} + + // Hard to test + function applyDiscount(cart): void { + cart.total -= discount; + } + ``` + +3. **Small surface area.** Fewer methods = fewer tests needed. Fewer params = simpler test setup. + +## Relationships + +- A **Module** has exactly one **Interface** (the surface it presents to callers and tests). +- **Depth** is a property of a **Module**, measured against its **Interface**. +- A **Seam** is where a **Module**'s **Interface** lives. +- An **Adapter** sits at a **Seam** and satisfies the **Interface**. +- **Depth** produces **Leverage** for callers and **Locality** for maintainers. + +## Rejected framings + +- **Depth as ratio of implementation-lines to interface-lines** (Ousterhout): rewards padding the implementation. We use depth-as-leverage instead. +- **"Interface" as the TypeScript `interface` keyword or a class's public methods**: too narrow: interface here includes every fact a caller must know. +- **"Boundary"**: overloaded with DDD's bounded context. Say **seam** or **interface**. + +## Going deeper + +- **Deepening a cluster given its dependencies**, see [DEEPENING.md](DEEPENING.md): dependency categories, seam discipline, and replace-don't-layer testing. +- **Exploring alternative interfaces**, see [DESIGN-IT-TWICE.md](DESIGN-IT-TWICE.md): spin up parallel sub-agents to design the interface several radically different ways, then compare on depth, locality, and seam placement. diff --git a/.agents/skills/codebase-design/agents/openai.yaml b/.agents/skills/codebase-design/agents/openai.yaml new file mode 100644 index 0000000000..3180715edb --- /dev/null +++ b/.agents/skills/codebase-design/agents/openai.yaml @@ -0,0 +1,3 @@ +interface: + display_name: "Codebase Design" + short_description: "Vocabulary for deep-module design" diff --git a/.agents/skills/color-system/PALETTE.css b/.agents/skills/color-system/PALETTE.css deleted file mode 100644 index d9e2283e92..0000000000 --- a/.agents/skills/color-system/PALETTE.css +++ /dev/null @@ -1,146 +0,0 @@ -:root { - --card: #fbfaf7; - --ring: #2e2e2e; - --input: #d6d6d6; - --muted: #ebeae6; - --accent: #e5e4df; - --border: #dedede; - --radius: 0.5rem; - --chart-1: #f26a4b; - --chart-2: #1e1e1e; - --chart-3: #626262; - --chart-4: #9b9b9b; - --chart-5: #c9c9c9; - --popover: #fbfaf7; - --primary: #2e2e2e; - --sidebar: #f3f2ee; - --spacing: 0.25rem; - --font-mono: "IBM Plex Mono", monospace; - --font-sans: "Instrument Sans", sans-serif; - --secondary: #f1f0ec; - --background: #f7f6f2; - --app-shell: #f3f2ee; - --font-serif: "Newsreader", serif; - --foreground: #1e1e1e; - --destructive: #dc2626; - --notice: #3f74c8; - --shadow-blur: 10px; - --shadow-color: #000000; - --sidebar-ring: #2e2e2e; - --shadow-spread: 0px; - --letter-spacing: 0.01em; - --shadow-opacity: 0.1; - --sidebar-accent: #e5e4df; - --sidebar-border: #dedede; - --card-foreground: #1e1e1e; - --shadow-offset-x: 0px; - --shadow-offset-y: 4px; - --sidebar-primary: #2e2e2e; - --muted-foreground: #626262; - --accent-foreground: #2e2e2e; - --popover-foreground: #1e1e1e; - --primary-foreground: #f7f7f7; - --sidebar-foreground: #1e1e1e; - --secondary-foreground: #2e2e2e; - --destructive-foreground: #ffffff; - --sidebar-accent-foreground: #2e2e2e; - --sidebar-primary-foreground: #f7f7f7; -} - -.dark { - --card: #1c1c1c; - --ring: #d1cfc0; - --input: #2c2c2c; - --muted: #2a2a2a; - --accent: #363636; - --border: #2c2c2c; - --radius: 0.5rem; - --chart-1: #f26a4b; - --chart-2: #d9cfc2; - --chart-3: #8e8a83; - --chart-4: #5c5a56; - --chart-5: #3b3b3b; - --popover: #1c1c1c; - --primary: #d9aa90; - --sidebar: #141414; - --spacing: 0.25rem; - --font-mono: "IBM Plex Mono", monospace; - --font-sans: "Instrument Sans", sans-serif; - --secondary: #222222; - --background: #141414; - --app-shell: #101010; - --font-serif: "Newsreader", serif; - --foreground: #e8e3da; - --destructive: #ef4444; - --notice: #3f74c8; - --shadow-blur: 15px; - --shadow-color: #000000; - --sidebar-ring: #d1cfc0; - --shadow-spread: 0px; - --letter-spacing: 0.01em; - --shadow-opacity: 0.3; - --sidebar-accent: #363636; - --sidebar-border: #2c2c2c; - --card-foreground: #e8e3da; - --shadow-offset-x: 0px; - --shadow-offset-y: 6px; - --sidebar-primary: #d1cfc0; - --muted-foreground: #8e8a83; - --accent-foreground: #d1cfc0; - --popover-foreground: #e8e3da; - --primary-foreground: #363636; - --sidebar-foreground: #e8e3da; - --secondary-foreground: #d1cfc0; - --destructive-foreground: #ffffff; - --sidebar-accent-foreground: #d1cfc0; - --sidebar-primary-foreground: #363636; -} - -@theme inline { - --color-card: var(--card); - --color-ring: var(--ring); - --color-input: var(--input); - --color-muted: var(--muted); - --color-accent: var(--accent); - --color-border: var(--border); - --color-radius: var(--radius); - --color-chart-1: var(--chart-1); - --color-chart-2: var(--chart-2); - --color-chart-3: var(--chart-3); - --color-chart-4: var(--chart-4); - --color-chart-5: var(--chart-5); - --color-popover: var(--popover); - --color-primary: var(--primary); - --color-sidebar: var(--sidebar); - --color-spacing: var(--spacing); - --color-font-mono: var(--font-mono); - --color-font-sans: var(--font-sans); - --color-secondary: var(--secondary); - --color-background: var(--background); - --color-app-shell: var(--app-shell); - --color-font-serif: var(--font-serif); - --color-foreground: var(--foreground); - --color-destructive: var(--destructive); - --color-notice: var(--notice); - --color-shadow-blur: var(--shadow-blur); - --color-shadow-color: var(--shadow-color); - --color-sidebar-ring: var(--sidebar-ring); - --color-shadow-spread: var(--shadow-spread); - --color-letter-spacing: var(--letter-spacing); - --color-shadow-opacity: var(--shadow-opacity); - --color-sidebar-accent: var(--sidebar-accent); - --color-sidebar-border: var(--sidebar-border); - --color-card-foreground: var(--card-foreground); - --color-shadow-offset-x: var(--shadow-offset-x); - --color-shadow-offset-y: var(--shadow-offset-y); - --color-sidebar-primary: var(--sidebar-primary); - --color-muted-foreground: var(--muted-foreground); - --color-accent-foreground: var(--accent-foreground); - --color-popover-foreground: var(--popover-foreground); - --color-primary-foreground: var(--primary-foreground); - --color-sidebar-foreground: var(--sidebar-foreground); - --color-secondary-foreground: var(--secondary-foreground); - --color-destructive-foreground: var(--destructive-foreground); - --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); - --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); -} diff --git a/.agents/skills/color-system/SKILL.md b/.agents/skills/color-system/SKILL.md deleted file mode 100644 index 5c0f7b7260..0000000000 --- a/.agents/skills/color-system/SKILL.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -name: color-system -description: Applies and reviews SurfSense's canonical light and dark color palette, semantic design tokens, typography, charts, borders, shadows, and theme mappings. Use when creating or changing frontend colors, themes, component styling, data visualizations, or design tokens. -disable-model-invocation: true ---- - -# SurfSense Color System - -Use [PALETTE.css](PALETTE.css) as the canonical palette contract. Preserve its -token names and values unless the user explicitly requests a palette change. - -## Principles - -1. Use semantic tokens such as `background`, `foreground`, `primary`, - `muted`, `accent`, `destructive`, `border`, and their foreground pairs. - Do not use raw hex values in components. -2. Use the matching foreground token for text and icons placed on a semantic - surface: `primary-foreground` on `primary`, `card-foreground` on `card`, - and so on. -3. Use `chart-1` through `chart-5` for data series. Do not repurpose chart - colors as component state colors. -4. Use `ring` for focus indicators and `border` or `input` for boundaries. - Never remove a visible keyboard focus indicator. -5. Use `muted-foreground` only for secondary text. Do not use it for small or - essential text unless its contrast passes WCAG. -6. Use `destructive` only for destructive actions, errors, or dangerous - states. Do not use chart colors to communicate errors. -7. Support both `:root` and `.dark`; never add a light-only semantic token. -8. Prefer existing semantic tokens over creating new aliases. Add a token only - when it represents a reusable semantic role that the palette does not cover. - -## Workflow - -1. Locate the active global CSS file from the target app's `components.json`; - this repository contains more than one frontend. -2. Compare the active theme with [PALETTE.css](PALETTE.css). Do not overwrite - unrelated CSS, animation, layout, or framework directives. -3. Apply palette values at the global token layer, not inside individual - components. -4. In components, use the project's semantic utility classes or CSS variables, - for example `bg-background text-foreground`, `bg-card - text-card-foreground`, and `border-border`. -5. Check affected foreground/background pairs in both themes. WCAG targets: - 4.5:1 for normal text, 3:1 for large text and meaningful UI boundaries. -6. Verify focus, hover, active, selected, disabled, destructive, chart, and - sidebar states when affected. - -## Guardrails - -- Do not invent intermediate shades to make one component look better. -- Do not use opacity to compensate for an incorrect semantic token when it - reduces text contrast. -- Do not replace the palette wholesale when the requested change concerns one - component. -- Report a contrast failure instead of silently changing canonical values. -- Treat the palette's `@theme inline` block as Tailwind v4 configuration. - Before applying it, verify the target app uses Tailwind v4 and preserve any - required non-color namespaces already present in its global stylesheet. - -## Known Accessibility Constraint - -The canonical dark `muted-foreground` (`#7a706a`) does not reach 4.5:1 for -normal text on dark `background` (4.11:1), `card` (3.90:1), or `muted` -(3.64:1). Preserve the palette, but do not use this token for essential or -small normal-weight dark-mode text. Report the conflict and request a palette -decision when no existing foreground token fits. - -## Coordination - -- For shadcn/ui composition and styling, also use `../shadcn/SKILL.md`. -- For React or Next.js implementation, also use - `../vercel-react-best-practices/SKILL.md`. -- For interface polish, color transitions, or reduced-motion behavior, use - `../make-interfaces-feel-better/SKILL.md` after implementation. - -## Output - -For implementation tasks, report: - -- which app and global stylesheet received the palette; -- whether both themes were updated; -- contrast or state risks that remain; -- validation performed. - -For audits, cite each issue by token pair and usage location, then recommend a -semantic-token correction before proposing a new color. diff --git a/.agents/skills/competitor-analysis/SKILL.md b/.agents/skills/competitor-analysis/SKILL.md deleted file mode 100644 index 11c1efc917..0000000000 --- a/.agents/skills/competitor-analysis/SKILL.md +++ /dev/null @@ -1,309 +0,0 @@ ---- -name: competitor-analysis -description: 'Analyze competitor SEO/GEO: keywords, content, backlinks, AI citations, traffic share gaps. 竞品分析/竞争对手' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when analyzing competitor SEO strategy, comparing domains, benchmarking against competitors, or finding competitor keywords and content gaps." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "medium" - tags: - - seo - - geo - - competitor-analysis - - competitive-intelligence - - benchmarking - - competitor-keywords - - competitor-backlinks - - market-analysis - - spyfu-alternative - - 竞品分析 - - 競合分析 - - 경쟁분석 - - analisis-competitivo - triggers: - # EN-formal - - "analyze competitors" - - "competitor SEO" - - "competitive analysis" - - "competitor keywords" - - "competitor backlinks" - - "market analysis" - - "competitive intelligence" - # EN-casual - - "what are my competitors doing" - - "what are they doing differently" - - "why do they rank higher" - - "spy on competitor SEO" - - "what are they doing better" - - "why do they outrank me" - # EN-question - - "who are my SEO competitors" - - "how do I beat my competitors" - - "why do competitors rank higher" - # EN-competitor - - "SpyFu alternative" - - "Semrush competitor analysis" - - "Ahrefs competitor tool" - # ZH-pro - - "竞品分析" - - "竞争对手分析" - - "竞品SEO" - - "对标分析" - - "竞争情报" - # ZH-casual - - "竞品怎么做的" - - "他们排名为什么比我高" - - "看看对手在干什么" - - "为什么他们排名好" - # JA - - "競合分析" - - "競合SEO分析" - - "ライバル分析" - # KO - - "경쟁 분석" - - "경쟁사 SEO" - - "경쟁사 키워드" - # ES - - "análisis de competidores" - - "análisis competitivo SEO" - # PT - - "análise de concorrentes" - # Misspellings - - "competitve analysis" - - "compeditor analysis" ---- - -# Competitor Analysis - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This research skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill provides comprehensive analysis of competitor SEO and GEO strategies, revealing what's working in your market and identifying opportunities to outperform the competition. - -**System role**: Research layer skill. It turns market signals into reusable strategic inputs for the rest of the library. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs reusable market intelligence that should influence strategy, not just an ad hoc answer. - -- Entering a new market or niche -- Planning content strategy based on competitor success -- Understanding why competitors rank higher -- Finding backlink and partnership opportunities -- Identifying content gaps competitors are missing -- Analyzing competitor AI citation strategies -- Benchmarking your SEO performance - -## What This Skill Does - -1. **Keyword Analysis**: Identifies keywords competitors rank for -2. **Content Audit**: Analyzes competitor content strategies and formats -3. **Backlink Profiling**: Reviews competitor link-building approaches -4. **Technical Assessment**: Evaluates competitor site health -5. **GEO Analysis**: Identifies how competitors appear in AI responses -6. **Gap Identification**: Finds opportunities competitors miss -7. **Strategy Extraction**: Reveals actionable insights from competitor success - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Basic Competitor Analysis - -``` -Analyze SEO strategy for [competitor URL] -``` - -``` -Compare my site [URL] against [competitor 1], [competitor 2], [competitor 3] -``` - -### Specific Analysis - -``` -What content is driving the most traffic for [competitor]? -``` - -``` -Analyze why [competitor] ranks #1 for [keyword] -``` - -### GEO-Focused Analysis - -``` -How is [competitor] getting cited in AI responses? What can I learn? -``` - -## Skill Contract - -**Expected output**: a prioritized research brief, evidence-backed findings, and a short handoff summary ready for `memory/research/`. - -- **Reads**: user goals, target market inputs, available tool data, and prior strategy from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing research deliverable plus a reusable summary that can be stored under `memory/research/`. -- **Promotes**: durable keyword priorities, competitor facts, entity candidates, and strategic decisions to `CLAUDE.md`, `memory/decisions.md`, and `memory/research/`; hand canonical entity work to `entity-optimizer`. -- **Next handoff**: use the `Next Best Skill` below when the findings are ready to drive action. - -## Data Sources - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~SEO tool + ~~analytics + ~~AI monitor connected:** -Automatically pull competitor keyword rankings, backlink profiles, top performing content, domain authority metrics from ~~SEO tool. Compare against your site's metrics from ~~analytics and ~~search console. Check AI citation patterns for both your site and competitors using ~~AI monitor. - -**With manual data only:** -Ask the user to provide: -1. Competitor URLs to analyze (2-5 recommended) -2. Your own site URL and current metrics (traffic, rankings if known) -3. Industry or niche context -4. Specific aspects to focus on (keywords, content, backlinks, etc.) -5. Any known competitor strengths or weaknesses - -Proceed with the full analysis using provided data. Note in the output which metrics are from automated collection vs. user-provided data. - -## Instructions - -When a user requests competitor analysis: - -1. **Identify Competitors** - - If not specified, help identify competitors: - - ```markdown - ### Competitor Identification Framework - - **Direct Competitors** (same product/service) - - Search "[your main keyword]" and note top 5 organic results - - Check who's advertising for your keywords - - Ask: Who do customers compare you to? - - **Indirect Competitors** (different solution, same problem) - - Search problem-focused keywords - - Look at alternative solutions - - **Content Competitors** (compete for same keywords) - - May not sell same product - - Rank for your target keywords - - Include media sites, blogs, aggregators - ``` - -2. **Gather Competitor Data** - - Collect for each competitor: URL, domain age, estimated traffic, domain authority, business model, target audience, and key offerings. - -3. **Analyze Keyword Rankings** - - Document total keywords ranking, top 10/top 3 counts, top performing keywords (with position, volume, traffic, page URL), keyword distribution by intent, and keyword gaps. - -4. **Audit Content Strategy** - - Analyze content volume by type, top performing content, content patterns (word count, frequency, formats), content themes, and success factors. - -5. **Analyze Backlink Profile** - - Review total backlinks, referring domains, link quality distribution, top linking domains, link acquisition patterns, and linkable assets. - -6. **Technical SEO Assessment** - - Evaluate Core Web Vitals, mobile-friendliness, site architecture, internal linking quality, URL structure, and technical strengths/weaknesses. - -7. **GEO/AI Citation Analysis** - - Test competitor content in AI systems: document which queries cite them, GEO strategies observed (definitions, statistics, Q&A, authority signals), and GEO opportunities they are missing. - -8. **Synthesize Competitive Intelligence** - - Produce a final report with: Executive Summary, Competitive Landscape comparison table, CITE domain authority comparison, Strengths to Learn From, Weaknesses to Exploit, Keyword Opportunities, Content Strategy Recommendations, and Action Plan (Immediate / Short-term / Long-term). - - > **Reference**: See [references/analysis-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/analysis-templates.md) for detailed templates for each step. - -## Validation Checkpoints - -### Input Validation -- [ ] Competitor URLs verified as relevant to your niche -- [ ] Analysis scope defined (comprehensive or specific focus area) -- [ ] Your own site metrics available for comparison -- [ ] Minimum 2-3 competitors identified for meaningful patterns - -### Output Validation -- [ ] Every recommendation cites specific data points (not generic advice) -- [ ] Competitor strengths backed by measurable evidence (metrics, rankings) -- [ ] Opportunities based on identifiable gaps, not assumptions -- [ ] Action plan items are specific and actionable (not vague strategies) -- [ ] Source of each data point clearly stated (~~SEO tool data, ~~analytics data, ~~AI monitor data, user-provided, or estimated) - -## Example - -> **Reference**: See [references/example-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/example-report.md) for a complete example analyzing HubSpot's marketing keyword dominance. - -## Advanced Analysis Types - -### Content Gap Analysis - -``` -Show me content [competitor] has that I don't, sorted by traffic potential -``` - -### Link Intersection - -``` -Find sites linking to [competitor 1] AND [competitor 2] but not me -``` - -### SERP Feature Analysis - -``` -What SERP features do competitors win? (Featured snippets, PAA, etc.) -``` - -### Historical Tracking - -``` -How has [competitor]'s SEO strategy evolved over the past year? -``` - -## Tips for Success - -1. **Analyze 3-5 competitors** for comprehensive view -2. **Include indirect competitors** - they often have innovative approaches -3. **Look beyond rankings** - analyze content quality, user experience -4. **Study their failures** - avoid their mistakes -5. **Monitor regularly** - competitor strategies evolve -6. **Focus on actionable insights** - what can you actually implement? - - - -### Save Results - -After delivering findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/research/competitor-analysis/YYYY-MM-DD-.md` containing: -- One-line headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - -## Reference Materials - -- [Analysis Templates](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/analysis-templates.md) — Detailed templates for each analysis step (profile, keywords, content, backlinks, technical, GEO, synthesis) -- [Battlecard Template](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/battlecard-template.md) — Quick-reference competitive battlecard for sales and marketing teams -- [Positioning Frameworks](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/positioning-frameworks.md) — Positioning maps, messaging matrices, narrative analysis, and differentiation frameworks -- [Example Report](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/example-report.md) — Complete example analyzing HubSpot's marketing keyword dominance - -## Next Best Skill - -- **Primary**: [content-gap-analysis](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/content-gap-analysis/SKILL.md) — turn competitor findings into a focused opportunity map. diff --git a/.agents/skills/competitor-analysis/references/analysis-templates.md b/.agents/skills/competitor-analysis/references/analysis-templates.md deleted file mode 100644 index 1e1e157167..0000000000 --- a/.agents/skills/competitor-analysis/references/analysis-templates.md +++ /dev/null @@ -1,285 +0,0 @@ -# Competitor Analysis — Analysis Templates - -Templates for each step of the competitor analysis workflow. Use these to structure your output. - -## Competitor Profile Template - -```markdown -## Competitor Profile: [Name] - -**Basic Info** -- URL: [website] -- Domain Age: [years] -- Estimated Traffic: [monthly visits] -- Domain Authority/Rating: [score] - -**Business Model** -- Type: [SaaS/E-commerce/Content/etc.] -- Target Audience: [description] -- Key Offerings: [products/services] -``` - -## Keyword Analysis Template - -```markdown -### Keyword Analysis: [Competitor] - -**Total Keywords Ranking**: [X] -**Keywords in Top 10**: [X] -**Keywords in Top 3**: [X] - -#### Top Performing Keywords - -| Keyword | Position | Volume | Traffic Est. | Page | -|---------|----------|--------|--------------|------| -| [kw 1] | [pos] | [vol] | [traffic] | [url] | -| [kw 2] | [pos] | [vol] | [traffic] | [url] | - -#### Keyword Distribution by Intent - -- Informational: [X]% ([keywords]) -- Commercial: [X]% ([keywords]) -- Transactional: [X]% ([keywords]) -- Navigational: [X]% ([keywords]) - -#### Keyword Gaps (They rank, you don't) - -| Keyword | Their Position | Volume | Opportunity | -|---------|----------------|--------|-------------| -| [kw 1] | [pos] | [vol] | [analysis] | -``` - -## Content Analysis Template - -```markdown -### Content Analysis: [Competitor] - -**Content Volume** -- Total Pages: [X] -- Blog Posts: [X] -- Landing Pages: [X] -- Resource Pages: [X] - -**Content Performance** - -#### Top Performing Content - -| Title | URL | Est. Traffic | Keywords | Backlinks | -|-------|-----|--------------|----------|-----------| -| [title 1] | [url] | [traffic] | [X] | [X] | - -**Content Patterns** - -- Average word count: [X] words -- Publishing frequency: [X] posts/month -- Content formats used: - - Blog posts: [X]% - - Guides/tutorials: [X]% - - Case studies: [X]% - - Tools/calculators: [X]% - - Videos: [X]% - -**Content Themes** - -| Theme | # Articles | Combined Traffic | -|-------|------------|------------------| -| [theme 1] | [X] | [traffic] | -| [theme 2] | [X] | [traffic] | - -**What Makes Their Content Successful** - -1. [Success factor 1 with example] -2. [Success factor 2 with example] -3. [Success factor 3 with example] -``` - -## Backlink Analysis Template - -```markdown -### Backlink Analysis: [Competitor] - -**Overview** -- Total Backlinks: [X] -- Referring Domains: [X] -- Domain Rating: [X] - -**Link Quality Distribution** -- High Authority (DR 70+): [X]% -- Medium Authority (DR 30-69): [X]% -- Low Authority (DR <30): [X]% - -**Top Linking Domains** - -| Domain | DR | Link Type | Target Page | -|--------|-----|-----------|-------------| -| [domain 1] | [DR] | [type] | [page] | - -**Link Acquisition Patterns** - -- Guest posts: [X]% -- Editorial/organic: [X]% -- Resource pages: [X]% -- Directories: [X]% -- Other: [X]% - -**Linkable Assets (Content attracting links)** - -| Asset | Type | Backlinks | Why It Works | -|-------|------|-----------|--------------| -| [asset 1] | [type] | [X] | [reason] | -``` - -## Technical SEO Assessment Template - -```markdown -### Technical Analysis: [Competitor] - -**Site Performance** -- Core Web Vitals: [Pass/Fail] -- LCP: [X]s -- FID: [X]ms -- CLS: [X] -- Mobile-friendly: [Yes/No] - -**Site Structure** -- Site architecture depth: [X] levels -- Internal linking quality: [Rating] -- URL structure: [Clean/Messy] -- Sitemap present: [Yes/No] - -**Technical Strengths** -1. [Strength 1] -2. [Strength 2] - -**Technical Weaknesses** -1. [Weakness 1] -2. [Weakness 2] -``` - -## GEO/AI Citation Analysis Template - -```markdown -### GEO Analysis: [Competitor] - -**AI Visibility Assessment** - -Test competitor content in AI systems for relevant queries: - -| Query | AI Mentions Competitor? | What's Cited | Why | -|-------|------------------------|--------------|-----| -| [query 1] | Yes/No | [content] | [reason] | -| [query 2] | Yes/No | [content] | [reason] | - -**GEO Strategies Observed** - -1. **Clear Definitions** - - Example: [quote from their content] - - Effectiveness: [rating] - -2. **Quotable Statistics** - - Example: [quote from their content] - - Effectiveness: [rating] - -3. **Q&A Format Content** - - Examples found: [X] pages - - Topics covered: [list] - -4. **Authority Signals** - - Expert authorship: [Yes/No] - - Citations to sources: [Yes/No] - - Original research: [Yes/No] - -**GEO Opportunities They're Missing** - -| Topic | Why Missing | Your Opportunity | -|-------|-------------|------------------| -| [topic 1] | [reason] | [action] | -``` - -## Synthesis Report Template - -```markdown -# Competitive Analysis Report - -**Analysis Date**: [Date] -**Competitors Analyzed**: [List] -**Your Site**: [URL] - -## Executive Summary - -[2-3 paragraph overview of key findings and recommendations] - -## Competitive Landscape - -| Metric | You | Competitor 1 | Competitor 2 | Competitor 3 | -|--------|-----|--------------|--------------|--------------| -| Domain Authority | [X] | [X] | [X] | [X] | -| Organic Traffic | [X] | [X] | [X] | [X] | -| Keywords Top 10 | [X] | [X] | [X] | [X] | -| Backlinks | [X] | [X] | [X] | [X] | -| Content Pages | [X] | [X] | [X] | [X] | - -**Domain Authority Comparison (Recommended)** - -When domain-level comparison is needed, run `domain-authority-auditor` for each competitor to get CITE scores: - -| Domain | CITE Score | C (Citation) | I (Identity) | T (Trust) | E (Eminence) | Veto | -|--------|-----------|-------------|-------------|----------|-------------|------| -| Your domain | [score] | [score] | [score] | [score] | [score] | [pass/fail] | -| Competitor 1 | [score] | [score] | [score] | [score] | [score] | [pass/fail] | -| Competitor 2 | [score] | [score] | [score] | [score] | [score] | [pass/fail] | - -This reveals domain authority gaps that inform link building and brand strategy beyond keyword-level competition. - -## Competitor Strengths to Learn From - -### [Competitor 1] -- **Strength**: [description] -- **Why It Works**: [analysis] -- **How to Apply**: [action item] - -[Repeat for each competitor] - -## Competitor Weaknesses to Exploit - -### Gap 1: [Description] -- Who's weak: [competitors] -- Opportunity size: [estimate] -- Recommended action: [specific steps] - -[Repeat for each gap] - -## Keyword Opportunities - -### Keywords to Target (Competitor overlap) -| Keyword | Volume | Avg Position | Best Strategy | -|---------|--------|--------------|---------------| -| [kw] | [vol] | [pos] | [strategy] | - -### Untapped Keywords (No competitor coverage) -| Keyword | Volume | Difficulty | Opportunity | -|---------|--------|------------|-------------| -| [kw] | [vol] | [diff] | [description] | - -## Content Strategy Recommendations - -Based on competitor analysis: - -1. **Create**: [Content type] about [topic] because [reason] -2. **Improve**: [Existing content] to match/exceed [competitor content] -3. **Promote**: [Content] to sites like [competitor's link sources] - -## Action Plan - -### Immediate (This Week) -1. [Action item] -2. [Action item] - -### Short-term (This Month) -1. [Action item] -2. [Action item] - -### Long-term (This Quarter) -1. [Action item] -2. [Action item] -``` diff --git a/.agents/skills/competitor-analysis/references/battlecard-template.md b/.agents/skills/competitor-analysis/references/battlecard-template.md deleted file mode 100644 index 66407a53c3..0000000000 --- a/.agents/skills/competitor-analysis/references/battlecard-template.md +++ /dev/null @@ -1,256 +0,0 @@ -# Competitive Battlecard Template - -A fill-in-the-blank template for creating competitive battlecards that equip sales, marketing, and content teams with actionable intelligence. Maintain one battlecard per major competitor and review quarterly. - -## Overview - -A competitive battlecard is a concise reference document that summarizes a competitor's strengths, weaknesses, positioning, and how to compete against them. Battlecards are living documents that should be updated as competitors evolve. - -**Audience:** Sales teams, content strategists, marketing leadership -**Update frequency:** Quarterly review minimum; update immediately on major competitor changes -**Length target:** 2-3 pages per competitor (scannable, not exhaustive) - ---- - -## Battlecard Template - -### Header - -``` -COMPETITIVE BATTLECARD: [Competitor Name] -Last Updated: [Date] -Updated By: [Name/Team] -Confidence Level: [High/Medium/Low] — based on data recency and source quality -``` - ---- - -### Section 1: Competitor Overview - -| Field | Details | -|-------|---------| -| **Company Name** | [Name] | -| **Website** | [URL] | -| **Founded** | [Year] | -| **Headquarters** | [Location] | -| **Company Size** | [Employees] | -| **Funding/Revenue** | [Known funding rounds or estimated revenue] | -| **Target Customer** | [Primary audience: SMB, mid-market, enterprise, etc.] | -| **Pricing Model** | [Freemium, subscription, usage-based, custom, etc.] | -| **Pricing Range** | [Entry price → Enterprise price] | - -**One-Sentence Summary:** -> [Competitor] is a [category] that helps [target audience] to [primary benefit] by [mechanism]. - ---- - -### Section 2: Their Pitch - -**Tagline/Slogan:** -> [Their exact tagline from website] - -**Top 3 Claimed Differentiators:** -1. [Differentiator 1 — their claim, not your assessment] -2. [Differentiator 2] -3. [Differentiator 3] - -**Positioning Statement (Reverse-Engineered):** -> For [their target audience], [product] is the [category] that [key benefit] because [reason to believe]. - -**Key Messages They Repeat:** -- [Message 1 — found in blog, ads, sales decks] -- [Message 2] -- [Message 3] - ---- - -### Section 3: Strengths (Be Honest) - -Credibility depends on honestly assessing where competitors are genuinely strong. Sugarcoating weaknesses or ignoring strengths reduces trust in the battlecard. - -| Strength | Evidence | Impact on Deals | -|----------|---------|----------------| -| [Strength 1] | [Where you observed this — reviews, demos, customer feedback] | [How this affects your competitive win rate] | -| [Strength 2] | [Evidence] | [Impact] | -| [Strength 3] | [Evidence] | [Impact] | - ---- - -### Section 4: Weaknesses - -| Weakness | Evidence | How to Exploit | -|----------|---------|---------------| -| [Weakness 1] | [Sources: G2 reviews, customer complaints, technical limitations] | [Talking point or demo moment that highlights this gap] | -| [Weakness 2] | [Evidence] | [How to exploit] | -| [Weakness 3] | [Evidence] | [How to exploit] | - -**Common Complaints (from review sites):** -- "[Exact quote from G2/Capterra/TrustRadius]" — [Source, Date] -- "[Exact quote]" — [Source, Date] -- "[Exact quote]" — [Source, Date] - ---- - -### Section 5: Your Differentiators - -For each differentiator, provide the claim AND the proof. Unsupported claims erode credibility. - -| Differentiator | Your Advantage | Proof Point | How to Demo/Show | -|---------------|---------------|------------|-----------------| -| [Differentiator 1] | [What you do better] | [Data, testimonial, or demo evidence] | [Specific demo step or slide] | -| [Differentiator 2] | [What you do better] | [Proof] | [How to show] | -| [Differentiator 3] | [What you do better] | [Proof] | [How to show] | - ---- - -### Section 6: Feature Comparison - -| Feature/Capability | You | [Competitor] | Notes | -|-------------------|-----|-------------|-------| -| [Feature 1] | [Yes/No/Partial + detail] | [Yes/No/Partial + detail] | [Context] | -| [Feature 2] | [Yes/No/Partial] | [Yes/No/Partial] | [Context] | -| [Feature 3] | [Yes/No/Partial] | [Yes/No/Partial] | [Context] | -| [Feature 4] | [Yes/No/Partial] | [Yes/No/Partial] | [Context] | -| [Feature 5] | [Yes/No/Partial] | [Yes/No/Partial] | [Context] | -| [Integration A] | [Yes/No] | [Yes/No] | [Context] | -| [Integration B] | [Yes/No] | [Yes/No] | [Context] | - ---- - -### Section 7: Pricing Comparison - -| Tier | You | [Competitor] | Advantage | -|------|-----|-------------|-----------| -| Entry/Free | [Price + what's included] | [Price + what's included] | [Who wins at this tier and why] | -| Mid-tier | [Price + what's included] | [Price + what's included] | [Who wins] | -| Enterprise | [Price + what's included] | [Price + what's included] | [Who wins] | - -**Hidden Costs to Highlight:** -- [Competitor charges extra for X, which you include] -- [Competitor requires Y add-on for common use case] -- [Competitor's pricing scales poorly because Z] - ---- - -### Section 8: Objection Handling - -| When They Say... | You Respond With... | Supporting Evidence | -|-----------------|--------------------|--------------------| -| "[Competitor] has more features" | "[Your response — focus on outcomes, not feature count]" | [Case study, data point, or demo] | -| "[Competitor] is cheaper" | "[Your response — focus on total value, ROI, or hidden costs]" | [TCO comparison, customer quote] | -| "[Competitor] is more established" | "[Your response — focus on innovation, agility, or support quality]" | [Growth metrics, customer satisfaction data] | -| "We're already using [Competitor]" | "[Your response — focus on switching ease, quick wins, or pain points]" | [Migration case study, time-to-value data] | -| "[Competitor] integrates with [tool]" | "[Your response — confirm your integration or alternative]" | [Integration docs, workaround, or roadmap] | - ---- - -### Section 9: Landmine Questions - -Questions to ask prospects early in the sales process that highlight your advantages and surface competitor weaknesses. Use these in discovery calls. - -| Landmine Question | What It Exposes | Your Advantage | -|------------------|----------------|---------------| -| "How important is [capability you have, they don't] to your workflow?" | Creates need for your differentiator | [Your capability] | -| "Have you experienced [common pain point with competitor]?" | Surfaces known competitor weakness | [Your solution to that pain] | -| "What's your timeline for seeing ROI from this tool?" | Highlights time-to-value differences | [Your faster time-to-value] | -| "How does your team currently handle [use case you excel at]?" | Opens discussion where you shine | [Your approach to this use case] | - ---- - -### Section 10: Win/Loss Analysis - -| Theme | Win Reasons (Why We Won) | Loss Reasons (Why We Lost) | -|-------|------------------------|---------------------------| -| Product | [What product features drove the win] | [What product gaps caused the loss] | -| Pricing | [Price advantage or value perception] | [Price disadvantage or perceived poor value] | -| Relationship | [Sales process, support quality] | [Better existing relationship with competitor] | -| Brand | [Brand trust, market perception] | [Competitor brand stronger in this segment] | - -**Recent Win Story:** -> [Brief narrative: who was the customer, what were they comparing, why did they choose you] - -**Recent Loss Story:** -> [Brief narrative: who was the customer, what were they comparing, why did they choose competitor] - ---- - -### Section 11: SEO/Content Intelligence - -| Dimension | [Competitor] | Your Position | -|-----------|-------------|--------------| -| Domain Authority/Rating | [Score] | [Score] | -| Organic traffic (est.) | [Monthly visits] | [Monthly visits] | -| Keywords in top 10 | [Count] | [Count] | -| Top-performing content | [URL + topic] | [Your equivalent or gap] | -| Content publishing cadence | [Posts/month] | [Posts/month] | -| Backlink count | [Count] | [Count] | -| AI citation frequency | [High/Medium/Low] | [High/Medium/Low] | - -**Content Strategy Observations:** -- [What content themes drive their traffic] -- [What formats they use effectively] -- [Where their content is weak or outdated] - ---- - -## Battlecard Maintenance Guidelines - -### Update Triggers - -Update the battlecard immediately when any of these occur: - -| Trigger | What to Update | -|---------|---------------| -| Competitor launches new feature | Feature comparison, differentiators, landmine questions | -| Competitor changes pricing | Pricing comparison, objection handling | -| Competitor raises funding / IPO | Overview section, positioning | -| Major review trends change | Weaknesses, objection handling, win/loss | -| You launch competing feature | Differentiators, feature comparison | -| Significant win or loss against them | Win/loss analysis, objection handling | -| Competitor publishes major content | SEO/content intelligence | - -### Quarterly Review Checklist - -- [ ] Verify all pricing information is current -- [ ] Check for new features launched by competitor -- [ ] Review latest G2/Capterra/TrustRadius reviews for new themes -- [ ] Update win/loss analysis with recent deals -- [ ] Refresh SEO/content metrics -- [ ] Confirm differentiators are still accurate -- [ ] Test all landmine questions with sales team for relevance -- [ ] Archive outdated information (don't delete -- move to "Historical" section) - -### Data Sources for Battlecard Updates - -| Source | What to Gather | Update Frequency | -|--------|---------------|-----------------| -| Competitor website | Pricing, features, messaging, positioning | Monthly | -| G2/Capterra reviews | Strengths, weaknesses, customer sentiment | Quarterly | -| LinkedIn/social media | Team changes, company news, content strategy | Monthly | -| Industry press/blogs | Funding, partnerships, market perception | As published | -| Sales team feedback | Win/loss themes, objection patterns | Monthly | -| SEO tools | Traffic, rankings, backlinks, content changes | Monthly | -| AI system queries | AI citation patterns, brand mentions | Quarterly | -| Customer feedback | Competitive switching reasons | Quarterly | - ---- - -## Example: Populated Battlecard Header - -``` -COMPETITIVE BATTLECARD: SEMrush -Last Updated: 2026-02-01 -Updated By: Content Strategy Team -Confidence Level: High - -One-Sentence Summary: -SEMrush is an all-in-one SEO and digital marketing platform that helps marketing -professionals manage SEO, PPC, content, and social media campaigns from a single dashboard. - -Tagline: "Online Marketing Can Be Easy" - -Positioning Statement: -For digital marketing professionals and agencies, SEMrush is the all-in-one marketing -toolkit that provides competitive intelligence and workflow automation because it combines -55+ tools across SEO, PPC, content, and social in one platform. -``` diff --git a/.agents/skills/competitor-analysis/references/example-report.md b/.agents/skills/competitor-analysis/references/example-report.md deleted file mode 100644 index 4ef522d40a..0000000000 --- a/.agents/skills/competitor-analysis/references/example-report.md +++ /dev/null @@ -1,75 +0,0 @@ -# Competitor Analysis — Example Report - -This is a complete example output showing the expected format for competitor analysis deliverables. - -## Prompt - -**User**: "Analyze why HubSpot ranks so well for marketing-related keywords" - -## Example Output - -```markdown -# Competitive Analysis: HubSpot - -## SEO Strategy Overview - -HubSpot dominates marketing keywords through a combination of: -1. **Massive content moat** - 10,000+ blog posts -2. **Free tools as linkbait** - Website grader, email signature generator -3. **Educational brand** - Academy, certifications, courses -4. **Topic cluster model** - Pioneered the pillar/cluster approach - -## What Makes Them Successful - -### Content Strategy - -**Publishing Volume**: 50-100 posts/month -**Average Word Count**: 2,500+ words -**Content Types**: -- In-depth guides (35%) -- How-to tutorials (25%) -- Templates & examples (20%) -- Data/research (10%) -- Tools & calculators (10%) - -**Top Performing Content Pattern**: -1. Ultimate guides on broad topics -2. Free templates with email gate -3. Statistics roundup posts -4. Definition posts ("What is [term]") - -### GEO Success Factors - -HubSpot appears in AI responses frequently because: - -1. **Clear definitions** at the start of every post - > "Inbound marketing is a business methodology that attracts customers by creating valuable content and experiences tailored to them." - -2. **Quotable statistics** - > "Companies that blog get 55% more website visitors" - -3. **Comprehensive coverage** - AI trusts their authority - -### Linkable Assets - -| Asset | Backlinks | Why It Works | -|-------|-----------|--------------| -| Website Grader | 45,000+ | Free, instant value | -| Marketing Statistics | 12,000+ | Quotable reference | -| Blog Ideas Generator | 8,500+ | Solves real problem | - -## Weaknesses to Exploit - -1. **Content becoming dated** - Many posts 3+ years old -2. **Generic advice** - Lacks industry-specific depth -3. **Enterprise focus** - Underserves solopreneurs -4. **Slow innovation** - Same formats for years - -## Your Opportunities - -1. Create more specific, niche content they can't cover -2. Target long-tail keywords they ignore -3. Build interactive tools in emerging areas -4. Add original research they don't have -5. Focus on GEO-optimized definitions in your niche -``` diff --git a/.agents/skills/competitor-analysis/references/positioning-frameworks.md b/.agents/skills/competitor-analysis/references/positioning-frameworks.md deleted file mode 100644 index 42cba0ac50..0000000000 --- a/.agents/skills/competitor-analysis/references/positioning-frameworks.md +++ /dev/null @@ -1,320 +0,0 @@ -# Positioning Frameworks - -Comprehensive frameworks for analyzing competitor positioning, developing your own positioning strategy, and identifying differentiation opportunities. Use these frameworks to map competitive landscapes, craft positioning statements, and find messaging vulnerabilities. - -## Overview - -Positioning is how your product or brand occupies a distinct place in the mind of your target audience relative to competitors. Strong positioning makes your value proposition clear, your differentiation obvious, and your target audience feel understood. - -This reference covers: -1. Positioning statement templates -2. 2x2 positioning map methodology -3. Category strategy options -4. Differentiation playbooks -5. Messaging vulnerability analysis - ---- - -## 1. Positioning Statement Templates - -### Classic Positioning Statement - -The foundational template used across industries: - -``` -For [target audience], -[product/brand] is the [category] -that [key benefit / point of difference] -because [reason to believe / proof point]. -``` - -**Example (SEO tool):** -> For growth-stage SaaS marketers, Clearscope is the content optimization platform that ensures every article ranks on page one because it uses real-time SERP data and NLP to grade content against ranking competitors. - -### Extended Positioning Statement - -Adds competitive context and emotional resonance: - -``` -For [target audience] who [situation/need], -[product/brand] is the [category] -that [functional benefit] -unlike [competitive alternative] -because [unique capability / proof]. -This matters because [emotional benefit / outcome]. -``` - -**Example:** -> For content teams who struggle to consistently rank new articles, MarketMuse is the content planning platform that identifies exactly which topics to cover, unlike manual keyword research tools, because it uses AI-driven topic models trained on ranking content. This matters because teams stop guessing and start publishing with confidence. - -### Before/After/Bridge Positioning - -Focuses on transformation: - -``` -BEFORE: [Current painful state for target audience] -AFTER: [Desired improved state] -BRIDGE: [Your product] makes this possible by [mechanism]. -``` - -### Problem-Agitation-Solution Positioning - -Focuses on pain point amplification: - -``` -PROBLEM: [What the target audience struggles with] -AGITATION: [Why this problem is worse than they think — consequences, hidden costs] -SOLUTION: [How your product solves it differently than alternatives] -``` - ---- - -## 2. Positioning Map (2x2 Matrix) Methodology - -### How to Build a Positioning Map - -A positioning map plots competitors on two key dimensions to visualize the competitive landscape and identify open positioning space. - -#### Step 1: Choose Your Axes - -Select two dimensions that matter most to your target audience. The axes should be: -- **Meaningful to buyers** (not internal metrics) -- **Differentiating** (competitors should spread across the map) -- **Independent** (the two dimensions should not be correlated) - -#### Common Axis Pairs for SEO/Marketing - -| Axis Pair | X-Axis | Y-Axis | Best For | -|-----------|--------|--------|---------| -| Value Positioning | Price (Low → High) | Capability (Basic → Advanced) | Understanding market tiers | -| UX Positioning | Ease of Use (Complex → Simple) | Power (Limited → Comprehensive) | Evaluating UX/capability tradeoffs | -| Audience Positioning | SMB Focus ← → Enterprise Focus | Point Solution ← → Full Platform | Identifying segment gaps | -| Innovation Positioning | Established/Stable ← → Innovative/Cutting-Edge | Niche ← → Broad | Timing market positioning | -| Content Positioning | Data-Driven ← → Opinion-Driven | Beginner ← → Expert | Content strategy differentiation | - -#### Step 2: Plot Competitors - -Place each competitor (including yourself) on the map based on objective assessment. - -``` - HIGH CAPABILITY - │ - Enterprise │ All-in-One - Suites │ Platforms - │ - LOW ───────────────┼─────────────── HIGH - PRICE │ PRICE - │ - Free/Basic │ Premium - Tools │ Specialists - │ - LOW CAPABILITY -``` - -#### Step 3: Identify White Space - -Look for quadrants or areas where: -- **No competitor exists** (market opportunity) -- **Only weak competitors exist** (displacement opportunity) -- **Crowded clusters exist** (differentiation challenge -- avoid or reframe) - -#### Step 4: Choose Your Position - -Select a position that: -- Aligns with your actual product capabilities -- Serves an underserved audience segment -- Is defensible (hard for competitors to copy) -- Connects to a clear value narrative - -### Positioning Map Anti-Patterns - -| Anti-Pattern | Description | Fix | -|-------------|------------|-----| -| Aspiration mapping | Plotting yourself where you want to be, not where you are | Be honest about current position; create a roadmap to desired position | -| Vanity axes | Choosing dimensions that make you look good but don't matter to buyers | Validate axes with customer research | -| Missing competitors | Only plotting direct competitors, missing substitutes | Include indirect competitors and alternative solutions | -| Static mapping | Building the map once and never updating | Refresh quarterly as market shifts | - ---- - -## 3. Category Strategy Options - -How you define your category determines your competitive set and positioning flexibility. - -### Strategy 1: Win the Existing Category - -**When to use:** You have a genuinely superior product in an established category. -**Approach:** Accept the existing category definition and compete on being the best. -**Messaging:** "The best [category] for [audience]" -**Risk:** Head-to-head competition with established players. - -**Example:** "The best SEO tool for content marketers" (competing within "SEO tools") - -### Strategy 2: Create a New Sub-Category - -**When to use:** You have a genuine differentiator that deserves its own label. -**Approach:** Segment the existing category and own the new segment. -**Messaging:** "The first [new sub-category]" -**Risk:** Market may not recognize the sub-category. - -**Example:** "Content optimization platform" (sub-category of "SEO tools" focused on content) - -### Strategy 3: Create a New Category - -**When to use:** Your product genuinely doesn't fit existing categories. -**Approach:** Define a new category and position yourself as the category creator. -**Messaging:** "Introducing [new category]: [definition]" -**Risk:** High education cost; market may not adopt the category. - -**Example:** "GEO optimization platform" (new category combining SEO + AI visibility) - -### Strategy 4: Reframe the Category - -**When to use:** The existing category frames competition in a way that disadvantages you. -**Approach:** Change how buyers think about the problem, shifting the evaluation criteria. -**Messaging:** "Stop thinking about [old category]. Start thinking about [new frame]." -**Risk:** Confusing prospects who use existing category to search/evaluate. - -**Example:** "Not another SEO tool -- a revenue intelligence platform" (reframing from SEO to revenue) - -### Category Strategy Decision Matrix - -| Factor | Win Existing | New Sub-Category | New Category | Reframe | -|--------|-------------|-----------------|-------------|---------| -| Product differentiation | Low-Medium | Medium-High | Very High | Medium | -| Market education cost | Low | Medium | Very High | High | -| Competitive intensity | High | Medium | Low (initially) | Medium | -| SEO/search difficulty | High (competitive terms) | Medium (new terms) | Low (no competition) | Medium | -| Sales cycle impact | Short (known category) | Medium | Long (education needed) | Medium-Long | - ---- - -## 4. Differentiation Playbooks - -### Types of Differentiation - -Not all differentiation is equal. Stronger forms are harder to copy. - -| Type | Strength | Durability | Examples | -|------|---------|-----------|---------| -| **Feature** | Low | Low (easily copied) | "We have feature X" | -| **Integration** | Medium | Medium | "We connect to Y ecosystem" | -| **Experience** | Medium-High | Medium-High | "Our UX is designed for Z workflow" | -| **Data/Network** | High | High | "Our data from N users makes results better" | -| **Methodology** | High | High | "We invented the X framework" | -| **Mission/Values** | High | Very High | "We're the only [category] focused on [mission]" | - -### Differentiation Audit Template - -For each competitor, assess your differentiation strength: - -| Dimension | Your Approach | Competitor's Approach | Differentiation Strength | Defensibility | -|-----------|-------------|---------------------|------------------------|--------------| -| Core technology | [Your tech] | [Their tech] | [Weak/Medium/Strong] | [Easy/Hard to copy] | -| Target audience | [Your audience] | [Their audience] | [Weak/Medium/Strong] | [Easy/Hard to copy] | -| Pricing model | [Your model] | [Their model] | [Weak/Medium/Strong] | [Easy/Hard to copy] | -| Content/education | [Your approach] | [Their approach] | [Weak/Medium/Strong] | [Easy/Hard to copy] | -| Customer support | [Your approach] | [Their approach] | [Weak/Medium/Strong] | [Easy/Hard to copy] | -| Data advantage | [Your data] | [Their data] | [Weak/Medium/Strong] | [Easy/Hard to copy] | -| Brand/community | [Your brand] | [Their brand] | [Weak/Medium/Strong] | [Easy/Hard to copy] | - -### The "Only We" Test - -For each claimed differentiator, apply this test: - -> "Only [your company] [does X] because [unique reason]." - -If a competitor could truthfully make the same claim, it is not a true differentiator. Revise until the statement is uniquely yours. - -**Strong example:** -> "Only Clearscope grades content against real-time SERP competitor analysis because we built a proprietary NLP model trained on ranking content patterns." - -**Weak example:** -> "Only we offer great customer support." (Every company claims this.) - -### Differentiation Messaging Formula - -``` -We're the only [category] that [unique capability] -which means [customer benefit] -so you can [desired outcome]. -``` - ---- - -## 5. Messaging Vulnerability Analysis - -### Identifying Competitor Messaging Vulnerabilities - -A messaging vulnerability is a gap between what a competitor claims and what they actually deliver, or a message that sounds good but collapses under scrutiny. - -#### Vulnerability Types - -| Vulnerability Type | Description | How to Exploit | -|-------------------|------------|---------------| -| **Promise-Reality Gap** | They claim X but reviews say Y | Reference review data; offer proof of your delivery | -| **Specificity Gap** | They use vague claims ("best-in-class") without proof | Be specific with your claims; use data | -| **Audience Mismatch** | Their messaging targets one audience but product serves another | Speak directly to the underserved audience | -| **Legacy Positioning** | Their positioning is outdated; product has evolved | Position against their OLD story, which is stuck in market perception | -| **Feature Overload** | They list features without connecting to outcomes | Lead with outcomes, not features | -| **Price Sensitivity** | They avoid discussing price, suggesting it's high or confusing | Be transparent about pricing; create TCO comparisons | - -#### Competitor Messaging Audit Worksheet - -For each competitor, analyze their messaging across touchpoints: - -| Touchpoint | Their Message | Vulnerability? | Your Counter-Message | -|-----------|-------------|---------------|---------------------| -| Homepage hero | [Their headline] | [Yes/No — what's weak?] | [Your alternative angle] | -| Pricing page | [How they frame pricing] | [Yes/No — what's hidden?] | [Your transparency approach] | -| Blog/content | [Content themes and tone] | [Yes/No — what's missing?] | [Your content differentiation] | -| Social media | [Social messaging and engagement] | [Yes/No — what's performative?] | [Your authentic approach] | -| Sales emails/outreach | [Known sales messaging] | [Yes/No — what's pushy/generic?] | [Your consultative approach] | -| Customer reviews | [What customers actually say] | [Yes/No — gap from their claims?] | [Highlight alignment of your claims + reviews] | - -### Counter-Positioning Strategies - -| Strategy | When to Use | Messaging Pattern | -|----------|-----------|-----------------| -| **Contrast** | When competitor is dominant but has clear weakness | "Unlike [competitor], we [your strength]" | -| **Flanking** | When competitor ignores a valuable segment | "Built specifically for [underserved segment]" | -| **Reframing** | When competitor's strength is actually a weakness in disguise | "[Competitor's feature] sounds good until you realize [negative consequence]" | -| **Elevation** | When competing on features is a losing game | "Stop comparing features. What matters is [higher-level outcome]" | -| **Specificity** | When competitor uses vague claims | "[Specific metric] for [specific audience] in [specific timeframe]" | - -### Messaging Strength Test - -Rate each competitor's messaging on these dimensions (1-5): - -| Dimension | What to Evaluate | Score | -|-----------|-----------------|-------| -| Clarity | Can you understand their value prop in 5 seconds? | [1-5] | -| Specificity | Do they use concrete numbers and claims? | [1-5] | -| Differentiation | Is their message distinct from competitors? | [1-5] | -| Proof | Do they back claims with evidence? | [1-5] | -| Audience fit | Does the message resonate with their target? | [1-5] | -| Consistency | Is messaging consistent across all touchpoints? | [1-5] | -| Emotional resonance | Does it connect with real pain or aspiration? | [1-5] | - -**Total Score** = Sum / 35 - -| Score Range | Assessment | -|------------|-----------| -| 0.8-1.0 | Very strong messaging -- find a niche angle to compete | -| 0.6-0.79 | Solid messaging -- exploit specific gaps | -| 0.4-0.59 | Average messaging -- multiple attack vectors available | -| Below 0.4 | Weak messaging -- opportunity to dominate with clarity | - ---- - -## Applying These Frameworks Together - -### Recommended Workflow - -1. **Start with Positioning Maps** to understand the landscape visually -2. **Analyze Category Strategy** to decide how to frame your competitive set -3. **Build Positioning Statement** using the templates -4. **Audit Differentiation** to ensure claims are defensible -5. **Identify Messaging Vulnerabilities** to find attack angles -6. **Create Battlecards** (see [battlecard-template.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/references/battlecard-template.md)) incorporating all insights -7. **Review quarterly** as the competitive landscape shifts diff --git a/.agents/skills/content-gap-analysis/SKILL.md b/.agents/skills/content-gap-analysis/SKILL.md deleted file mode 100644 index 0e4e0e7883..0000000000 --- a/.agents/skills/content-gap-analysis/SKILL.md +++ /dev/null @@ -1,287 +0,0 @@ ---- -name: content-gap-analysis -description: 'Find content gaps: topics and keywords competitors cover that you don''t, with editorial calendar. 内容缺口/选题规划' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when finding content gaps between two domains, discovering missing topics, or identifying coverage holes versus competitors." -argument-hint: " " -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "medium" - tags: - - seo - - geo - - content-gaps - - topic-analysis - - content-strategy - - editorial-calendar - - competitive-gap - - content-opportunities - - 内容缺口 - - コンテンツギャップ - - 콘텐츠갭 - - brechas-contenido - triggers: - # EN-formal - - "find content gaps" - - "content opportunities" - - "topic analysis" - - "content strategy gaps" - - "editorial calendar" - - "untapped topics" - # EN-casual - - "what am I missing" - - "topics to cover" - - "what do competitors write about" - - "what should I cover next" - - "topics I haven't written about" - - "they cover this but I don't" - # EN-question - - "what topics am I missing" - - "what content should I create" - # ZH-pro - - "内容缺口分析" - - "选题规划" - - "内容机会" - - "竞品话题" - # ZH-casual - - "缺什么内容" - - "竞品写了什么" - - "还应该写什么" - # JA - - "コンテンツギャップ" - - "コンテンツ機会" - # KO - - "콘텐츠 갭 분석" - - "콘텐츠 기회" - # ES - - "brechas de contenido" - - "oportunidades de contenido" - # PT - - "lacunas de conteúdo" - # Misspellings - - "content gab analysis" ---- - -# Content Gap Analysis - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This research skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -Identifies content opportunities by analyzing gaps between a site's content and competitors'. Surfaces missing topics, untapped keywords, and content formats worth creating. - -**System role**: Research layer skill. It turns market signals into reusable strategic inputs for the rest of the library. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs reusable market intelligence that should influence strategy, not just an ad hoc answer. - -- Planning content strategy and editorial calendar -- Finding quick-win content opportunities -- Understanding where competitors outperform you -- Identifying underserved topics in your niche -- Expanding into adjacent topic areas -- Prioritizing content creation efforts -- Finding GEO opportunities competitors miss - -## What This Skill Does - -1. **Keyword Gap Analysis**: Finds keywords competitors rank for that you don't -2. **Topic Coverage Mapping**: Identifies topic areas needing more content -3. **Content Format Gaps**: Reveals missing content types (videos, tools, guides) -4. **Audience Need Mapping**: Matches gaps to audience journey stages -5. **GEO Opportunity Detection**: Finds AI-answerable topics you're missing -6. **Priority Scoring**: Ranks gaps by impact and effort -7. **Content Calendar Creation**: Plans gap-filling content schedule - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Basic Gap Analysis - -``` -Find content gaps between my site [URL] and [competitor URLs] -``` - -``` -What content am I missing compared to my top 3 competitors? -``` - -### Topic-Specific Analysis - -``` -Find content gaps in [topic area] compared to industry leaders -``` - -``` -What [content type] do competitors have that I don't? -``` - -### Audience-Focused - -``` -What content gaps exist for [audience segment] in my niche? -``` - -## Skill Contract - -**Expected output**: a prioritized research brief, evidence-backed findings, and a short handoff summary ready for `memory/research/`. - -- **Reads**: user goals, target market inputs, available tool data, and prior strategy from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing research deliverable plus a reusable summary that can be stored under `memory/research/`. -- **Promotes**: durable keyword priorities, competitor facts, entity candidates, and strategic decisions to `CLAUDE.md`, `memory/decisions.md`, and `memory/research/`; hand canonical entity work to `entity-optimizer`. -- **Next handoff**: use the `Next Best Skill` below when the findings are ready to drive action. - -## Data Sources - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~SEO tool + ~~search console + ~~analytics + ~~AI monitor connected:** -Automatically pull your site's content inventory from ~~search console and ~~analytics (indexed pages, traffic per page, keywords ranking), competitor content data from ~~SEO tool (ranking keywords, top pages, backlink counts), and AI citation patterns from ~~AI monitor. Keyword overlap analysis and gap identification can be automated. - -**With manual data only:** -Ask the user to provide: -1. Your site URL and content inventory (list of published content with topics) -2. Competitor URLs (3-5 sites) -3. Your current traffic and keyword performance (if available) -4. Known content strengths and weaknesses -5. Industry context and business goals - -Proceed with the full analysis using provided data. Note in the output which metrics are from automated collection vs. user-provided data. - -## Instructions - -When a user requests content gap analysis: - -1. **Define Analysis Scope** - - Clarify parameters: - - ```markdown - ### Analysis Parameters - - **Your Site**: [URL] - **Competitors to Analyze**: [URLs or "identify for me"] - **Topic Focus**: [specific area or "all"] - **Content Types**: [blogs, guides, tools, videos, or "all"] - **Audience**: [target audience] - **Business Goals**: [traffic, leads, authority, etc.] - ``` - -2. **Audit Your Existing Content** - - Document total indexed pages, content by type and topic cluster, top performing content, and content strengths/weaknesses. - -3. **Analyze Competitor Content** - - For each competitor: document content volume, monthly traffic, content distribution by type, topic coverage vs. yours, and unique content they have. - -4. **Identify Keyword Gaps** - - Find keywords competitors rank for that you do not. Categorize into High Priority (high volume, achievable difficulty), Quick Wins (lower volume, low difficulty), and Long-term (high volume, high difficulty). Include keyword overlap analysis. - -5. **Map Topic Gaps** - - Create a topic coverage comparison matrix across all competitors. For each missing topic cluster, document business relevance, competitor coverage, opportunity size, sub-topics, and recommended pillar/cluster approach. - -6. **Identify Content Format Gaps** - - Compare format distribution (guides, tutorials, comparisons, case studies, tools, templates, video, infographics, research) against competitors and industry averages. For each gap, assess effort and expected impact. - -7. **Analyze GEO/AI Gaps** - - Identify topics where competitors get AI citations but you do not. Document missing Q&A content, definition/explanation content, and comparison content. Score each by traditional SEO value and GEO value. - -8. **Map to Audience Journey** - - Compare funnel stage coverage (Awareness, Consideration, Decision, Retention) against competitor averages. Detail specific gaps at each stage. - -9. **Prioritize and Create Action Plan** - - Produce a final report with: Executive Summary, Prioritized Gap List (Tier 1 Quick Wins, Tier 2 Strategic Builds, Tier 3 Long-term), Content Calendar, and Success Metrics. - - > **Reference**: See [references/analysis-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/content-gap-analysis/references/analysis-templates.md) for detailed templates for each step. - -## Validation Checkpoints - -### Input Validation -- [ ] Your content inventory is complete or representative sample provided -- [ ] Competitor URLs identified (minimum 2-3 competitors) -- [ ] Analysis scope defined (specific topics or comprehensive) -- [ ] Business goals and priorities clarified - -### Output Validation -- [ ] Every recommendation cites specific data points (not generic advice) -- [ ] Gap analysis compares like-to-like content (topic clusters to topic clusters) -- [ ] Priority scoring based on measurable criteria (volume, difficulty, business fit) -- [ ] Content calendar maps gaps to realistic timeframes -- [ ] Source of each data point clearly stated (~~SEO tool data, ~~analytics data, ~~AI monitor data, user-provided, or estimated) - -## Example - -> **Reference**: See [references/example-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/content-gap-analysis/references/example-report.md) for a complete example analyzing SaaS marketing blog gaps vs. HubSpot and Drift. - -## Advanced Analysis - -### Competitive Cluster Comparison - -``` -Compare our topic cluster coverage for [topic] vs top 5 competitors -``` - -### Temporal Gap Analysis - -``` -What content have competitors published in the last 6 months that we haven't covered? -``` - -### Intent-Based Gaps - -``` -Find gaps in our [commercial/informational] intent content -``` - -## Tips for Success - -1. **Focus on actionable gaps** - Not all gaps are worth filling -2. **Consider your resources** - Prioritize based on ability to execute -3. **Quality over quantity** - Better to fill 5 gaps well than 20 poorly -4. **Track what works** - Measure gap-filling success -5. **Update regularly** - Gaps change as competitors publish -6. **Include GEO opportunities** - Don't just optimize for traditional search - - - -### Save Results - -After delivering findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/research/content-gap-analysis/YYYY-MM-DD-.md` containing: -- One-line headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - -## Reference Materials - -- [Analysis Templates](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/content-gap-analysis/references/analysis-templates.md) — Detailed templates for each analysis step (inventory, competitor content, keyword gaps, topic gaps, format gaps, GEO gaps, journey, prioritized report) -- [Gap Analysis Frameworks](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/content-gap-analysis/references/gap-analysis-frameworks.md) — Content audit matrices, funnel mapping, and gap prioritization scoring methodologies -- [Example Report](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/content-gap-analysis/references/example-report.md) — Complete example analyzing SaaS marketing blog gaps vs. HubSpot and Drift - -## Next Best Skill - -- **Primary**: [seo-content-writer](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/seo-content-writer/SKILL.md) — turn missing topics into a draft or content roadmap. diff --git a/.agents/skills/content-gap-analysis/references/analysis-templates.md b/.agents/skills/content-gap-analysis/references/analysis-templates.md deleted file mode 100644 index 487919a67e..0000000000 --- a/.agents/skills/content-gap-analysis/references/analysis-templates.md +++ /dev/null @@ -1,335 +0,0 @@ -# Content Gap Analysis — Analysis Templates - -Templates for each step of the content gap analysis workflow. Use these to structure your output. - -## Content Inventory Template - -```markdown -## Your Content Inventory - -**Total Indexed Pages**: [X] -**Content by Type**: -- Blog posts: [X] -- Landing pages: [X] -- Resource pages: [X] -- Tools/calculators: [X] -- Case studies: [X] - -**Content by Topic Cluster**: - -| Topic | Articles | Keywords Ranking | Traffic | -|-------|----------|------------------|---------| -| [topic 1] | [X] | [X] | [X] | -| [topic 2] | [X] | [X] | [X] | -| [topic 3] | [X] | [X] | [X] | - -**Top Performing Content**: -1. [Title] - [traffic] visits - [keywords] keywords -2. [Title] - [traffic] visits - [keywords] keywords -3. [Title] - [traffic] visits - [keywords] keywords - -**Content Strengths**: -- [Strength 1] -- [Strength 2] - -**Content Weaknesses**: -- [Weakness 1] -- [Weakness 2] -``` - -## Competitor Content Analysis Template - -```markdown -## Competitor Content Analysis - -### Competitor 1: [Name/URL] - -**Content Volume**: [X] pages -**Monthly Traffic**: [X] visits - -**Content Distribution**: -| Type | Count | Est. Traffic | -|------|-------|--------------| -| Blog posts | [X] | [X] | -| Guides | [X] | [X] | -| Tools | [X] | [X] | -| Videos | [X] | [X] | - -**Topic Coverage**: -| Topic | Articles | Your Coverage | -|-------|----------|---------------| -| [topic] | [X] | [X or "None"] | - -**Unique Content They Have**: -1. [Content piece] - [traffic] - [why it works] -2. [Content piece] - [traffic] - [why it works] - -[Repeat for each competitor] -``` - -## Keyword Gap Analysis Template - -```markdown -## Keyword Gap Analysis - -### Keywords Competitors Rank For (You Don't) - -**High Priority Gaps** (High volume, achievable difficulty) - -| Keyword | Volume | Difficulty | Competitor | Their Position | -|---------|--------|------------|------------|----------------| -| [kw 1] | [vol] | [diff] | [comp] | [pos] | -| [kw 2] | [vol] | [diff] | [comp] | [pos] | -| [kw 3] | [vol] | [diff] | [comp] | [pos] | - -**Quick Win Gaps** (Lower volume, low difficulty) - -| Keyword | Volume | Difficulty | Competitor | Their Position | -|---------|--------|------------|------------|----------------| -| [kw 1] | [vol] | [diff] | [comp] | [pos] | - -**Long-term Gaps** (High volume, high difficulty) - -| Keyword | Volume | Difficulty | Competitor | Their Position | -|---------|--------|------------|------------|----------------| -| [kw 1] | [vol] | [diff] | [comp] | [pos] | - -### Keyword Overlap Analysis - -``` -Venn Diagram Representation: - - You Competitor 1 - ○ ○ - / \ / \ - / \ / \ - / A \ B / C \ - / \ / \ - ○─────────○─────○─────────○ - Competitor 2 - -A: Keywords only you rank for: [X] -B: Overlap with Comp 1: [X] -C: Keywords all competitors share: [X] -Gap: Keywords they all have, you don't: [X] -``` - -**Unique Keywords (Your Advantage)**: -| Keyword | Your Position | Volume | -|---------|---------------|--------| -| [kw] | [pos] | [vol] | -``` - -## Topic Gap Analysis Template - -```markdown -## Topic Gap Analysis - -### Topic Coverage Comparison - -| Topic Area | You | Comp 1 | Comp 2 | Comp 3 | Gap? | -|------------|-----|--------|--------|--------|------| -| [Topic 1] | ✅ [X] | ✅ [X] | ✅ [X] | ✅ [X] | No | -| [Topic 2] | ❌ 0 | ✅ [X] | ✅ [X] | ✅ [X] | **Yes** | -| [Topic 3] | ✅ [X] | ✅ [X] | ❌ 0 | ✅ [X] | Partial | -| [Topic 4] | ❌ 0 | ✅ [X] | ✅ [X] | ❌ 0 | **Yes** | - -### Missing Topic Clusters - -#### Gap 1: [Topic Area] - -**Why it matters**: [Business relevance] -**Competitor coverage**: [Who covers it and how] -**Opportunity size**: [Traffic/keyword potential] - -**Sub-topics to cover**: -1. [Sub-topic] - [X] search volume -2. [Sub-topic] - [X] search volume -3. [Sub-topic] - [X] search volume - -**Recommended approach**: -- Pillar content: [topic] -- Cluster articles: [list] -- Supporting content: [list] -``` - -## Content Format Gap Template - -```markdown -## Content Format Gap Analysis - -### Format Distribution Comparison - -| Format | You | Comp 1 | Comp 2 | Industry Avg | -|--------|-----|--------|--------|--------------| -| Long-form guides | [X] | [X] | [X] | [X] | -| Tutorials | [X] | [X] | [X] | [X] | -| Comparison posts | [X] | [X] | [X] | [X] | -| Case studies | [X] | [X] | [X] | [X] | -| Tools/calculators | [X] | [X] | [X] | [X] | -| Templates | [X] | [X] | [X] | [X] | -| Video content | [X] | [X] | [X] | [X] | -| Infographics | [X] | [X] | [X] | [X] | -| Original research | [X] | [X] | [X] | [X] | - -### Format Gaps to Fill - -#### Gap: [Format Type] - -**Current state**: You have [X], competitors average [Y] -**Best examples**: [Competitor content examples] -**Opportunity**: [Description] -**Effort to create**: [Low/Medium/High] -**Expected impact**: [Low/Medium/High] - -**Recommended first project**: -[Specific content idea] -``` - -## GEO Content Gap Template - -```markdown -## GEO Content Gap Analysis - -### AI-Answerable Topics Assessment - -**Topics where competitors get AI citations (you don't)**: - -| Topic | AI Cites | Why They're Cited | Your Gap | -|-------|----------|-------------------|----------| -| [topic 1] | [Comp] | [reason] | [what you need] | -| [topic 2] | [Comp] | [reason] | [what you need] | - -### GEO-Optimized Content Gaps - -**Missing Q&A Content**: -| Question | Search Volume | Currently Answered By | -|----------|---------------|----------------------| -| [question] | [vol] | [competitor] | - -**Missing Definition/Explanation Content**: -| Term | Search Volume | Best Current Source | -|------|---------------|---------------------| -| [term] | [vol] | [source] | - -**Missing Comparison Content**: -| Comparison | Search Volume | Best Current Source | -|------------|---------------|---------------------| -| [A vs B] | [vol] | [source] | - -### GEO Opportunity Score - -| Topic | Traditional SEO Value | GEO Value | Combined Priority | -|-------|----------------------|-----------|-------------------| -| [topic] | [score] | [score] | [priority] | -``` - -## Audience Journey Gap Template - -```markdown -## Audience Journey Gap Analysis - -### Funnel Stage Coverage - -| Stage | Your Content | Competitor Avg | Gap | -|-------|--------------|----------------|-----| -| Awareness | [X] articles | [X] articles | [+/-X] | -| Consideration | [X] articles | [X] articles | [+/-X] | -| Decision | [X] articles | [X] articles | [+/-X] | -| Retention | [X] articles | [X] articles | [+/-X] | - -### Journey Gap Details - -#### Awareness Stage Gaps -- Missing: [topics/content] -- Opportunity: [description] - -#### Consideration Stage Gaps -- Missing: [topics/content] -- Opportunity: [description] - -#### Decision Stage Gaps -- Missing: [topics/content] -- Opportunity: [description] -``` - -## Prioritized Report Template - -```markdown -# Content Gap Analysis Report - -## Executive Summary - -**Analysis Date**: [Date] -**Sites Analyzed**: [Your site] vs [Competitors] - -**Key Findings**: -1. [Most significant gap] -2. [Second significant gap] -3. [Third significant gap] - -**Total Opportunity**: -- Keywords gaps identified: [X] -- Estimated traffic opportunity: [X]/month -- Quick wins available: [X] pieces - ---- - -## Prioritized Gap List - -### Tier 1: Quick Wins (Do Now) - -| Content to Create | Target Keyword | Volume | Difficulty | Impact | -|-------------------|----------------|--------|------------|--------| -| [Title idea] | [keyword] | [vol] | [diff] | High | -| [Title idea] | [keyword] | [vol] | [diff] | High | - -**Why prioritize**: Low effort, immediate ranking potential - -### Tier 2: Strategic Builds (This Quarter) - -| Content to Create | Target Keyword | Volume | Difficulty | Impact | -|-------------------|----------------|--------|------------|--------| -| [Title idea] | [keyword] | [vol] | [diff] | High | - -**Why prioritize**: High value, requires more resources - -### Tier 3: Long-term Investments (This Year) - -| Content to Create | Target Keyword | Volume | Difficulty | Impact | -|-------------------|----------------|--------|------------|--------| -| [Title idea] | [keyword] | [vol] | [diff] | High | - -**Why prioritize**: Builds authority, competitive differentiator - ---- - -## Content Calendar Recommendation - -### Month 1 -| Week | Content | Type | Target Keyword | Status | -|------|---------|------|----------------|--------| -| 1 | [Title] | [Type] | [Keyword] | Planned | -| 2 | [Title] | [Type] | [Keyword] | Planned | -| 3 | [Title] | [Type] | [Keyword] | Planned | -| 4 | [Title] | [Type] | [Keyword] | Planned | - -### Month 2 -[Continue...] - -### Month 3 -[Continue...] - ---- - -## Success Metrics - -Track these to measure gap-filling success: - -| Metric | Current | 3-Month Target | 6-Month Target | -|--------|---------|----------------|----------------| -| Keyword coverage | [X] | [X] | [X] | -| Topic clusters complete | [X] | [X] | [X] | -| Traffic from new content | [X] | [X] | [X] | -| AI citations | [X] | [X] | [X] | -``` diff --git a/.agents/skills/content-gap-analysis/references/example-report.md b/.agents/skills/content-gap-analysis/references/example-report.md deleted file mode 100644 index 0a4438dce6..0000000000 --- a/.agents/skills/content-gap-analysis/references/example-report.md +++ /dev/null @@ -1,47 +0,0 @@ -# Content Gap Analysis — Example Report - -This is a complete example output showing the expected format for content gap analysis deliverables. - -## Prompt - -**User**: "Find content gaps for my SaaS marketing blog compared to HubSpot and Drift" - -## Example Output - -```markdown -# Content Gap Analysis: SaaS Marketing Blog - -## Executive Summary - -Compared to HubSpot and Drift, your blog has significant gaps in: -1. **Interactive tools** - They have 15+, you have 0 -2. **Comparison content** - Missing "[Your Tool] vs [Competitor]" pages -3. **GEO-optimized definitions** - No glossary or term definitions - -Total opportunity: ~25,000 monthly visits from 45 keyword gaps - -## Top Keyword Gaps - -### Quick Wins (Difficulty <40) - -| Keyword | Volume | Difficulty | Who Ranks | -|---------|--------|------------|-----------| -| saas marketing metrics | 1,200 | 32 | HubSpot #3 | -| b2b email sequences | 890 | 28 | Drift #5 | -| saas onboarding emails | 720 | 25 | Neither! | -| marketing qualified lead definition | 1,800 | 35 | HubSpot #1 | - -### Content Format Gaps - -**You're missing**: -- [ ] Interactive ROI calculator (HubSpot gets 15k visits/mo from theirs) -- [ ] Email template library (Drift's gets 8k visits/mo) -- [ ] Marketing glossary (HubSpot's definition pages rank for 500+ keywords) - -## Recommended Content Calendar - -**Week 1**: "SaaS Marketing Metrics: Complete Guide" (Quick win) -**Week 2**: "What is a Marketing Qualified Lead?" (GEO opportunity) -**Week 3**: "B2B Email Sequence Templates" (Format gap) -**Week 4**: "[Your Tool] vs HubSpot" (Comparison gap) -``` diff --git a/.agents/skills/content-gap-analysis/references/gap-analysis-frameworks.md b/.agents/skills/content-gap-analysis/references/gap-analysis-frameworks.md deleted file mode 100644 index a9ebc5a462..0000000000 --- a/.agents/skills/content-gap-analysis/references/gap-analysis-frameworks.md +++ /dev/null @@ -1,318 +0,0 @@ -# Gap Analysis Frameworks - -Complete frameworks for systematic keyword gap identification, content format gap analysis, funnel stage gap mapping, opportunity scoring models, and content calendar integration. Use these frameworks to transform gap analysis from ad-hoc observation into repeatable process. - -## Overview - -Content gap analysis answers three questions: -1. **What are competitors covering that you are not?** (Keyword and topic gaps) -2. **What content formats are expected but missing?** (Format gaps) -3. **Where in the buyer journey does your content fail?** (Funnel gaps) - -This reference provides structured methodologies for each question, plus scoring and prioritization systems. - ---- - -## 1. Systematic Keyword Gap Methodology - -### Step-by-Step Process - -#### Step 1: Define Your Keyword Universe - -Before finding gaps, establish what you already rank for: - -| Data Source | What It Provides | How to Collect | -|------------|-----------------|---------------| -| ~~search console | Keywords driving impressions and clicks to your site | Export Performance report (last 6 months) | -| ~~SEO tool | Full keyword profile including positions 1-100 | Domain keyword export | -| Content audit | Topics covered by existing content | Manual or automated URL crawl + categorization | - -**Output:** A master list of all keywords you currently rank for (positions 1-100). - -#### Step 2: Build Competitor Keyword Profiles - -For each competitor (3-5 recommended): - -| Competitor | Total Keywords | Keywords in Top 10 | Keywords in Top 3 | Estimated Traffic | -|-----------|---------------|-------------------|-------------------|------------------| -| [Competitor A] | [Count] | [Count] | [Count] | [Monthly est.] | -| [Competitor B] | [Count] | [Count] | [Count] | [Monthly est.] | -| [Competitor C] | [Count] | [Count] | [Count] | [Monthly est.] | -| **You** | [Count] | [Count] | [Count] | [Monthly est.] | - -#### Step 3: Calculate Keyword Overlap - -Identify four keyword segments: - -``` -┌──────────────────────────────────────────────────────────┐ -│ │ -│ A: Keywords ONLY you rank for (your unique advantage) │ -│ │ -│ B: Keywords you AND competitors share (competitive) │ -│ │ -│ C: Keywords ONLY competitors rank for (your gaps) │ -│ │ -│ D: Keywords NO ONE ranks for (market opportunity) │ -│ │ -└──────────────────────────────────────────────────────────┘ -``` - -| Segment | Size | Strategic Meaning | Action | -|---------|------|------------------|--------| -| A (Only you) | [Count] | Your content moat | Protect and strengthen | -| B (Shared) | [Count] | Competitive battleground | Improve rankings | -| C (Only them) | [Count] | Your content gaps | Prioritize and create | -| D (No one) | [Count] | Untapped market | Evaluate and pioneer | - -#### Step 4: Filter and Categorize Gaps (Segment C) - -Not all gaps are worth filling. Filter by: - -| Filter | Threshold | Purpose | -|--------|-----------|---------| -| Minimum search volume | >100/month (adjust for niche) | Ensure traffic potential | -| Maximum keyword difficulty | 70% bounce rate | Missing "next step" content or CTAs | -| Interest → Consideration | Pages per session | <2 pages/session | Missing comparison/evaluation content | -| Consideration → Intent | Demo/trial requests | Low conversion from comparison pages | Missing trust content (case studies, reviews) | -| Intent → Purchase | Cart/signup abandonment | High abandonment rate | Missing objection-handling content | -| Purchase → Retention | Churn rate | High early churn | Missing onboarding/help content | - ---- - -## 4. Opportunity Scoring Model - -### Multi-Factor Gap Scoring - -Score each identified gap on 5 factors (1-5 scale each): - -| Factor | Weight | Score 1 (Low) | Score 3 (Medium) | Score 5 (High) | -|--------|--------|--------------|-----------------|----------------| -| **Search Demand** | 25% | <100 monthly searches | 500-2,000 searches | >5,000 searches | -| **Competitive Density** | 20% | All competitors cover it well | 1-2 competitors cover it | No competitor covers it | -| **Business Relevance** | 25% | Tangential to your offering | Related to your offering | Core to your offering | -| **Creation Effort** | 15% | Requires new capabilities | Moderate effort | Quick to create | -| **Conversion Potential** | 15% | Pure awareness (top-funnel) | Consideration stage | Decision/transactional stage | - -**Gap Priority Score** = Σ (Factor Weight x Score) - -### Priority Tiers - -| Tier | Score Range | Timeline | Action | -|------|-----------|----------|--------| -| **P0 -- Fill Immediately** | 4.0-5.0 | This sprint / this week | High demand + low competition + high relevance | -| **P1 -- Fill This Quarter** | 3.0-3.9 | Next 1-3 months | Good demand + some competition + relevant | -| **P2 -- Plan for Future** | 2.0-2.9 | Next 3-6 months | Moderate opportunity, requires more resources | -| **P3 -- Monitor** | 1.0-1.9 | Track quarterly | Low priority but may become relevant | - -### Quick-Win Identification - -A "quick win" gap scores high on Demand + Relevance and low on Effort + Competition: - -``` -Quick Win Score = (Search Demand + Business Relevance) - (Creation Effort + Competitive Density) - Higher is better -``` - -| Quick Win Score | Assessment | -|----------------|-----------| -| 4+ | Strong quick win -- create this content first | -| 2-3 | Moderate quick win -- include in next content sprint | -| 0-1 | Not a quick win -- may still be strategically important | -| Negative | Avoid -- high effort/competition relative to demand | - ---- - -## 5. Content Calendar Integration - -### Gap-to-Calendar Workflow - -#### Step 1: Group Gaps by Topic Cluster - -Group related gaps into clusters rather than addressing them individually: - -| Cluster | Related Gaps | Combined Volume | Pillar Needed? | Cluster Pages Needed | -|---------|-------------|----------------|---------------|---------------------| -| [Cluster A] | Gap 1, Gap 3, Gap 7 | [Sum of volumes] | [Yes/No] | [Count] | -| [Cluster B] | Gap 2, Gap 5 | [Sum of volumes] | [Yes/No] | [Count] | -| [Cluster C] | Gap 4, Gap 6, Gap 8, Gap 9 | [Sum of volumes] | [Yes/No] | [Count] | - -#### Step 2: Sequence by Priority and Dependencies - -| Order | Content Piece | Gap(s) Addressed | Priority | Dependencies | Target Publish | -|-------|-------------|-----------------|---------|-------------|---------------| -| 1 | [Pillar page for Cluster A] | Gap 1, 3, 7 (overview) | P0 | None | Week 1 | -| 2 | [Quick-win article] | Gap 2 | P0 | None | Week 2 | -| 3 | [Cluster A subtopic 1] | Gap 3 (deep dive) | P0 | Pillar published | Week 3 | -| 4 | [Cluster A subtopic 2] | Gap 7 (deep dive) | P1 | Pillar published | Week 4 | -| 5 | [Comparison page] | Gap 5 | P1 | None | Week 5 | - -#### Step 3: Assign Resources and Track - -| Content Piece | Writer | Reviewer | Target Date | Status | Gap Closed? | -|-------------|--------|---------|------------|--------|------------| -| [Title] | [Name] | [Name] | [Date] | [Draft/Review/Published] | [Yes/No/Partial] | - -### Calendar Cadence by Team Size - -| Team Size | Monthly Output | Gap Fill Rate | Full Gap Close Timeline | -|-----------|---------------|--------------|----------------------| -| Solo | 4-6 pieces/month | 3-4 gaps/month | 6-12 months for 30 gaps | -| Small (2-3) | 8-15 pieces/month | 6-10 gaps/month | 3-6 months for 30 gaps | -| Medium (4-6) | 15-25 pieces/month | 10-20 gaps/month | 2-3 months for 30 gaps | -| Large (7+) | 25+ pieces/month | 20+ gaps/month | 1-2 months for 30 gaps | - -### Post-Publication Gap Validation - -After publishing gap-filling content, validate that the gap is actually closed: - -| Checkpoint | Timeframe | What to Check | Success Criteria | -|-----------|-----------|--------------|-----------------| -| Indexing | 1-2 weeks | Is the page indexed? | Appears in Google index | -| Initial ranking | 2-4 weeks | Is it ranking for target keyword? | Appears in top 100 | -| Competitive ranking | 2-3 months | Has it reached competitive positions? | Top 20 or improving trend | -| Traffic impact | 3-6 months | Is it driving meaningful traffic? | Meeting traffic projections | -| Gap closure | 6 months | Has the gap been effectively closed? | Ranking comparable to competitors | - ---- - -## Framework Selection Guide - -| Situation | Primary Framework | Secondary Framework | -|-----------|------------------|-------------------| -| "We need more traffic" | Keyword Gap Methodology | Content Calendar Integration | -| "Competitors outrank us everywhere" | Keyword Gap + Format Gap | Opportunity Scoring | -| "Our conversion rate is low" | Funnel Stage Gap | Format Gap (decision-stage formats) | -| "We don't know what to write next" | All three gap types | Opportunity Scoring + Calendar | -| "We have limited resources" | Opportunity Scoring (quick wins) | Keyword Gap (filtered by difficulty) | -| "We're entering a new market" | Keyword Gap (comprehensive) | Format Gap + Funnel Gap | diff --git a/.agents/skills/content-quality-auditor/SKILL.md b/.agents/skills/content-quality-auditor/SKILL.md deleted file mode 100644 index c3679a8e27..0000000000 --- a/.agents/skills/content-quality-auditor/SKILL.md +++ /dev/null @@ -1,403 +0,0 @@ ---- -name: content-quality-auditor -description: 'Publish-readiness gate: 80-item CORE-EEAT audit with weighted scoring, veto checks, and fix plan. 内容质量/EEAT评分' -version: "6.0.0" -license: Apache-2.0 -allowed-tools: WebFetch -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when auditing content quality before publishing. Runs CORE-EEAT 80-item scoring with veto checks. Also when the user asks for E-E-A-T analysis or publish readiness." -argument-hint: " [keyword]" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "high" - tags: - - seo - - geo - - e-e-a-t - - core-eeat - - content-quality - - content-scoring - - helpful-content - - publish-readiness - - 内容质量 - - コンテンツ品質 - - 콘텐츠품질 - - auditoria-eeat - triggers: - # EN-formal - - "audit content quality" - - "EEAT score" - - "CORE-EEAT audit" - - "content quality check" - - "content assessment" - - "quality score" - # EN-casual - - "is this ready to publish" - - "grade my article" - - "check before publishing" - - "how good is my content" - - "is my content good enough to rank" - - "rate my content quality" - # EN-question - - "is my content ready to publish" - - "how do I improve content quality" - # ZH-pro - - "内容质量审计" - - "EEAT评分" - - "内容评估" - # ZH-casual - - "文章能发吗" - - "内容打几分" - - "文章写得怎么样" - # JA - - "コンテンツ品質監査" - - "E-E-A-T評価" - # KO - - "콘텐츠 품질 감사" - - "EEAT 점수" - # ES - - "auditoría de calidad de contenido" - - "puntuación EEAT" - # PT - - "auditoria de qualidade" - # Misspellings - - "EEAT scroe" ---- - -# Content Quality Auditor - -> Based on [CORE-EEAT Content Benchmark](https://github.com/aaron-he-zhu/core-eeat-content-benchmark). Full benchmark reference: [references/core-eeat-benchmark.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md) - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This cross-cutting skill is part of the protocol layer and follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill evaluates content quality across 80 standardized criteria organized in 8 dimensions. It produces a comprehensive audit report with per-item scoring, dimension and system scores, weighted totals by content type, and a prioritized action plan. - -**System role**: Publish Readiness Gate. It decides whether content is ready to ship, what blocks publication, and what should be promoted into durable project memory. - -## When This Must Trigger - -Use this when content needs a quality check before publishing — even if the user doesn't use audit terminology: - -- User asks "is this ready to publish" or "how good is this" -- User just finished writing with seo-content-writer or content-refresher -- **PostToolUse hook auto-triggers**: after content is written or substantially edited, the hook recommends this audit. When hook-triggered, skip setup questions — audit the content that was just produced. -- Auditing content quality before publishing -- Evaluating existing content for improvement opportunities -- Benchmarking content against CORE-EEAT standards -- Comparing content quality against competitors -- Assessing both GEO readiness (AI citation potential) and SEO strength (source credibility) -- Running periodic content quality checks as part of a content maintenance program -- After writing or optimizing content with seo-content-writer or geo-content-optimizer - -## What This Skill Does - -1. **Full 80-Item Audit**: Scores every CORE-EEAT check item as Pass/Partial/Fail -2. **Dimension Scoring**: Calculates scores for all 8 dimensions (0-100 each) -3. **System Scoring**: Computes GEO Score (CORE) and SEO Score (EEAT) -4. **Weighted Totals**: Applies content-type-specific weights for final score -5. **Veto Detection**: Flags critical trust violations (T04, C01, R10) -6. **Priority Ranking**: Identifies Top 5 improvements sorted by impact -7. **Action Plan**: Generates specific, actionable improvement steps - -## Quick Start - -Start with one of these prompts. Finish with a publish verdict and a handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Audit Content - -``` -Audit this content against CORE-EEAT: [content text or URL] -``` - -``` -Run a content quality audit on [URL] as a [content type] -``` - -### Audit with Content Type - -``` -CORE-EEAT audit for this product review: [content] -``` - -``` -Score this how-to guide against the 80-item benchmark: [content] -``` - -### Comparative Audit - -``` -Audit my content vs competitor: [your content] vs [competitor content] -``` - -## Skill Contract - -**Gate verdict**: **SHIP** (no veto items, dimension scores above threshold) / **FIX** (issues found but no veto) / **BLOCK** (veto item T04, C01, or R10 failed). Always state the verdict prominently at the top of the report. - -**Expected output**: a CORE-EEAT audit report, a publish-readiness verdict, and a short handoff summary ready for `memory/audits/content/`. - -- **Reads**: the target content, content type, supporting evidence, and any prior decisions from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing audit report plus a reusable summary that can be stored under `memory/audits/content/`. -- **Promotes**: veto items and publish blockers to `memory/hot-cache.md` (auto-saved, no user confirmation needed). Top improvement priorities to `memory/open-loops.md`. -- **Next handoff**: use the `Next Best Skill` below once the verdict is clear. - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~web crawler + ~~SEO tool connected:** -Automatically fetch page content, extract HTML structure, check schema markup, verify internal/external links, and pull competitor content for comparison. - -**With manual data only:** -Ask the user to provide: -1. Content text, URL, or file path -2. Content type (if not auto-detectable): Product Review, How-to Guide, Comparison, Landing Page, Blog Post, FAQ Page, Alternative, Best-of, or Testimonial -3. Optional: competitor content for benchmarking - -Proceed with the full 80-item audit using provided data. Note in the output which items could not be fully evaluated due to missing access (e.g., backlink data, schema markup, site-level signals). - -## Decision Gates - -When stopping to ask, always: (1) state the specific value and threshold, (2) offer numbered options with outcomes. - -**Stop and ask the user when:** -- Content is under minimum word count for its type (blog/guide: 300 words; product/landing page: 150 words; FAQ: fewer than 3 entries with 50+ words each) — state the actual count and offer: (1) expand to minimum, (2) continue audit with Insufficient Data flags, (3) cancel -- Content type cannot be auto-detected — state what you detected and ask to confirm before proceeding -- Content is primarily media (video/image) with minimal text — ask whether to audit transcript, alt text, or skip -- More than 50% of a dimension's items are N/A — name the dimension and ask: (1) provide supplementary data, (2) mark entire dimension as Insufficient Data -- Any veto item triggers — flag it immediately with the item ID and ask: (1) stop for immediate fix, (2) continue full audit and flag in report - -**Continue silently (never stop for):** -- Individual Partial scores within a dimension -- Missing SEO tool data (mark items as N/A and continue) -- Low overall score (the report is the deliverable, not a judgment call) -- User not specifying content type (auto-detect and state your assumption) - -## Instructions - -When a user requests a content quality audit: - -### Step 1: Preparation - -```markdown -### Audit Setup - -**Content**: [title or URL] -**Content Type**: [auto-detected or user-specified] -**Dimension Weights**: [loaded from content-type weight table] - -#### Veto Check (Emergency Brake) - -| Veto Item | Status | Action | -|-----------|--------|--------| -| T04: Disclosure Statements | ✅ Pass / ⚠️ VETO | [If VETO: "Add disclosure banner at page top immediately"] | -| C01: Intent Alignment | ✅ Pass / ⚠️ VETO | [If VETO: "Rewrite title and first paragraph"] | -| R10: Content Consistency | ✅ Pass / ⚠️ VETO | [If VETO: "Verify all data before publishing"] | -``` - -If any veto item triggers, flag it prominently at the top of the report and recommend immediate action before continuing the full audit. - -### Step 2: CORE Audit (40 items) - -Evaluate each item against the criteria in [references/core-eeat-benchmark.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md). - -Score each item: -- **Pass** = 10 points (fully meets criteria) -- **Partial** = 5 points (partially meets criteria) -- **Fail** = 0 points (does not meet criteria) - -```markdown -### C — Contextual Clarity - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| C01 | Intent Alignment | Pass/Partial/Fail | [specific observation] | -| C02 | Direct Answer | Pass/Partial/Fail | [specific observation] | -| ... | ... | ... | ... | -| C10 | Semantic Closure | Pass/Partial/Fail | [specific observation] | - -**C Score**: [X]/100 -``` - -Repeat the same table format for **O** (Organization), **R** (Referenceability), and **E** (Exclusivity), scoring all 10 items per dimension. - -### Step 3: EEAT Audit (40 items) - -```markdown -### Exp — Experience - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| Exp01 | First-Person Narrative | Pass/Partial/Fail | [specific observation] | -| ... | ... | ... | ... | - -**Exp Score**: [X]/100 -``` - -Repeat the same table format for **Ept** (Expertise), **A** (Authority), and **T** (Trust), scoring all 10 items per dimension. - -See [references/item-reference.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/references/item-reference.md) for the complete 80-item ID lookup table and site-level item handling notes. - -### Step 4: Scoring & Report - -Calculate scores and generate the final report: - -```markdown -## CORE-EEAT Audit Report - -### Overview - -- **Content**: [title] -- **Content Type**: [type] -- **Audit Date**: [date] -- **Total Score**: [score]/100 ([rating]) -- **GEO Score**: [score]/100 | **SEO Score**: [score]/100 -- **Veto Status**: ✅ No triggers / ⚠️ [item] triggered - -### Dimension Scores - -| Dimension | Score | Rating | Weight | Weighted | -|-----------|-------|--------|--------|----------| -| C — Contextual Clarity | [X]/100 | [rating] | [X]% | [X] | -| O — Organization | [X]/100 | [rating] | [X]% | [X] | -| R — Referenceability | [X]/100 | [rating] | [X]% | [X] | -| E — Exclusivity | [X]/100 | [rating] | [X]% | [X] | -| Exp — Experience | [X]/100 | [rating] | [X]% | [X] | -| Ept — Expertise | [X]/100 | [rating] | [X]% | [X] | -| A — Authority | [X]/100 | [rating] | [X]% | [X] | -| T — Trust | [X]/100 | [rating] | [X]% | [X] | -| **Weighted Total** | | | | **[X]/100** | - -**Score Calculation**: -- GEO Score = (C + O + R + E) / 4 -- SEO Score = (Exp + Ept + A + T) / 4 -- Weighted Score = Σ (dimension_score × content_type_weight) - -**Rating Scale**: 90-100 Excellent | 75-89 Good | 60-74 Medium | 40-59 Low | 0-39 Poor - -### N/A Item Handling - -When an item cannot be evaluated (e.g., A01 Backlink Profile requires site-level data not available): - -1. Mark the item as "N/A" with reason -2. Exclude N/A items from the dimension score calculation -3. Dimension Score = (sum of scored items) / (number of scored items x 10) x 100 -4. If more than 50% of a dimension's items are N/A, flag the dimension as "Insufficient Data" and exclude it from the weighted total -5. Recalculate weighted total using only dimensions with sufficient data, re-normalizing weights to sum to 100% - -**Example**: Authority dimension with 8 N/A items and 2 scored items (A05=8, A07=5): -- Dimension score = (8+5) / (2 x 10) x 100 = 65 -- But 8/10 items are N/A (>50%), so flag as "Insufficient Data -- Authority" -- Exclude A dimension from weighted total; redistribute its weight proportionally to remaining dimensions - -### Per-Item Scores - -#### CORE — Content Body (40 Items) - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| C01 | Intent Alignment | [Pass/Partial/Fail] | [observation] | -| C02 | Direct Answer | [Pass/Partial/Fail] | [observation] | -| ... | ... | ... | ... | - -#### EEAT — Source Credibility (40 Items) - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| Exp01 | First-Person Narrative | [Pass/Partial/Fail] | [observation] | -| ... | ... | ... | ... | - -### Top 5 Priority Improvements - -Sorted by: weight × points lost (highest impact first) - -1. **[ID] [Name]** — [specific modification suggestion] - - Current: [Fail/Partial] | Potential gain: [X] weighted points - - Action: [concrete step] - -2. **[ID] [Name]** — [specific modification suggestion] - - Current: [Fail/Partial] | Potential gain: [X] weighted points - - Action: [concrete step] - -3–5. [Same format] - -### Action Plan - -#### Quick Wins (< 30 minutes each) -- [ ] [Action 1] -- [ ] [Action 2] - -#### Medium Effort (1-2 hours) -- [ ] [Action 3] -- [ ] [Action 4] - -#### Strategic (Requires planning) -- [ ] [Action 5] -- [ ] [Action 6] - -### Recommended Next Steps - -- For full content rewrite: use `seo-content-writer` with CORE-EEAT constraints -- For GEO optimization: use `geo-content-optimizer` targeting failed GEO-First items -- For content refresh: use `content-refresher` with weak dimensions as focus -- For technical fixes: run `/seo:check-technical` for site-level issues -``` - -### Save Results - -After delivering findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to the appropriate `memory/` path using filename `YYYY-MM-DD-.md` containing: -- One-line verdict or headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to `memory/hot-cache.md` without asking. - -## Validation Checkpoints - -### Input Validation -- [ ] Content source identified (text, URL, or file path) -- [ ] Content type confirmed (auto-detected or user-specified) -- [ ] Content is substantial enough for meaningful audit (≥300 words) -- [ ] If comparative audit, competitor content also provided - -### Output Validation -- [ ] All 80 items scored (or marked N/A with reason) -- [ ] All 8 dimension scores calculated correctly -- [ ] Weighted total matches content-type weight configuration -- [ ] Veto items checked and flagged if triggered -- [ ] Top 5 improvements sorted by weighted impact, not arbitrary -- [ ] Every recommendation is specific and actionable (not generic advice) -- [ ] Action plan includes concrete steps with effort estimates - -## Example - -See [references/item-reference.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/references/item-reference.md) for a complete scored example showing the C dimension with all 10 items, priority improvements, and weighted scoring. - -## Tips for Success - -1. **Start with veto items** — T04, C01, R10 are deal-breakers regardless of total score - > These veto items are consistent with the CORE-EEAT benchmark (Section 3), which defines them as items that can override the overall score. -2. **Focus on high-weight dimensions** — Different content types prioritize different dimensions -3. **GEO-First items matter most for AI visibility** — Prioritize items tagged GEO 🎯 if AI citation is the goal -4. **Some EEAT items need site-level data** — Don't penalize content for things only observable at the site level (backlinks, brand recognition) -5. **Use the weighted score, not just the raw average** — A product review with strong Exclusivity matters more than strong Authority -6. **Re-audit after improvements** — Run again to verify score improvements and catch regressions -7. **Pair with CITE for domain-level context** — A high content score on a low-authority domain signals a different priority than the reverse; run [domain-authority-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/domain-authority-auditor/SKILL.md) for the full 120-item picture - -## Reference Materials - -- [CORE-EEAT Content Benchmark](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md) — Full 80-item benchmark with dimension definitions, scoring criteria, and GEO-First item markers -- [references/item-reference.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/references/item-reference.md) — All 80 item IDs in a compact lookup table + site-level item handling notes + scored example report - -## Next Best Skill - -- **Primary**: [content-refresher](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/SKILL.md) — turn failed checks into a concrete rewrite plan. diff --git a/.agents/skills/content-quality-auditor/references/item-reference.md b/.agents/skills/content-quality-auditor/references/item-reference.md deleted file mode 100644 index 49bf51045a..0000000000 --- a/.agents/skills/content-quality-auditor/references/item-reference.md +++ /dev/null @@ -1,99 +0,0 @@ -# CORE-EEAT Item Reference - -Quick reference for all 80 CORE-EEAT audit items. Full scoring criteria in [core-eeat-benchmark.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md). - -## Complete Item Reference - -| ID | Item | ID | Item | -|----|------|----|------| -| C01 | Intent Alignment | Exp01 | First-Person Narrative | -| C02 | Direct Answer | Exp02 | Sensory Details | -| C03 | Query Coverage | Exp03 | Process Documentation | -| C04 | Definition First | Exp04 | Tangible Proof | -| C05 | Topic Scope | Exp05 | Usage Duration | -| C06 | Audience Targeting | Exp06 | Problems Encountered | -| C07 | Semantic Coherence | Exp07 | Before/After Comparison | -| C08 | Use Case Mapping | Exp08 | Quantified Metrics | -| C09 | FAQ Coverage | Exp09 | Repeated Testing | -| C10 | Semantic Closure | Exp10 | Limitations Acknowledged | -| O01 | Heading Hierarchy | Ept01 | Author Identity | -| O02 | Summary Box | Ept02 | Credentials Display | -| O03 | Data Tables | Ept03 | Professional Vocabulary | -| O04 | List Formatting | Ept04 | Technical Depth | -| O05 | Schema Markup | Ept05 | Methodology Rigor | -| O06 | Section Chunking | Ept06 | Edge Case Awareness | -| O07 | Visual Hierarchy | Ept07 | Historical Context | -| O08 | Anchor Navigation | Ept08 | Reasoning Transparency | -| O09 | Information Density | Ept09 | Cross-domain Integration | -| O10 | Multimedia Structure | Ept10 | Editorial Process | -| R01 | Data Precision | A01 | Backlink Profile | -| R02 | Citation Density | A02 | Media Mentions | -| R03 | Source Hierarchy | A03 | Industry Awards | -| R04 | Evidence-Claim Mapping | A04 | Publishing Record | -| R05 | Methodology Transparency | A05 | Brand Recognition | -| R06 | Timestamp & Versioning | A06 | Social Proof | -| R07 | Entity Precision | A07 | Knowledge Graph Presence | -| R08 | Internal Link Graph | A08 | Entity Consistency | -| R09 | HTML Semantics | A09 | Partnership Signals | -| R10 | Content Consistency | A10 | Community Standing | -| E01 | Original Data | T01 | Legal Compliance | -| E02 | Novel Framework | T02 | Contact Transparency | -| E03 | Primary Research | T03 | Security Standards | -| E04 | Contrarian View | T04 | Disclosure Statements | -| E05 | Proprietary Visuals | T05 | Editorial Policy | -| E06 | Gap Filling | T06 | Correction & Update Policy | -| E07 | Practical Tools | T07 | Ad Experience | -| E08 | Depth Advantage | T08 | Risk Disclaimers | -| E09 | Synthesis Value | T09 | Review Authenticity | -| E10 | Forward Insights | T10 | Customer Support | - -**Note on site-level items**: Most Authority items (A01-A10) and several Trust items (T01-T03, T05, T07, T10) require site-level or organization-level data that may not be observable from a single page. When auditing a standalone page without site context, mark these as "N/A — requires site-level data" and exclude from the dimension average. - -## Example Audit Report - -**User**: "Audit this blog post against CORE-EEAT: [paste of 'Best Project Management Tools for Remote Teams 2025']" - -**Output** (partial — showing one dimension to demonstrate format): - -```markdown -## CORE-EEAT Audit Report - -### Overview - -- **Content**: "Best Project Management Tools for Remote Teams 2025" -- **Content Type**: Blog Post / Comparison -- **Audit Date**: 2025-06-15 -- **Veto Status**: No triggers - -### C -- Contextual Clarity (scored dimension example) - -| ID | Check Item | Score | Points | Notes | -|-----|--------------------|---------|--------|-------------------------------------------------------------| -| C01 | Intent Alignment | Pass | 10 | Matches "best X" comparison intent; title and body aligned | -| C02 | Direct Answer | Partial | 5 | Answer appears in first 300 words but no summary box | -| C03 | Query Coverage | Pass | 10 | Covers "project management tools", "remote team software", "best PM tools" | -| C04 | Definition First | Pass | 10 | Key terms ("PM tool", "async collaboration") defined on first use | -| C05 | Topic Scope | Partial | 5 | States what's covered but not what's excluded | -| C06 | Audience Targeting | Pass | 10 | Explicitly targets "remote team leads and managers" | -| C07 | Semantic Coherence | Pass | 10 | Logical flow: intro > criteria > tools > comparison > verdict | -| C08 | Use Case Mapping | Pass | 10 | Decision matrix for team size, budget, and features | -| C09 | FAQ Coverage | Fail | 0 | No FAQ section despite long-tail potential ("free PM tools for small teams") | -| C10 | Semantic Closure | Partial | 5 | Conclusion present but doesn't loop back to opening promise | - -**C Dimension Score**: 75/100 (Good) -**Blog Post weight for C**: 25% -**Weighted contribution**: 18.75 - -#### Priority Improvements from C Dimension - -1. **C09 FAQ Coverage** -- Add FAQ section with 3-5 long-tail questions - - Current: Fail (0) | Potential gain: 2.5 weighted points - - Action: Add FAQ with "Are there free PM tools for small remote teams?", "How to migrate between PM tools?", etc. - -2. **C02 Direct Answer** -- Add a summary box above the fold - - Current: Partial (5) | Potential gain: 1.25 weighted points - - Action: Insert a "Top 3 Picks" callout box in the first 150 words - -[... remaining 7 dimensions (O, R, E, Exp, Ept, A, T) follow the same per-item format ...] -[... then: Dimension Scores table, Top 5 Priority Improvements, Action Plan, Recommended Next Steps ...] -``` diff --git a/.agents/skills/content-refresher/SKILL.md b/.agents/skills/content-refresher/SKILL.md deleted file mode 100644 index 5c1967a909..0000000000 --- a/.agents/skills/content-refresher/SKILL.md +++ /dev/null @@ -1,396 +0,0 @@ ---- -name: content-refresher -description: 'Refresh outdated posts with current stats, new sections, freshness signals to restore rankings. 内容更新/排名恢复' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when updating outdated content, refreshing old articles, improving declining pages, or adding new information to existing content." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "medium" - tags: - - seo - - geo - - content-refresh - - content-update - - content-decay - - ranking-recovery - - evergreen-content - - content-lifecycle - - 内容更新 - - コンテンツ更新 - - 콘텐츠갱신 - - actualizar-contenido - triggers: - # EN-formal - - "update old content" - - "refresh content" - - "content is outdated" - - "improve declining rankings" - - "revive old blog posts" - - "content decay" - - "content refresh strategy" - # EN-casual - - "traffic is dropping" - - "ranking dropped" - - "this post is outdated" - - "traffic is declining on this page" - - "rankings dropped for this article" - - "my old content needs updating" - # EN-question - - "how to fix declining traffic" - - "why is my content losing rankings" - - "how often should I update content" - # EN-competitor - - "Clearscope content refresh" - - "MarketMuse content update" - # ZH-pro - - "内容更新" - - "内容刷新" - - "排名恢复" - - "内容衰减" - - "内容生命周期" - # ZH-casual - - "排名下降了" - - "文章过时了" - - "流量掉了" - - "老文章怎么办" - # JA - - "コンテンツ更新" - - "コンテンツリフレッシュ" - - "記事更新" - # KO - - "콘텐츠 갱신" - - "콘텐츠 업데이트" - - "순위 하락" - - "순위 하락 원인" - - "오래된 글 어떻게 해?" - # ES - - "actualizar contenido" - - "refrescar contenido antiguo" - # PT - - "atualizar conteúdo" - # Misspellings - - "content refesh" - - "content refreshh" ---- - -# Content Refresher - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This optimization skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill helps identify and revitalize outdated content to reclaim lost rankings and traffic. It analyzes content freshness, identifies update opportunities, and guides the refresh process for maximum SEO and GEO impact. - -**System role**: Optimization layer skill. It turns weak pages, structures, and technical issues into prioritized repair work. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs a diagnosis or repair plan that should feed directly into remediation work, not just a one-time opinion. - -- Content has lost rankings or traffic over time -- Statistics and information are outdated -- Competitors have published better content -- Content needs updating for a new year -- Industry changes require content updates -- Adding new sections to existing content -- Converting old content for GEO optimization - -## What This Skill Does - -1. **Freshness Analysis**: Identifies outdated content needing updates -2. **Performance Tracking**: Finds content with declining traffic -3. **Gap Identification**: Spots missing information competitors have -4. **Update Prioritization**: Ranks content by refresh potential -5. **Refresh Recommendations**: Provides specific update guidance -6. **GEO Enhancement**: Updates content for AI citation potential -7. **Republishing Strategy**: Advises on date and promotion tactics - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Identify Content to Refresh - -``` -Find content on [domain] that needs refreshing -``` - -``` -Which of my blog posts have lost the most traffic? -``` - -### Refresh Specific Content - -``` -Refresh this article for [current year]: [URL/content] -``` - -``` -Update this content to outrank [competitor URL]: [your URL] -``` - -### Content Refresh Strategy - -``` -Create a content refresh strategy for [domain/topic] -``` - -## Skill Contract - -**Expected output**: a scored diagnosis, prioritized repair plan, and a short handoff summary ready for `memory/audits/`. - -- **Reads**: the current page or site state, symptoms, prior audits, and current priorities from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing audit or optimization plan plus a reusable summary that can be stored under `memory/audits/`. -- **Promotes**: blocking defects, repeated weaknesses, and fix priorities to `memory/open-loops.md` and `memory/decisions.md`. -- **Next handoff**: use the `Next Best Skill` below when the repair path is clear. - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~analytics + ~~search console + ~~SEO tool connected:** -Claude can automatically pull historical traffic trends from ~~analytics, fetch impression and ranking data from ~~search console, retrieve keyword position history from ~~SEO tool, and identify content with declining performance. This enables data-driven refresh prioritization. - -**With manual data only:** -Ask the user to provide: -1. Traffic data or screenshots showing performance trends -2. Ranking screenshots or history for key pages -3. Content publish dates and last update dates -4. List of pages the user suspects need refreshing - -Proceed with the analysis using provided data. Note in the output which findings are from automated data vs. manual review. - -## Instructions - -When a user requests content refresh help: - -1. **CORE-EEAT Quick Score — Identify Weak Dimensions** - - Before refreshing, run a quick CORE-EEAT assessment to focus effort on the weakest areas. Reference: [CORE-EEAT Benchmark](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md) - - ```markdown - ### CORE-EEAT Quick Assessment - - **Content**: [title or URL] - **Content Type**: [type] - - Rapidly score each dimension (estimate 0-100): - - | Dimension | Quick Score | Key Weakness | Refresh Priority | - |-----------|-----------|--------------|-----------------| - | C — Contextual Clarity | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | O — Organization | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | R — Referenceability | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | E — Exclusivity | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | Exp — Experience | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | Ept — Expertise | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | A — Authority | [X]/100 | [main issue] | 🔴/🟡/🟢 | - | T — Trust | [X]/100 | [main issue] | 🔴/🟡/🟢 | - - **Weakest Dimensions** (focus refresh here): - 1. [Dimension] — [what needs fixing] - 2. [Dimension] — [what needs fixing] - - **Refresh Strategy**: Focus on 🔴 dimensions first, then 🟡. - - _For full 80-item audit, use [content-quality-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/SKILL.md)_ - ``` - -2. **Identify Content Refresh Candidates** - - ```markdown - ## Content Refresh Analysis - - ### Refresh Candidate Identification - - **Criteria for Content Refresh**: - - Published more than 6 months ago - - Contains dated information (years, statistics) - - Declining traffic trend - - Lost keyword rankings - - Outdated references or broken links - - Missing topics competitors now cover - - No GEO optimization - - ### Content Audit Results - - | Content | Published | Last Updated | Traffic Trend | Priority | - |---------|-----------|--------------|---------------|----------| - | [Title 1] | [date] | [date] | ↓ -45% | 🔴 High | - | [Title 2] | [date] | Never | ↓ -30% | 🔴 High | - | [Title 3] | [date] | [date] | ↓ -20% | 🟡 Medium | - | [Title 4] | [date] | [date] | → 0% | 🟡 Medium | - - ### Refresh Prioritization Matrix - - ``` - High Traffic + High Decline = 🔴 Refresh Immediately - High Traffic + Low Decline = 🟡 Schedule Refresh - Low Traffic + High Decline = 🟡 Evaluate & Decide - Low Traffic + Low Decline = 🟢 Low Priority - ``` - ``` - -3. **Analyze Individual Content for Refresh** - - ```markdown - ## Content Refresh Analysis: [Title] - - **URL**: [URL] - **Published**: [date] - **Last Updated**: [date] - **Word Count**: [X] - - ### Performance Metrics - - | Metric | 6 Mo Ago | Current | Change | - |--------|----------|---------|--------| - | Organic Traffic | [X]/mo | [X]/mo | [+/-X]% | - | Avg Position | [X] | [X] | [+/-X] | - | Impressions | [X] | [X] | [+/-X]% | - | CTR | [X]% | [X]% | [+/-X]% | - - ### Keywords Analysis - - | Keyword | Old Position | Current Position | Change | - |---------|--------------|------------------|--------| - | [kw 1] | [X] | [X] | ↓ [X] | - | [kw 2] | [X] | [X] | ↓ [X] | - | [kw 3] | [X] | [X] | ↓ [X] | - - ### Why This Content Needs Refresh - - 1. **Outdated information**: [specific examples] - 2. **Competitive gap**: [what competitors added] - 3. **Missing topics**: [new subtopics to cover] - 4. **SEO issues**: [current optimization problems] - 5. **GEO potential**: [AI citation opportunities] - ``` - -4. **Identify Specific Updates Needed** - - ```markdown - ## Refresh Requirements - - ### Outdated Elements - - | Element | Current | Update Needed | - |---------|---------|---------------| - | Year references | "[old year]" | Update to [current year] | - | Statistics | "[old stat]" | Find current data | - | Tool mentions | "[old tool]" | Add newer tools | - | Links | [X] broken | Fix or replace | - | Screenshots | Outdated UI | Recapture | - - ### Missing Information - - **Topics competitors now cover that you don't**: - - | Topic | Competitor Coverage | Words Needed | Priority | - |-------|---------------------|--------------|----------| - | [Topic 1] | 3/5 competitors | ~300 words | High | - | [Topic 2] | 2/5 competitors | ~200 words | Medium | - | [Topic 3] | 4/5 competitors | ~400 words | High | - - ### SEO Updates Needed - - - [ ] Update title tag with current year - - [ ] Refresh meta description - - [ ] Add new H2 sections for [topics] - - [ ] Update internal links to newer content - - [ ] Add FAQ section for featured snippets - - [ ] Refresh images and add new alt text - - ### GEO Updates Needed - - - [ ] Add clear definition at start - - [ ] Include quotable statistics with sources - - [ ] Add Q&A formatted sections - - [ ] Update sources with current citations - - [ ] Create standalone factual statements - ``` - -5. **Create Refresh Plan** — Structural changes, content additions, statistics/links/images to update - - > **Reference**: See [references/refresh-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-templates.md) for the full refresh plan template (Step 5). - -6. **Write Refresh Content** — Updated introduction, new sections, refreshed statistics, new FAQ section - - > **Reference**: See [references/refresh-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-templates.md) for the refresh content writing template (Step 6). - -7. **Optimize for GEO During Refresh** — Clear definitions, quotable statements, Q&A sections, updated citations - - > **Reference**: See [references/refresh-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-templates.md) for the GEO enhancement template (Step 7). - -8. **Generate Republishing Strategy** — Date strategy (update/add "last updated"/keep original), technical implementation, promotion plan - - > **Reference**: See [references/refresh-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-templates.md) for the republishing strategy template (Step 8). - -9. **Create Refresh Report** — Summary of changes, updates completed, expected outcomes, next review date - - > **Reference**: See [references/refresh-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-templates.md) for the refresh report template (Step 9). - -## Validation Checkpoints - -### Input Validation -- [ ] Target content URL or title clearly identified -- [ ] Historical performance data available (traffic trends, rankings) -- [ ] Content publish/update dates known -- [ ] If comparing to competitors, competitor URLs provided - -### Output Validation -- [ ] Every recommendation cites specific data points (not generic advice) -- [ ] Outdated elements identified with specific examples and replacement data -- [ ] All suggested additions include word counts and section locations -- [ ] Source of each data point clearly stated (~~analytics data, ~~search console, ~~SEO tool, user-provided, or estimated) - -## Example - -> **Reference**: See [references/refresh-example.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-example.md) for a full worked example (cloud hosting refresh) and the comprehensive content refresh checklist. - -## Tips for Success - -1. **Prioritize by ROI** - Refresh high-potential content first -2. **Don't just add dates** - Make substantial improvements -3. **Beat competitors** - Add what they have and more -4. **Track results** - Monitor ranking changes post-refresh -5. **Schedule regular audits** - Check content health quarterly -6. **Optimize for GEO** - Every refresh is a GEO opportunity - -> **Reference data**: For content decay signal taxonomy, lifecycle stages, refresh vs. rewrite decision framework, and update strategy by content type, see [references/content-decay-signals.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/content-decay-signals.md). - - -### Save Results - -After delivering audit or optimization findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/audits/content-refresher/YYYY-MM-DD-.md` containing: -- One-line verdict or headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to `memory/hot-cache.md` without asking. - - -**Gate check recommended**: Run content-quality-auditor on refreshed content before republishing. - -## Reference Materials - -- [Content Decay Signals](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/content-decay-signals.md) — Decay indicators, lifecycle stages, and refresh triggers by content type -- [Refresh Templates](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-templates.md) — Detailed output templates for steps 5-9 (refresh plan, content writing, GEO enhancement, republishing, report) -- [Refresh Example & Checklist](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/references/refresh-example.md) — Full worked example and pre/post-refresh checklist - -## Next Best Skill - -- **Primary**: [content-quality-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/SKILL.md) — re-score the refreshed content before shipping. diff --git a/.agents/skills/content-refresher/references/content-decay-signals.md b/.agents/skills/content-refresher/references/content-decay-signals.md deleted file mode 100644 index 9eb7dd3c55..0000000000 --- a/.agents/skills/content-refresher/references/content-decay-signals.md +++ /dev/null @@ -1,404 +0,0 @@ -# Content Decay Signals - -Comprehensive decay detection system with automated monitoring setup, severity scoring, refresh playbooks by content type, and ROI estimation for content refresh investments. - -## Decay Signal Detection System - -### Primary Signals (High Reliability) - -These signals directly indicate content performance decline and should trigger immediate investigation. - -#### 1. Organic Traffic Decline - -| Severity | Threshold | Detection Window | Action | -|----------|-----------|-----------------|--------| -| Watch | 10-20% decline | Month-over-month | Add to monitoring list | -| Warning | 20-40% decline | Month-over-month | Schedule refresh within 2 weeks | -| Critical | 40-60% decline | Month-over-month | Refresh this week | -| Emergency | >60% decline | Month-over-month | Investigate immediately (may be technical issue) | - -**Detection method**: Compare current month's organic sessions to same month previous year (to account for seasonality) and to previous month (for trend detection). - -**False positive check**: Before attributing traffic decline to content decay, rule out: -- Seasonal variations (compare year-over-year, not just month-over-month) -- Algorithm updates (check if decline coincides with known Google updates) -- Technical issues (crawl errors, indexation problems, site speed regression) -- Tracking code changes (analytics misconfiguration) - -#### 2. Ranking Position Drops - -| Severity | Threshold | Detection Window | Action | -|----------|-----------|-----------------|--------| -| Watch | 1-3 positions lost | 2-week average | Monitor | -| Warning | 3-5 positions lost | 2-week average | Investigate cause | -| Critical | 5-10 positions lost | 2-week average | Schedule immediate refresh | -| Emergency | Dropped off page 1 to page 3+ | Any timeframe | Priority refresh or rewrite | - -**Detection method**: Track primary keyword positions weekly. Use 2-week rolling averages to smooth daily fluctuations. - -#### 3. Click-Through Rate Decline - -| Severity | Threshold | Context | Action | -|----------|-----------|---------|--------| -| Watch | CTR below expected for position | Position stable, CTR dropping | Review title and meta description | -| Warning | CTR dropped 20%+ vs. baseline | With stable impressions | Rewrite title tag and meta description | -| Critical | CTR dropped 40%+ vs. baseline | May indicate stale SERP appearance | Full refresh of title, description, and structured data | - -**Expected CTR by position** (organic, desktop, approximate): - -| Position | Expected CTR Range | Below This = Investigate | -|----------|--------------------|-------------------------| -| 1 | 25-35% | <20% | -| 2 | 12-18% | <10% | -| 3 | 8-12% | <6% | -| 4-5 | 5-8% | <4% | -| 6-10 | 2-5% | <2% | - ---- - -### Secondary Signals (Moderate Reliability) - -These signals suggest potential decay but may have other causes. Use them to corroborate primary signals. - -#### 4. Engagement Metric Decline - -| Metric | Decay Indicator | Possible Cause | -|--------|----------------|---------------| -| Bounce rate increase >15% | Content no longer satisfies intent | Outdated information, better competitor content | -| Time on page decrease >20% | Users leaving faster | Content not comprehensive enough | -| Scroll depth decrease | Users not reading full content | Front-loading outdated info, losing interest | -| Pages per session decrease | Users not exploring further | Poor internal linking, irrelevant content | - -#### 5. Content Freshness Indicators - -| Indicator | Decay Risk | Detection | -|-----------|-----------|-----------| -| Published >12 months ago, never updated | High | CMS date audit | -| Contains year references 2+ years old | High | Text search for year patterns | -| Statistics from 3+ years ago | Medium | Manual review or text search for "20XX" | -| Broken external links (>10% of total) | Medium | Monthly crawl report | -| Screenshots of outdated UI | Medium | Manual visual review | -| References to discontinued products/tools | High | Manual review | - -#### 6. Competitive Displacement Signals - -| Signal | Detection Method | Severity | -|--------|-----------------|----------| -| New competitor content ranking above you | SERP monitoring | High | -| Competitor content is longer and more comprehensive | Manual comparison | Medium | -| Competitor has more recent publication date displayed in SERP | SERP monitoring | Medium | -| Featured snippet lost to competitor | SERP monitoring | High | -| AI overview now answers query without click | SERP monitoring | High | - ---- - -### Tertiary Signals (Low Reliability, Supporting Evidence) - -These signals alone do not indicate decay but strengthen the case when combined with primary or secondary signals. - -| Signal | What It Suggests | -|--------|-----------------| -| Fewer social shares over time | Content less share-worthy (may be stale) | -| Decrease in backlink acquisition | Content no longer being cited as a resource | -| Fewer comments or engagement | Community interest waning | -| Content not appearing in AI responses | Not structured for GEO or information is outdated | - ---- - -## Automated Monitoring Setup - -### Monitoring Dashboard Configuration - -Set up these automated checks to catch decay early. - -#### Weekly Checks - -| Check | Data Source | Alert Threshold | -|-------|-----------|----------------| -| Keyword position changes | Rank tracker | Any target keyword drops >3 positions | -| Crawl errors on key pages | Search Console | Any new crawl error on monitored pages | -| Index coverage changes | Search Console | Any page drops from index | - -#### Monthly Checks - -| Check | Data Source | Alert Threshold | -|-------|-----------|----------------| -| Traffic comparison (MoM) | Analytics | >15% decline on any monitored page | -| CTR comparison | Search Console | >20% CTR decline for any target keyword | -| Broken link scan | Crawler | Any new broken links on monitored pages | -| Competitor SERP changes | SERP tracker | New competitor enters top 5 | - -#### Quarterly Checks - -| Check | Data Source | Process | -|-------|-----------|---------| -| Content freshness audit | CMS + manual | Review all content older than 6 months | -| Statistics accuracy check | Manual | Verify top 20 pages have current data | -| Engagement trend review | Analytics | Compare engagement metrics across quarters | -| Full competitive content gap | SEO tool | Identify new competitor content opportunities | - -### Alert Priority Matrix - -When multiple signals fire simultaneously, use this matrix to determine response urgency. - -| Primary Signal + Secondary Signal | Priority | Response | -|----------------------------------|----------|----------| -| Traffic decline + Position drop | P1 (Critical) | Refresh within 48 hours | -| Traffic decline + CTR decline | P1 (Critical) | Rewrite title/description immediately, schedule content refresh | -| Position drop + Competitor displacement | P2 (High) | Refresh within 1 week | -| Traffic decline + Engagement decline | P2 (High) | Refresh within 1 week | -| CTR decline only | P3 (Medium) | Rewrite title and meta description this week | -| Freshness indicators only | P3 (Medium) | Schedule refresh within 2 weeks | -| Engagement decline only | P4 (Low) | Investigate and schedule if confirmed | - ---- - -## Decay Severity Scoring - -### Composite Decay Score - -Calculate a 0-100 decay severity score by summing weighted signal scores. - -| Signal Category | Weight | Score Range | -|----------------|--------|-------------| -| Traffic decline | 30% | 0 = no decline, 100 = >60% decline | -| Position drops | 25% | 0 = stable, 100 = dropped off page 1 | -| CTR decline | 15% | 0 = stable, 100 = >40% decline | -| Content freshness | 15% | 0 = updated this quarter, 100 = >2 years stale | -| Competitive displacement | 15% | 0 = no new competitors, 100 = displaced from top 3 | - -### Score Interpretation - -| Composite Score | Decay Stage | Action | -|----------------|-------------|--------| -| 0-20 | Healthy | Continue monitoring | -| 21-40 | Early decay | Add to refresh queue (next month) | -| 41-60 | Active decay | Schedule refresh (this week) | -| 61-80 | Significant decay | Immediate refresh or rewrite decision | -| 81-100 | Terminal decay | Rewrite, redirect, or retire | - ---- - -## Refresh Playbooks by Content Type - -### Blog Post / Article Refresh Playbook - -| Step | Action | Time Estimate | -|------|--------|--------------| -| 1 | Update title with current year or hook | 10 min | -| 2 | Rewrite introduction with fresh angle | 20 min | -| 3 | Update all statistics with current sources | 30-60 min | -| 4 | Add 1-2 new sections covering gaps | 60-90 min | -| 5 | Update screenshots and images | 30 min | -| 6 | Add or update FAQ section | 20 min | -| 7 | Refresh internal links | 15 min | -| 8 | Update meta description | 5 min | -| 9 | Add/update schema markup | 10 min | -| 10 | Update dateModified and republish | 5 min | -| **Total** | | **3-4 hours** | - -### Product/Service Page Refresh Playbook - -| Step | Action | Time Estimate | -|------|--------|--------------| -| 1 | Update pricing, features, specifications | 30 min | -| 2 | Add new customer testimonials/reviews | 20 min | -| 3 | Update product images | 30 min | -| 4 | Refresh comparison tables | 20 min | -| 5 | Update internal links to related products | 15 min | -| 6 | Verify and update schema markup | 10 min | -| **Total** | | **2-2.5 hours** | - -### Statistics/Data Roundup Refresh Playbook - -| Step | Action | Time Estimate | -|------|--------|--------------| -| 1 | Verify every statistic is still current | 60-90 min | -| 2 | Replace outdated stats with current data | 60 min | -| 3 | Add new statistics from recent studies | 30 min | -| 4 | Update source links and citations | 30 min | -| 5 | Update year references throughout | 15 min | -| 6 | Add new visualization if data changed significantly | 30 min | -| 7 | Update title, meta description with year | 10 min | -| **Total** | | **4-5 hours** | - -### How-To Guide Refresh Playbook - -| Step | Action | Time Estimate | -|------|--------|--------------| -| 1 | Verify all steps are still accurate | 30 min | -| 2 | Update screenshots for UI changes | 60 min | -| 3 | Add new methods or alternative approaches | 30 min | -| 4 | Update tool recommendations | 15 min | -| 5 | Add troubleshooting section if missing | 20 min | -| 6 | Update FAQ with new common questions | 15 min | -| 7 | Test all links and embedded resources | 15 min | -| **Total** | | **3-3.5 hours** | - ---- - -## ROI Estimation for Content Refresh - -### Cost-Benefit Framework - -| Factor | Measurement | -|--------|------------| -| **Cost of refresh** | Writer hours x hourly rate + tool costs | -| **Current monthly traffic value** | Organic sessions x conversion rate x avg order value | -| **Projected traffic recovery** | Based on decay stage and content potential | -| **Time to recover** | Typically 4-8 weeks for rankings to respond | - -### Traffic Recovery Benchmarks - -Based on industry data for content refreshes (not rewrites): - -| Decay Stage at Refresh | Typical Traffic Recovery | Recovery Timeline | -|------------------------|------------------------|-------------------| -| Early decay | 90-110% of peak (often exceeds) | 2-4 weeks | -| Active decay | 70-90% of peak | 4-8 weeks | -| Significant decay | 40-70% of peak | 6-12 weeks | -| Terminal decay | 10-40% of peak (rewrite may be better) | 8-16 weeks | - -### ROI Calculation Template - -``` -Refresh Cost: - Writer time: [X hours] x [$Y/hour] = $[Z] - Tool costs: $[A] (one-time crawl, research tools) - Total cost: $[Z + A] - -Monthly Traffic Value (before decay): - Peak monthly organic sessions: [N] - Conversion rate: [X]% - Average conversion value: $[Y] - Peak monthly value: [N] x [X]% x $[Y] = $[V] - -Expected Recovery: - Projected recovery: [%] of peak = $[V x %] per month - Current monthly value: $[current] - Monthly value increase: $[V x % - current] - -ROI: - Payback period: $[total cost] / $[monthly value increase] = [months] - 12-month ROI: ($[monthly value increase] x 12 - $[total cost]) / $[total cost] x 100 = [X]% -``` - -### Refresh Priority Scoring - -When choosing which content to refresh first, score each candidate: - -| Factor | Weight | Score (1-10) | -|--------|--------|-------------| -| Current traffic value | 25% | Higher traffic = higher score | -| Decay severity | 20% | More decay = more urgency | -| Competitive opportunity | 20% | Weaker competition = higher score | -| Refresh difficulty | 15% | Easier refresh = higher score | -| Strategic importance | 10% | Aligns with business goals = higher score | -| Backlink equity | 10% | More backlinks = more worth preserving | - -**Priority formula**: Weighted score total. Refresh highest-scoring content first. - ---- - -## Content Retirement Decision - -Not all decaying content should be refreshed. Use this checklist to decide when to retire content instead. - -### Retire When - -- [ ] Content targets a keyword with zero search volume -- [ ] Topic is no longer relevant to your business -- [ ] No backlinks worth preserving -- [ ] Content never ranked well even when fresh -- [ ] Cost to refresh exceeds projected 12-month value recovery -- [ ] Content cannibalizes a better-performing page on the same topic - -### Retirement Options - -| Option | When to Use | Implementation | -|--------|------------|---------------| -| 301 redirect | Content has backlinks or residual traffic | Redirect to best related page | -| Consolidate | Multiple weak pages on same topic | Merge into one strong page, redirect others | -| Noindex | Page has internal utility but should not rank | Add noindex, keep page accessible | -| Delete (410) | Content has no value, no links, no traffic | Return 410 Gone status | - -### Post-Retirement Monitoring - -After retiring content, monitor for 4 weeks: -- Verify redirects are working (no 404 errors) -- Check that target pages are receiving redirected traffic -- Monitor rankings of consolidated/target pages -- Ensure no orphan pages were created by removing internal links - ---- - -## Content Decay Signal Taxonomy - -### Decay Indicators - -| Signal | Source | Severity | Detection Method | -|--------|--------|----------|-----------------| -| Traffic decline >20% MoM | Analytics | High | Monthly traffic comparison | -| Position drop >5 positions | Rank tracker | High | Weekly rank monitoring | -| Outdated statistics/dates | Manual review | Medium | Annual content audit | -| Broken external links | Crawler | Medium | Monthly crawl reports | -| Decreased CTR | Search Console | Medium | Quarterly CTR analysis | -| Competitor new content | SERP monitoring | Medium | Monthly SERP checks | -| User engagement drop | Analytics | Low | Quarterly engagement review | -| Index coverage issues | Search Console | High | Weekly coverage monitoring | - -### Content Decay Stages - -| Stage | Symptoms | Urgency | Recommended Action | -|-------|---------|---------|-------------------| -| **Early decay** | Slight traffic/position dip | Low | Monitor for 2-4 weeks | -| **Active decay** | Consistent decline across 2+ months | Medium | Schedule refresh within 2 weeks | -| **Significant decay** | 50%+ traffic loss, page 2+ | High | Immediate refresh or rewrite | -| **Terminal decay** | No organic traffic, deindexed | Critical | Rewrite, redirect, or retire | - -## Refresh vs. Rewrite Decision Framework - -| Factor | Refresh (Update) | Rewrite (New version) | -|--------|-----------------|---------------------| -| Content quality | Good foundation, needs updating | Fundamentally flawed or outdated approach | -| Position | Was ranking well, now dropping | Never ranked well despite optimization | -| URL age | 1+ years, has earned backlinks | Young URL with no backlink equity | -| Backlinks | Has external links pointing to it | No backlinks worth preserving | -| Scope of changes needed | <50% of content changing | >50% needs rewriting | -| Search intent | Intent hasn't changed | Search intent has evolved | - -**Decision rule:** If the URL has backlinks and was ranking, REFRESH. If not, consider REWRITE at a new URL (with 301 redirect if old URL has any equity). - -## Content Lifecycle Model - -``` -CREATE → PROMOTE → MAINTAIN → REFRESH → [REFRESH again] or RETIRE - │ │ │ │ │ - │ Month 1 Month 2-6 Month 6-12 When terminal - │ Social, Monitor Update facts, 301 redirect - │ outreach, rankings, add new sections, to related - │ email fix issues improve depth content -``` - -### Lifecycle Actions by Phase - -| Phase | Duration | Key Actions | Metrics to Track | -|-------|----------|------------|-----------------| -| Create | Week 1 | Publish, submit to Search Console | Indexation | -| Promote | Month 1 | Social shares, email, outreach | Referral traffic, backlinks | -| Maintain | Months 2-6 | Monitor, fix broken links, respond to comments | Rankings, traffic trend | -| Refresh | Months 6-12+ | Update data, add sections, improve structure | Traffic recovery, new keywords | -| Retire | When terminal | 301 redirect to best alternative | Redirect traffic recovery | - -## Update Strategy by Content Type - -| Content Type | Refresh Frequency | Key Updates | Shelf Life | -|-------------|-------------------|------------|-----------| -| Statistics roundups | Every 6 months | Replace old stats, add new sources | 6-12 months | -| Tool comparisons | Every 3-6 months | Update pricing, features, screenshots | 3-6 months | -| How-to guides | Annually | Update steps, screenshots, links | 12-18 months | -| Evergreen guides | Every 12-18 months | Add new sections, update examples | 18-24 months | -| News/trend content | Don't refresh | Archive or redirect | 1-3 months | -| Case studies | Rarely | Update results if available | 2-3 years | -| Glossary/definitions | As needed | Update when definitions evolve | 2-5 years | - diff --git a/.agents/skills/content-refresher/references/refresh-example.md b/.agents/skills/content-refresher/references/refresh-example.md deleted file mode 100644 index 40dcc1887d..0000000000 --- a/.agents/skills/content-refresher/references/refresh-example.md +++ /dev/null @@ -1,143 +0,0 @@ -# Content Refresher — Worked Example & Checklist - -Referenced from [SKILL.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/SKILL.md). - ---- - -## Worked Example - -**User**: "Refresh my blog post about 'best cloud hosting providers'" - -**Output**: - -```markdown -### CORE-EEAT Quick Assessment - -**Content**: "Best Cloud Hosting Providers 2023" -**Content Type**: Commercial comparison / listicle - -| Dimension | Quick Score | Key Weakness | Refresh Priority | -|-----------|-----------|--------------|-----------------| -| C — Contextual Clarity | 60/100 | Title says 2023 — stale intent signal | 🔴 | -| O — Organization | 75/100 | Good comparison tables but no summary box | 🟡 | -| R — Referenceability | 35/100 | Pricing from Q1 2023, 3 broken links | 🔴 | -| E — Exclusivity | 50/100 | No original benchmarks or test data | 🟡 | -| Exp — Experience | 30/100 | No first-person testing narrative | 🔴 | -| Ept — Expertise | 65/100 | Author bio present but lacks credentials | 🟡 | -| A — Authority | 55/100 | 12 backlinks, was ranking page 1 | 🟢 | -| T — Trust | 60/100 | Affiliate links present but not disclosed | 🔴 | - -**Weakest Dimensions** (focus refresh here): -1. **Experience** — Add hands-on testing results ("We migrated a test site to each provider") -2. **Referenceability** — Replace all 2023 pricing/uptime data with current figures - -## Content Refresh Analysis: Best Cloud Hosting Providers 2023 - -**URL**: cloudhosting.com/best-cloud-hosting -**Published**: 2023-02-14 -**Last Updated**: Never -**Word Count**: 2,100 - -### Performance Metrics - -| Metric | 6 Mo Ago | Current | Change | -|--------|----------|---------|--------| -| Organic Traffic | 3,200/mo | 1,400/mo | -56% | -| Avg Position | 4.2 | 14.8 | ↓ 10.6 | -| Impressions | 18,000 | 9,500 | -47% | -| CTR | 6.1% | 2.3% | -3.8% | - -### Content Decay Signals Identified - -1. **Outdated year in title and H1** — "2023" signals stale content to users and search engines -2. **Pricing data 18+ months old** — AWS Lightsail listed at $3.50/mo (now $5/mo), DigitalOcean at $4/mo (now $6/mo) -3. **Missing new entrants** — No mention of Hetzner Cloud or Vultr, which 4/5 top competitors now cover -4. **3 broken outbound links** — Provider comparison pages that have moved or been retired - -### Refresh vs. Rewrite Decision - -| Factor | Assessment | -|--------|-----------| -| Content quality | Good structure, solid comparison tables — foundation is sound | -| URL equity | 12 referring domains, 18 months old | -| Scope of changes | ~40% of content needs updating | -| Search intent | Unchanged — still commercial comparison | - -**Decision**: **REFRESH** — The URL has earned backlinks, the structure is solid, and less than 50% needs rewriting. Keep the URL, update in place. - -## Content Refresh Plan - -**Current Title**: "Best Cloud Hosting Providers 2023" -**Refreshed Title**: "Best Cloud Hosting Providers 2024: 7 Platforms Tested & Compared" - -### Specific Refresh Actions - -1. **Update all pricing and specs** (~30 min) - - Replace 2023 pricing for all 5 listed providers with current data - - Add uptime stats from the last 12 months (source: UptimeRobot public status pages) - - Update feature comparison table with current plan tiers - -2. **Add 2 missing providers + testing narrative** (~600 words) - - Add Hetzner Cloud and Vultr sections with same comparison format - - Write intro paragraph: "We deployed a WordPress benchmark site to each provider and measured TTFB, uptime, and support response times over 30 days" - -3. **Add affiliate disclosure and FAQ section** (~200 words) - - Add disclosure statement below introduction: "This post contains affiliate links. See our editorial policy." - - Add FAQ with 4 questions targeting People Also Ask (e.g., "What is the cheapest cloud hosting?", "Is cloud hosting faster than shared hosting?") - - Implement FAQ schema markup for rich result eligibility - -4. **Fix broken links and update internal links** (~15 min) - - Replace 3 broken outbound links with current provider URLs - - Add internal links to cloudhosting.com/vps-vs-cloud and cloudhosting.com/hosting-speed-test - -### Republishing Strategy - -**Recommendation**: Update Published Date — this is a major overhaul (40%+ new content, new providers, fresh test data). Update `dateModified` in Article schema, resubmit URL in Search Console, and share on social as "Updated for 2024." - -### Expected Outcomes - -| Metric | Current | 30-Day Target | 90-Day Target | -|--------|---------|---------------|---------------| -| Avg Position | 14.8 | 8-10 | 3-6 | -| Organic Traffic | 1,400/mo | 2,200/mo | 3,500/mo | -| Featured Snippets | 0 | 1 (FAQ) | 2+ | -``` - ---- - -## Content Refresh Checklist - -```markdown -### Pre-Refresh -- [ ] Analyze current performance metrics -- [ ] Identify outdated information -- [ ] Research competitor updates -- [ ] Note missing topics - -### Content Updates -- [ ] Update year references -- [ ] Refresh statistics with sources -- [ ] Add new examples and case studies -- [ ] Expand thin sections -- [ ] Add new relevant sections -- [ ] Create FAQ section - -### SEO Updates -- [ ] Update title tag -- [ ] Refresh meta description -- [ ] Optimize headers -- [ ] Update internal links -- [ ] Add new images with alt text - -### GEO Updates -- [ ] Add clear definition -- [ ] Include quotable statements -- [ ] Add Q&A formatted content -- [ ] Update source citations - -### Technical -- [ ] Update schema dateModified -- [ ] Clear page cache -- [ ] Update sitemap -- [ ] Test page speed -``` diff --git a/.agents/skills/content-refresher/references/refresh-templates.md b/.agents/skills/content-refresher/references/refresh-templates.md deleted file mode 100644 index 8b9e52a826..0000000000 --- a/.agents/skills/content-refresher/references/refresh-templates.md +++ /dev/null @@ -1,253 +0,0 @@ -# Content Refresh Templates - -Detailed output templates for content-refresher steps 5-9. Referenced from [SKILL.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/content-refresher/SKILL.md). - ---- - -## Step 5: Create Refresh Plan - -```markdown -## Content Refresh Plan - -### Title/URL -**Current**: [current title] -**Refreshed**: [updated title with year/hook] - -### Structural Changes - -**Keep As-Is**: -- [Section 1] - Still relevant and accurate -- [Section 2] - Still relevant and accurate - -**Update/Expand**: -- [Section 3] - Update statistics, add [X] words -- [Section 4] - Add new examples from [current year] - -**Add New Sections**: -- [New Section 1] - [description, ~X words] -- [New Section 2] - [description, ~X words] -- FAQ Section - [X questions for featured snippets] - -**Remove/Consolidate**: -- [Section 5] - Outdated, remove or redirect topic - -### Content Additions - -**New Word Count Target**: [X] words (+[Y] from current) - -| Section | Current | After Refresh | Notes | -|---------|---------|---------------|-------| -| Introduction | [X] | [X] | Add hook, update context | -| [Section 1] | [X] | [X] | Keep | -| [Section 2] | [X] | [X] | Update stats | -| [New Section] | 0 | [X] | Add entirely | -| FAQ | 0 | [X] | Add for GEO | -| Conclusion | [X] | [X] | Update CTA | - -### Specific Updates - -**Statistics to Update**: - -| Old Statistic | New Statistic | Source | -|---------------|---------------|--------| -| "[old stat]" | "[find current]" | [source] | -| "[old stat]" | "[find current]" | [source] | - -**Links to Update**: - -| Anchor Text | Old URL | New URL | Reason | -|-------------|---------|---------|--------| -| "[anchor]" | [old] | [new] | Broken | -| "[anchor]" | [old] | [new] | Better resource | - -**Images to Update**: - -| Image | Action | New Alt Text | -|-------|--------|--------------| -| [img 1] | Replace | "[keyword-rich alt]" | -| [img 2] | Keep | Update alt text | -``` - ---- - -## Step 6: Write Refresh Content - -```markdown -## Refreshed Content Sections - -### Updated Introduction - -[Write new introduction with:] -- Updated hook for current year -- Fresh statistics -- Clear value proposition -- Primary keyword in first 100 words - -### New Section: [Title] - -[Write new section covering:] -- [Topic competitors now cover] -- Current information and examples -- GEO-optimized with quotable statements - -### Updated Statistics Section - -**Replace**: -> "[Old statement with outdated stat]" - -**With**: -> "[New statement with current stat] (Source, [current year])" - -### New FAQ Section - -## Frequently Asked Questions - -### [Question matching PAA/common query]? - -[Direct answer in 40-60 words, optimized for featured snippets] - -### [Question 2]? - -[Direct answer] - -### [Question 3]? - -[Direct answer] -``` - ---- - -## Step 7: Optimize for GEO During Refresh - -```markdown -## GEO Enhancement Opportunities - -### Add Clear Definitions - -**Add at start of article**: -> **[Topic]** is [clear, quotable definition in 40-60 words that -> AI systems can cite directly]. - -### Add Quotable Statements - -**Transform**: -> "Email marketing is effective for businesses." - -**Into**: -> "Email marketing delivers an average ROI of $42 for every $1 -> invested, making it the highest-ROI digital marketing channel -> according to the Data & Marketing Association ([current year])." - -### Add Q&A Sections - -Structure content with questions AI might answer: -- What is [topic]? -- How does [topic] work? -- Why is [topic] important? -- What are the benefits of [topic]? - -### Update Citations - -- Add sources for all statistics -- Link to authoritative references -- Include publication dates -- Use recent sources (last 2 years) -``` - ---- - -## Step 8: Generate Republishing Strategy - -```markdown -## Republishing Strategy - -### Date Strategy - -**Options**: - -1. **Update Published Date** - - Use when: Major overhaul (50%+ new content) - - Pros: Signals freshness to Google - - Cons: Loses "original" authority - -2. **Add "Last Updated" Date** - - Use when: Moderate updates (20-50% new) - - Pros: Shows both original and fresh - - Cons: Original date visible - -3. **Keep Original Date** - - Use when: Minor updates (<20% new) - - Pros: Maintains authority - - Cons: Doesn't signal update - -**Recommendation**: [Option X] because [reason] - -### Technical Implementation - -- [ ] Update `dateModified` in schema -- [ ] Update sitemap lastmod -- [ ] Clear cache after publishing -- [ ] Resubmit to ~~search console - -### Promotion Strategy - -**Immediately after refresh**: -- [ ] Share on social media as "updated for [current year]" -- [ ] Send to email list if significant update -- [ ] Update internal links with fresh anchors -- [ ] Reach out for new backlinks - -**Track Results**: -- [ ] Monitor rankings for 4-6 weeks -- [ ] Track traffic changes -- [ ] Watch for featured snippet capture -- [ ] Check AI citation improvements -``` - ---- - -## Step 9: Create Refresh Report - -```markdown -# Content Refresh Report - -## Summary - -**Content**: [Title] -**Refresh Date**: [Date] -**Refresh Level**: [Major/Moderate/Minor] - -## Changes Made - -| Element | Before | After | -|---------|--------|-------| -| Word count | [X] | [Y] (+[Z]%) | -| Sections | [X] | [Y] | -| Statistics | [X] outdated | [Y] current | -| Internal links | [X] | [Y] | -| Images | [X] | [Y] | -| FAQ questions | 0 | [X] | - -## Updates Completed - -- [x] Updated title with current year -- [x] Refreshed meta description -- [x] Added [X] new sections -- [x] Updated [X] statistics with sources -- [x] Fixed [X] broken links -- [x] Added FAQ section for GEO -- [x] Implemented FAQ schema -- [x] Updated images and alt text - -## Expected Outcomes - -| Metric | Current | 30-Day Target | 90-Day Target | -|--------|---------|---------------|---------------| -| Avg Position | [X] | [Y] | [Z] | -| Organic Traffic | [X]/mo | [Y]/mo | [Z]/mo | -| Featured Snippets | 0 | 1+ | 2+ | - -## Next Review - -Schedule next refresh review: [Date - 6 months from now] -``` diff --git a/.agents/skills/domain-authority-auditor/SKILL.md b/.agents/skills/domain-authority-auditor/SKILL.md deleted file mode 100644 index 1840cfd38e..0000000000 --- a/.agents/skills/domain-authority-auditor/SKILL.md +++ /dev/null @@ -1,394 +0,0 @@ ---- -name: domain-authority-auditor -description: '40-item CITE domain audit: citation, impact, trust, entity scoring with veto checks. 域名权威/网站可信度' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when auditing domain trust and authority. Runs CITE 40-item scoring with veto checks. Also when the user asks about domain credibility or citation trustworthiness." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "medium" - tags: - - seo - - geo - - domain-authority - - domain-rating - - domain-trust - - cite-framework - - site-authority - - 域名权威 - - ドメイン権威 - - 도메인권위 - - autoridad-dominio - triggers: - # EN-formal - - "audit domain authority" - - "CITE audit" - - "domain trust score" - - "domain credibility check" - - "domain rating" - - "site authority" - # EN-casual - - "how trustworthy is my site" - - "is my domain credible" - - "is my domain trustworthy" - - "domain credibility score" - - "Google penalty recovery" - - "my site got penalized" - # EN-question - - "how authoritative is my site" - - "what is my domain authority" - # ZH-pro - - "域名权威审计" - - "网站可信度" - - "域名评分" - # ZH-casual - - "域名可信吗" - - "权威度多少" - - "网站可信度怎么样" - # JA - - "ドメイン権威" - - "ドメイン評価" - # KO - - "도메인 권위" - - "도메인 신뢰도" - # ES - - "autoridad de dominio" - - "auditoría de dominio" - # PT - - "autoridade de domínio" - # Misspellings - - "domain autority" ---- - -# Domain Authority Auditor - -> Based on [CITE Domain Rating](https://github.com/aaron-he-zhu/cite-domain-rating). Full benchmark reference: [references/cite-domain-rating.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/cite-domain-rating.md) - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This cross-cutting skill is part of the protocol layer and follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill evaluates domain authority across 40 standardized criteria organized in 4 dimensions. It produces a comprehensive audit report with per-item scoring, dimension and weighted scores by domain type, veto item checks, and a prioritized action plan. - -**Sister skill**: [content-quality-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/SKILL.md) evaluates content at the page level (80 items). This skill evaluates the domain behind the content (40 items). Together they provide a complete 120-item assessment. - -> **Namespace note**: CITE uses C01-C10 for Citation items; CORE-EEAT uses C01-C10 for Contextual Clarity items. In combined 120-item assessments, prefix with the framework name (e.g., CITE-C01 vs CORE-C01) to avoid confusion. - -**System role**: Citation Trust Gate. It decides whether a domain is credible enough to support ranking, citation, and brand authority work. - -## When This Must Trigger - -Use this when domain credibility or citation trustworthiness is in question — even if the user doesn't use audit terminology: - -- User asks "how trustworthy is my site" or "is my domain credible" -- When backlink-analyzer finds toxic link ratio above 15%, its handoff summary recommends this gate check -- Evaluating domain authority before a GEO campaign -- Benchmarking your domain against competitors -- Assessing whether a domain is trustworthy as a citation source -- Running periodic domain health checks or after link building campaigns -- Identifying manipulation red flags (PBNs, link farms, penalty history) -- Cross-referencing with content-quality-auditor for full 120-item assessment - -## What This Skill Does - -1. **Full 40-Item Audit**: Scores every CITE check item as Pass/Partial/Fail -2. **Dimension Scoring**: Calculates scores for all 4 dimensions (0-100 each) -3. **Weighted Totals**: Applies domain-type-specific weights for CITE Score -4. **Veto Detection**: Flags critical manipulation signals (T03, T05, T09) -5. **Priority Ranking**: Identifies Top 5 improvements sorted by impact -6. **Action Plan**: Generates specific, actionable improvement steps -7. **Cross-Reference**: Optionally pairs with CORE-EEAT for combined diagnosis - -## Quick Start - -Start with one of these prompts. Finish with a citation-trust verdict and a handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Audit Your Domain - -``` -Audit domain authority for [domain] -Run a CITE domain audit on [domain] as a [domain type] -``` - -### Audit with Domain Type - -``` -CITE audit for example.com as an e-commerce site -Score this SaaS domain against the 40-item benchmark: [domain] -``` - -### Comparative Audit - -``` -Compare domain authority: [your domain] vs [competitor 1] vs [competitor 2] -``` - -### Combined Assessment - -``` -Run full 120-item assessment on [domain]: CITE domain audit + CORE-EEAT content audit on [sample pages] -``` - -## Skill Contract - -**Gate verdict**: **TRUSTED** (no veto items, scores above threshold) / **CAUTIOUS** (issues found but no veto) / **UNTRUSTED** (veto item T03, T05, or T09 failed). Always state the verdict prominently at the top of the report. - -**Expected output**: a CITE audit report, a citation-trust verdict, and a short handoff summary ready for `memory/audits/domain/`. - -- **Reads**: the target domain, supporting authority signals, comparison domains, and prior decisions from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing authority report plus a reusable summary that can be stored under `memory/audits/domain/`. -- **Promotes**: veto items and domain risks to `memory/hot-cache.md` (auto-saved). Authority context to `memory/audits/domain/`. Results feed into entity-optimizer as authority input for brand's canonical profile. -- **Next handoff**: use the `Next Best Skill` below once the trust picture is clear. - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -**With ~~link database + ~~SEO tool + ~~AI monitor + ~~knowledge graph + ~~brand monitor connected:** -Automatically pull backlink profiles and link quality metrics from ~~link database, domain authority scores and keyword rankings from ~~SEO tool, AI citation data from ~~AI monitor, entity presence from ~~knowledge graph, and brand mention data from ~~brand monitor. - -**With manual data only:** -Ask the user to provide: -1. Domain to evaluate -2. Domain type (if not auto-detectable): Content Publisher, Product & Service, E-commerce, Community & UGC, Tool & Utility, or Authority & Institutional -3. Backlink data: referring domains count, domain authority, top linking domains -4. Traffic estimates (from any SEO tool or SimilarWeb) -5. Competitor domains for comparison (optional) - -Proceed with the full 40-item audit using provided data. Note in the output which items could not be fully evaluated due to missing access (e.g., AI citation data, knowledge graph queries, WHOIS history). - -## Instructions - -When a user requests a domain authority audit: - -### Step 1: Preparation - -```markdown -### Audit Setup - -**Domain**: [domain] -**Domain Type**: [auto-detected or user-specified] -**Dimension Weights**: [from domain-type weight table below] - -#### Domain-Type Weight Table - -> Canonical source: `references/cite-domain-rating.md`. This inline copy is for convenience. - -| Dim | Default | Content Publisher | Product & Service | E-commerce | Community & UGC | Tool & Utility | Authority & Institutional | -|-----|:-------:|:-:|:-:|:-:|:-:|:-:|:-:| -| C | 35% | **40%** | 25% | 20% | 35% | 25% | **45%** | -| I | 20% | 15% | **30%** | 20% | 10% | **30%** | 20% | -| T | 25% | 20% | 25% | **35%** | 25% | 25% | 20% | -| E | 20% | 25% | 20% | 25% | **30%** | 20% | 15% | - -#### Veto Check (Emergency Brake) - -| Veto Item | Status | Action | -|-----------|--------|--------| -| T03: Link-Traffic Coherence | ✅ Pass / ⚠️ VETO | [If VETO: "Audit backlink profile; disavow toxic links"] | -| T05: Backlink Profile Uniqueness | ✅ Pass / ⚠️ VETO | [If VETO: "Flag as manipulation network; investigate link sources"] | -| T09: Penalty & Deindex History | ✅ Pass / ⚠️ VETO | [If VETO: "Address penalty first; all other optimization is futile"] | -``` - -If any veto item triggers, flag it prominently at the top of the report. CITE Score is capped at 39 (Poor) regardless of other scores. - -### Step 2: C + I Audit (20 items) - -Evaluate each item against the criteria in [references/cite-domain-rating.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/cite-domain-rating.md). - -Score each item: -- **Pass** = 10 points (fully meets criteria) -- **Partial** = 5 points (partially meets criteria) -- **Fail** = 0 points (does not meet criteria) - -```markdown -### C — Citation - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| C01 | Referring Domains Volume | Pass/Partial/Fail | [specific observation] | -| C02 | Referring Domains Quality | Pass/Partial/Fail | [specific observation] | -| ... | ... | ... | ... | -| C10 | Link Source Diversity | Pass/Partial/Fail | [specific observation] | - -**C Score**: [X]/100 - -### I — Identity - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| I01 | Knowledge Graph Presence | Pass/Partial/Fail | [specific observation] | -| ... | ... | ... | ... | - -**I Score**: [X]/100 -``` - -### Step 3: T + E Audit (20 items) - -Same format for Trust and Eminence dimensions. - -```markdown -### T — Trust - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| T01 | Link Profile Naturalness | Pass/Partial/Fail | [specific observation] | -| ... | ... | ... | ... | - -**T Score**: [X]/100 - -### E — Eminence - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| E01 | Organic Search Visibility | Pass/Partial/Fail | [specific observation] | -| ... | ... | ... | ... | - -**E Score**: [X]/100 -``` - -**Note**: Some items require specialized data (C05-C08 AI citation data, I01 knowledge graph queries, T04-T05 IP/profile analysis). Score what is observable; mark unverifiable items as "N/A — requires [data source]" and exclude from dimension average. - -### Step 4: Scoring & Report - -Calculate scores and generate the final report: - -```markdown -## CITE Domain Authority Report - -### Overview - -- **Domain**: [domain] -- **Domain Type**: [type] -- **Audit Date**: [date] -- **CITE Score**: [score]/100 ([rating]) -- **Veto Status**: ✅ No triggers / ⚠️ [item] triggered — Score capped at 39 - -### Dimension Scores - -| Dimension | Score | Rating | Weight | Weighted | -|-----------|-------|--------|--------|----------| -| C — Citation | [X]/100 | [rating] | [X]% | [X] | -| I — Identity | [X]/100 | [rating] | [X]% | [X] | -| T — Trust | [X]/100 | [rating] | [X]% | [X] | -| E — Eminence | [X]/100 | [rating] | [X]% | [X] | -| **CITE Score** | | | | **[X]/100** | - -**Score Calculation**: CITE Score = C × [w_C] + I × [w_I] + T × [w_T] + E × [w_E] - -**Rating Scale**: 90-100 Excellent | 75-89 Good | 60-74 Medium | 40-59 Low | 0-39 Poor - -### Per-Item Scores - -| ID | Check Item | Score | Notes | -|----|-----------|-------|-------| -| C01 | Referring Domains Volume | [Pass/Partial/Fail] | [observation] | -| C02 | Referring Domains Quality | [Pass/Partial/Fail] | [observation] | -| ... | ... | ... | ... | -| E10 | Industry Share of Voice | [Pass/Partial/Fail] | [observation] | - -### Top 5 Priority Improvements - -Sorted by: weight × points lost (highest impact first) - -1. **[ID] [Name]** — [specific modification suggestion] - - Current: [Fail/Partial] | Potential gain: [X] weighted points - - Action: [concrete step] -2. **[ID] [Name]** — [specific modification suggestion] - - Current: [Fail/Partial] | Potential gain: [X] weighted points - - Action: [concrete step] -3–5. [Same format] - -### Action Plan - -#### Quick Wins (< 1 week) -- [ ] [Action 1] -- [ ] [Action 2] -#### Medium Effort (1-4 weeks) -- [ ] [Action 3] -- [ ] [Action 4] -#### Strategic (1-3 months) -- [ ] [Action 5] -- [ ] [Action 6] - -### Cross-Reference with CORE-EEAT - -For a complete assessment, pair this CITE audit with a CORE-EEAT content audit: - -| Assessment | Score | Rating | -|-----------|-------|--------| -| CITE (Domain) | [X]/100 | [rating] | -| CORE-EEAT (Content) | [Run content-quality-auditor on sample pages] | — | - -**Diagnosis Matrix**: -- High CITE + High CORE-EEAT → Maintain and expand -- High CITE + Low CORE-EEAT → Prioritize content quality -- Low CITE + High CORE-EEAT → Build domain authority -- Low CITE + Low CORE-EEAT → Start with content, then domain - -### Recommended Next Steps - -- For domain authority building: focus on top 5 priorities above -- For content improvement: use `content-quality-auditor` on key pages -- For backlink strategy: use `backlink-analyzer` for detailed link analysis -- For competitor benchmarking: use `competitor-analysis` with CITE scores -- For tracking progress: run `/seo:report` with CITE score trends -``` - -### Save Results - -After delivering findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to the appropriate `memory/` path using filename `YYYY-MM-DD-.md` containing: -- One-line verdict or headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to `memory/hot-cache.md` without asking. - -## Validation Checkpoints - -### Input Validation -- [ ] Domain identified and accessible -- [ ] Domain type confirmed (auto-detected or user-specified) -- [ ] Backlink data available (at minimum: referring domains count, DA/DR) -- [ ] If comparative audit, competitor domains also specified - -### Output Validation -- [ ] All 40 items scored (or marked N/A with reason) -- [ ] All 4 dimension scores calculated correctly -- [ ] Weighted CITE Score matches domain-type weight configuration -- [ ] All 3 veto items checked first and flagged if triggered -- [ ] Top 5 improvements sorted by weighted impact, not arbitrary -- [ ] Every recommendation is specific and actionable (not generic advice) -- [ ] Action plan includes concrete steps with effort estimates - -## Example - -See [references/example-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/domain-authority-auditor/references/example-report.md) for a complete CITE audit of cloudhosting.com showing veto check, dimension scores, top 5 improvements, action plan, and cross-reference with CORE-EEAT. - -## Tips for Success - -1. **Start with veto items** — T03, T05, T09 can invalidate the entire score -2. **Identify domain type first** — Different types have very different weight profiles -3. **AI citation items (C05-C08) matter most for GEO** — Test by querying AI engines with niche-relevant questions -4. **Some items need specialized tools** — Knowledge graph queries, AI citation monitoring, and IP diversity analysis may require manual research if tools aren't connected -5. **Pair with CORE-EEAT for full picture** — Domain authority without content quality (or vice versa) tells only half the story - -## Reference Materials - -- [CITE Domain Rating](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/cite-domain-rating.md) — Full 40-item benchmark with dimension definitions, scoring criteria, domain-type weight tables, and veto items -- [references/example-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/domain-authority-auditor/references/example-report.md) — Complete CITE audit example with scored dimensions, top 5 improvements, action plan, and CORE-EEAT cross-reference - -## Next Best Skill - -- **Primary**: [backlink-analyzer](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/monitor/backlink-analyzer/SKILL.md) — turn trust or citation issues into link-level investigation. diff --git a/.agents/skills/domain-authority-auditor/references/example-report.md b/.agents/skills/domain-authority-auditor/references/example-report.md deleted file mode 100644 index fe20e30132..0000000000 --- a/.agents/skills/domain-authority-auditor/references/example-report.md +++ /dev/null @@ -1,99 +0,0 @@ -# CITE Domain Authority Report — Example - -Full example audit output for reference. See the [SKILL.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/domain-authority-auditor/SKILL.md) for the complete workflow. - -## Example - -**User**: "Audit domain authority for cloudhosting.com as a content publisher" - -**Output**: - -```markdown -## CITE Domain Authority Report - -### Overview - -- **Domain**: cloudhosting.com -- **Domain Type**: Content Publisher -- **Audit Date**: 2025-02-03 -- **CITE Score**: 69.9/100 (Medium) -- **Veto Status**: ✅ No triggers - -#### Veto Check (Emergency Brake) - -| Veto Item | Status | Action | -|-----------|--------|--------| -| T03: Link-Traffic Coherence | ✅ Pass | Link growth correlates with traffic growth | -| T05: Backlink Profile Uniqueness | ✅ Pass | No PBN patterns detected; diverse link sources | -| T09: Penalty & Deindex History | ✅ Pass | No manual actions; clean penalty history | - -### Dimension Scores - -| Dimension | Score | Rating | Weight | Weighted | -|-----------|-------|--------|--------|----------| -| C — Citation | 72/100 | Medium | 40% | 28.8 | -| I — Identity | 58/100 | Low | 15% | 8.7 | -| T — Trust | 81/100 | Good | 20% | 16.2 | -| E — Eminence | 65/100 | Medium | 25% | 16.25 | -| **CITE Score** | | | | **69.9/100** | - -**Score Calculation**: -- CITE Score = 72 × 0.40 + 58 × 0.15 + 81 × 0.20 + 65 × 0.25 = 69.9 - -**Rating Scale**: 90-100 Excellent | 75-89 Good | 60-74 Medium | 40-59 Low | 0-39 Poor - -### Top 5 Priority Improvements - -Sorted by: weight × points lost (highest impact first) - -1. **I01 Knowledge Graph Presence** — Create entity entry in Google Knowledge Graph - - Current: Fail | Potential gain: 1.5 weighted points - - Action: Create Wikidata entry for CloudHost Inc. with P856 (website), P452 (industry), P571 (inception) - -2. **C05 AI Citation Volume** — Increase citations in AI-generated answers - - Current: Partial | Potential gain: 2.0 weighted points - - Action: Optimize top 10 pages for GEO; add definitive statements AI can quote directly - -3. **I03 Brand SERP Control** — Branded SERP shows only 4 of 10 results from owned properties - - Current: Partial | Potential gain: 0.75 weighted points - - Action: Claim Google Business Profile; build out social profiles; create CrunchBase entry - -4. **E04 Content Freshness Cadence** — 40% of content is >12 months without update - - Current: Partial | Potential gain: 1.25 weighted points - - Action: Establish monthly content refresh schedule; prioritize top 20 traffic pages - -5. **I05 Schema.org Completeness** — Organization schema missing sameAs, founder, foundingDate - - Current: Partial | Potential gain: 0.75 weighted points - - Action: Add complete Organization schema with sameAs links to Wikidata, LinkedIn, CrunchBase - -### Action Plan - -#### Quick Wins (< 1 week) -- [ ] Add sameAs, founder, and foundingDate to Organization schema -- [ ] Claim Google Business Profile for branded SERP control - -#### Medium Effort (1-4 weeks) -- [ ] Create Wikidata entry with complete properties and references -- [ ] Optimize top 10 pages with GEO-friendly definitive statements -- [ ] Create or complete CrunchBase, LinkedIn company page profiles - -#### Strategic (1-3 months) -- [ ] Launch monthly content refresh program targeting stale pages -- [ ] Build topical authority through 3-4 pillar content clusters -- [ ] Pursue digital PR to earn mentions on industry publications (TechCrunch, G2) - -### Cross-Reference with CORE-EEAT - -| Assessment | Score | Rating | -|-----------|-------|--------| -| CITE (Domain) | 69.9/100 | Medium | -| CORE-EEAT (Content) | Run content-quality-auditor on sample pages | — | - -**Diagnosis**: Low CITE + unknown CORE-EEAT → Run `/seo:audit-page` on top 5 landing pages to determine whether to prioritize content quality or domain authority first. - -### Recommended Next Steps - -- For entity building: run `entity-optimizer` to strengthen I-dimension signals -- For content audit: use `content-quality-auditor` on key pages -- For tracking progress: run `/seo:report` with CITE score trends quarterly -``` diff --git a/.agents/skills/entity-optimizer/SKILL.md b/.agents/skills/entity-optimizer/SKILL.md deleted file mode 100644 index 7a2a6c5029..0000000000 --- a/.agents/skills/entity-optimizer/SKILL.md +++ /dev/null @@ -1,376 +0,0 @@ ---- -name: entity-optimizer -description: 'Build entity presence in Knowledge Graph, Wikidata, AI systems for brand recognition and citations. 实体优化/知识图谱' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when optimizing entity presence for Knowledge Graph, Wikidata, or AI engine disambiguation. Also for brand entity canonicalization." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "high" - tags: - - seo - - geo - - entity-optimization - - knowledge-graph - - knowledge-panel - - brand-entity - - wikidata - - entity-disambiguation - - 实体优化 - - エンティティ - - 엔티티 - - entidad-seo - triggers: - # EN-formal - - "optimize entity presence" - - "build knowledge graph" - - "improve knowledge panel" - - "entity audit" - - "establish brand entity" - - "entity disambiguation" - # EN-casual - - "Google doesn't know my brand" - - "no knowledge panel" - - "establish my brand" - - "establish my brand as an entity" - - "get a Google knowledge card" - - "no Wikipedia entry" - # EN-question - - "how to get a knowledge panel" - - "how to build brand entity" - # ZH-pro - - "实体优化" - - "知识图谱" - - "品牌实体" - - "知识面板" - - "品牌词" - - "品牌词优化" - # ZH-casual - - "品牌搜不到" - - "没有知识面板" - - "Google不认识我的品牌" - # JA - - "エンティティ最適化" - - "ナレッジパネル" - # KO - - "엔티티 최적화" - - "지식 패널" - - "구글이 내 브랜드 모르는데?" - - "지식 패널 만들려면?" - # ES - - "optimización de entidad" - - "panel de conocimiento" - # PT - - "otimização de entidade" - # Misspellings - - "knowlege panel" - - "enity optimization" ---- - -# Entity Optimizer - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This cross-cutting skill is part of the protocol layer and follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -Audits, builds, and maintains entity identity across search engines and AI systems. Entities — the people, organizations, products, and concepts that search engines and AI systems recognize as distinct things — are the foundation of how both Google and LLMs decide *what a brand is* and *whether to cite it*. - -**Why entities matter for SEO + GEO:** - -- **SEO**: Google's Knowledge Graph powers Knowledge Panels, rich results, and entity-based ranking signals. A well-defined entity earns SERP real estate. -- **GEO**: AI systems resolve queries to entities before generating answers. If an AI cannot identify an entity, it cannot cite it — no matter how good the content is. - -**System role**: Canonical Entity Profile. It acts as the source of truth for entity identity, associations, and disambiguation across the library. - -## When This Must Trigger - -Use this when brand or entity identity needs to be established or verified — even if the user doesn't use entity terminology: - -- User says "Google doesn't know my brand" or "no knowledge panel" -- Auto-recommended when `memory/entities/candidates.md` accumulates 3 or more uncanonized entity candidates from other skills -- Establishing a new brand/person/product as a recognized entity -- Auditing current entity presence across Knowledge Graph, Wikidata, and AI systems -- Improving or correcting a Knowledge Panel -- Building entity associations (entity ↔ topic, entity ↔ industry) -- Resolving entity disambiguation issues (your entity confused with another) -- Strengthening entity signals for AI citation -- After launching a new brand, product, or organization -- Preparing for a site migration (preserving entity identity) -- Running periodic entity health checks - -## What This Skill Does - -1. **Entity Audit**: Evaluates current entity presence across search and AI systems -2. **Knowledge Graph Analysis**: Checks Google Knowledge Graph, Wikidata, and Wikipedia status -3. **AI Entity Resolution Test**: Queries AI systems to see how they identify and describe the entity -4. **Entity Signal Mapping**: Identifies all signals that establish entity identity -5. **Gap Analysis**: Finds missing or weak entity signals -6. **Entity Building Plan**: Creates actionable plan to establish or strengthen entity presence -7. **Disambiguation Strategy**: Resolves confusion with similarly-named entities - -## Quick Start - -Start with one of these prompts. Finish with a canonical entity profile and a handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Entity Audit - -``` -Audit entity presence for [brand/person/organization] -``` - -``` -How well do search engines and AI systems recognize [entity name]? -``` - -### Build Entity Presence - -``` -Build entity presence for [new brand] in the [industry] space -``` - -``` -Establish [person name] as a recognized expert in [topic] -``` - -### Fix Entity Issues - -``` -My Knowledge Panel shows incorrect information — fix entity signals for [entity] -``` - -``` -AI systems confuse [my entity] with [other entity] — help me disambiguate -``` - -## Skill Contract - -**Expected output**: an entity audit, a canonical entity profile, and a short handoff summary ready for `memory/entities/`. - -- **Reads**: the entity name, primary domain, known profiles, topic associations, and prior brand context from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing entity report plus a reusable profile that can be stored under `memory/entities/`. -- **Promotes**: canonical names, sameAs links, disambiguation notes, and entity gaps to `CLAUDE.md`, `memory/entities/`, and `memory/open-loops.md`. - -This skill is the sole writer of canonical entity profiles at `memory/entities/.md`. Other skills write entity candidates to `memory/entities/candidates.md` only. When 3+ candidates accumulate, this skill should be recommended. - -- **Next handoff**: use the `Next Best Skill` below once the entity truth is clear. - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~knowledge graph + ~~SEO tool + ~~AI monitor + ~~brand monitor connected:** -Query Knowledge Graph API for entity status, pull branded search data from ~~SEO tool, test AI citation with ~~AI monitor, track brand mentions with ~~brand monitor. - -**With manual data only:** -Ask the user to provide: -1. Entity name, type (Person, Organization, Brand, Product, Creative Work, Event) -2. Primary website / domain -3. Known existing profiles (Wikipedia, Wikidata, social media, industry directories) -4. Top 3-5 topics/industries the entity should be associated with -5. Any known disambiguation issues (other entities with same/similar name) - -Without tools, Claude provides entity optimization strategy and recommendations based on information the user provides. The user must run search queries, check Knowledge Panels, and test AI responses to supply the raw data for analysis. - -Proceed with the audit using public search results, AI query testing, and SERP analysis. Note which items require tool access for full evaluation. - -## Instructions - -When a user requests entity optimization: - -### Step 1: Entity Discovery - -Establish the entity's current state across all systems. - -```markdown -### Entity Profile - -**Entity Name**: [name] -**Entity Type**: [Person / Organization / Brand / Product / Creative Work / Event] -**Primary Domain**: [URL] -**Target Topics**: [topic 1, topic 2, topic 3] - -#### Current Entity Presence - -| Platform | Status | Details | -|----------|--------|---------| -| Google Knowledge Panel | ✅ Present / ❌ Absent / ⚠️ Incorrect | [details] | -| Wikidata | ✅ Listed / ❌ Not listed | [QID if exists] | -| Wikipedia | ✅ Article / ⚠️ Mentioned only / ❌ Absent | [notability assessment] | -| Google Knowledge Graph API | ✅ Entity found / ❌ Not found | [entity ID, types, score] | -| Schema.org on site | ✅ Complete / ⚠️ Partial / ❌ Missing | [Organization/Person/Product schema] | - -#### AI Entity Resolution Test - -**Note**: Claude cannot directly query other AI systems or perform real-time web searches without tool access. When running without ~~AI monitor or ~~knowledge graph tools, ask the user to run these test queries and report the results, or use the user-provided information to assess entity presence. - -Test how AI systems identify this entity by querying: -- "What is [entity name]?" -- "Who founded [entity name]?" (for organizations) -- "What does [entity name] do?" -- "[entity name] vs [competitor]" - -| AI System | Recognizes Entity? | Description Accuracy | Cites Entity's Content? | -|-----------|-------------------|---------------------|------------------------| -| ChatGPT | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] | -| Claude | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] | -| Perplexity | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] | -| Google AI Overview | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] | -``` - -### Step 2: Entity Signal Audit - -Evaluate entity signals across 6 categories. For the detailed 47-signal checklist with verification methods, see [references/entity-signal-checklist.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/entity-signal-checklist.md). - -Evaluate each signal as Pass / Fail / Partial with a specific action for each gap. The 6 categories are: - -1. **Structured Data Signals** -- Organization/Person schema, sameAs links, @id consistency, author schema -2. **Knowledge Base Signals** -- Wikidata, Wikipedia, CrunchBase, industry directories -3. **Consistent NAP+E Signals** -- Name/description/logo/social consistency across platforms -4. **Content-Based Entity Signals** -- About page, author pages, topical authority, branded backlinks -5. **Third-Party Entity Signals** -- Authoritative mentions, co-citation, reviews, press coverage -6. **AI-Specific Entity Signals** -- Clear definitions, disambiguation, verifiable claims, crawlability - -> **Reference**: Use the audit template in [references/entity-signal-checklist.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/entity-signal-checklist.md) for the full 47-signal checklist with verification methods for each category. - -### Step 3: Report & Action Plan - -```markdown -## Entity Optimization Report - -### Overview - -- **Entity**: [name] -- **Entity Type**: [type] -- **Audit Date**: [date] - -### Signal Category Summary - -| Category | Status | Key Findings | -|----------|--------|-------------| -| Structured Data | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] | -| Knowledge Base | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] | -| Consistency (NAP+E) | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] | -| Content-Based | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] | -| Third-Party | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] | -| AI-Specific | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] | - -### Critical Issues - -[List any issues that severely impact entity recognition — disambiguation problems, incorrect Knowledge Panel, missing from Knowledge Graph entirely] - -### Top 5 Priority Actions - -Sorted by: impact on entity recognition × effort required - -1. **[Signal]** — [specific action] - - Impact: [High/Medium] | Effort: [Low/Medium/High] - - Why: [explanation of how this improves entity recognition] - -2. **[Signal]** — [specific action] - - Impact: [High/Medium] | Effort: [Low/Medium/High] - - Why: [explanation] - -3–5. [Same format] - -### Entity Building Roadmap - -#### Week 1-2: Foundation (Structured Data + Consistency) -- [ ] Implement/fix Organization or Person schema with full properties -- [ ] Add sameAs links to all authoritative profiles -- [ ] Audit and fix NAP+E consistency across all platforms -- [ ] Ensure About page is entity-rich and well-structured - -#### Month 1: Knowledge Bases -- [ ] Create or update Wikidata entry with complete properties -- [ ] Ensure CrunchBase / industry directory profiles are complete -- [ ] Build Wikipedia notability (or plan path to notability) -- [ ] Submit to relevant authoritative directories - -#### Month 2-3: Authority Building -- [ ] Secure mentions on authoritative industry sites -- [ ] Build co-citation signals with established entities -- [ ] Create topical content clusters that reinforce entity-topic associations -- [ ] Pursue PR opportunities that generate entity mentions - -#### Ongoing: AI-Specific Optimization -- [ ] Test AI entity resolution quarterly -- [ ] Update factual claims to remain current and verifiable -- [ ] Monitor AI systems for incorrect entity information -- [ ] Ensure new content reinforces entity identity signals - -### Cross-Reference - -- **CORE-EEAT relevance**: Items A07 (Knowledge Graph Presence) and A08 (Entity Consistency) directly overlap — entity optimization strengthens Authority dimension -- **CITE relevance**: CITE I01-I10 (Identity dimension) measures entity signals at domain level — entity optimization feeds these scores -- For content-level audit: `content-quality-auditor` -- For domain-level audit: `domain-authority-auditor` -``` - -### Save Results - -After delivering findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to the appropriate `memory/` path using filename `YYYY-MM-DD-.md` containing: -- One-line verdict or headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to `memory/hot-cache.md` without asking. - -## Validation Checkpoints - -### Input Validation -- [ ] Entity name and type identified -- [ ] Primary domain/website confirmed -- [ ] Target topics/industries specified -- [ ] Disambiguation context provided (if entity name is common) - -### Output Validation -- [ ] All 6 signal categories evaluated -- [ ] AI entity resolution tested with at least 3 queries -- [ ] Knowledge Panel status checked -- [ ] Wikidata/Wikipedia status verified -- [ ] Schema.org markup on primary site audited -- [ ] Every recommendation is specific and actionable -- [ ] Roadmap includes concrete steps with timeframes -- [ ] Cross-reference with CORE-EEAT A07/A08 and CITE I01-I10 noted - -## Example - -> **Reference**: See [references/example-audit-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/example-audit-report.md) for a complete example entity audit report for a B2B SaaS company (CloudMetrics), including AI entity resolution test results, entity health summary, top 3 priority actions, and CORE-EEAT/CITE cross-references. - -## Tips for Success - -1. **Start with Wikidata** — It's the single most influential editable knowledge base; a complete Wikidata entry with references often triggers Knowledge Panel creation within weeks -2. **sameAs is your most powerful Schema.org property** — It directly tells search engines "I am this entity in the Knowledge Graph"; always include Wikidata URL first -3. **Test AI recognition before and after** — Query ChatGPT, Claude, Perplexity, and Google AI Overview before optimizing, then again after; this is the most direct GEO metric -4. **Entity signals compound** — Unlike content SEO, entity signals from different sources reinforce each other; 5 weak signals together are stronger than 1 strong signal alone -5. **Consistency beats completeness** — A consistent entity name and description across 10 platforms beats a perfect profile on just 2 -6. **Don't neglect disambiguation** — If your entity name is shared with anything else, disambiguation is the first priority; all other signals are wasted if they're attributed to the wrong entity -7. **Pair with CITE I-dimension for domain context** — Entity audit tells you how well the entity is recognized; CITE Identity (I01-I10) tells you how well the domain represents that entity; use both together - -## Entity Type Reference - -> **Reference**: See [references/entity-type-reference.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/entity-type-reference.md) for entity types with key signals, schemas, and disambiguation strategies by situation. - -## Knowledge Panel & Wikidata Optimization - -> **Reference**: See [references/knowledge-panel-wikidata-guide.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/knowledge-panel-wikidata-guide.md) for Knowledge Panel claiming/editing, common issues and fixes, Wikidata entry creation, key properties by entity type, and AI entity resolution optimization. - -## Reference Materials - -Detailed guides for entity optimization: -- [references/entity-signal-checklist.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/entity-signal-checklist.md) — Complete signal checklist with verification methods -- [references/knowledge-graph-guide.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/knowledge-graph-guide.md) — Wikidata, Wikipedia, and Knowledge Graph optimization playbook - -## Next Best Skill - -- **Primary**: [schema-markup-generator](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/schema-markup-generator/SKILL.md) — turn entity truth into machine-readable implementation. diff --git a/.agents/skills/entity-optimizer/references/entity-signal-checklist.md b/.agents/skills/entity-optimizer/references/entity-signal-checklist.md deleted file mode 100644 index b6b772fe59..0000000000 --- a/.agents/skills/entity-optimizer/references/entity-signal-checklist.md +++ /dev/null @@ -1,137 +0,0 @@ -# Entity Signal Checklist - -> Part of [entity-optimizer](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/SKILL.md). See also: [knowledge-graph-guide.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/knowledge-graph-guide.md) - -Complete checklist of entity signals organized by priority and verification method. Use this as a systematic audit guide — work through each signal, verify its status, and note actions needed. - -## Priority 1: Foundation Signals (Must-Have) - -These signals form the minimum viable entity identity. Without them, search engines and AI systems cannot reliably identify the entity. - -### On-Site Structured Data - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 1 | Organization or Person schema on homepage | Run Google Rich Results Test on homepage | Schema present with name, url, logo, description | -| 2 | sameAs property links to all authoritative profiles | Inspect schema markup | Links to Wikipedia, Wikidata, LinkedIn, social profiles | -| 3 | Consistent @id used across all pages | Inspect schema on 5+ pages | Same @id (typically homepage URL + #organization) on every page | -| 4 | About page exists with entity-rich content | Manual review | First paragraph defines entity clearly; includes founding date, key people, mission | -| 5 | Contact page with verifiable information | Manual review | Physical address, phone, email — matches other directory listings | - -### Key External Profiles - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 6 | Wikidata entry exists | Search wikidata.org | Entry with label, description, key properties, and references | -| 7 | Google Business Profile (if applicable) | Search "[entity] Google Business" | Claimed, verified, complete profile | -| 8 | LinkedIn company/person page | Search LinkedIn | Complete profile matching entity name and description | -| 9 | CrunchBase profile (for companies/products) | Search crunchbase.com | Entry with description, founding info, key people | -| 10 | Primary industry directory listing | Search top 3 industry directories | Listed with correct entity information | - -### Branded Search Presence - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 11 | Branded search returns correct entity | Google "[entity name]" | Entity's website is #1; Knowledge Panel appears or SERP clearly identifies entity | -| 12 | No disambiguation confusion | Google "[entity name]" | No other prominent entity dominates results for the same name | -| 13 | Branded search volume exists | Check ~~SEO tool | Measurable branded search volume (any amount > 0) | - -## Priority 2: Authority Signals (Should-Have) - -These signals establish the entity as recognized and authoritative. They separate a "registered entity" from a "known entity." - -### Knowledge Graph Depth - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 14 | Google Knowledge Panel present | Google "[entity name]" | Knowledge Panel displayed with correct information | -| 15 | Knowledge Panel attributes complete | Review Knowledge Panel | Key attributes filled (founded, CEO, location, industry, etc.) | -| 16 | Knowledge Panel image correct | Review Knowledge Panel | Preferred image displayed | -| 17 | Wikipedia article (or strong notability path) | Search Wikipedia | Article exists, or entity has 3+ independent reliable sources for future article | -| 18 | Wikidata properties complete | Review Wikidata entry | 10+ properties with references | - -### Third-Party Validation - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 19 | Authoritative media mentions | Google News search for entity | 3+ mentions in recognized publications | -| 20 | Industry awards or recognitions | Search "[entity] award" | At least 1 verifiable award or recognition | -| 21 | Co-citation with established entities | Search for entity alongside competitors | Appears in "X vs Y" comparisons, listicles, or industry roundups | -| 22 | Speaking engagements or publications | Search event/conference sites | Appears as speaker, author, or contributor | -| 23 | Reviews on third-party platforms | Check G2, Trustpilot, Yelp, etc. | Reviews exist with reasonable volume and rating | - -### Content Authority - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 24 | Topical content depth in target areas | Site search for target topics | 10+ pages covering target topics in depth | -| 25 | Author pages with credentials | Review author pages | Author schema, credentials, sameAs to external profiles | -| 26 | Original research or data published | Review content | At least 1 piece of original data/research cited by others | -| 27 | Entity mentioned in own content naturally | Search site for entity name | Entity name appears contextually (not just in header/footer) | - -## Priority 3: AI-Specific Signals (Must-Have for GEO) - -These signals specifically help AI systems recognize, understand, and cite the entity. - -### AI Recognition - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 28 | ChatGPT recognizes entity | Ask "What is [entity]?" | Correct description returned | -| 29 | Perplexity recognizes entity | Ask "What is [entity]?" | Correct description with source citations | -| 30 | Google AI Overview mentions entity | Search branded + topical queries | Entity appears in AI-generated overview | -| 31 | AI description is accurate | Compare AI output to entity's self-description | No factual errors in AI's response | -| 32 | AI associates entity with correct topics | Ask "[entity] expertise areas" | Correct topic associations returned | - -### AI Optimization - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 33 | Entity definition quotable in first paragraph | Review About page and key pages | Clear, factual, self-contained definition suitable for AI quotation | -| 34 | Factual claims are verifiable | Cross-reference claims with external sources | All claims about entity can be verified via third-party sources | -| 35 | Entity name used consistently | Audit all platforms | Identical name format everywhere (no abbreviations in some places, full name in others) | -| 36 | Content is crawlable by AI systems | Check robots.txt for AI bot access | Not blocking GPTBot, ClaudeBot, or other AI crawlers (unless intentional) | -| 37 | Fresh information available | Check update dates | Key entity pages updated within last 6 months | - -## Priority 4: Advanced Signals (Nice-to-Have) - -These signals provide marginal gains but demonstrate thoroughness and maturity. - -### Extended Knowledge Base Presence - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 38 | Multiple language entries in Wikidata | Check Wikidata labels | Labels and descriptions in languages matching target markets | -| 39 | DBpedia entry | Search dbpedia.org | Entry exists (auto-generated from Wikipedia) | -| 40 | Google Knowledge Graph ID known | Search Google Knowledge Graph API | Entity has a kg: identifier | -| 41 | ISNI or VIAF identifier (for persons) | Search isni.org or viaf.org | Identifier exists and links correctly | - -### Social Entity Signals - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 42 | Social profiles bidirectionally linked | Check website links to social AND social links to website | Both directions verified on all platforms | -| 43 | Consistent entity description across social | Compare bios on all platforms | Same core description, adapted for platform length limits | -| 44 | Social engagement demonstrates real audience | Review engagement metrics | Engagement patterns consistent with genuine audience (not bot-like) | - -### Technical Entity Signals - -| # | Signal | Verification Method | Pass Criteria | -|---|--------|-------------------|---------------| -| 45 | Entity homepage has strong backlink profile | Check ~~link database | Homepage DR/DA above industry median | -| 46 | Branded anchor text in backlinks | Analyze anchor text distribution | Entity name appears naturally in inbound link anchor text | -| 47 | Entity subdomain consistency | Check all subdomains | Same entity schema and branding across all subdomains | - -## How to Use This Checklist - -Work through signals by priority tier. For each signal, mark status as ✅ (present and correct), ⚠️ (present but incomplete), or ❌ (absent). Focus on completing each priority tier before moving to the next. - -### Priority Action Matrix - -| Current State | Focus Area | Expected Timeline | -|--------------|-----------|-------------------| -| Most Priority 1 signals ❌ | Priority 1 foundation signals only | 2-4 weeks | -| Priority 1 mostly ✅, Priority 2 mixed | Priority 2 authority signals | 1-2 months | -| Priority 1-2 mostly ✅ | Priority 3 AI-specific signals | 2-3 months | -| Priority 1-3 mostly ✅ | Selective Priority 4 for completeness | Ongoing | -| All tiers mostly ✅ | Maintenance + quarterly re-audit | Quarterly review | diff --git a/.agents/skills/entity-optimizer/references/entity-type-reference.md b/.agents/skills/entity-optimizer/references/entity-type-reference.md deleted file mode 100644 index 1bffe7d948..0000000000 --- a/.agents/skills/entity-optimizer/references/entity-type-reference.md +++ /dev/null @@ -1,24 +0,0 @@ -# Entity Type Reference - -Reference tables for entity types, key signals, and disambiguation strategies. - -## Entity Types and Key Signals - -| Entity Type | Primary Signals | Secondary Signals | Key Schema | -|-------------|----------------|-------------------|------------| -| **Person** | Author pages, social profiles, publication history | Speaking, awards, media mentions | Person, ProfilePage | -| **Organization** | Registration records, Wikidata, industry listings | Press coverage, partnerships, awards | Organization, Corporation | -| **Brand** | Trademark, branded search volume, social presence | Reviews, brand mentions, visual identity | Brand, Organization | -| **Product** | Product pages, reviews, comparison mentions | Awards, expert endorsements, market share | Product, SoftwareApplication | -| **Creative Work** | Publication record, citations, reviews | Awards, adaptations, cultural impact | CreativeWork, Book, Movie | -| **Event** | Event listings, press coverage, social buzz | Sponsorships, speaker profiles, attendance | Event | - -## Disambiguation Strategy by Situation - -| Situation | Strategy | -|-----------|----------| -| **Common name, unique entity** | Strengthen all signals; let signal volume resolve ambiguity | -| **Name collision with larger entity** | Add qualifier consistently (e.g., "Acme Software" not just "Acme"); use sameAs extensively; build topic-specific authority that differentiates | -| **Name collision with similar entity** | Geographic, industry, or product qualifiers; ensure Schema @id is unique and consistent; prioritize Wikidata disambiguation | -| **Abbreviation/acronym conflict** | Prefer full name in structured data; use abbreviation only in contexts where entity is already established | -| **Merged or renamed entity** | Redirect old entity signals; update all structured data; create explicit "formerly known as" content; update Wikidata | diff --git a/.agents/skills/entity-optimizer/references/example-audit-report.md b/.agents/skills/entity-optimizer/references/example-audit-report.md deleted file mode 100644 index dc3648167f..0000000000 --- a/.agents/skills/entity-optimizer/references/example-audit-report.md +++ /dev/null @@ -1,59 +0,0 @@ -# Example: Entity Optimization Report - -Full example output for an entity audit request. - -**User**: "Audit entity presence for CloudMetrics, our B2B SaaS analytics platform at cloudmetrics.io" - -**Output**: - -```markdown -## Entity Optimization Report - -### Entity Profile - -**Entity Name**: CloudMetrics -**Entity Type**: Organization (B2B SaaS) -**Primary Domain**: cloudmetrics.io -**Target Topics**: analytics platform, business intelligence, enterprise analytics - -### AI Entity Resolution Test - -Queries tested with results reported by user: - -| Query | Result | Assessment | -|-------|--------|------------| -| "What is CloudMetrics?" | Described as "an analytics tool" with no further detail | Partial recognition -- generic description, no mention of B2B focus or key features | -| "Best analytics platforms for enterprises" | CloudMetrics not mentioned in any AI response | Not recognized as a player in the enterprise analytics space | -| "CloudMetrics vs Datadog" | Correctly identified as a competitor to Datadog, but feature comparison was incomplete and partially inaccurate | Partial -- entity is associated with the right category but attributes are thin | -| "Who founded CloudMetrics?" | No answer found by any AI system tested | Entity leadership not present in AI knowledge bases | - -### Entity Health Summary - -| Signal Category | Status | Key Findings | -|-----------------|--------|--------------| -| Knowledge Graph | Missing | No Wikidata entry exists; no Google Knowledge Panel triggers for branded queries | -| Structured Data | Partial | Organization schema present on homepage with name, url, and logo; missing Person schema for CEO and leadership team; no sameAs links to external profiles | -| Web Presence | Strong | Consistent NAP across LinkedIn, Twitter/X, G2, and Crunchbase; social profiles link back to cloudmetrics.io; branded search returns owned properties in top 5 | -| Content-Based | Partial | About page exists but opens with marketing copy rather than an entity-defining statement; no dedicated author pages for leadership | -| Third-Party | Partial | Listed on G2 and Crunchbase; 2 industry publication mentions found; no awards or analyst coverage | -| AI-Specific | Weak | AI systems have only surface-level awareness; entity definition is not quotable from any authoritative source | - -### Top 3 Priority Actions - -1. **Create Wikidata entry** with key properties: instance of (P31: business intelligence software company), official website (P856: cloudmetrics.io), inception (P571), country (P17) - - Impact: High | Effort: Low - - Why: Wikidata is the foundational knowledge base that feeds Google Knowledge Graph, Bing, and AI training pipelines; without it, the entity cannot be formally resolved - -2. **Add Person schema for leadership team** on the About/Team page, including name, jobTitle, sameAs links to LinkedIn profiles, and worksFor pointing to the Organization entity - - Impact: High | Effort: Low - - Why: Addresses the "Who founded CloudMetrics?" gap directly; Person schema for key people creates bidirectional entity associations that strengthen organizational identity - -3. **Build Wikipedia notability through independent press coverage** -- target 3-5 articles in industry publications (TechCrunch, VentureBeat, Analytics India Magazine) that mention CloudMetrics by name with verifiable claims - - Impact: High | Effort: High - - Why: Wikipedia notability requires coverage in independent reliable sources; press mentions simultaneously feed AI training data, build third-party entity signals, and create the citation foundation for a future Wikipedia article - -### Cross-Reference - -- **CORE-EEAT**: A07 (Knowledge Graph Presence) scored Fail, A08 (Entity Consistency) scored Pass -- entity optimization should focus on knowledge base gaps rather than consistency -- **CITE**: I-dimension weakest area is I01 (Knowledge Graph Presence) -- completing Wikidata entry and earning Knowledge Panel directly improves domain identity score -``` diff --git a/.agents/skills/entity-optimizer/references/knowledge-graph-guide.md b/.agents/skills/entity-optimizer/references/knowledge-graph-guide.md deleted file mode 100644 index ed7ad0aeb4..0000000000 --- a/.agents/skills/entity-optimizer/references/knowledge-graph-guide.md +++ /dev/null @@ -1,399 +0,0 @@ -# Knowledge Graph Optimization Guide - -> Part of [entity-optimizer](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/SKILL.md). See also: [entity-signal-checklist.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/entity-signal-checklist.md) - -Comprehensive playbook for establishing and maintaining entity presence across Google Knowledge Graph, Wikidata, Wikipedia, and other knowledge bases. - -## How Knowledge Graphs Work - -### The Entity Web - -Knowledge graphs are interconnected databases of entities and their relationships. Search engines and AI systems use them as ground truth for entity understanding. - -``` -Your Entity -├── is described by → Wikidata entry -├── is described by → Wikipedia article -├── is described by → Schema.org markup on your site -├── is linked to → Social profiles (LinkedIn, X, etc.) -├── is mentioned by → News articles, industry sites -├── is associated with → Topics, industries, other entities -└── is recognized by → Google Knowledge Graph, Bing Satori, AI training data -``` - -### Which Knowledge Graphs Matter - -| Knowledge Graph | Who Uses It | Impact | -|----------------|-------------|--------| -| **Google Knowledge Graph** | Google Search, Google AI | Powers Knowledge Panels, rich results, entity understanding in search | -| **Wikidata** | Google, Bing, Apple, Amazon, AI systems | Open data feeds multiple knowledge graphs; primary structured data source | -| **Wikipedia** | Google, all AI systems | Training data for every major LLM; Knowledge Panel descriptions often sourced here | -| **Bing Satori** | Bing, Copilot | Powers Bing's entity understanding and Microsoft Copilot | -| **Schema.org (your site)** | All search engines, AI crawlers | First-party structured data you control directly | -| **DBpedia** | Research, some AI systems | Auto-extracted from Wikipedia; relevant for academic/research entities | - -### Data Flow - -``` -Your Website (Schema.org) ─┐ -Wikidata ──────────────────┤ -Wikipedia ─────────────────┼──→ Google Knowledge Graph ──→ Knowledge Panel -Industry Directories ──────┤ AI Search Results -News/Media Mentions ───────┤ Rich Results -Social Profiles ───────────┘ -``` - -Understanding this flow is key: you influence the Knowledge Graph by controlling the **source signals** that feed it. - -## Google Knowledge Graph - -### Getting Into the Knowledge Graph - -There is no "submit to Knowledge Graph" form. Google builds its Knowledge Graph from multiple sources. To get included: - -1. **Have a Wikidata entry** — This is the most direct path -2. **Earn a Wikipedia article** — Strongest single signal -3. **Implement Schema.org markup** — Provides structured self-description -4. **Get mentioned on authoritative sites** — Third-party validation -5. **Build branded search demand** — Signals that users look for your entity - -### Checking Your Knowledge Graph Status - -**Method 1: Google Search** -Search for your entity name in quotes. If a Knowledge Panel appears on the right, you're in the Knowledge Graph. - -**Method 2: Knowledge Graph API** -``` -GET https://kgsearch.googleapis.com/v1/entities:search?query=[entity]&key=[API_KEY] -``` - -Response includes: -- `@id`: Your Knowledge Graph ID (e.g., `kg:/m/0wrt4g`) -- `name`: Entity name as Google understands it -- `description`: Short entity description -- `detailedDescription`: Longer description (usually from Wikipedia) -- `resultScore`: Confidence score (higher = more established entity) - -**Method 3: ~~knowledge graph** -If connected, query directly for entity status and attributes. - -### Claiming Your Knowledge Panel - -1. Search for your entity on Google -2. If Knowledge Panel appears, look for "Claim this knowledge panel" link at bottom -3. Verify via official website, Search Console, YouTube, or other Google property -4. Once claimed, you can suggest edits (but Google has final say) - -### Common Knowledge Panel Fixes - -| Problem | Solution | -|---------|----------| -| **No Knowledge Panel** | Build Wikidata entry + Schema.org + authoritative mentions. Timeline: 2-6 months. | -| **Wrong image** | Update preferred image on: Wikidata (P18), About page, social profiles. Claim panel and suggest preferred image. | -| **Wrong description** | Edit Wikidata description. Update first paragraph of About page and Wikipedia article. | -| **Missing attributes** | Add properties to Wikidata and Schema.org. Claim panel and suggest additions. | -| **Outdated information** | Update Wikidata, About page, Wikipedia, and social profiles. Request refresh via claimed panel. | -| **Wrong entity shown** | Disambiguation needed. See Wikidata section below for disambiguation strategy. | - -## Wikidata - -### Why Wikidata Is Critical - -Wikidata is the **single most influential editable knowledge base** for entity optimization: -- Google uses it as a primary source for Knowledge Panels -- Bing uses it for Satori knowledge graph -- AI systems reference it during entity resolution -- It's open and you can edit it (within their guidelines) - -### Creating a Wikidata Entry - -#### Step 1: Check Eligibility - -Wikidata requires "notability" — the entity must be referenced in at least one external source. Unlike Wikipedia, the notability bar is lower: a company mentioned in a news article, a product with reviews, or a person with published work typically qualifies. - -#### Step 2: Create the Item - -1. Go to https://www.wikidata.org/wiki/Special:NewItem -2. Fill in: - - **Label**: Official entity name - - **Description**: Short description (e.g., "American software company" or "SEO optimization tool") - - **Aliases**: Alternative names, abbreviations, former names - -#### Step 3: Add Core Statements - -Essential properties for each entity type: - -**Organizations:** -| Property | Code | Example | -|----------|------|---------| -| instance of | P31 | business (Q4830453) or specific type | -| official website | P856 | https://example.com | -| inception | P571 | 2020-01-15 | -| country | P17 | United States (Q30) | -| headquarters location | P159 | San Francisco (Q62) | -| industry | P452 | software industry (Q638608) | -| founded by | P112 | [founder's Wikidata item] | -| CEO | P169 | [CEO's Wikidata item] | - -**Persons:** -| Property | Code | Example | -|----------|------|---------| -| instance of | P31 | human (Q5) | -| occupation | P106 | software engineer (Q183888) | -| employer | P108 | [company Wikidata item] | -| educated at | P69 | [university Wikidata item] | -| country of citizenship | P27 | [country item] | -| official website | P856 | https://example.com | - -**Products/Software:** -| Property | Code | Example | -|----------|------|---------| -| instance of | P31 | software (Q7397) or web application (Q189210) | -| developer | P178 | [company Wikidata item] | -| official website | P856 | https://example.com | -| programming language | P277 | Python (Q28865) | -| operating system | P306 | Linux (Q388) | -| software license | P275 | Apache-2.0 (Q13785927) | -| inception | P571 | 2023-06-01 | - -#### Step 4: Add External Identifiers - -These link your Wikidata item to other knowledge bases: - -| Identifier | Code | Purpose | -|-----------|------|---------| -| official website | P856 | Primary web presence | -| X (Twitter) username | P2002 | Social presence | -| LinkedIn organization ID | P4264 | Professional presence | -| GitHub username | P2037 | Technical presence | -| CrunchBase ID | P2087 | Business data | -| Google Knowledge Graph ID | P2671 | Google entity link | -| App Store ID | P3861 | Mobile presence | - -#### Step 5: Add References - -**Every statement must have a reference.** Unreferenced statements may be removed. - -Good reference sources: -- Official website (for factual claims like founding date) -- News articles (for events, milestones) -- Industry reports (for market position) -- Government registries (for legal entity information) - -### Wikidata Maintenance - -| Task | Frequency | Why | -|------|-----------|-----| -| Review existing statements | Quarterly | Ensure accuracy; update changed information | -| Add new properties | When new information available | Keep entry comprehensive | -| Check for vandalism | Monthly | Others can edit your entry | -| Add new references | When new coverage appears | Strengthen statement credibility | -| Update identifiers | When new profiles created | Keep links current | - -## Wikipedia - -### Notability Requirements - -Wikipedia requires entities to meet "general notability guidelines" (GNG): -- **Significant coverage** in **reliable, independent sources** -- Coverage must be **non-trivial** (not just a mention or directory listing) -- Sources must be **independent** of the entity (not press releases, not entity's own content) - -### Building Toward Notability - -If the entity doesn't have a Wikipedia article yet: - -1. **Audit existing coverage**: Search Google News, academic databases, and industry publications for mentions -2. **Identify gaps**: What kinds of coverage are missing? -3. **Build coverage first, then article**: The article is the last step, not the first - -Coverage-building strategies: -| Strategy | Timeline | Notability Impact | -|----------|----------|-------------------| -| Industry report mentions | 3-6 months | Medium — depends on report authority | -| News article coverage | 1-3 months | High — especially from recognized publications | -| Conference speaking + coverage | 3-12 months | Medium — needs post-event coverage | -| Academic paper citations | 6-12+ months | High — very strong for GNG | -| Award recognition | Variable | Medium — depends on award authority | -| Book publication or feature | 6-12+ months | High — strong independent source | - -### Wikipedia Article Best Practices - -**DO:** -- Write in neutral, encyclopedic tone -- Use only independent, reliable sources as references -- Follow Wikipedia's Manual of Style -- Disclose any conflict of interest on your Talk page -- Let the community review and improve the article - -**DO NOT:** -- Write promotional content -- Use the entity's own website as a primary source -- Create the article from a company account without disclosure -- Remove criticism or negative but sourced information -- Pay someone to write the article without disclosure (violates Wikipedia policy) - -### Wikipedia's Impact on AI - -Wikipedia is disproportionately important for AI systems because: -- It's in the training data of every major LLM -- AI systems treat it as a high-trust source -- Wikipedia's structured format makes it easy for AI to extract and cite -- The first paragraph of a Wikipedia article often becomes the AI's entity definition - -This makes Wikipedia presence one of the highest-impact entity optimization actions for GEO. - -## Schema.org Entity Markup - -### Minimum Viable Entity Schema - -Every entity should have at minimum this markup on the homepage: - -**Organization:** -```json -{ - "@context": "https://schema.org", - "@type": "Organization", - "@id": "https://example.com/#organization", - "name": "Example Corp", - "url": "https://example.com", - "logo": "https://example.com/logo.png", - "description": "Example Corp is a [what it is] that [what it does].", - "foundingDate": "2020-01-15", - "founder": { - "@type": "Person", - "name": "Jane Smith", - "@id": "https://example.com/about/jane-smith#person" - }, - "sameAs": [ - "https://www.wikidata.org/wiki/Q12345678", - "https://en.wikipedia.org/wiki/Example_Corp", - "https://www.linkedin.com/company/example-corp", - "https://x.com/examplecorp", - "https://www.crunchbase.com/organization/example-corp" - ] -} -``` - -**Person:** -```json -{ - "@context": "https://schema.org", - "@type": "Person", - "@id": "https://example.com/about/jane-smith#person", - "name": "Jane Smith", - "url": "https://example.com/about/jane-smith", - "image": "https://example.com/photos/jane-smith.jpg", - "jobTitle": "CEO", - "worksFor": { - "@type": "Organization", - "@id": "https://example.com/#organization" - }, - "description": "Jane Smith is [who they are] specializing in [expertise areas].", - "sameAs": [ - "https://www.wikidata.org/wiki/Q87654321", - "https://www.linkedin.com/in/janesmith", - "https://x.com/janesmith" - ] -} -``` - -### sameAs Best Practices - -The `sameAs` property is the **primary entity disambiguation signal** in Schema.org. It tells search engines "this is the same entity as the one on these other platforms." - -**Must include (when available):** -1. Wikidata URL (most important for Knowledge Graph) -2. Wikipedia URL -3. LinkedIn URL -4. Official social media profiles - -**Include when relevant:** -5. CrunchBase URL -6. GitHub URL -7. IMDb URL (for people in entertainment) -8. Industry directory URLs - -**Common mistakes:** -- Linking to generic pages instead of entity-specific URLs -- Inconsistent: Schema says "Example Corp" but LinkedIn says "Example Corporation" -- Missing Wikidata link (this is the single most impactful sameAs) -- Including dead or redirecting URLs - -### Cross-Page Entity Consistency - -Every page on the site should reference the same entity with the same `@id`: - -```json -{ - "@type": "WebPage", - "publisher": { - "@type": "Organization", - "@id": "https://example.com/#organization" - } -} -``` - -For articles: -```json -{ - "@type": "Article", - "author": { - "@type": "Person", - "@id": "https://example.com/about/jane-smith#person" - }, - "publisher": { - "@type": "Organization", - "@id": "https://example.com/#organization" - } -} -``` - -This creates a consistent entity graph that search engines can confidently map to Knowledge Graph entries. - -## Monitoring Entity Health - -### Quarterly Entity Health Check - -| Check | How | What to Look For | -|-------|-----|-----------------| -| Knowledge Panel accuracy | Google entity name | Correct info, image, attributes | -| Wikidata entry | Visit Wikidata page | No vandalism, info still current | -| AI entity resolution | Query 3+ AI systems | Accurate recognition and description | -| Schema.org validation | Google Rich Results Test | No errors, complete entity data | -| Branded search SERP | Google "[entity name]" | Clean SERP, no disambiguation issues | -| Social profile consistency | Visit all profiles | Same name, description, links | - -### Entity Health Metrics to Track - -| Metric | Tool | Target | -|--------|------|--------| -| Knowledge Panel presence | Google Search | Present and accurate | -| Branded search CTR | ~~search console | > 50% for exact brand name | -| AI recognition rate | Manual testing | Recognized by 3/3 major AI systems | -| Wikidata completeness | Wikidata | 15+ properties with references | -| Schema.org error count | Google Search Console | 0 errors | -| Brand mention volume | ~~brand monitor | Stable or growing trend | - -### Recovery Playbooks - -**Entity disappeared from Knowledge Graph:** -1. Check if Wikidata entry was deleted or merged -2. Verify Schema.org markup hasn't changed -3. Look for major algorithm updates that might have affected entity recognition -4. Rebuild signals: start with Wikidata, then Schema.org, then external mentions -5. Timeline: 2-8 weeks for recovery - -**AI systems giving incorrect entity info:** -1. Identify which sources have incorrect information -2. Correct information at source (Wikidata, Wikipedia, About page) -3. AI systems will update over time (training data refresh + live search) -4. For urgent issues, some AI systems have feedback mechanisms -5. Timeline: weeks to months depending on AI system update cycles - -**Knowledge Panel showing wrong entity:** -1. Claim the Knowledge Panel (if you haven't already) -2. Strengthen disambiguation signals (see SKILL.md Disambiguation Strategy) -3. Add qualifier to entity name if needed -4. Build more unique entity signals (original content, specific topic associations) -5. Timeline: 1-3 months diff --git a/.agents/skills/entity-optimizer/references/knowledge-panel-wikidata-guide.md b/.agents/skills/entity-optimizer/references/knowledge-panel-wikidata-guide.md deleted file mode 100644 index b718f4ef88..0000000000 --- a/.agents/skills/entity-optimizer/references/knowledge-panel-wikidata-guide.md +++ /dev/null @@ -1,81 +0,0 @@ -# Knowledge Panel & Wikidata Optimization Guide - -Detailed instructions for Knowledge Panel optimization, Wikidata entry management, and AI entity resolution. - -## Knowledge Panel Optimization - -### Claiming and Editing - -1. **Google Knowledge Panel**: Claim via Google's verification process (search for entity -> click "Claim this knowledge panel") -2. **Bing Knowledge Panel**: Driven by Wikidata and LinkedIn -- update those sources -3. **AI Knowledge**: Driven by training data -- ensure authoritative sources describe entity correctly - -### Common Knowledge Panel Issues - -| Issue | Root Cause | Fix | -|-------|-----------|-----| -| No panel appears | Entity not in Knowledge Graph | Build Wikidata entry + structured data + authoritative mentions | -| Wrong image | Image sourced from incorrect page | Update Wikidata image; ensure preferred image on About page and social profiles | -| Wrong description | Description pulled from wrong source | Edit Wikidata description; ensure About page has clear entity description in first paragraph | -| Missing attributes | Incomplete structured data | Add properties to Schema.org markup and Wikidata entry | -| Wrong entity shown | Disambiguation failure | Strengthen unique signals; add qualifiers; resolve Wikidata disambiguation | -| Outdated info | Source data not updated | Update Wikidata, About page, and all profile pages | - -## Wikidata Best Practices - -### Creating a Wikidata Entry - -1. **Check notability**: Entity must have at least one authoritative reference -2. **Create item**: Add label, description, and aliases in relevant languages -3. **Add statements**: instance of, official website, social media links, founding date, founders, industry -4. **Add identifiers**: official website (P856), social media IDs, CrunchBase ID, ISNI, VIAF -5. **Add references**: Every statement should have a reference to an authoritative source - -**Important**: Wikipedia's Conflict of Interest (COI) policy prohibits individuals and organizations from creating or editing articles about themselves. Instead of directly editing Wikipedia: (1) Focus on building notability through independent reliable sources (press coverage, industry publications, academic citations); (2) If you believe a Wikipedia article is warranted, consider engaging an independent Wikipedia editor through the Requested Articles process; (3) Ensure all claims about the entity are verifiable through third-party sources before any Wikipedia involvement. - -### Key Wikidata Properties by Entity Type - -| Property | Code | Person | Org | Brand | Product | -|----------|------|:------:|:---:|:-----:|:-------:| -| instance of | P31 | human | organization type | brand | product type | -| official website | P856 | yes | yes | yes | yes | -| occupation / industry | P106/P452 | yes | yes | -- | -- | -| founded by | P112 | -- | yes | yes | -- | -| inception | P571 | -- | yes | yes | yes | -| country | P17 | yes | yes | -- | -- | -| social media | various | yes | yes | yes | yes | -| employer | P108 | yes | -- | -- | -- | -| developer | P178 | -- | -- | -- | yes | - -## AI Entity Optimization - -### How AI Systems Resolve Entities - -``` -User query -> Entity extraction -> Entity resolution -> Knowledge retrieval -> Answer generation -``` - -AI systems follow this pipeline: -1. **Extract** entity mentions from the query -2. **Resolve** each mention to a known entity (or fail -> "I'm not sure") -3. **Retrieve** associated knowledge about the entity -4. **Generate** response citing sources that confirmed the entity's attributes - -### Signals AI Systems Use for Entity Resolution - -| Signal Type | What AI Checks | How to Optimize | -|-------------|---------------|-----------------| -| **Training data presence** | Was entity in pre-training corpus? | Get mentioned in high-quality, widely-crawled sources | -| **Retrieval augmentation** | Does entity appear in live search results? | Strong SEO presence for branded queries | -| **Structured data** | Can entity be matched to Knowledge Graph? | Complete Wikidata + Schema.org | -| **Contextual co-occurrence** | What topics/entities appear alongside? | Build consistent topic associations across content | -| **Source authority** | Are sources about entity trustworthy? | Get mentioned by authoritative, well-known sources | -| **Recency** | Is information current? | Keep all entity profiles and content updated | - -### Entity-Specific GEO Tactics - -1. **Define clearly**: First paragraph of About page and key pages should define the entity in a way AI can quote directly -2. **Be consistent**: Use identical entity description across all platforms -3. **Build associations**: Create content that explicitly connects entity to target topics -4. **Earn mentions**: Third-party authoritative mentions are stronger entity signals than self-description -5. **Stay current**: Outdated entity information causes AI to lose confidence and stop citing diff --git a/.agents/skills/fastapi b/.agents/skills/fastapi new file mode 120000 index 0000000000..34204a79c4 --- /dev/null +++ b/.agents/skills/fastapi @@ -0,0 +1 @@ +../../surfsense_backend/.venv/lib/python3.12/site-packages/fastapi/.agents/skills/fastapi \ No newline at end of file diff --git a/.agents/skills/frontend-workflow/SKILL.md b/.agents/skills/frontend-workflow/SKILL.md deleted file mode 100644 index 32ec87d694..0000000000 --- a/.agents/skills/frontend-workflow/SKILL.md +++ /dev/null @@ -1,108 +0,0 @@ ---- -name: frontend-workflow -description: Coordinates frontend and UI implementation, refactoring, and review using the project's color system, React performance, shadcn/ui, interface-polish, Apple-style motion behavior, and animation-vocabulary skills. Use when the user explicitly requests /frontend-workflow for a frontend task. -disable-model-invocation: true ---- - -# Frontend Workflow - -Use this as the single entry point for frontend work. It orchestrates specialist -skills; it does not duplicate their rules. - -## Specialist Skills - -Load only the skills relevant to the task: - -- **React or Next.js code:** read - `../vercel-react-best-practices/SKILL.md`, then load only the applicable files - from its `rules/` directory. Do not load its full compiled guide by default. -- **shadcn/ui components or a project with `components.json`:** read - `../shadcn/SKILL.md` and follow its project-inspection, component-reuse, - documentation, composition, styling, and accessibility workflow. -- **Colors, themes, charts, or visual styling:** read - `../color-system/SKILL.md` and use its canonical palette and semantic-token - rules. -- **UI polish, typography, surfaces, icons, interactions, or motion:** read - `../make-interfaces-feel-better/SKILL.md`, then load only the applicable - supporting reference files. -- **Gesture-driven or physical motion — drag, swipe, sheets, springs, momentum, - interruptible or reversible transitions, translucent materials and depth, or - the `prefers-reduced-transparency` and `prefers-contrast` preferences:** read - `../apple-design/SKILL.md`. It governs how motion behaves; the interface-polish - skill governs concrete values and static detail. Where they conflict, prefer - springs and current-value interpolation for anything the user can touch or - interrupt, and CSS transitions for everything else. -- **A vaguely described motion effect:** read - `../animation-vocabulary/SKILL.md` first to identify the exact term. This - glossary names effects; it does not decide whether to build them. - -Do not load animation vocabulary, Apple-style motion, or animation references -for frontend work that has no motion concern. Apple-style motion in particular -is for gesture, physics, and material work; a hover state or a colour change -does not need it. - -## Workflow - -1. **Understand the task** - - Inspect the relevant implementation and trace the affected interaction. - - Clarify only decisions that materially change behavior or design. - - Reuse existing components, helpers, tokens, and patterns. - -2. **Select guidance** - - Apply the specialist-skill conditions above. - - Read detailed reference files only when the touched code needs them. - - Treat current project configuration and installed APIs as authoritative. - -3. **Implement** - - Make the smallest complete change that satisfies the request. - - Preserve established visual language and component APIs. - - Cover loading, empty, error, disabled, responsive, keyboard, focus, and - reduced-motion states when they are relevant. - -4. **Validate** - - Run the smallest relevant lint, type, and test checks. - - For visible interaction changes, verify the rendered behavior when a - runnable frontend is available. - - If visual details or motion changed, apply the interface-polish review - only after functional implementation is complete and resolve blocking - findings within scope. - -5. **Report** - - Summarize the user-visible result, checks run, and unresolved risks. - - Use the interface-polish skill's review format only when the user - requested a review. For implementation tasks, include relevant visual or - motion findings in the normal completion summary. - -## Precedence and Conflicts - -Resolve conflicting guidance in this order: - -1. The user's explicit requirements. -2. Correctness, security, and accessibility. -3. Existing project conventions and configuration. -4. The canonical color system and shadcn/ui composition rules. -5. React and Next.js performance guidance. -6. Motion behavior for gesture-driven and interruptible interactions. -7. Interface and motion polish. - -Never sacrifice correctness or accessibility for visual polish or a -micro-optimization. If a specialist rule conflicts with the installed library -version or project configuration, verify the current API and follow the -project's actual version. - -## Invocation - -Use: - -```text -/frontend-workflow -``` - -Examples: - -```text -/frontend-workflow build a responsive settings dialog -/frontend-workflow improve the performance of this React page -/frontend-workflow add the subtle grow-from-trigger effect to this popover -/frontend-workflow review this component's UI and motion -``` diff --git a/.agents/skills/geo-content-optimizer/SKILL.md b/.agents/skills/geo-content-optimizer/SKILL.md deleted file mode 100644 index 5ee0a58407..0000000000 --- a/.agents/skills/geo-content-optimizer/SKILL.md +++ /dev/null @@ -1,420 +0,0 @@ ---- -name: geo-content-optimizer -description: 'Optimize content for AI citations in ChatGPT, Perplexity, AI Overviews, Gemini, Claude. AI引用优化/GEO优化/AI搜索' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when optimizing content for AI engines like ChatGPT, Perplexity, AI Overviews, Gemini, Claude, or Copilot. Also for AI citation optimization and generative engine visibility." -argument-hint: " [target AI engine]" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "high" - tags: - - geo - - ai-citations - - chatgpt-optimization - - perplexity-optimization - - google-ai-overview - - gemini - - generative-engine-optimization - - llm-citations - - ai-seo - - quotable-content - - AI引用优化 - - GEO优化 - - AI最適化 - - AI최적화 - - optimizacion-ia - triggers: - # EN-formal - - "optimize for AI" - - "get cited by ChatGPT" - - "AI optimization" - - "appear in AI answers" - - "GEO optimization" - - "generative engine optimization" - - "AI-friendly content" - - "LLM citations" - # EN-casual - - "get cited by AI" - - "show up in ChatGPT answers" - - "AI doesn't mention my brand" - - "make content AI-quotable" - - "how do I get AI to mention me" - - "ChatGPT doesn't know my brand" - # EN-question - - "how to appear in AI answers" - - "how to get cited by AI" - - "why doesn't AI mention my brand" - # EN-competitor - - "Perplexity optimization" - - "how to rank in ChatGPT answers" - - "AI Overview optimization tool" - # ZH-pro - - "AI引用优化" - - "GEO优化" - - "生成式引擎优化" - - "AI搜索优化" - - "AI可引用内容" - # ZH-casual - - "让AI引用我" - - "ChatGPT搜不到我" - - "AI不提我的品牌" - - "怎么让AI推荐我" - - "AI搜不到" - # JA - - "AIコンテンツ最適化" - - "AI引用されるコンテンツ" - - "ChatGPT対策" - - "AI検索最適化" - # KO - - "AI 최적화" - - "AI 검색 최적화" - - "ChatGPT 인용" - # ES - - "optimización para IA" - - "aparecer en respuestas de IA" - - "citación de IA" - # PT - - "otimização para IA" - - "aparecer nas respostas da IA" - # Misspellings - - "generative engine optimisation" - - "GEO optimisation" ---- - -# GEO Content Optimizer - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This build skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill optimizes content to appear in AI-generated responses. As AI systems increasingly answer user queries directly, getting cited by these systems directly impacts visibility. - -**System role**: Build layer skill. It turns briefs and signals into assets that other skills can review, publish, and monitor. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs a shippable asset or transformation that should feed directly into quality review, deployment, or monitoring. - -- Optimizing existing content for AI citations -- Creating new content designed for both SEO and GEO -- Improving chances of appearing in AI Overviews -- Making content more quotable by AI systems -- Adding authority signals that AI systems trust -- Structuring content for AI comprehension -- Competing for visibility in the AI-first search era - -## What This Skill Does - -1. **Citation Optimization**: Makes content more likely to be quoted by AI -2. **Structure Enhancement**: Formats content for AI comprehension -3. **Authority Building**: Adds signals that AI systems trust -4. **Factual Enhancement**: Improves accuracy and verifiability -5. **Quote Creation**: Creates memorable, citeable statements -6. **Source Attribution**: Adds proper citations that AI can verify -7. **GEO Scoring**: Evaluates content's AI-friendliness - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Optimize Existing Content - -``` -Optimize this content for GEO/AI citations: [content or URL] -``` - -``` -Make this article more likely to be cited by AI systems -``` - -### Create GEO-Optimized Content - -``` -Write content about [topic] optimized for both SEO and GEO -``` - -### GEO Audit - -``` -Audit this content for GEO readiness and suggest improvements -``` - -## Skill Contract - -**Expected output**: a ready-to-use asset or implementation-ready transformation plus a short handoff summary ready for `memory/content/`. - -- **Reads**: the brief, target keywords, entity inputs, quality constraints, and prior decisions from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing content, metadata, or schema deliverable plus a reusable summary that can be stored under `memory/content/`. -- **Promotes**: approved angles, messaging choices, missing evidence, and publish blockers to `CLAUDE.md`, `memory/decisions.md`, and `memory/open-loops.md`. -- **Next handoff**: use the `Next Best Skill` below when the asset is ready for review or deployment. - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -**With ~~AI monitor + ~~SEO tool connected:** -Automatically pull AI citation patterns (which content is being cited by ChatGPT, Claude, Perplexity), current AI visibility scores, competitor citation frequency, and AI Overview appearance tracking. - -**With manual data only:** -Ask the user to provide: -1. Target queries where they want AI citations -2. Current content URL or full content text -3. Any known instances where competitors are being cited by AI - -Proceed with the full workflow using provided data. Note in the output which metrics are from automated collection vs. user-provided data. - -## Instructions - -When a user requests GEO optimization: - -1. **Load CORE-EEAT GEO-First Optimization Targets** - - Before optimizing, load GEO-critical items from the [CORE-EEAT Benchmark](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md): - - ```markdown - ### CORE-EEAT GEO-First Targets - - These items have the highest impact on AI engine citation. Use as optimization checklist: - - **Top 6 Priority Items**: - | Rank | ID | Standard | Why It Matters | - |------|----|----------|---------------| - | 1 | C02 | Direct Answer in first 150 words | All engines extract from first paragraph | - | 2 | C09 | Structured FAQ with Schema | Directly matches AI follow-up queries | - | 3 | O03 | Data in tables, not prose | Most extractable structured format | - | 4 | O05 | JSON-LD Schema Markup | Helps AI understand content type | - | 5 | E01 | Original first-party data | AI prefers exclusive, verifiable sources | - | 6 | O02 | Key Takeaways / Summary Box | First choice for AI summary citations | - - **All GEO-First Items** (optimize for all when possible): - C02, C04, C05, C07, C08, C09 | O02, O03, O04, O05, O06, O09 - R01, R02, R03, R04, R05, R07, R09 | E01, E02, E03, E04, E06, E08, E09, E10 - Exp10 | Ept05, Ept08 | A08 - - **AI Engine Preferences**: - | Engine | Priority Items | - |--------|----------------| - | Google AI Overview | C02, O03, O05, C09 | - | ChatGPT Browse | C02, R01, R02, E01 | - | Perplexity AI | E01, R03, R05, Ept05 | - | Claude | R04, Ept08, Exp10, R03 | - - _Full benchmark: [references/core-eeat-benchmark.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md)_ - ``` - -2. **Analyze Current Content** - - ```markdown - ## GEO Analysis: [Content Title] - - ### Current State Assessment - - | GEO Factor | Current Score (1-10) | Notes | - |------------|---------------------|-------| - | Clear definitions | [X] | [notes] | - | Quotable statements | [X] | [notes] | - | Factual density | [X] | [notes] | - | Source citations | [X] | [notes] | - | Q&A format | [X] | [notes] | - | Authority signals | [X] | [notes] | - | Content freshness | [X] | [notes] | - | Structure clarity | [X] | [notes] | - | **GEO Readiness** | **[avg]/10** | **Average across factors** | - - **Primary Weaknesses**: - 1. [Weakness 1] - 2. [Weakness 2] - 3. [Weakness 3] - - **Quick Wins**: - 1. [Quick improvement 1] - 2. [Quick improvement 2] - ``` - -3. **Apply GEO Optimization Techniques** - - > **GEO fundamentals**: AI systems prioritize content that is authoritative (expert credentials, proper citations), accurate (verifiable, up-to-date), clear (well-structured, unambiguous), and quotable (standalone answers, specific data). See [references/geo-optimization-techniques.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/geo-content-optimizer/references/geo-optimization-techniques.md) for details. - - Apply the six core optimization techniques: definition optimization, quotable statement creation, authority signal enhancement, structure optimization, factual density improvement, and FAQ schema implementation. - - > **Reference**: See [references/geo-optimization-techniques.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/geo-content-optimizer/references/geo-optimization-techniques.md) for detailed before/after examples, templates, and checklists for each technique. - - Key principles: - - **Definitions**: 25-50 words, standalone, starting with the term - - **Quotable statements**: Specific statistics with sources, verifiable facts - - **Authority signals**: Expert quotes with credentials, proper source citations - - **Structure**: Q&A format, comparison tables, numbered lists - - **Factual density**: Replace vague claims with specific data points - - **FAQ schema**: JSON-LD FAQPage markup matching visible content - -4. **Generate GEO-Optimized Output** - - ```markdown - ## GEO Optimization Report - - ### Changes Made - - **Definitions Added/Improved**: - 1. [Definition 1] - [location in content] - 2. [Definition 2] - [location in content] - - **Quotable Statements Created**: - 1. "[Statement 1]" - 2. "[Statement 2]" - - **Authority Signals Added**: - 1. [Expert quote/citation] - 2. [Source attribution] - - **Structural Improvements**: - 1. [Change 1] - 2. [Change 2] - - ### Before/After GEO Score - - | GEO Factor | Before (1-10) | After (1-10) | Change | - |------------|---------------|--------------|--------| - | Clear definitions | [X] | [X] | +[X] | - | Quotable statements | [X] | [X] | +[X] | - | Factual density | [X] | [X] | +[X] | - | Source citations | [X] | [X] | +[X] | - | Q&A format | [X] | [X] | +[X] | - | Authority signals | [X] | [X] | +[X] | - | **Overall GEO Score** | **[avg]/10** | **[avg]/10** | **+[X]** | - - ### AI Query Coverage - - This content is now optimized to answer: - - "What is [topic]?" ✅ - - "How does [topic] work?" ✅ - - "Why is [topic] important?" ✅ - - "[Topic] vs [alternative]" ✅ - - "Best [topic] for [use case]" ✅ - ``` - -5. **CORE-EEAT GEO Self-Check** - - After optimization, verify GEO-First items: - - ```markdown - ### CORE-EEAT GEO Post-Optimization Check - - | ID | Standard | Status | Notes | - |----|----------|--------|-------| - | C02 | Direct Answer in first 150 words | ✅/⚠️/❌ | [notes] | - | C04 | Key terms defined on first use | ✅/⚠️/❌ | [notes] | - | C09 | Structured FAQ with Schema | ✅/⚠️/❌ | [notes] | - | O02 | Summary Box / Key Takeaways | ✅/⚠️/❌ | [notes] | - | O03 | Comparisons in tables | ✅/⚠️/❌ | [notes] | - | O05 | JSON-LD Schema Markup | ✅/⚠️/❌ | [notes] | - | O06 | Section chunking (3–5 sentences) | ✅/⚠️/❌ | [notes] | - | R01 | ≥5 precise data points with units | ✅/⚠️/❌ | [notes] | - | R02 | ≥1 citation per 500 words | ✅/⚠️/❌ | [notes] | - | R04 | Claims backed by evidence | ✅/⚠️/❌ | [notes] | - | R07 | Full entity names | ✅/⚠️/❌ | [notes] | - | E01 | Original first-party data | ✅/⚠️/❌ | [notes] | - | Exp10 | Limitations acknowledged | ✅/⚠️/❌ | [notes] | - | Ept08 | Reasoning transparency | ✅/⚠️/❌ | [notes] | - - **Items Needing Attention**: [list any ⚠️/❌ items] - - _For full 80-item audit, use [content-quality-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/SKILL.md)_ - ``` - -## Validation Checkpoints - -### Input Validation -- [ ] Content source identified (URL, full text, or content draft) -- [ ] Target AI queries or topics clearly defined -- [ ] Current GEO baseline assessed (if optimizing existing content) - -### Output Validation -- [ ] At least 3 clear, quotable definitions added -- [ ] Factual density improved with at least 5 verifiable statistics -- [ ] All claims have source citations from authoritative sources -- [ ] Q&A format sections cover top 5 user queries -- [ ] GEO score improvement of at least 50% from baseline -- [ ] Source of each data point clearly stated (~~AI monitor data, user-provided, or estimated) - -## Example - -**User**: "Optimize this paragraph for GEO: 'Email marketing is a good way to reach customers. It's been around for a while and many businesses use it. You can send newsletters, promotions, and other messages.'" - -**Output**: - -```markdown -## GEO-Optimized Version - -**Email marketing** is a digital marketing strategy that uses email to promote -products, build customer relationships, and drive conversions. According to -the Data & Marketing Association, email marketing delivers an average ROI -of $42 for every $1 invested, making it the highest-performing digital -marketing channel. - -### Key email marketing formats include: - -1. **Newsletters**: Regular updates sharing company news, industry insights, - and valuable content to maintain subscriber engagement -2. **Promotional emails**: Time-sensitive offers, discounts, and product - announcements designed to drive immediate sales -3. **Transactional emails**: Order confirmations, shipping updates, and - account notifications with 8x higher open rates than marketing emails -4. **Automated sequences**: Pre-scheduled email series triggered by user - actions, such as welcome series or abandoned cart reminders - -> **Key statistic**: 81% of small businesses rely on email as their primary -> customer acquisition channel (Emarsys, 2024). - ---- - -### Changes Made: - -Changes: added clear definition, 2 stats with sources, structured list, DMA/Emarsys citations, 3 quotable facts. **GEO Score**: 1/10 → 8/10. -``` - -## GEO Optimization Checklist - -> **Reference**: See the GEO Readiness Checklist in [references/geo-optimization-techniques.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/geo-content-optimizer/references/geo-optimization-techniques.md) for the full checklist covering definitions, quotable content, authority, structure, and technical elements. - -## Tips for Success - -1. **Answer the question first** - Put the answer in the first sentence -2. **Be specific** - Vague content doesn't get cited -3. **Cite sources** - AI systems trust verifiable information -4. **Stay current** - Update statistics and facts regularly -5. **Match query format** - Questions deserve direct answers -6. **Build authority** - Expert credentials increase citation likelihood - - -### Save Results - -After delivering content or optimization output to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/content/YYYY-MM-DD-.md` containing: -- One-line description of what was created -- Target keyword and content type -- Open loops or items needing review -- Source data references - -**Gate check recommended**: Run content-quality-auditor before publishing (PostToolUse hook will remind automatically). - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - -## Reference Materials - -- [AI Citation Patterns](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/geo-content-optimizer/references/ai-citation-patterns.md) - How Google AI Overviews, ChatGPT, Perplexity, and Claude select and cite sources -- [Quotable Content Examples](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/geo-content-optimizer/references/quotable-content-examples.md) - Before/after examples of content optimized for AI citation - -## Next Best Skill - -- **Primary**: [content-quality-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/SKILL.md) — verify the optimized content is strong enough to ship and cite. diff --git a/.agents/skills/geo-content-optimizer/references/ai-citation-patterns.md b/.agents/skills/geo-content-optimizer/references/ai-citation-patterns.md deleted file mode 100644 index 6430c20a9c..0000000000 --- a/.agents/skills/geo-content-optimizer/references/ai-citation-patterns.md +++ /dev/null @@ -1,504 +0,0 @@ -# AI Citation Patterns - -How different AI systems select and cite content. Understanding these patterns helps optimize content for AI visibility. - -## Google AI Overviews - -### Citation Behavior - -**Format preferences**: -- Prefers structured, factual content -- Cites multiple sources per overview -- Shows source links as footnotes -- Displays "Sources" section at bottom - -**What gets cited**: -- Clear, direct answers to queries -- Statistics with recent dates -- Step-by-step instructions -- Comparison tables -- Definition blocks -- List-formatted content - -**Content structure preferences**: -- Short paragraphs (2-3 sentences) -- Bullet points and numbered lists -- Clear headings matching query intent -- Tables for comparison data -- FAQ formats - -**Authority signals**: -- Domain authority (trusted sites favored) -- E-E-A-T signals (expertise, authoritativeness, trustworthiness) -- Recent publication/update dates -- Author credentials visible -- Citations to other authoritative sources - -**Citation frequency**: Typically cites 3-8 sources per AI Overview - ---- - -## ChatGPT (with Browsing) - -### Citation Behavior - -**Format preferences**: -- Inline citations with numbers [1], [2] -- "Sources" list at end of response -- Clickable source links -- Sometimes quotes directly with quotation marks - -**What gets cited**: -- Specific facts and statistics -- Expert quotes -- Technical explanations -- Recent information (prioritizes freshness) -- Authoritative domain content -- Well-structured, scannable content - -**Source selection patterns**: -- Favors .edu, .gov, .org domains -- Prioritizes recognized brands/publishers -- Values comprehensive content over thin pages -- Prefers content with clear attribution -- Looks for consensus across multiple sources - -**Quoting behavior**: -- Pulls exact quotes when information is distinctive -- Paraphrases general information -- Combines information from multiple sources -- Attributes specific claims to sources - -**Citation frequency**: 1-6 sources per response depending on complexity - ---- - -## Perplexity AI - -### Citation Behavior - -**Format preferences**: -- Superscript numbers [1] inline -- Numbered source list with snippets -- Shows brief excerpt from each source -- Displays domain name and publish date - -**What gets cited**: -- Recent content (strong freshness bias) -- Authoritative sources -- Content with clear, quotable statements -- Statistical data with sources -- Primary sources over secondary -- Content matching query intent precisely - -**Content structure preferences**: -- Extremely well-structured content -- Clear topic sentences -- Quotable, standalone statements -- Factual density (stats, data, specifics) -- Headings that match question formats - -**Authority signals**: -- Domain credibility -- Author expertise -- Publication reputation -- Recency of content -- Depth of coverage - -**Citation frequency**: Typically 5-10 sources per response (more than others) - -**Unique behavior**: Often shows "Follow-up Questions" that can reveal additional citation opportunities - ---- - -## Claude (Knowledge-Based Responses) - -### Citation Behavior - -**Note**: Claude typically relies on training data rather than live web access, but understanding preferences helps create citeable content. - -**Format preferences**: -- When citing, uses clear attribution phrases -- "According to [source]..." -- "Research from [source] shows..." -- May reference general knowledge without specific citations - -**What gets remembered/prioritized**: -- Clear, authoritative definitions -- Widely-accepted facts and statistics -- Well-established methodologies -- Consensus information -- Content from recognized authorities - -**Content characteristics valued**: -- Factual accuracy and precision -- Logical structure and clarity -- Comprehensive explanations -- Technical accuracy -- Unambiguous language - ---- - -## Common Traits Across All AI Systems - -### Universal Citation Factors - -**Content quality**: -- Factual accuracy (incorrect info won't be cited) -- Clear, unambiguous language -- Proper grammar and spelling -- Comprehensive coverage -- Up-to-date information - -**Structure**: -- Scannable format (headings, lists, tables) -- Logical organization -- Clear topic segmentation -- Short paragraphs -- Visual hierarchy - -**Authority**: -- Domain credibility -- Author credentials -- Source citations in content -- Expertise signals -- Editorial quality - -**Relevance**: -- Precise match to query intent -- Topic focus (not meandering) -- Keyword-topic alignment -- Depth of coverage on specific topic - ---- - -## Optimal Content Structures for Citation - -### 1. Definition Blocks - -AI systems love clear, quotable definitions. - -**Structure**: -```markdown -**[Term]** is [clear category] that [primary function], [key characteristic]. -``` - -**Example**: -> **Search Engine Optimization (SEO)** is a digital marketing practice that improves website visibility in organic search results by optimizing content, technical elements, and authority signals. - -**Why it works**: Standalone, complete, unambiguous, proper scope. - ---- - -### 2. Statistic Blocks - -Facts with sources are highly citeable. - -**Structure**: -```markdown -According to [Source], [specific statistic] as of [timeframe]. -``` - -**Example**: -> According to HubSpot's 2024 State of Marketing Report, 82% of marketers actively invest in content marketing, making it the most widely adopted digital marketing strategy. - -**Why it works**: Specific, attributed, recent, verifiable. - ---- - -### 3. Q&A Pairs - -Question-answer formats match AI query patterns. - -**Structure**: -```markdown -### [Question matching common query]? - -[Direct answer in 40-60 words] - -[Optional supporting detail] -``` - -**Example**: -> ### How long does SEO take to show results? -> -> SEO typically takes 3-6 months to show significant results for new websites, though this varies based on competition, domain authority, and strategy. Established sites may see improvements in 1-3 months for less competitive keywords. - -**Why it works**: Matches query format, provides concise answer, includes qualifiers. - ---- - -### 4. Comparison Tables - -Structured comparisons are easy for AI to parse and cite. - -**Structure**: -```markdown -| Feature | Option A | Option B | -|---------|----------|----------| -| [Factor 1] | [Specific value] | [Specific value] | -| [Factor 2] | [Specific value] | [Specific value] | -| **Best for** | [Use case] | [Use case] | -``` - -**Example**: -| Factor | Technical SEO | On-Page SEO | -|--------|---------------|-------------| -| Focus | Site infrastructure | Content optimization | -| Timeframe | 1-3 months | Ongoing | -| Complexity | High | Medium | -| **Best for** | Site-wide issues | Individual page improvements | - -**Why it works**: Clear comparison, specific values, scannable format. - ---- - -### 5. Step-by-Step Processes - -Numbered lists for "how to" queries. - -**Structure**: -```markdown -1. **[Action]** - [Brief explanation] -2. **[Action]** - [Brief explanation] -3. **[Action]** - [Brief explanation] -``` - -**Example**: -> To conduct keyword research: -> 1. **Identify seed keywords** - List 5-10 topics your audience searches for -> 2. **Use keyword research tools** - Expand seed keywords into hundreds of variations -> 3. **Analyze search intent** - Determine what content format each keyword requires -> 4. **Evaluate competition** - Assess ranking difficulty for each keyword -> 5. **Prioritize keywords** - Choose based on volume, difficulty, and relevance - -**Why it works**: Clear process, actionable steps, logical sequence. - ---- - -### 6. List-Based Content - -Curated lists with brief explanations. - -**Structure**: -```markdown -**[Item name]**: [Clear description with key benefit] -``` - -**Example**: -> Top on-page SEO factors: -> - **Title tags**: Most important on-page element; include primary keyword within first 60 characters -> - **Header tags**: Structure content hierarchically; use one H1, multiple H2s for main sections -> - **Meta descriptions**: Don't directly impact rankings but affect CTR; keep under 160 characters -> - **URL structure**: Use descriptive, keyword-rich URLs without unnecessary parameters - -**Why it works**: Scannable, specific, actionable. - ---- - -### 7. Before/After Examples - -Concrete examples showing transformation. - -**Structure**: -```markdown -**Before**: [Weak example] -**After**: [Strong example] -**Why it's better**: [Explanation] -``` - -**Example**: -> **Before**: "Email marketing is pretty effective." -> **After**: "Email marketing delivers an average ROI of $42 for every $1 spent, according to the Data & Marketing Association." -> **Why it's better**: Specific statistic, attributed source, quantifiable claim. - -**Why it works**: Shows concrete improvement, demonstrates principle. - ---- - -### 8. Key Insight Callouts - -Highlighted important points. - -**Structure**: -```markdown -> **Key insight**: [Memorable, quotable statement] -``` - -**Example**: -> **Key insight**: According to Google's John Mueller, internal linking is one of the most underutilized SEO tactics, with properly structured internal links often delivering faster ranking improvements than external link building. - -**Why it works**: Visually distinct, authoritative, quotable. - ---- - -## Content Optimization by Query Type - -### Informational Queries ("What is...", "How does...", "Why...") - -**AI citation priorities**: -1. Clear definitions -2. Comprehensive explanations -3. Expert perspectives -4. Supporting statistics -5. Real-world examples - -**Optimal structure**: -- Definition in first paragraph -- "Why it matters" section -- How it works explanation -- Common use cases -- Expert quotes or citations - ---- - -### Comparison Queries ("[A] vs [B]", "Best [category]") - -**AI citation priorities**: -1. Comparison tables -2. Clear pros/cons lists -3. Use case recommendations -4. Specific differentiators -5. Verdict or recommendation - -**Optimal structure**: -- Quick comparison table upfront -- Individual descriptions -- Feature-by-feature comparison -- "Choose X if..." recommendations -- Summary verdict - ---- - -### How-To Queries ("How to...", "Steps to...") - -**AI citation priorities**: -1. Numbered step-by-step processes -2. Required tools/prerequisites -3. Time estimates -4. Success indicators -5. Troubleshooting tips - -**Optimal structure**: -- Prerequisites listed first -- Clear numbered steps -- Sub-steps where needed -- Visual indicators of progress -- Common problems and solutions - ---- - -### Statistical Queries ("How much...", "How many...", "Statistics about...") - -**AI citation priorities**: -1. Specific numbers with sources -2. Recent data (within 1-2 years) -3. Multiple data points -4. Context for statistics -5. Trend information - -**Optimal structure**: -- Lead with key statistic -- Source attribution immediately after -- Context and interpretation -- Related statistics -- Takeaways from data - ---- - -## Citation Likelihood Factors - -### High Citation Likelihood - -- [ ] Content from recognized authority domains -- [ ] Published or updated within 12 months -- [ ] Clear, standalone statements -- [ ] Proper source attribution -- [ ] Specific statistics with dates -- [ ] Structured with headings/lists/tables -- [ ] Comprehensive topic coverage -- [ ] Author credentials visible -- [ ] Technical accuracy verified -- [ ] Consensus with other sources - -### Medium Citation Likelihood - -- [ ] Content from less-known but quality domains -- [ ] Published 1-2 years ago -- [ ] Clear but requires slight context -- [ ] General industry claims -- [ ] Good structure but less scannable -- [ ] Moderate depth of coverage -- [ ] No author listed but quality content -- [ ] Some supporting evidence - -### Low Citation Likelihood - -- [ ] Content from unknown/low-authority domains -- [ ] Published 3+ years ago without updates -- [ ] Vague or ambiguous statements -- [ ] No sources cited -- [ ] Poor content structure (walls of text) -- [ ] Thin or superficial coverage -- [ ] Promotional or biased tone -- [ ] Factual inconsistencies -- [ ] No expertise signals - ---- - -## AI System Comparison Summary - -| Factor | Google AI Overviews | ChatGPT | Perplexity | Claude | -|--------|---------------------|---------|------------|--------| -| **Freshness bias** | High | Medium | Very high | N/A (training data) | -| **Authority weight** | Very high | High | High | High | -| **Structure importance** | High | Medium | Very high | Medium | -| **Citation count** | 3-8 | 1-6 | 5-10 | N/A | -| **Quotable focus** | High | Medium | Very high | High | -| **Domain trust** | Very high | High | Medium | High | -| **Factual density** | High | High | Very high | Very high | - ---- - -## Tracking AI Citations - -### Manual Monitoring - -**Check if your content appears in**: -- Google AI Overviews for target keywords -- ChatGPT responses (search your domain in ChatGPT) -- Perplexity results for relevant queries -- Other AI search engines - -**Test queries**: -- Exact-match questions from your FAQ -- Definitions of terms you've defined -- Statistics you've cited with attribution -- Processes you've documented - -### Indicators of AI Visibility - -- Increased direct traffic (AI users clicking sources) -- Traffic spikes from unusual referrers -- Engagement metrics: low bounce rate, high time-on-page -- Return visitors (AI users coming back for more depth) - ---- - -## Optimization Checklist for AI Citations - -Content ready for AI citation should have: - -- [ ] At least 3 clear, quotable definitions -- [ ] 5+ specific statistics with sources and dates -- [ ] Q&A format sections covering top queries -- [ ] Comparison tables where relevant -- [ ] Numbered lists for processes -- [ ] Content published or updated within 12 months -- [ ] Author credentials visible -- [ ] External citations to authoritative sources -- [ ] Structured with clear H2/H3 headings -- [ ] Short paragraphs (2-4 sentences) -- [ ] No promotional language -- [ ] Technical accuracy verified -- [ ] Mobile-friendly formatting diff --git a/.agents/skills/geo-content-optimizer/references/geo-optimization-techniques.md b/.agents/skills/geo-content-optimizer/references/geo-optimization-techniques.md deleted file mode 100644 index cfc343a748..0000000000 --- a/.agents/skills/geo-content-optimizer/references/geo-optimization-techniques.md +++ /dev/null @@ -1,235 +0,0 @@ -# GEO Optimization Techniques - -Detailed techniques for optimizing content for AI citation across Google AI Overviews, ChatGPT, Perplexity AI, Claude, and Gemini. - -## Definition Optimization - -AI systems love clear, quotable definitions. - -**Before** (Weak for GEO): -> SEO is really important for businesses and involves various -> techniques to improve visibility online through search engines. - -**After** (Strong for GEO): -> **Search Engine Optimization (SEO)** is the practice of optimizing -> websites and content to rank higher in search engine results pages -> (SERPs), increasing organic traffic and visibility. - -**Definition Template**: -"[Term] is [clear category/classification] that [primary function/purpose], -[key characteristic or benefit]." - -**Checklist for GEO-Optimized Definitions**: -- [ ] Starts with the term being defined -- [ ] Provides clear category (what type of thing it is) -- [ ] Explains primary function or purpose -- [ ] Uses precise, unambiguous language -- [ ] Can stand alone as a complete answer -- [ ] Is 25-50 words for optimal citation length - -## Quotable Statement Optimization - -AI systems cite specific, standalone statements. Transform vague -content into quotable facts. - -**Weak (Not quotable)**: -> Email marketing is pretty effective and lots of companies use it. - -**Strong (Quotable)**: -> Email marketing delivers an average ROI of $42 for every $1 spent, -> making it one of the highest-performing digital marketing channels. - -**Types of Quotable Statements**: - -1. **Statistics** - - Include specific numbers - - Cite the source - - Add context (timeframe, comparison) - - Example: "According to [Source], [specific statistic] as of [date]." - -2. **Facts** - - Verifiable information - - Unambiguous language - - Authoritative source - - Example: "[Subject] was [fact], according to [authoritative source]." - -3. **Definitions** (covered above) - -4. **Comparisons** - - Clear comparison structure - - Specific differentiators - - Example: "Unlike [A], [B] [specific difference], which means [implication]." - -5. **How-to Steps** - - Numbered, clear steps - - Action-oriented language - - Example: "To [achieve goal], [step 1], then [step 2], and finally [step 3]." - -## Authority Signal Enhancement - -**Expert Attribution** - -Add expert quotes and credentials: - -> "AI will transform how we search for information," says Dr. Jane Smith, -> AI Research Director at Stanford University. - -**Source Citations** - -Properly cite sources that AI can verify: - -Before: -> Studies show that most people prefer video content. - -After: -> According to Wyzowl's 2024 Video Marketing Statistics report, -> 91% of consumers want to see more online video content from brands. - -**Authority Elements to Add**: -- [ ] Author byline with credentials -- [ ] Expert quotes with attribution -- [ ] Citations to peer-reviewed research -- [ ] References to recognized authorities -- [ ] Original data or research -- [ ] Case studies with named companies -- [ ] Industry statistics with sources - -## Structure Optimization for GEO - -AI systems parse structured content more effectively. - -**Q&A Format** - -Transform content into question-answer pairs: - -```html -

What is [Topic]?

-

[Direct answer in 40-60 words]

- -

How does [Topic] work?

-

[Clear explanation with steps if applicable]

- -

Why is [Topic] important?

-

[Specific reasons with evidence]

-``` - -**Comparison Tables** - -For comparison queries, use clear tables: - -| Feature | Option A | Option B | -|---------|----------|----------| -| [Feature 1] | [Specific value] | [Specific value] | -| [Feature 2] | [Specific value] | [Specific value] | -| **Best for** | [Use case] | [Use case] | - -**Numbered Lists** - -For process or list queries: - -1. **Step 1: [Action]** - [Brief explanation] -2. **Step 2: [Action]** - [Brief explanation] -3. **Step 3: [Action]** - [Brief explanation] - -**Definition Boxes** - -Highlight key definitions: - -> **Key Definition**: [Term] refers to [clear definition]. - -## Factual Density Improvement - -AI systems prefer fact-rich content over opinion-heavy content. - -**Content Transformation**: - -**Low factual density**: -> Social media marketing is very popular nowadays. Many businesses -> use it and find it helpful for reaching customers. - -**High factual density**: -> Social media marketing reaches 4.9 billion users globally (Statista, 2024). -> Businesses using social media marketing report 66% higher lead generation -> rates compared to non-users (HubSpot State of Marketing Report, 2024). -> The most effective platforms for B2B marketing are LinkedIn (96% usage), -> Twitter (82%), and Facebook (80%). - -**Factual Enhancement Checklist**: -- [ ] Add specific statistics with sources -- [ ] Include exact dates, numbers, percentages -- [ ] Replace vague claims with verified facts -- [ ] Add recent data (within last 2 years) -- [ ] Include multiple data points per section -- [ ] Cross-reference with authoritative sources - -## FAQ Optimization for GEO - -FAQ sections are highly effective for GEO because: -- They match question-based AI queries -- They provide concise, structured answers -- FAQ schema helps AI understand Q&A pairs - -**FAQ Structure**: - -## Frequently Asked Questions - -### [Question matching common query]? - -[Direct answer: 40-60 words] -[Supporting detail or example] - -### [Question matching common query]? - -[Direct answer: 40-60 words] -[Supporting detail or example] - -**FAQ Schema (JSON-LD)**: - -```json -{ - "@context": "https://schema.org", - "@type": "FAQPage", - "mainEntity": [{ - "@type": "Question", - "name": "[Question text]", - "acceptedAnswer": { - "@type": "Answer", - "text": "[Answer text]" - } - }] -} -``` - -## GEO Readiness Checklist - -Use this checklist for any content: - -**Definitions & Clarity** -- [ ] Key terms are clearly defined -- [ ] Definitions can stand alone as answers -- [ ] Language is precise and unambiguous - -**Quotable Content** -- [ ] Specific statistics included -- [ ] Facts have source citations -- [ ] Memorable statements created - -**Authority** -- [ ] Expert quotes or credentials present -- [ ] Authoritative sources cited -- [ ] Original data or research included - -**Structure** -- [ ] Q&A format sections included -- [ ] Clear headings match common queries -- [ ] Comparison tables where relevant -- [ ] Numbered lists for processes - -**Technical** -- [ ] FAQ schema markup added -- [ ] Content freshness indicated -- [ ] Sources are verifiable diff --git a/.agents/skills/geo-content-optimizer/references/quotable-content-examples.md b/.agents/skills/geo-content-optimizer/references/quotable-content-examples.md deleted file mode 100644 index ddc73201f1..0000000000 --- a/.agents/skills/geo-content-optimizer/references/quotable-content-examples.md +++ /dev/null @@ -1,484 +0,0 @@ -# Quotable Content Examples - -Before/after examples of content optimized for AI citation. Each example shows the transformation from generic to quotable. - -## Example 1: Definition Block - -### Before (Generic, Not Quotable) - -> SEO is really important for businesses and involves various techniques to improve visibility online through search engines. It's been around for a while and many businesses use it to get more traffic to their websites. - -**Problems**: -- Vague language ("really important", "various techniques") -- No clear classification -- Meandering structure -- Not standalone - -**Citation likelihood**: 1/10 - ---- - -### After (Clear, Quotable) - -> **Search Engine Optimization (SEO)** is a digital marketing practice that improves website rankings in organic search results through content optimization, technical improvements, and authority building. According to BrightEdge research, organic search drives 53% of all website traffic, making SEO the highest-impact channel for sustainable online visibility. - -**Improvements**: -- Clear term + category classification -- Specific components listed -- Authoritative statistic with source -- Standalone statement -- Precise language - -**Citation likelihood**: 9/10 - ---- - -## Example 2: Statistical Content - -### Before (Weak) - -> Email marketing is pretty effective and lots of companies use it. It has a good return on investment compared to other marketing channels. - -**Problems**: -- No specific numbers -- No source attribution -- Vague comparisons ("pretty effective", "lots", "good") -- Not verifiable - -**Citation likelihood**: 2/10 - ---- - -### After (Strong) - -> Email marketing delivers an average ROI of $42 for every $1 spent, according to the Data & Marketing Association's 2024 research. This 4,200% return makes email the highest-performing digital marketing channel, outperforming social media (28% ROI) and paid search (23% ROI) by significant margins. - -**Improvements**: -- Specific statistic with exact numbers -- Source attribution with year -- Comparative data -- Verifiable claim -- Context provided - -**Citation likelihood**: 9/10 - ---- - -## Example 3: Process/How-To Content - -### Before (Unclear) - -> To do keyword research, you should think about what your customers might search for and then use some tools to find more keywords. Look at what your competitors are doing too. Then pick the best keywords for your content. - -**Problems**: -- Vague steps ("think about", "some tools") -- No clear structure -- Missing specifics -- No actionable guidance - -**Citation likelihood**: 2/10 - ---- - -### After (Clear Process) - -> To conduct effective keyword research: -> -> 1. **Identify seed keywords** - List 5-10 core topics your target audience searches for -> 2. **Expand with research tools** - Use tools like Google Keyword Planner or Ahrefs to generate 100+ related keywords -> 3. **Analyze search intent** - Categorize each keyword as informational, commercial, or transactional -> 4. **Assess competition** - Evaluate ranking difficulty using domain authority and SERP analysis -> 5. **Prioritize strategically** - Select 10-15 keywords balancing search volume (1,000+ monthly searches) with achievable competition -> -> This process typically takes 2-4 hours for a comprehensive initial keyword list. - -**Improvements**: -- Numbered, sequential steps -- Specific actions in each step -- Concrete numbers and tools -- Time estimate included -- Actionable guidance - -**Citation likelihood**: 8/10 - ---- - -## Example 4: Comparison Content - -### Before (Vague Comparison) - -> WordPress and Shopify are both popular website builders. WordPress is more flexible while Shopify is easier to use. The choice depends on what you need. - -**Problems**: -- No specific differentiators -- Vague claims ("more flexible", "easier") -- No clear recommendation criteria -- Not helpful for decision-making - -**Citation likelihood**: 2/10 - ---- - -### After (Structured Comparison) - -> | Factor | WordPress | Shopify | -> |--------|-----------|---------| -> | **Best for** | Content-heavy sites, blogs, custom needs | E-commerce, quick setup | -> | **Setup time** | 4-8 hours | 1-2 hours | -> | **Monthly cost** | $10-50 (hosting + theme) | $29-299 (subscription) | -> | **Customization** | Unlimited (50,000+ plugins) | Limited to Shopify apps | -> | **E-commerce** | Requires WooCommerce plugin | Built-in, optimized | -> | **SEO control** | Complete control | Good but limited | -> -> **Choose WordPress if**: You need maximum flexibility, run a content-first site, or have technical resources. -> -> **Choose Shopify if**: E-commerce is your primary goal, you want fast setup, or you lack technical expertise. - -**Improvements**: -- Structured table format -- Specific, comparable metrics -- Clear use case recommendations -- Quantifiable differences -- Decision framework - -**Citation likelihood**: 9/10 - ---- - -## Example 5: Expert Content - -### Before (No Authority) - -> Many people think that social media is important for SEO. It can help you get more visibility and traffic. - -**Problems**: -- No expert attribution -- Vague claims ("many people think") -- No specific mechanism explained -- Weak authority signals - -**Citation likelihood**: 1/10 - ---- - -### After (Expert-Backed) - -> While social media doesn't directly impact search rankings, it influences SEO through indirect channels. "Social signals drive discovery, which leads to backlinks and brand searches—both powerful ranking factors," explains Rand Fishkin, founder of SparkToro and former CEO of Moz. Research from Hootsuite found that content promoted on social media earns 2.3x more backlinks than non-promoted content within the first 30 days of publication. - -**Improvements**: -- Expert quote with credentials -- Specific mechanism explained -- Research data with source -- Quantifiable impact (2.3x) -- Authority signals - -**Citation likelihood**: 9/10 - ---- - -## Example 6: Q&A Content - -### Before (Incomplete Answer) - -> **How long does SEO take?** -> -> It depends on a lot of factors. Sometimes it's fast, sometimes it takes a while. New sites usually take longer than established ones. - -**Problems**: -- No specific timeframe -- "It depends" without explaining what it depends on -- Vague language ("fast", "a while", "longer") -- Not actionable - -**Citation likelihood**: 1/10 - ---- - -### After (Complete Answer) - -> **How long does SEO take to show results?** -> -> SEO typically takes 3-6 months to show significant results for new websites. Established sites with existing authority may see improvements in 1-3 months for less competitive keywords. Results depend on four key factors: -> -> 1. **Domain authority** - New domains take 6-12 months; established domains see faster results -> 2. **Competition level** - Low-competition keywords rank in 1-3 months; high-competition may take 12+ months -> 3. **Implementation quality** - Comprehensive optimization accelerates results -> 4. **Existing backlink profile** - Sites with 20+ quality backlinks see 40% faster improvements (Ahrefs study) -> -> Most businesses should budget 6 months for measurable ROI from SEO efforts. - -**Improvements**: -- Specific timeframes with ranges -- Factors clearly explained -- Differentiated by scenario -- Supporting data with source -- Actionable planning guidance - -**Citation likelihood**: 9/10 - ---- - -## Example 7: List Content - -### Before (Thin List) - -> Here are some important on-page SEO factors: -> - Title tags -> - Meta descriptions -> - Headers -> - Content -> - Links -> - Images - -**Problems**: -- No explanations -- No prioritization -- Not actionable -- Missing context - -**Citation likelihood**: 2/10 - ---- - -### After (Rich List) - -> Critical on-page SEO factors ranked by impact: -> -> 1. **Title tags** - Most important on-page element; include primary keyword within first 60 characters. Title tags directly influence both rankings and CTR. -> -> 2. **Content quality and depth** - Comprehensive content (1,500+ words for competitive keywords) outranks thin content. Focus on topical authority over keyword density. -> -> 3. **Header structure (H1-H6)** - Use one H1 with primary keyword, multiple H2s for main sections. Proper hierarchy helps search engines understand content structure. -> -> 4. **Internal linking** - Link to 3-5 related pages using descriptive anchor text. Internal links distribute page authority and help search engines discover content. -> -> 5. **Image optimization** - Compress images to under 100KB, use descriptive alt text with target keywords. Page speed and accessibility both impact rankings. -> -> 6. **URL structure** - Use descriptive, keyword-rich URLs (example.com/keyword-topic) avoiding parameters. Clean URLs improve click-through rates by 25% (Backlinko study). -> -> Optimize these six factors before moving to advanced tactics. - -**Improvements**: -- Prioritized ranking -- Specific guidance for each -- Actionable recommendations -- Supporting data -- Clear next step - -**Citation likelihood**: 8/10 - ---- - -## Example 8: Trend/Prediction Content - -### Before (Speculation) - -> AI is going to change SEO a lot in the future. Things will be different and SEO professionals will need to adapt. - -**Problems**: -- Vague predictions ("a lot", "different") -- No specific changes identified -- No timeframe -- No evidence - -**Citation likelihood**: 1/10 - ---- - -### After (Evidence-Based Trend) - -> AI is fundamentally transforming SEO search behavior. According to Gartner's 2024 research, AI-powered search will reduce traditional search engine traffic by 25% by 2026, as users increasingly rely on AI chatbots for information. -> -> Three specific shifts SEO professionals must prepare for: -> -> 1. **Zero-click content consumption** - Users get answers without visiting websites, requiring optimization for AI citations rather than clicks -> -> 2. **Conversation-based queries** - Natural language questions replace keyword-based searches, demanding content that answers specific questions -> -> 3. **Source attribution value** - Being cited by AI systems (Generative Engine Optimization/GEO) becomes as important as ranking #1 -> -> Forrester predicts that by 2025, 30% of organic traffic will come from AI system citations rather than traditional search results, making GEO optimization critical for visibility. - -**Improvements**: -- Specific statistics with sources -- Clear timeframes -- Concrete changes identified -- Multiple research citations -- Actionable implications - -**Citation likelihood**: 9/10 - ---- - -## Example 9: Technical Explanation - -### Before (Complex, Unclear) - -> Core Web Vitals are metrics that Google uses to measure user experience. They look at how fast your site loads and how stable it is when people use it. You should try to improve them. - -**Problems**: -- Oversimplified without specifics -- No concrete metrics -- Vague improvement guidance -- Missing thresholds - -**Citation likelihood**: 2/10 - ---- - -### After (Clear, Specific) - -> Core Web Vitals are three specific metrics Google uses to measure page experience: -> -> | Metric | Measures | Good Score | Impact | -> |--------|----------|------------|--------| -> | **LCP (Largest Contentful Paint)** | Loading performance | <2.5 seconds | How quickly main content appears | -> | **FID (First Input Delay)** | Interactivity | <100 milliseconds | How quickly page responds to user input | -> | **CLS (Cumulative Layout Shift)** | Visual stability | <0.1 | How much content shifts during loading | -> -> According to Google's 2024 documentation, pages meeting all three thresholds rank higher and receive 24% more clicks than pages failing these metrics. Core Web Vitals became an official ranking factor in June 2021 as part of Google's Page Experience update. -> -> Test your Core Web Vitals with ~~page speed tool. - -**Improvements**: -- Three specific metrics defined -- Clear thresholds provided -- Table format for scannability -- Official data with source -- Testing tool referenced - -**Citation likelihood**: 9/10 - ---- - -## Example 10: Case Study Content - -### Before (Vague Results) - -> We improved our client's SEO and they got more traffic. They were really happy with the results and saw an increase in their business. - -**Problems**: -- No specific numbers -- No timeframe -- No methodology shared -- Not reproducible - -**Citation likelihood**: 1/10 - ---- - -### After (Data-Rich Case Study) - -> **Case Study: 312% Organic Traffic Increase in 6 Months** -> -> **Client**: B2B SaaS company (project management software) -> -> **Challenge**: Only 450 monthly organic visitors, ranking #20+ for target keywords -> -> **Strategy implemented**: -> 1. Created 24 pillar pages targeting high-intent keywords -> 2. Built internal linking hub connecting 150 existing pages -> 3. Optimized 80 existing pages for featured snippets -> 4. Secured 45 high-authority backlinks through expert roundups -> -> **Results (6 months)**: -> - Organic traffic: 450 → 1,854 monthly visitors (+312%) -> - Keyword rankings: 3 keywords in top 10 → 47 keywords in top 10 -> - Featured snippets: 0 → 12 owned snippets -> - Lead conversions: 8 → 41 monthly leads (+412%) -> -> **Key insight**: 80% of traffic growth came from optimizing existing content rather than creating new pages, demonstrating the power of content refreshing strategies. - -**Improvements**: -- Specific metrics with percentages -- Clear timeframe -- Detailed methodology -- Reproducible tactics -- Key learning identified - -**Citation likelihood**: 9/10 - ---- - -## Transformation Checklist - -To make any content more quotable: - -**Definitions**: -- [ ] Replace vague language with precise terms -- [ ] Add clear category classification -- [ ] Make statement standalone -- [ ] Include scope and boundaries - -**Statistics**: -- [ ] Add specific numbers -- [ ] Include source attribution -- [ ] Specify timeframe/date -- [ ] Add context for interpretation -- [ ] Verify accuracy - -**Processes**: -- [ ] Number steps clearly -- [ ] Make each step actionable -- [ ] Add time estimates -- [ ] Include specific tools/methods -- [ ] Provide success criteria - -**Comparisons**: -- [ ] Use table format -- [ ] Include specific, comparable metrics -- [ ] Add "choose if..." recommendations -- [ ] Quantify differences -- [ ] Cover key decision factors - -**Lists**: -- [ ] Prioritize items -- [ ] Add explanations to each -- [ ] Include specific guidance -- [ ] Provide examples -- [ ] Make actionable - -**Expert content**: -- [ ] Add attributed quotes -- [ ] Include credentials -- [ ] Reference studies/research -- [ ] Cite authoritative sources -- [ ] Quantify claims - ---- - -## Content Optimization Pattern - -**Generic → Quotable transformation follows this pattern**: - -1. **Add specificity** - Replace "many" with "73%", "fast" with "under 2.5 seconds" -2. **Include attribution** - Add sources for all claims -3. **Structure clearly** - Use tables, lists, numbered steps -4. **Quantify everything** - Numbers > adjectives -5. **Make standalone** - Each statement should work in isolation -6. **Add context** - Explain why, not just what -7. **Use precise language** - Eliminate ambiguity -8. **Include timeframes** - When was this measured/true? -9. **Show authority** - Reference experts, studies, data -10. **Enable verification** - Link to sources, cite methodology - ---- - -## Quick Quotability Test - -Ask these questions about your content: - -1. **Can AI quote this without additional context?** - If no, add context -2. **Does it include specific numbers?** - If no, add data -3. **Is the source clear?** - If no, add attribution -4. **Is language precise and unambiguous?** - If no, clarify -5. **Would an expert approve this?** - If no, add authority signals -6. **Is it scannable?** - If no, add structure -7. **Is it up-to-date?** - If no, update or add date context -8. **Can it be verified?** - If no, add sources -9. **Is it specific to a use case?** - If no, add context -10. **Does it answer a complete question?** - If no, expand - -If you answer "yes" to 8+ questions, content is highly quotable. -If you answer "yes" to 5-7 questions, content is moderately quotable. -If you answer "yes" to fewer than 5, content needs significant optimization. diff --git a/.agents/skills/humanizer/.claude-plugin/marketplace.json b/.agents/skills/humanizer/.claude-plugin/marketplace.json deleted file mode 100644 index 7e281e72fe..0000000000 --- a/.agents/skills/humanizer/.claude-plugin/marketplace.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "$schema": "https://json.schemastore.org/claude-code-marketplace-manifest.json", - "name": "humanizer", - "owner": { - "name": "blader", - "url": "https://github.com/blader" - }, - "description": "Install Humanizer as a Claude plugin.", - "plugins": [ - { - "name": "humanizer", - "source": "./", - "description": "Rewrite AI-sounding text so it reads naturally without changing what it says.", - "license": "MIT", - "keywords": ["writing", "editing", "ai-detection", "humanize", "prose", "style"] - } - ] -} diff --git a/.agents/skills/humanizer/.claude-plugin/plugin.json b/.agents/skills/humanizer/.claude-plugin/plugin.json deleted file mode 100644 index 6ca35e9211..0000000000 --- a/.agents/skills/humanizer/.claude-plugin/plugin.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json", - "name": "humanizer", - "description": "Rewrite AI-sounding text so it reads naturally without changing what it says.", - "version": "2.11.2", - "author": { - "name": "blader", - "url": "https://github.com/blader" - }, - "homepage": "https://github.com/blader/humanizer", - "repository": "https://github.com/blader/humanizer", - "license": "MIT", - "keywords": ["writing", "editing", "ai-detection", "humanize", "prose", "style"], - "skills": ["./"] -} diff --git a/.agents/skills/humanizer/.github/workflows/validate.yml b/.agents/skills/humanizer/.github/workflows/validate.yml deleted file mode 100644 index 80ecf79f4b..0000000000 --- a/.agents/skills/humanizer/.github/workflows/validate.yml +++ /dev/null @@ -1,29 +0,0 @@ -name: Check package - -on: - pull_request: - push: - branches: [main] - -permissions: - contents: read - -jobs: - check: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-node@v4 - with: - node-version: 22 - - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - name: Check package files - run: python3 scripts/validate-package.py - - name: Check skill discovery - run: npx --yes skills@1.5.20 add . --list - - name: Check Claude marketplace - run: | - npm install --global @anthropic-ai/claude-code - claude plugin validate . diff --git a/.agents/skills/humanizer/AGENTS.md b/.agents/skills/humanizer/AGENTS.md deleted file mode 100644 index 30618899f4..0000000000 --- a/.agents/skills/humanizer/AGENTS.md +++ /dev/null @@ -1,49 +0,0 @@ -# Guide for agents - -This file explains how to change Humanizer without breaking its package or prompt. - -## What this repo contains - -Humanizer is an agent skill written in Markdown. `SKILL.md` is the prompt that agents read. The repo has no build step. - -Keep the skill portable. Do not write instructions that limit it to one or two agent tools. - -## Key files - -- `SKILL.md` is the source of truth and the repo's only skill file. It contains portable YAML metadata, 35 numbered patterns, and their examples. -- `README.md` explains installation, use, patterns, and version history. -- `.claude-plugin/plugin.json` describes the Claude plugin and points its skill loader at the root `SKILL.md`. -- `.claude-plugin/marketplace.json` lets users add this repo as a Claude marketplace. -- `scripts/validate-package.py` checks package files and shared values. - -## Rules for changes - -Keep `SKILL.md` and `README.md` in sync. - -- **Patterns:** The skill has 35 numbered patterns. If you add, remove, or renumber a pattern, update the README table, heading, validator, and every pattern reference. -- **Version:** Keep the same version in `SKILL.md` under `metadata.version`, the first README version entry, and `.claude-plugin/plugin.json`. Do not add a top-level `version` field to the skill. -- **Compatibility:** Keep install and use instructions neutral across agents. Names such as Claude Code, OpenCode, and Codex are examples, not limits. -- **History:** Add a short README version note for any behavior change or non-obvious fix. -- **Checks:** Before publishing, run `python3 scripts/validate-package.py`, `npx skills add . --list`, and `claude plugin validate .`. - -## Writing style - -Use Plain Language in code comments, prompts, documentation, descriptions, validation messages, and progress reports. - -- Lead with the main point. -- Use common words and active voice. -- Keep sentences and paragraphs short. -- Use one term for the same item. -- Use `must` for requirements. -- Use headings, lists, and tables when they help the reader. -- Remove repeated or unnecessary words. -- Limit acronyms and explain technical terms. -- Avoid double negatives. -- Keep exact identifiers, commands, paths, schema fields, quotations, watched phrases, and behavior-bearing examples. -- Keep the full technical meaning. - -## Editing the skill - -- Keep the YAML metadata valid. -- Treat the prompt below the metadata as the product. -- Prefer a short, clear instruction over another exception or repeated explanation. diff --git a/.agents/skills/humanizer/LICENSE b/.agents/skills/humanizer/LICENSE deleted file mode 100644 index 625297fb77..0000000000 --- a/.agents/skills/humanizer/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2025 Siqi Chen - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/.agents/skills/humanizer/README.md b/.agents/skills/humanizer/README.md deleted file mode 100644 index aea9a0b493..0000000000 --- a/.agents/skills/humanizer/README.md +++ /dev/null @@ -1,209 +0,0 @@ -# Humanizer - -[![skills.sh installs](https://skills.sh/b/blader/humanizer)](https://skills.sh/blader/humanizer) - -Humanizer rewrites AI-sounding text so it reads like a person wrote it, without changing what it says. Because it is just Markdown, it works with any agent that supports skills. - -## How it works - -Humanizer uses 35 patterns from Wikipedia's ["Signs of AI writing"](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. It makes a first pass without treating the original structure as fixed. Then it checks the draft against those patterns and the original claims before rewriting whatever still needs work. - -> "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases." - -It does not make things up. A name, number, date, quote, citation, or other factual detail must come from the source or the writer. For personal writing, Humanizer keeps the writer's style. Technical and reference prose stays neutral and plain. If you provide a writing sample, Humanizer follows that sample instead of its default style rules. - -When you paste text, Humanizer shows its work before giving you the final version. You see the first rewrite and a short critique of anything that still sounds artificial. Point it at a file and it changes only the prose, leaving code, data, frontmatter, and link targets alone. - -## Usage - -Call the skill directly: - -``` -/humanizer - -[paste your text here] -``` - -Or ask in plain language: - -``` -Please humanize this text: [your text] -``` - -To rewrite a file, give Humanizer its path: - -``` -Humanize the prose in docs/launch-post.md -``` - -### Match your voice - -If you want the rewrite to sound more like you, include a sample: - -``` -/humanizer - -Here's a sample of my writing for voice matching: -[paste 2-3 paragraphs of your own writing] - -Now humanize this text: -[paste AI text to humanize] -``` - -Humanizer follows the sample's rhythm, word choice, punctuation, and deliberate quirks. - -## The 35 patterns - -### Content patterns - -| # | Pattern | Before | After | -|---|---------|--------|-------| -| 1 | **Inflated importance and legacy** | "marking a pivotal moment in the evolution of..." | "was established in 1989 as part of a wider decentralization" | -| 2 | **Name-dropping to prove importance** | "cited in NYT, BBC, FT, and The Hindu" | Keep only useful, sourced context | -| 3 | **Shallow -ing analysis** | "symbolizing... reflecting... showcasing..." | Keep only what the source supports | -| 4 | **Sales language** | "nestled within the breathtaking region" | "is a town in the Gonder region" | -| 5 | **Vague sources** | "Experts believe it plays a crucial role" | Name a real source or remove the claim | -| 6 | **Formulaic challenges and outlook** | "Despite challenges... continues to thrive" | Keep the facts and remove the sales pitch | - -### Language and grammar patterns - -| # | Pattern | Before | After | -|---|---------|--------|-------| -| 7 | **Overused AI words** | "Actually... additionally... gated on... quietly... testament... landscape... showcasing" | "also... needs... remain common" | -| 8 | **Avoiding is and are** | "serves as... features... boasts" | "is... has" | -| 9 | **Not X but Y and clipped endings** | "It's not just X, it's Y", "..., no guessing" | State the point directly | -| 10 | **Forced groups of three** | "innovation, inspiration, and insights" | Use the number of items the meaning needs | -| 11 | **Changing names and repeated openings** | "protagonist... main character... hero" or "She noted... She noted... She filed..." | Use one name or merge the repeated sentences | -| 12 | **False from X to Y ranges** | "from the Big Bang to dark matter" | List the topics directly | -| 13 | **Passive voice and missing subjects** | "No configuration file needed" | Name the actor when that helps | - -### Style patterns - -| # | Pattern | Before | After | -|---|---------|--------|-------| -| 14 | **Em/en dashes** | "institutions—not the people—yet this continues—" | Cut them: periods, commas, colons, or parentheses | -| 15 | **Too much bold text** | "**OKRs**, **KPIs**, **BMC**" | "OKRs, KPIs, BMC" | -| 16 | **Lists with bold mini-headings** | "**Performance:** Performance improved" | Use prose when a list adds no value | -| 17 | **Title case in headings** | "Strategic Negotiations And Partnerships" | "Strategic negotiations and partnerships" | -| 18 | **Emojis** | "🚀 Launch Phase: 💡 Key Insight:" | Remove emojis | -| 19 | **Curly quotes** | `said “the project”` | `said "the project"` | -| 26 | **Too many hyphenated word pairs** | “cross-functional, data-driven, client-facing” | Keep only the hyphens grammar needs | -| 27 | **A fake deeper truth** | "At its core, what matters is..." | State the point directly | -| 28 | **Announcing the next point** | "Let's dive in", or "one thing that bit me" | Start with the content | -| 29 | **A heading repeated below itself** | "## Performance" + "Speed matters." | Let the heading do the work | -| 30 | **Writing about the old version** | "This function was added to replace..." | Describe what it does now | -| 31 | **Forced punchlines and fragments** | "It had no preference. No prior. No nostalgia." | Use natural sentence lengths and specific claims | -| 32 | **Formulaic sayings** | "Symmetry is the language of trust" | State the specific claim | -| 33 | **Fake-candid openings** | "Honestly? It depends..." | State the answer directly | -| 34 | **Answering objections no one raised** | "This isn't mainly about prompt length..." | Remove the unsupported defense and keep any real claim | -| 35 | **Rejecting fake alternatives** | "A tempting option would be to..., but" | Remove the fake option and keep real choices | - -### Chatbot patterns - -| # | Pattern | Before | After | -|---|---------|--------|-------| -| 20 | **Chatbot text left in the answer** | "I hope this helps! Let me know if..." | Remove it | -| 21 | **Knowledge-limit disclaimers and guesses** | "While details are limited in available sources..." | State what is known or remove the claim | -| 22 | **Overly agreeable tone** | "Great question! You're absolutely right!" | Answer directly | - -### Filler and hedging - -| # | Pattern | Before | After | -|---|---------|--------|-------| -| 23 | **Filler phrases** | "In order to", "Due to the fact that" | "To", "Because" | -| 24 | **Too many qualifiers** | "could potentially possibly" | "may" | -| 25 | **Generic positive endings** | "The future looks bright" | End with a fact or a sourced plan | - -## Full example - -*Details such as the month and neighborhood need to come from the writer. If they are missing, Humanizer should ask instead of making them up.* - -**Before (AI-sounding):** -> I recently spent five unforgettable days in Lisbon, and let me tell you — this city completely stole my heart. From the moment I arrived, I knew I was somewhere truly special. -> -> Nestled along the banks of the Tagus River, Lisbon stands as a vibrant testament to Portugal's enduring spirit, where rich history and modern energy intertwine at every turn. Yes, the famous hills are challenging — my legs certainly felt it! — but every climb rewards you with breathtaking, panoramic views that make it all worthwhile. -> -> No trip would be complete without riding the iconic Tram 28, winding through the city's most historic neighborhoods. And the food? Simply divine. The original pastéis de nata at Pastéis de Belém are a beloved national treasure, and savoring one still warm was a moment I will never forget. -> -> But what truly makes Lisbon special isn't just the sights — it's the feeling. Wander a few steps off the main squares and you'll discover a quieter, more authentic side: sun-drenched alleys, charming tiled facades, and friendly locals going about their daily lives, inviting you to slow down and savor every moment. -> -> Of course, the must-see São Jorge Castle offers stunning views over the rooftops below. The queues can be long, but the experience is absolutely worth it for any history buff or curious traveler. -> -> Would I go back? Absolutely. Lisbon isn't just a place to visit — it's a place to fall in love with, again and again. If you're dreaming of your next getaway, this is one destination that promises memories to last a lifetime. ✨ - -**After:** -> I spent five days in Lisbon last October and still have mixed feelings about it. Beautiful, yes. Also harder on the knees than anyone warned me. -> -> The hills are the whole story and somehow never make the brochures. My hotel was up in Alfama, which photographs beautifully and translates, in practice, to climbing what felt like a six-story staircase every time I wanted coffee. By the second day my calves had opinions. I started planning each walk around which way was downhill, which is a strange way to see a city but probably an honest one. -> -> Everyone says to ride Tram 28, so I did, wedged against a stranger's backpack for forty minutes while three tour groups filmed the same corner. I would walk the route next time, or go before breakfast. The custard tarts, though, earn the fuss. I had one at a plain little place in Graça, still warm, and for about thirty seconds I understood why people build trips around pastry. -> -> What I did not expect was how quiet the city gets away from the main squares. Two blocks uphill from any plaza it turns into laundry lines, chipped tile, open windows, and old men watching football with the sound turned up. That is the Lisbon I keep thinking about, not the castle. -> -> The castle is fine. The view is great, the queue is long, and I spent more time shuffling toward the entrance than looking at anything once I got inside. If I had only two days, I would trade it for an afternoon of getting lost. -> -> I would go back, but in spring and with better shoes. Lisbon does not bend over backward to make things easy for you. I think I liked that, even when my legs disagreed. - -## Sources - -- [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) is the source for the pattern list. -- [WikiProject AI Cleanup](https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup) maintains the page. - -## Version history - -
-Show release notes - -- **2.11.2** - Removed the plugin symlink and separate Claude Desktop package. Current Claude Code loads the root `SKILL.md` directly, so GitHub's source ZIP now works in Claude Desktop. No change to the 35 patterns. -- **2.11.1** - Added a Claude Desktop-ready release package with one regular `humanizer/SKILL.md` file. GitHub's source archive still keeps the plugin symlink (fixes #224). No change to the 35 patterns. -- **2.11.0** - Rewrote all repo guidance, descriptions, checks, and skill instructions in Plain Language. Kept all 35 patterns and their behavior. -- **2.10.2** - Added the standard `skills/humanizer/` plugin path for Claude Desktop and older loaders. The path links to the root skill, so there is still one prompt (fixes #202). -- **2.10.1** - Added figurative uses of `gate`, `gated`, and `gating` to §7. Kept real technical uses, such as feature gating and CI quality gates. -- **2.10.0** - Added patterns #34 and #35 for old drafting ideas left in final text. Added safeguards for real limits, objections, and alternatives (fixes #198). Also improved §24 and the final rewrite step. 35 patterns total. -- **2.9.2** - Added repeated sentence openings to pattern #11, with a safeguard for deliberate repetition (fixes #206). Expanded §28 to cover casual announcements. 33 patterns total. -- **2.9.1** - Improved installation and package checks. Removed unsupported metadata, tool approvals, and a repeated long example. 33 patterns total. -- **2.9.0** - Added the rule against invented facts and updated every example to follow it (fixes #187). Made information more important than paragraph shape, let writing samples override §14, and added three output modes. 33 patterns total. -- **2.8.3** - Moved the version to `metadata.version` for Agent Skills compatibility. 33 patterns total. -- **2.8.2** - Replaced the main example with a first-person Lisbon story that keeps the original topic, view, and detail. 33 patterns total. -- **2.8.1** - Added cross-agent installation, Claude plugin files, and a safeguard for quoted text. 33 patterns total. -- **2.8.0** - Added patterns #31-33 and expanded pattern #20 to catch chatbot offers. 33 patterns total. -- **2.7.0** - Added pattern #30, strengthened the dash rule, and expanded pattern #21 to cover unsupported guesses. 30 patterns total. -- **2.6.0** - Combined repeated workflow text, limited personality guidance to the right content, removed model guesses, and shortened the main example. 29 patterns total. -- **2.5.1** - Added passive voice and missing subjects. 29 patterns total. -- **2.5.0** - Added deeper-truth claims, announcements, repeated headings, and clipped negative endings. Tightened the dash rule and corrected the frontmatter. 28 patterns total. -- **2.4.0** - Added writing-sample matching. -- **2.3.0** - Added hyphenated word pairs. -- **2.2.0** - Added a draft check and second rewrite. -- **2.1.1** - Corrected the curly-quote example. -- **2.1.0** - Added before/after examples for all 24 patterns. -- **2.0.0** - Rewrote the skill from the Wikipedia source. -- **1.0.0** - First release. - -
- -## License - -MIT - -## Installation - -Install Humanizer with the Skills CLI: - -```bash -npx skills add blader/humanizer --global -``` - -Leave off `--global` to install Humanizer only in the current project. Add `--agent ` or `--agent '*'` to choose which agents receive it, then reload their skills. - -Claude Code 2.1.142 or newer can install the plugin instead: - -```text -/plugin marketplace add blader/humanizer -/plugin install humanizer@humanizer -``` - -The plugin command is `/humanizer:humanizer`. - -In Claude Desktop, download this repository as a ZIP and upload it as a skill. - -For a manual install, copy `SKILL.md` into the agent's skill folder. diff --git a/.agents/skills/humanizer/SKILL.md b/.agents/skills/humanizer/SKILL.md deleted file mode 100644 index c9c22422f8..0000000000 --- a/.agents/skills/humanizer/SKILL.md +++ /dev/null @@ -1,456 +0,0 @@ ---- -name: humanizer -description: | - Rewrite AI-sounding text so it reads naturally without changing what it says. - Use when editing or reviewing prose for inflated claims, - sales language, vague sources, repetitive structure, stock AI words, passive - voice, filler, or chatbot artifacts. Based on Wikipedia's "Signs of AI writing." -license: MIT -metadata: - version: "2.11.2" ---- - -# Humanizer: remove AI writing patterns - -Rewrite AI-sounding text so it reads like the writer, not a chatbot. Do not change what it says or make up details. - -The patterns below come from Wikipedia's ["Signs of AI writing"](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. - -## What to do - -When given text to humanize: - -1. **Find AI patterns.** Check the text against the patterns below. -2. **Keep every claim.** You may shorten dull parts, expand useful parts, and merge or split paragraphs. Keep the information even when you change the structure. -3. **Do not invent facts.** Do not add a fact, name, number, date, quote, or citation unless it comes from the source or the user. If a sentence needs a missing detail, ask for it or use a simpler sentence. You may add an opinion or reaction when the writer's voice calls for one, but you may not add a factual claim. Fiction is exempt because invented details are part of the task. -4. **Match the voice.** Use the right tone for the text, such as formal, casual, or technical. Add personality only when the text and the writer call for it. - -The input type controls what you return. See [How to return the result](#how-to-return-the-result). Use the same rewrite process in every mode. - -## Match the writer's voice - -If the user provides a writing sample (their own previous writing), analyze it before rewriting: - -1. Read the sample first. Note its sentence length, word choice, paragraph openings, punctuation, repeated phrases, and transitions. -2. Match those habits. Do not replace casual words with formal ones or remove deliberate quirks. -3. If there is no sample, use the guidance below. - -A writing sample takes priority over these style rules. If the sample uses em dashes, keep them at about the same rate. Do not apply §14 as a ban. - -## Add personality only when it fits - -Removing AI patterns is only half the job. The result should still sound like a person. - -Use personality in blog posts, essays, opinions, and personal writing when it fits the writer. Keep reference, technical, legal, and factual text neutral. Do not add opinions or first-person language where they do not belong. - -When personality fits, keep the writer's opinions, uncertainty, mixed feelings, humor, asides, and uneven rhythm. Never invent facts to make the text feel personal. - -## Content patterns - -### 1. Inflated claims about importance and legacy - -**Words to watch:** stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance/significance, reflects broader, symbolizing its ongoing/enduring/lasting, contributing to the, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted -**Problem:** AI writing often claims that ordinary details mark a major change, prove a legacy, or reflect a broad trend. -**Before:** -> The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance. -**After:** -> The Statistical Institute of Catalonia was established in 1989, part of a wider decentralization of administrative functions in Spain. - -### 2. Name-dropping to prove importance - -**Words to watch:** independent coverage, local/regional/national media outlets, written by a leading expert, active social media presence -**Problem:** AI writing often lists well-known publications or follower counts to prove that a person matters. The list usually gives no useful context. -**Before:** -> Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. She maintains an active social media presence with over 500,000 followers. -**After:** -> Her views have been cited in The New York Times and the BBC. - -If the source explains what the person said and where, keep that useful citation. Do not invent context for a shorter version. - -### 3. Shallow analysis with -ing phrases - -**Words to watch:** highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., encompassing..., showcasing... -**Problem:** AI writing often adds an -ing phrase to make a simple fact sound deeper than it is. -**Before:** -> The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land. -**After:** -> The temple is painted blue, green, and gold, colors meant to evoke Texas bluebonnets and the Gulf of Mexico. - -### 4. Sales language - -**Words to watch:** boasts a, vibrant, rich (figurative), profound, enhancing its, showcasing, exemplifies, commitment to, natural beauty, nestled, in the heart of, groundbreaking (figurative), renowned, breathtaking, must-visit, stunning -**Problem:** AI writing often sounds like an advertisement, especially when it describes places, culture, products, or organizations. -**Before:** -> Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty. -**After:** -> Alamata Raya Kobo is a town in the Gonder region of Ethiopia. - -### 5. Vague sources - -**Words to watch:** Industry reports, Observers have cited, Experts argue, Some critics argue, several sources/publications (when few cited) -**Problem:** AI writing often assigns a claim to unnamed experts, critics, reports, or observers. -**Before:** -> Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. Experts believe it plays a crucial role in the regional ecosystem. -**After:** -> Researchers and conservationists study the Haolai River for its unusual characteristics. - -Name a real source when the source text provides one. Otherwise, remove the unsupported claim. Never invent a source. - -### 6. Formulaic challenges and outlook sections - -**Words to watch:** Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook -**Problem:** AI articles often add a stock section about challenges, future prospects, or continued growth. These sections usually repeat vague claims instead of adding facts. -**Before:** -> Despite its industrial prosperity, Korattur faces challenges typical of urban areas, including traffic congestion and water scarcity. Despite these challenges, with its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of Chennai's growth. -**After:** -> Korattur has recurring traffic congestion and water shortages. - -Add details such as dates or public actions only when they come from the source or the user. - -## Language and grammar patterns - -### 7. Overused AI words - -**High-frequency AI words:** Actually, additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, gate/gated/gating (figurative; preserve established technical usage), highlight (verb), interplay, intricate/intricacies, key (adjective), landscape (abstract noun), pivotal, quietly, showcase, tapestry (abstract noun), testament, underscore (verb), valuable, vibrant -**Problem:** AI writing uses these words much more often than most people do, especially in groups. -**Before:** -> Additionally, a distinctive feature of Somali cuisine is the incorporation of camel meat. An enduring testament to Italian colonial influence is the widespread adoption of pasta in the local culinary landscape, showcasing how these dishes have integrated into the traditional diet. -**After:** -> Somali cuisine also includes camel meat, which is considered a delicacy. Pasta dishes, introduced during Italian colonization, remain common, especially in the south. - -### 8. Avoiding is and are - -**Words to watch:** serves as/stands as/marks/represents [a], boasts/features/offers [a] -**Problem:** AI writing often replaces simple verbs such as *is*, *are*, and *has* with longer phrases. -**Before:** -> Gallery 825 serves as LAAA's exhibition space for contemporary art. The gallery features four separate spaces and boasts over 3,000 square feet. -**After:** -> Gallery 825 is LAAA's exhibition space for contemporary art. The gallery has four rooms totaling 3,000 square feet. - -### 9. Not X but Y and clipped negative endings -**Problem:** AI writing overuses forms such as "Not only...but..." and "It's not just X, it's Y." - -It also adds clipped endings such as "no guessing" instead of writing a clear clause. -**Before:** -> It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere. It's not merely a song, it's a statement. -**After:** -> The heavy beat adds to the aggressive tone. -**Before (tailing negation):** -> The options come from the selected item, no guessing. -**After:** -> The options come from the selected item without forcing the user to guess. - -### 10. Forced groups of three -**Problem:** AI writing often forces ideas into groups of three to sound complete. -**Before:** -> The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights. -**After:** -> The event includes talks and panels. There's also time for informal networking between sessions. - -### 11. Changing names and repeating sentence openings -**Problem:** AI writing handles repetition by rule instead of by ear. It may keep renaming the same person or thing. It may also start several sentences with the same subject, often *she* or *he*. - -Use one clear name for the same subject. For repeated openings, merge sentences, change the subject when that helps, or begin with the action. -**Before (synonym cycling):** -> The protagonist faces many challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home. -**After:** -> The protagonist faces many challenges but eventually triumphs and returns home. -**Before (repeated openings):** -> She noted the door. She noted the lock on it. She filed both away. -**After:** -> She noted the door and its lock, then filed both away. - -Do not ban the repeated word. Fix the repeated sentence pattern. The remaining sentence may still start with "She." - -### 12. False from X to Y ranges -**Problem:** AI writing often uses "from X to Y" when X and Y do not form a real range. -**Before:** -> Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to the enigmatic dance of dark matter. -**After:** -> The book covers the Big Bang, star formation, and current theories about dark matter. - -### 13. Passive voice and missing subjects -**Problem:** AI writing often hides who acts or drops the subject. Use active voice when it makes the actor and action clearer. -**Before:** -> No configuration file needed. The results are preserved automatically. -**After:** -> You do not need a configuration file. The system preserves the results automatically. - -## Style patterns - -### 14. Em and en dashes - -**Rule:** The final rewrite must not contain em dashes (—) or en dashes (–), unless the writer's sample uses them. Replace a dash with a period, comma, colon, or parentheses, or rewrite the sentence. Also check for spaced dashes (` — `) and double hyphens (` -- `) used as dashes. -**Before:** -> The term is primarily promoted by Dutch institutions—not by the people themselves. You don't say "Netherlands, Europe" as an address—yet this mislabeling continues—even in official documents. -**After:** -> The term is primarily promoted by Dutch institutions, not by the people themselves. You don't say "Netherlands, Europe" as an address, yet this mislabeling continues in official documents. -**Before:** -> The new policy — announced without warning — affects thousands of workers. The changes -- long overdue according to critics -- will take effect immediately. -**After:** -> The new policy, announced without warning, affects thousands of workers. The changes, long overdue according to critics, will take effect immediately. - -Before returning the rewrite, search for `—` and `–`. Remove each one unless the writer's sample uses that mark. In that case, match the sample's rate. - -### 15. Too much bold text -**Problem:** AI chatbots often bold words and phrases without a clear reason. -**Before:** -> It blends **OKRs (Objectives and Key Results)**, **KPIs (Key Performance Indicators)**, and visual strategy tools such as the **Business Model Canvas (BMC)** and **Balanced Scorecard (BSC)**. -**After:** -> It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard. - -### 16. Lists with bold mini-headings -**Problem:** AI writing often uses vertical lists in which every item starts with a bold label and a colon. -**Before:** -> - **User Experience:** The user experience has been significantly improved with a new interface. -> - **Performance:** Performance has been enhanced through optimized algorithms. -> - **Security:** Security has been strengthened with end-to-end encryption. -**After:** -> The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption. - -### 17. Title case in headings -**Problem:** AI chatbots often capitalize every main word in a heading. -**Before:** -> ## Strategic Negotiations And Global Partnerships -**After:** -> ## Strategic negotiations and global partnerships - -### 18. Emojis -**Problem:** AI chatbots often add emojis to headings and list items as decoration. -**Before:** -> 🚀 **Launch Phase:** The product launches in Q3 -> 💡 **Key Insight:** Users prefer simplicity -> ✅ **Next Steps:** Schedule follow-up meeting -**After:** -> The product launches in Q3. User research showed a preference for simplicity. Next step: schedule a follow-up meeting. - -### 19. Curly quotation marks -**Problem:** ChatGPT often uses curly quotes (“...”) where the writer or target format uses straight quotes ("..."). -**Before:** -> He said “the project is on track” but others disagreed. -**After:** -> He said "the project is on track" but others disagreed. - -## Chatbot patterns - -### 20. Chatbot text left in the answer - -**Words to watch:** I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., Want me to...?, Want me to give examples?, Should I continue?, let me know, here is a... -**Problem:** A chatbot's greeting, offer, or closing sometimes remains in text that should stand on its own. -**Before:** -> Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section. -**After:** -> The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest. - -### 21. Knowledge-limit disclaimers and guesses - -**Words to watch:** as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information, not publicly available, maintains a low profile, keeps personal details private, prefers to stay out of the spotlight, likely [grew up/studied/began], it is believed that -**Problem:** Older models may mention the date when their knowledge ends. A model may also explain that it could not find a source, then fill the gap with a plausible guess. State what the source does not show, or remove the sentence. Do not present a guess as a fact. -**Before (cutoff disclaimer):** -> While specific details about the company's founding are not extensively documented in readily available sources, it appears to have been established sometime in the 1990s. -**After:** -> The company's founding date is not documented in the available sources. (Or cut the sentence. State a date only if a source provides one.) -**Before (speculative gap-fill):** -> Information about her early life is not publicly available, suggesting she maintains a low profile and keeps personal details private. She likely grew up in a middle-class household, which shaped her later interest in education reform. -**After:** -> Her early life is not documented in the available sources. (Or omit the section.) - -### 22. Overly agreeable tone -**Problem:** AI assistants often praise the user or agree before giving the answer. -**Before:** -> Great question! You're absolutely right that this is a complex topic. That's an excellent point about the economic factors. -**After:** -> The economic factors you mentioned are relevant here. - -## Filler and hedging - -### 23. Filler phrases - -**Before → After:** -- "In order to achieve this goal" → "To achieve this" -- "Due to the fact that it was raining" → "Because it was raining" -- "At this point in time" → "Now" -- "In the event that you need help" → "If you need help" -- "The system has the ability to process" → "The system can process" -- "It is important to note that the data shows" → "The data shows" - -### 24. Too many qualifiers - -**Phrases to watch:** to be fair, it's also possible, could potentially, might arguably, in some cases it may, this is an inference -**Problem:** Repeated editing can add one qualifier after another until every claim sounds uncertain. Keep a qualifier only when the source supports it and the meaning needs it. Remove caveats that only repair an earlier overstatement. -**Before:** -> It could potentially possibly be argued that the policy might have some effect on outcomes. -**After:** -> The policy may affect outcomes. - -### 25. Generic positive endings -**Problem:** AI writing often ends with vague optimism instead of the last useful fact. -**Before:** -> The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence. This represents a major step in the right direction. -**After:** -> (Cut the paragraph. End on the last concrete fact instead of a send-off. If the source states real plans, use those.) - -### 26. Too many hyphenated word pairs - -**Words to watch:** third-party, cross-functional, client-facing, data-driven, decision-making, well-known, high-quality, real-time, long-term, end-to-end -**Problem:** AI writing often hyphenates these pairs everywhere. Keep the hyphen before a noun when grammar needs it, as in `a high-quality report`. Drop it after the noun, as in `the report is high quality`. -**Before:** -> The cross-functional team delivered a high-quality, data-driven report. The team is cross-functional, the report is high-quality, and the methodology is data-driven. -**After:** -> The cross-functional team delivered a high-quality, data-driven report. The team is cross functional, the report is high quality, and the methodology is data driven. - -### 27. Pretending to reveal a deeper truth - -**Phrases to watch:** The real question is, at its core, in reality, what really matters, fundamentally, the deeper issue, the heart of the matter -**Problem:** AI writing uses these phrases to make an ordinary point sound like a hidden truth. -**Before:** -> The real question is whether teams can adapt. At its core, what really matters is organizational readiness. -**After:** -> The question is whether teams can adapt. That mostly depends on whether the organization is ready to change its habits. - -### 28. Announcing the next point - -**Phrases to watch:** Let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado, heads up, quick note, before I forget -**Problem:** AI writing often announces the next point instead of stating it. A casual phrase such as "one thing that bit me" can have the same problem. Remove the announcement, not just its formal tone. -**Before:** -> Let's dive into how caching works in Next.js. Here's what you need to know. -**After:** -> Next.js caches data at multiple layers, including request memoization, the data cache, and the router cache. -**Before (casual register):** -> One thing that bit me hard, so pay attention to this part: the webpack dev server doesn't send the CORS header by default. -**After:** -> The webpack dev server doesn't send the CORS header by default. - -### 29. A heading repeated in the first sentence - -**Signs to watch:** A heading followed by a one-line paragraph that simply restates the heading before the real content begins. -**Problem:** AI writing often follows a heading with a sentence that only repeats the heading. Remove the repeated sentence. -**Before:** -> ## Performance -> -> Speed matters. -> -> When users hit a slow page, they leave. -**After:** -> ## Performance -> -> When users hit a slow page, they leave. - -### 30. Writing about the previous version -**Problem:** Documentation and comments should describe the current behavior. Mention the previous version only in change logs, release notes, migration guides, and other documents about change. -**Before:** -> This function was added to replace the previous approach of iterating through all items, which caused O(n²) performance. -**After:** -> This function uses a hash map for O(1) lookups, avoiding the O(n²) cost of naive iteration. - -### 31. Forced punchlines and dramatic fragments -**Problem:** AI writing often turns each sentence into a dramatic closing line. One short sentence can add emphasis. A row of short fragments usually feels forced. -**Before:** -> Then AlphaEvolve arrived. It had no preference for symmetry. No aesthetic prior. No nostalgia for human taste. The old rules were gone. -**After:** -> AlphaEvolve changed the search because it did not favor symmetry or human-looking designs. That made some of the older assumptions less useful. - -### 32. Formulaic sayings - -**Words to watch:** X is the Y of Z, X becomes a trap, X is not a tool but a mirror, the language of, the currency of, the architecture of -**Problem:** AI writing often turns an ordinary claim into a saying that sounds deep but adds no detail. Replace the saying with the specific claim. -**Before:** -> Symmetry is the language of trust. Efficiency becomes a trap when teams forget the human layer. -**After:** -> Symmetric layouts often feel more predictable to users. Teams can over-optimize workflows and miss how people actually use them. - -### 33. Fake-candid openings - -**Phrases to watch:** Honestly?, Look, Here's the thing, The thing is, Let's be honest, Real talk, when used as standalone hooks or fake-candid pauses before an ordinary point. -**Problem:** AI writing often starts with a staged pause or claim of honesty before making a routine point. State the point directly. -**Before:** -> Is it worth the price? Honestly? It depends on how often you'll use it. -**After:** -> Whether it's worth the price depends on how often you'll use it. - -### 34. Answering objections no one raised - -**Phrases to watch:** This isn't (mainly/really) about, I'm not saying/arguing/trying to, To be clear, Don't get me wrong, This is not to say, You could argue/frame this differently but, Some might say... but -**Problem:** AI writing may answer an objection that does not appear in the text. Watch for an unattributed statement about what the writer does not mean, especially when the topic appears nowhere else. A direct claim such as "the API is not thread-safe" is not this pattern. -**Before:** -> This isn't mainly about prompt length, and I'm not arguing that documentation doesn't matter. You could categorize the problem another way, but the issue is whether the agent can use the instruction when it acts. -**After:** -> The issue is whether the agent can use the instruction when it acts. - -Remove only the unsupported defense. If it contains a real claim, state that claim directly. Keep an objection when the text names its source or answers it in full. - -### 35. Rejecting fake alternatives - -**Phrases to watch:** A tempting option/approach would be, One might be tempted to, An obvious approach would be, You might think... but, It would be easy to just, Some would suggest -**Problem:** AI writing may introduce an option that no reader would consider, reject it in a clause, and never mention it again. This often leaves an old drafting idea in the final text. Remove the fake option and state the real constraint directly. -**Before:** -> Session tokens are rotated every 24 hours. A tempting approach would be to rotate them by restarting the auth service on a cron job, but that would drop every active session. Rotation happens in place, and clients refresh transparently. -**After:** -> Session tokens are rotated every 24 hours, in place, and clients refresh transparently. - -One rejected option may be valid. Several short, unrelated rejections are a stronger sign. Ask what new information each sentence adds. If it only records an earlier edit, rewrite the paragraph around its main point. - -## Check for false positives - -### What not to flag - -A person may use some of these patterns. Do not treat any item below as proof by itself: - -- **Perfect grammar and consistent style.** Many writers are professionals or have been edited. Polish does not equal AI. -- **Mixed casual and formal styles.** This can reflect the writer's field, age, or personal habits. -- **"Bland" or "robotic" prose.** AI prose has *specific* tells. Generic dryness without those tells is just dry writing. -- **Formal or academic words.** §7 lists specific words that AI writing overuses. Do not simplify every formal word. -- **Letter-style opening or closing on a comment.** Salutations and sign-offs predate ChatGPT by centuries. -- **Common transition words in isolation.** *Additionally*, *moreover*, *consequently* are AI-coded only when piled up. One *however* is not a tell. -- **Curly quotes alone.** macOS, Word, Google Docs, and most CMSes auto-curl by default. Curly quotes only count when stacked with other tells. -- **Em dashes alone.** Many editors and journalists use them often. Em dashes are evidence only when paired with formulaic sales-y rhythm. -- **One short sentence for emphasis.** Flag dramatic fragments only when several appear in a row. -- **Deliberate repeated openings.** Writers may repeat an opening to build rhythm or pressure, as in "She came. She saw. She conquered." Change it only when the repetition adds nothing. -- **"Honestly" or "look" mid-sentence.** These are ordinary in casual writing. The tell is the standalone theatrical opener, not the word itself. -- **Useful limits and disclaimers.** Keep scope statements, legal and safety notices, real corrections, named objections, replies, and FAQ answers. -- **Real alternatives.** Keep options that a reader may consider in a design document, tutorial, or argument. Remove only an unlikely option that the text dismisses and never uses again. -- **Unsourced claims.** Most of the web is unsourced. Lack of citations doesn't prove anything. -- **Correct, complex formatting.** Visual editors and templates produce clean output without any AI. -- **Secondhand text.** Do not rewrite watched phrases inside quotations, titles, proper names, or examples where the phrase is being discussed rather than used. - -When unsure, look for several patterns together. One em dash proves nothing. Several stock patterns in the same passage are stronger evidence. - -### Human details to keep - -These details often carry the writer's voice. Keep them unless they hurt the meaning: - -- **Specific, unusual details.** Keep a real address, an odd quote, or a phrase such as "the lawyer who used to work upstairs from my dentist." -- **Mixed feelings and unresolved tension.** Keep lines such as "I think this is mostly good, but it bothers me, and I can't fully explain why." -- **Dated, era-bound references.** Slang, memes, or in-jokes that map to a specific year and subculture. Models lag by a year or more. -- **Deliberate first-person choices.** Keep a cut or word choice when the writer can explain why it belongs. -- **Variety in sentence length.** Real writing alternates short and long. AI writing tends toward an even, mid-length cadence. -- **Genuine asides, parentheticals, or self-corrections.** "(I keep wanting to say 'almost' here, but it really was certain.)" Models rarely interrupt themselves like this. -- **Edits made before November 30, 2022.** ChatGPT's public launch. Anything older than that is, with very rare exceptions, not AI-written. - ---- - -## How to return the result - -**Pasted text (default).** Return the draft, a short list of remaining AI patterns, and the final rewrite. - -**File mode.** When the user names a file, run the full rewrite process but write only the final text to the file. Change prose only. Keep code blocks, YAML metadata, data, and link targets unchanged. Then give the user a short summary. - -**Embedded mode.** When another task uses this skill for a pull request, commit message, or document, return only the final text. - -## Rewrite process - -1. Read the source and mark each AI pattern. -2. Write a draft. Read it aloud. Check the rhythm, details, simple verbs such as *is* and *has*, and the right level of formality. -3. Ask two questions: - - **"What still sounds AI-generated?"** - - **"Did the rewrite add or remove any fact, name, number, date, quote, citation, ranking, or other claim?"** - Treat any unsupported addition or lost claim as an error. -4. Write the final version. State each point naturally instead of patching one flagged phrase at a time. If a sentence stays awkward, rewrite the paragraph around its main point. Apply the dash rule in §14. - -Return the result required by [How to return the result](#how-to-return-the-result). - -## Source - -This skill is based on [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. Its patterns come from reviews of AI-generated text on Wikipedia. - -Wikipedia's main point: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases." diff --git a/.agents/skills/humanizer/agents/openai.yaml b/.agents/skills/humanizer/agents/openai.yaml deleted file mode 100644 index 16df2a8e1c..0000000000 --- a/.agents/skills/humanizer/agents/openai.yaml +++ /dev/null @@ -1,4 +0,0 @@ -interface: - display_name: "Humanizer" - short_description: "Make AI-written text sound like the writer" - default_prompt: "Use $humanizer to rewrite this text in my voice without changing its facts." diff --git a/.agents/skills/humanizer/scripts/validate-package.py b/.agents/skills/humanizer/scripts/validate-package.py deleted file mode 100644 index 30f0779722..0000000000 --- a/.agents/skills/humanizer/scripts/validate-package.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 -"""Check Humanizer's package files without external dependencies.""" - -from __future__ import annotations - -import json -import re -from pathlib import Path - - -ROOT = Path(__file__).resolve().parent.parent -SKILL_PATH = ROOT / "SKILL.md" -SKILL = SKILL_PATH.read_text(encoding="utf-8") -README = (ROOT / "README.md").read_text(encoding="utf-8") -AGENTS = (ROOT / "AGENTS.md").read_text(encoding="utf-8") -PLUGIN = json.loads((ROOT / ".claude-plugin" / "plugin.json").read_text(encoding="utf-8")) - - -def require_match(match: re.Match[str] | None, message: str) -> re.Match[str]: - if match is None: - raise SystemExit(message) - return match - - -yaml_metadata = require_match( - re.match(r"\A---\n(.*?)\n---\n", SKILL, re.DOTALL), - "SKILL.md must begin with YAML metadata", -).group(1) - -for unsupported_field in ("compatibility:", "allowed-tools:"): - if re.search(rf"(?m)^{re.escape(unsupported_field)}", yaml_metadata): - raise SystemExit(f"Remove unsupported YAML field: {unsupported_field[:-1]}") - -skill_version = require_match( - re.search(r'(?m)^\s+version:\s*["\']([^"\']+)["\']\s*$', yaml_metadata), - "Add metadata.version to SKILL.md", -).group(1) -readme_version = require_match( - re.search(r"(?m)^- \*\*([0-9]+\.[0-9]+\.[0-9]+)\*\*", README), - "Add a version entry to README.md", -).group(1) - -package_versions = {skill_version, readme_version, str(PLUGIN.get("version", ""))} -if len(package_versions) != 1: - raise SystemExit( - f"Use one package version in all files: {sorted(package_versions)}" - ) - -skill_files = {path.relative_to(ROOT) for path in ROOT.rglob("SKILL.md")} -if SKILL_PATH.is_symlink() or skill_files != {Path("SKILL.md")}: - raise SystemExit("Keep one regular SKILL.md at the repo root") -if PLUGIN.get("skills") != ["./"]: - raise SystemExit("Point the Claude plugin skill loader at the repo root") - -plain_language_rules = ( - "## Writing style", - "Lead with the main point.", - "Use common words and active voice.", - "Keep sentences and paragraphs short.", - "Use `must` for requirements.", - "Keep the full technical meaning.", -) -missing_plain_language_rules = [ - rule for rule in plain_language_rules if rule not in AGENTS -] -if missing_plain_language_rules: - raise SystemExit( - "Add the missing Plain Language rules to AGENTS.md: " - + ", ".join(missing_plain_language_rules) - ) - -pattern_numbers = [ - int(number) - for number in re.findall(r"(?m)^### ([0-9]+)\. ", SKILL) -] -if pattern_numbers != list(range(1, 36)): - raise SystemExit(f"Number SKILL.md patterns from 1 through 35: {pattern_numbers}") - -readme_numbers = { - int(number) for number in re.findall(r"(?m)^\| ([0-9]+) \|", README) -} -if readme_numbers != set(range(1, 36)): - raise SystemExit("List patterns 1 through 35 in the README table") - -if len(SKILL.splitlines()) > 500: - raise SystemExit("Keep SKILL.md at 500 lines or fewer") - -print(f"Humanizer package v{skill_version} is valid") diff --git a/.agents/skills/improve-codebase-architecture/LANGUAGE.md b/.agents/skills/improve-codebase-architecture/LANGUAGE.md deleted file mode 100644 index 530c27630a..0000000000 --- a/.agents/skills/improve-codebase-architecture/LANGUAGE.md +++ /dev/null @@ -1,53 +0,0 @@ -# Language - -Shared vocabulary for every suggestion this skill makes. Use these terms exactly — don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point. - -## Terms - -**Module** -Anything with an interface and an implementation. Deliberately scale-agnostic — applies equally to a function, class, package, or tier-spanning slice. -_Avoid_: unit, component, service. - -**Interface** -Everything a caller must know to use the module correctly. Includes the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. -_Avoid_: API, signature (too narrow — those refer only to the type-level surface). - -**Implementation** -What's inside a module — its body of code. Distinct from **Adapter**: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise. - -**Depth** -Leverage at the interface — the amount of behaviour a caller (or test) can exercise per unit of interface they have to learn. A module is **deep** when a large amount of behaviour sits behind a small interface. A module is **shallow** when the interface is nearly as complex as the implementation. - -**Seam** _(from Michael Feathers)_ -A place where you can alter behaviour without editing in that place. The *location* at which a module's interface lives. Choosing where to put the seam is its own design decision, distinct from what goes behind it. -_Avoid_: boundary (overloaded with DDD's bounded context). - -**Adapter** -A concrete thing that satisfies an interface at a seam. Describes *role* (what slot it fills), not substance (what's inside). - -**Leverage** -What callers get from depth. More capability per unit of interface they have to learn. One implementation pays back across N call sites and M tests. - -**Locality** -What maintainers get from depth. Change, bugs, knowledge, and verification concentrate at one place rather than spreading across callers. Fix once, fixed everywhere. - -## Principles - -- **Depth is a property of the interface, not the implementation.** A deep module can be internally composed of small, mockable, swappable parts — they just aren't part of the interface. A module can have **internal seams** (private to its implementation, used by its own tests) as well as the **external seam** at its interface. -- **The deletion test.** Imagine deleting the module. If complexity vanishes, the module wasn't hiding anything (it was a pass-through). If complexity reappears across N callers, the module was earning its keep. -- **The interface is the test surface.** Callers and tests cross the same seam. If you want to test *past* the interface, the module is probably the wrong shape. -- **One adapter means a hypothetical seam. Two adapters means a real one.** Don't introduce a seam unless something actually varies across it. - -## Relationships - -- A **Module** has exactly one **Interface** (the surface it presents to callers and tests). -- **Depth** is a property of a **Module**, measured against its **Interface**. -- A **Seam** is where a **Module**'s **Interface** lives. -- An **Adapter** sits at a **Seam** and satisfies the **Interface**. -- **Depth** produces **Leverage** for callers and **Locality** for maintainers. - -## Rejected framings - -- **Depth as ratio of implementation-lines to interface-lines** (Ousterhout): rewards padding the implementation. We use depth-as-leverage instead. -- **"Interface" as the TypeScript `interface` keyword or a class's public methods**: too narrow — interface here includes every fact a caller must know. -- **"Boundary"**: overloaded with DDD's bounded context. Say **seam** or **interface**. diff --git a/.agents/skills/improve-codebase-architecture/SKILL.md b/.agents/skills/improve-codebase-architecture/SKILL.md deleted file mode 100644 index 05984a6096..0000000000 --- a/.agents/skills/improve-codebase-architecture/SKILL.md +++ /dev/null @@ -1,71 +0,0 @@ ---- -name: improve-codebase-architecture -description: Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable. ---- - -# Improve Codebase Architecture - -Surface architectural friction and propose **deepening opportunities** — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability. - -## Glossary - -Use these terms exactly in every suggestion. Consistent language is the point — don't drift into "component," "service," "API," or "boundary." Full definitions in [LANGUAGE.md](LANGUAGE.md). - -- **Module** — anything with an interface and an implementation (function, class, package, slice). -- **Interface** — everything a caller must know to use the module: types, invariants, error modes, ordering, config. Not just the type signature. -- **Implementation** — the code inside. -- **Depth** — leverage at the interface: a lot of behaviour behind a small interface. **Deep** = high leverage. **Shallow** = interface nearly as complex as the implementation. -- **Seam** — where an interface lives; a place behaviour can be altered without editing in place. (Use this, not "boundary.") -- **Adapter** — a concrete thing satisfying an interface at a seam. -- **Leverage** — what callers get from depth. -- **Locality** — what maintainers get from depth: change, bugs, knowledge concentrated in one place. - -Key principles (see [LANGUAGE.md](LANGUAGE.md) for the full list): - -- **Deletion test**: imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep. -- **The interface is the test surface.** -- **One adapter = hypothetical seam. Two adapters = real seam.** - -This skill is _informed_ by the project's domain model. The domain language gives names to good seams; ADRs record decisions the skill should not re-litigate. - -## Process - -### 1. Explore - -Read the project's domain glossary and any ADRs in the area you're touching first. - -Then use the Agent tool with `subagent_type=Explore` to walk the codebase. Don't follow rigid heuristics — explore organically and note where you experience friction: - -- Where does understanding one concept require bouncing between many small modules? -- Where are modules **shallow** — interface nearly as complex as the implementation? -- Where have pure functions been extracted just for testability, but the real bugs hide in how they're called (no **locality**)? -- Where do tightly-coupled modules leak across their seams? -- Which parts of the codebase are untested, or hard to test through their current interface? - -Apply the **deletion test** to anything you suspect is shallow: would deleting it concentrate complexity, or just move it? A "yes, concentrates" is the signal you want. - -### 2. Present candidates - -Present a numbered list of deepening opportunities. For each candidate: - -- **Files** — which files/modules are involved -- **Problem** — why the current architecture is causing friction -- **Solution** — plain English description of what would change -- **Benefits** — explained in terms of locality and leverage, and also in how tests would improve - -**Use CONTEXT.md vocabulary for the domain, and [LANGUAGE.md](LANGUAGE.md) vocabulary for the architecture.** If `CONTEXT.md` defines "Order," talk about "the Order intake module" — not "the FooBarHandler," and not "the Order service." - -**ADR conflicts**: if a candidate contradicts an existing ADR, only surface it when the friction is real enough to warrant revisiting the ADR. Mark it clearly (e.g. _"contradicts ADR-0007 — but worth reopening because…"_). Don't list every theoretical refactor an ADR forbids. - -Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?" - -### 3. Grilling loop - -Once the user picks a candidate, drop into a grilling conversation. Walk the design tree with them — constraints, dependencies, the shape of the deepened module, what sits behind the seam, what tests survive. - -Side effects happen inline as decisions crystallize: - -- **Naming a deepened module after a concept not in `CONTEXT.md`?** Add the term to `CONTEXT.md` — same discipline as `/grill-with-docs` (see [CONTEXT-FORMAT.md](../grill-with-docs/CONTEXT-FORMAT.md)). Create the file lazily if it doesn't exist. -- **Sharpening a fuzzy term during the conversation?** Update `CONTEXT.md` right there. -- **User rejects the candidate with a load-bearing reason?** Offer an ADR, framed as: _"Want me to record this as an ADR so future architecture reviews don't re-suggest it?"_ Only offer when the reason would actually be needed by a future explorer to avoid re-suggesting the same thing — skip ephemeral reasons ("not worth it right now") and self-evident ones. See [ADR-FORMAT.md](../grill-with-docs/ADR-FORMAT.md). -- **Want to explore alternative interfaces for the deepened module?** See [INTERFACE-DESIGN.md](INTERFACE-DESIGN.md). diff --git a/.agents/skills/internal-linking-optimizer/SKILL.md b/.agents/skills/internal-linking-optimizer/SKILL.md deleted file mode 100644 index d83f3eb972..0000000000 --- a/.agents/skills/internal-linking-optimizer/SKILL.md +++ /dev/null @@ -1,351 +0,0 @@ ---- -name: internal-linking-optimizer -description: 'Optimize internal links: site architecture, authority distribution, orphan pages, crawl depth analysis. 内链优化/站内架构' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when improving internal link structure, anchor text distribution, orphan pages, or site architecture." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "low" - tags: - - seo - - internal-linking - - site-architecture - - link-equity - - orphan-pages - - topical-authority - - crawl-depth - - 内链优化 - - 内部リンク - - 내부링크 - - enlaces-internos - triggers: - # EN-formal - - "fix internal links" - - "improve site architecture" - - "link structure" - - "distribute page authority" - - "internal linking strategy" - - "site navigation" - - "link equity" - # EN-casual - - "orphan pages" - - "site architecture is messy" - - "pages have no links pointing to them" - - "pages have no links" - - "site structure is messy" - # EN-question - - "how to improve internal linking" - - "how to fix orphan pages" - # ZH-pro - - "内链优化" - - "站内链接" - - "网站架构" - - "权重传递" - - "锚文本优化" - # ZH-casual - - "内链怎么做" - - "孤立页面" - - "网站结构乱" - # JA - - "内部リンク最適化" - - "サイト構造" - # KO - - "내부 링크 최적화" - - "사이트 구조" - # ES - - "enlaces internos" - - "arquitectura del sitio" - # PT - - "links internos" - # Misspellings - - "internal linkng" ---- - -# Internal Linking Optimizer - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This optimization skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill analyzes your site's internal link structure and provides recommendations to improve SEO through strategic internal linking. It helps distribute authority, establish topical relevance, and improve crawlability. - -**System role**: Optimization layer skill. It turns weak pages, structures, and technical issues into prioritized repair work. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs a diagnosis or repair plan that should feed directly into remediation work, not just a one-time opinion. - -- Improving site architecture for SEO -- Distributing authority to important pages -- Fixing orphan pages with no internal links -- Creating topic cluster internal link strategies -- Optimizing anchor text for SEO -- Recovering pages that have lost rankings -- Planning internal links for new content - -## What This Skill Does - -1. **Link Structure Analysis**: Maps current internal linking patterns -2. **Authority Flow Mapping**: Shows how PageRank flows through site -3. **Orphan Page Detection**: Finds pages with no internal links -4. **Anchor Text Optimization**: Improves anchor text diversity -5. **Topic Cluster Linking**: Creates pillar-cluster link strategies -6. **Link Opportunity Finding**: Identifies where to add links -7. **Navigation Optimization**: Improves site-wide link elements - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Analyze Current Structure - -``` -Analyze internal linking structure for [domain/sitemap] -``` - -``` -Find internal linking opportunities for [URL] -``` - -### Create Linking Strategy - -``` -Create internal linking plan for topic cluster about [topic] -``` - -``` -Suggest internal links for this new article: [content/URL] -``` - -### Fix Issues - -``` -Find orphan pages on [domain] -``` - -``` -Optimize anchor text across the site -``` - -## Skill Contract - -**Expected output**: a scored diagnosis, prioritized repair plan, and a short handoff summary ready for `memory/audits/`. - -- **Reads**: the current page or site state, symptoms, prior audits, and current priorities from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing audit or optimization plan plus a reusable summary that can be stored under `memory/audits/`. -- **Promotes**: blocking defects, repeated weaknesses, and fix priorities to `memory/open-loops.md` and `memory/decisions.md`. -- **Next handoff**: use the `Next Best Skill` below when the repair path is clear. - -## Data Sources - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~web crawler + ~~analytics connected:** -Claude can automatically perform a full site crawl via ~~web crawler to map the complete link graph, fetch page performance metrics from ~~analytics to identify high-value pages, and analyze link flow throughout the site. This enables data-driven internal linking strategies. - -**With manual data only:** -Ask the user to provide: -1. Sitemap URL or list of important pages -2. Key page URLs that need more internal links -3. Content categories or topic clusters -4. Any existing link structure documentation - -Proceed with the analysis using provided data. Note in the output which findings are from automated crawl vs. manual review. - -## Instructions - -When a user requests internal linking optimization: - -1. **Analyze Current Internal Link Structure** - - ```markdown - ## Internal Link Structure Analysis - - ### Overview - - **Domain**: [domain] - **Total Pages Analyzed**: [X] - **Total Internal Links**: [X] - **Average Links per Page**: [X] - - ### Link Distribution - - | Links per Page | Page Count | Percentage | - |----------------|------------|------------| - | 0 (Orphan) | [X] | [X]% | - | 1-5 | [X] | [X]% | - | 6-10 | [X] | [X]% | - | 11-20 | [X] | [X]% | - | 20+ | [X] | [X]% | - - ### Top Linked Pages - - | Page | Internal Links | Authority | Notes | - |------|----------------|-----------|-------| - | [URL 1] | [X] | High | [notes] | - | [URL 2] | [X] | High | [notes] | - | [URL 3] | [X] | Medium | [notes] | - - ### Under-Linked Important Pages - - | Page | Current Links | Traffic | Recommended Links | - |------|---------------|---------|-------------------| - | [URL 1] | [X] | [X]/mo | [X]+ | - | [URL 2] | [X] | [X]/mo | [X]+ | - - **Structure Score**: [X]/10 - ``` - -2. **Identify Orphan Pages** - - ```markdown - ## Orphan Page Analysis - - ### Definition - Orphan pages have no internal links pointing to them, making them - hard for users and search engines to discover. - - ### Orphan Pages Found: [X] - - | Page | Traffic | Priority | Recommended Action | - |------|---------|----------|-------------------| - | [URL 1] | [X]/mo | High | Link from [pages] | - | [URL 2] | [X]/mo | Medium | Add to navigation | - | [URL 3] | 0 | Low | Consider deleting/redirecting | - - ### Fix Strategy - - **High Priority Orphans** (have traffic/rankings): - 1. [URL] - Add links from: [relevant pages] - 2. [URL] - Add links from: [relevant pages] - - **Medium Priority Orphans** (potentially valuable): - 1. [URL] - Add to category/tag page - 2. [URL] - Link from related content - - **Low Priority Orphans** (consider removing): - 1. [URL] - Redirect to [better page] - 2. [URL] - Delete or noindex - ``` - -3. **Analyze Anchor Text Distribution** - - > **CORE-EEAT alignment**: Internal linking quality maps to R08 (Internal Link Graph) in the CORE-EEAT benchmark -- use descriptive anchors, ensure links support topical authority. See [content-quality-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/content-quality-auditor/SKILL.md) for full audit. - - ```markdown - ## Anchor Text Analysis - - ### Current Anchor Text Patterns - - **Most Used Anchors**: - - | Anchor Text | Count | Target Pages | Assessment | - |-------------|-------|--------------|------------| - | "click here" | [X] | [X] pages | ❌ Not descriptive | - | "read more" | [X] | [X] pages | ❌ Not descriptive | - | "[exact keyword]" | [X] | [page] | ⚠️ May be over-optimized | - | "[descriptive phrase]" | [X] | [page] | ✅ Good | - - ### Anchor Text Distribution by Page - - **Page: [Important URL]** - - | Anchor Text | Source Page | Status | - |-------------|-------------|--------| - | "[anchor 1]" | [source URL] | ✅/⚠️/❌ | - | "[anchor 2]" | [source URL] | ✅/⚠️/❌ | - - **Issues Found**: - - Over-optimized anchors: [X] instances - - Generic anchors: [X] instances - - Same anchor to multiple pages: [X] instances - - ### Anchor Text Recommendations - - **For Page: [URL]** - - Current: "[current anchor]" used [X] times - - Recommended variety: - - "[variation 1]" - Use from [page type] - - "[variation 2]" - Use from [page type] - - "[variation 3]" - Use from [page type] - - **Anchor Score**: [X]/10 - ``` - -4. **Create Topic Cluster Link Strategy** — Map current pillar/cluster links, recommend link structure, list specific links to add - - > **Reference**: See [references/linking-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-templates.md) for the topic cluster link strategy template (Step 4). - -5. **Find Contextual Link Opportunities** — Analyze each page for topic-relevant link opportunities, prioritize high-impact additions - - > **Reference**: See [references/linking-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-templates.md) for the contextual link opportunities template (Step 5). - -6. **Optimize Navigation and Footer Links** — Analyze main/footer/sidebar/breadcrumb navigation, recommend pages to add or remove - - > **Reference**: See [references/linking-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-templates.md) for the navigation optimization template (Step 6). - -7. **Generate Link Implementation Plan** — Executive summary, current state metrics, phased priority actions (weeks 1-4+), implementation guide, tracking plan - - > **Reference**: See [references/linking-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-templates.md) for the full implementation plan template (Step 7). - -## Validation Checkpoints - -### Input Validation -- [ ] Site structure or sitemap provided (URL or file) -- [ ] Target pages or topic clusters clearly defined -- [ ] If optimizing specific page, page URL or content provided - -### Output Validation -- [ ] Every recommendation cites specific data points (not generic advice) -- [ ] All link suggestions include source page, target page, and recommended anchor text -- [ ] Orphan page lists include URLs and recommended actions -- [ ] Source of each data point clearly stated (~~web crawler data, ~~analytics, user-provided, or manual analysis) - -## Example - -> **Reference**: See [references/linking-example.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-example.md) for a full worked example (email marketing best practices internal linking opportunities). - -## Tips for Success - -1. **Quality over quantity** - Add relevant links, not random ones -2. **User-first thinking** - Links should help users navigate -3. **Vary anchor text** - Avoid over-optimization -4. **Link to important pages** - Distribute authority strategically -5. **Regular audits** - Internal links need maintenance as content grows - - -### Save Results - -After delivering audit or optimization findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/audits/internal-linking-optimizer/YYYY-MM-DD-.md` containing: -- One-line verdict or headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to `memory/hot-cache.md` without asking. - -## Reference Materials - -- [Link Architecture Patterns](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/link-architecture-patterns.md) — Architecture models (hub-and-spoke, silo, flat, pyramid, mesh), anchor text diversity framework, link equity flow model, and internal link audit checklist -- [Linking Templates](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-templates.md) — Detailed output templates for steps 6-7 (navigation optimization, implementation plan) -- [Linking Example](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/references/linking-example.md) — Full worked example for internal linking opportunities - -## Next Best Skill - -- **Primary**: [on-page-seo-auditor](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/on-page-seo-auditor/SKILL.md) — verify that revised internal links support the page-level goals. diff --git a/.agents/skills/internal-linking-optimizer/references/link-architecture-patterns.md b/.agents/skills/internal-linking-optimizer/references/link-architecture-patterns.md deleted file mode 100644 index 24d2f3de2f..0000000000 --- a/.agents/skills/internal-linking-optimizer/references/link-architecture-patterns.md +++ /dev/null @@ -1,362 +0,0 @@ -# Link Architecture Patterns - -Detailed architecture models with implementation guides, migration strategies, and measurement frameworks for internal linking optimization. - -## Architecture Model Deep Dives - -### 1. Hub-and-Spoke (Topic Cluster) Model - -#### Overview - -The hub-and-spoke model organizes content around central "pillar" pages (hubs) that link to and from related "cluster" articles (spokes). This is the most widely recommended architecture for content-driven sites targeting topical authority. - -#### Structure Diagram - -``` - ┌──────────────┐ - │ Homepage │ - └──────┬───────┘ - │ - ┌──────────────┼──────────────┐ - │ │ │ - ┌──────▼──────┐ ┌────▼────┐ ┌──────▼──────┐ - │ Hub A │ │ Hub B │ │ Hub C │ - │ (Pillar) │ │(Pillar) │ │ (Pillar) │ - └──┬───┬───┬──┘ └────┬────┘ └──┬───┬───┬──┘ - │ │ │ │ │ │ │ - A1 A2 A3 B1 B2 C1 C2 C3 - └───┼───┘ └───┼───┘ - cross-links cross-links -``` - -#### Implementation Steps - -1. **Identify 3-7 core topics** that define your business expertise -2. **Create pillar pages** (2,000-5,000 words) that broadly cover each core topic -3. **Map cluster articles** (800-2,000 words) that dive deep into subtopics -4. **Implement bidirectional links**: every cluster article links to its pillar, every pillar links to all its clusters -5. **Add cross-links** between related cluster articles within the same hub -6. **Add bridge links** between hubs where subtopics overlap - -#### Link Rules - -| Link Type | Direction | Anchor Text Strategy | -|-----------|-----------|---------------------| -| Pillar → Cluster | Pillar links to each cluster | Descriptive: "learn about [subtopic]" | -| Cluster → Pillar | Every cluster links back to pillar | Partial match: "our complete [topic] guide" | -| Cluster ↔ Cluster | Between related clusters in same hub | Natural: "as we covered in [related article]" | -| Hub ↔ Hub (bridge) | Between related pillar pages | Branded/natural: "see also our [topic] resource" | - -#### When to Use -- Content marketing sites and blogs -- SaaS companies building topical authority -- Publishers covering defined topic areas -- Any site with 50-500 content pages - -#### Measurement - -| Metric | Target | Tool | -|--------|--------|------| -| Pillar page rankings for head terms | Top 10 | Rank tracker | -| Cluster article rankings for long-tail | Top 20 | Rank tracker | -| Internal links per cluster article | 3-5 minimum | Crawl report | -| Click depth from homepage to cluster | ≤3 clicks | Crawl report | -| Organic traffic to hub pages | Month-over-month growth | Analytics | - ---- - -### 2. Silo Structure - -#### Overview - -The silo model creates strict vertical hierarchies where content is organized into isolated "silos" (categories). Links flow vertically within a silo but rarely cross between silos. This concentrates topical relevance within each silo. - -#### Structure Diagram - -``` - ┌──────────────┐ - │ Homepage │ - └──────┬───────┘ - │ - ┌─────────────────┼─────────────────┐ - │ │ │ - ┌────▼─────┐ ┌────▼─────┐ ┌────▼─────┐ - │ Silo A │ │ Silo B │ │ Silo C │ - │ Category │ │ Category │ │ Category │ - └────┬─────┘ └────┬─────┘ └────┬─────┘ - │ │ │ - ┌────▼─────┐ ┌────▼─────┐ ┌────▼─────┐ - │ Sub-cat │ │ Sub-cat │ │ Sub-cat │ - └────┬─────┘ └────┬─────┘ └────┬─────┘ - │ │ │ - ┌────▼────┐ ┌────▼────┐ ┌────▼────┐ - │ Pages │ │ Pages │ │ Pages │ - └─────────┘ └─────────┘ └─────────┘ - - No horizontal links between silos (strict model) -``` - -#### Implementation Steps - -1. **Define 5-15 top-level categories** (silos) based on your product/service taxonomy -2. **Create category landing pages** with overview content and links to subcategories -3. **Build subcategory pages** linking down to individual product/content pages -4. **Enforce vertical linking**: pages link up to their parent and down to their children -5. **Use breadcrumbs** to reinforce the hierarchy visually and structurally -6. **Limit cross-silo links** to only the most relevant connections (strict model) or allow them strategically (modified model) - -#### Link Rules - -| Link Type | Direction | Allowed? | -|-----------|-----------|----------| -| Parent → Child | Downward within silo | Always | -| Child → Parent | Upward within silo | Always | -| Sibling ↔ Sibling | Horizontal within same parent | Yes | -| Cross-silo | Between different silos | Strict: No. Modified: Sparingly | -| All pages → Homepage | Upward to root | Yes (via navigation) | - -#### When to Use -- Large e-commerce sites (100+ product categories) -- Directory sites with clear taxonomy -- Sites where categories are truly distinct topics -- Enterprises with separate business lines - -#### Limitations -- Overly strict silos can trap link equity in one branch -- Cross-topic content becomes difficult to place -- Users may need to navigate up and over to find related content -- Modified silo (allowing some cross-links) often works better in practice - ---- - -### 3. Flat Architecture - -#### Overview - -A flat architecture keeps all pages within 2-3 clicks of the homepage. There is minimal hierarchy; instead, pages are broadly interlinked. This maximizes crawlability and distributes link equity evenly. - -#### Structure Diagram - -``` - ┌──────────┐ - │ Homepage │ - └────┬─────┘ - │ - ┌──────────────┼──────────────┐ - │ │ │ │ │ │ │ - P1 P2 P3 P4 P5 P6 P7 - └────┼────┼────┼────┼────┼────┘ - └────┴────┴────┘ - (cross-linked freely) -``` - -#### Implementation Steps - -1. **Link all key pages from the homepage** (directly or via a comprehensive sitemap page) -2. **Keep URL structure shallow**: /category/page, not /category/subcategory/year/page -3. **Cross-link freely** between related pages at the same level -4. **Use comprehensive navigation** menus, footer links, or HTML sitemaps -5. **Limit total pages** to keep the architecture manageable - -#### When to Use -- Small sites with fewer than 100 pages -- Portfolio sites -- Small business brochure sites -- Startups with limited content - -#### Scaling Limits - -| Site Size | Flat Architecture Feasibility | -|-----------|------------------------------| -| <50 pages | Ideal | -| 50-100 pages | Manageable with good navigation | -| 100-500 pages | Difficult; consider hub-and-spoke | -| 500+ pages | Not recommended; switch to hierarchical model | - ---- - -### 4. Pyramid Architecture - -#### Overview - -The pyramid model mirrors traditional website hierarchies: a single homepage at the top, branching into categories, subcategories, and finally individual pages. Authority flows from top to bottom, concentrating at higher levels. - -#### Structure Diagram - -``` -Level 0: Homepage - / \ -Level 1: Category A Category B - / \ / \ -Level 2: Sub A1 Sub A2 Sub B1 Sub B2 - / \ / \ / \ / \ -Level 3: P1 P2 P3 P4 P5 P6 P7 P8 -``` - -#### Implementation Steps - -1. **Design a clear hierarchy** with 3-4 levels maximum -2. **Homepage links to all top-level categories** prominently -3. **Category pages link to all subcategories** within them -4. **Subcategory pages link to all child pages** -5. **Implement breadcrumbs** to support the hierarchy -6. **Add "related content" cross-links** at the page level to offset authority concentration - -#### Authority Flow Considerations - -| Level | Typical Authority | Action to Improve | -|-------|-------------------|-------------------| -| Homepage | Highest | Ensure links to priority categories are prominent | -| Categories | High | Link from blog content, not just navigation | -| Subcategories | Medium | Add contextual links from other sections | -| Individual pages | Lowest | Cross-link, feature in "popular posts" widgets | - -#### When to Use -- News and media sites -- Large blogs (500+ posts) -- Corporate sites with many divisions -- Government/educational sites - ---- - -### 5. Mesh/Matrix Architecture - -#### Overview - -The mesh model allows free-form linking between any related pages, regardless of hierarchy. Every page can link to any other relevant page. This creates a dense web of connections, similar to Wikipedia's link structure. - -#### Structure Diagram - -``` - P1 ←──→ P2 ←──→ P3 - ↕ ╲ ↕ ╱ ↕ - P4 ←──→ P5 ←──→ P6 - ↕ ╱ ↕ ╲ ↕ - P7 ←──→ P8 ←──→ P9 -``` - -#### Implementation Steps - -1. **Set linking rules** to prevent chaos: link only when topically relevant -2. **Use contextual anchors** that describe the destination page -3. **Set a link budget** per page (5-15 contextual links per 1,000 words) -4. **Review link density regularly** to prune irrelevant connections -5. **Maintain a link map** (spreadsheet or tool) to track the network - -#### Governance Rules - -| Rule | Purpose | -|------|---------| -| Every link must have topical relevance | Prevents link dilution | -| Maximum 15 contextual links per 1,000 words | Prevents link farms | -| Review links quarterly | Prunes outdated connections | -| Use descriptive anchor text only | Maintains semantic value | -| No reciprocal link trading between unrelated pages | Prevents manipulation patterns | - -#### When to Use -- Knowledge bases and documentation sites -- Wikis and encyclopedias -- Research repositories -- FAQ/help center sites - ---- - -## Migration Between Models - -### Common Migration Paths - -| From | To | Reason | Difficulty | -|------|----|--------|-----------| -| Flat → Hub-and-Spoke | Site grew beyond 100 pages | Medium | -| Silo → Hub-and-Spoke | Silos too rigid, need cross-topic links | Medium | -| Pyramid → Hub-and-Spoke | Want to build topical clusters | High | -| No structure → Any model | Starting from disorganized state | High | -| Hub-and-Spoke → Hybrid | Need both clusters and strict categories | Medium | - -### Migration Steps (General) - -1. **Audit current state**: Map all existing internal links using a crawler -2. **Design target architecture**: Choose model, map pages to their new positions -3. **Create a link change plan**: Document every link addition, removal, and anchor text change -4. **Implement in phases**: Start with highest-priority cluster/silo, then expand -5. **Preserve existing equity**: Do not remove links that pass significant value without replacement -6. **Monitor impact**: Track rankings and traffic for 4-8 weeks after each phase -7. **Iterate**: Adjust the plan based on measured results - -### Migration Risk Mitigation - -| Risk | Mitigation | -|------|-----------| -| Temporary ranking drops | Migrate one section at a time, not all at once | -| Broken internal links | Run crawl after each phase to verify | -| Lost link equity | Ensure no orphan pages created during migration | -| Anchor text disruption | Change anchors gradually, not all at once | - ---- - -## Measurement Framework - -### Key Metrics by Architecture Model - -| Metric | Hub-and-Spoke | Silo | Flat | Pyramid | Mesh | -|--------|---------------|------|------|---------|------| -| Avg click depth | ≤3 | ≤4 | ≤2 | ≤4 | ≤3 | -| Orphan pages | 0 | 0 | 0 | 0 | 0 | -| Avg internal links per page | 5-10 | 3-7 | 8-15 | 3-5 | 8-15 | -| Cross-section links | Many | Few | N/A | Some | Many | -| Authority concentration | Distributed to hubs | Concentrated in silo tops | Even | Top-heavy | Even | - -### Monthly Monitoring Checklist - -| Check | Tool | Action if Failing | -|-------|------|-------------------| -| Orphan pages count | Crawl report | Add internal links immediately | -| Average click depth | Crawl report | Add shortcuts to deep pages | -| Crawl depth distribution | Crawl report | Flatten deep branches | -| Internal link count per page | Crawl report | Add links to under-linked pages | -| Anchor text diversity | Manual audit | Vary anchors for over-optimized pages | -| Broken internal links | Crawl report | Fix or remove broken links | -| New content linked within 48 hours | Editorial process | Add to related pages upon publishing | - -### ROI Estimation - -| Architecture Change | Typical Impact | Timeline to See Results | -|--------------------|---------------|----------------------| -| Fix orphan pages | +15-30% traffic to those pages | 2-4 weeks | -| Build first topic cluster | +10-25% traffic to cluster pages | 4-8 weeks | -| Reduce click depth by 1 level | +5-15% crawl efficiency | 2-6 weeks | -| Anchor text optimization | +5-10% ranking improvement for target terms | 4-12 weeks | -| Full architecture migration | +20-50% overall organic traffic | 3-6 months | - ---- - -## Hybrid Architecture Strategies - -Most real-world sites combine elements from multiple models. Common hybrid patterns: - -### Hub-and-Spoke + Silo (Recommended for Medium-Large Sites) - -``` -Homepage - ├── Category Silo A - │ ├── Hub A1 (pillar) ←→ Cluster articles - │ └── Hub A2 (pillar) ←→ Cluster articles - ├── Category Silo B - │ ├── Hub B1 (pillar) ←→ Cluster articles - │ └── Hub B2 (pillar) ←→ Cluster articles - └── Cross-category bridge links (A1 ↔ B2 where relevant) -``` - -- **Silos** provide category organization for navigation and URL structure -- **Hubs** within each silo build topical authority for specific keyword clusters -- **Bridge links** connect related content across silos where user intent overlaps - -### Implementation Priority Order - -1. Fix structural issues first (orphan pages, broken links) -2. Implement primary architecture model -3. Add cross-linking strategy -4. Optimize anchor text -5. Monitor and iterate - -This order ensures each phase builds on a solid foundation rather than optimizing details on a broken structure. diff --git a/.agents/skills/internal-linking-optimizer/references/linking-example.md b/.agents/skills/internal-linking-optimizer/references/linking-example.md deleted file mode 100644 index ef8703eea6..0000000000 --- a/.agents/skills/internal-linking-optimizer/references/linking-example.md +++ /dev/null @@ -1,42 +0,0 @@ -# Internal Linking Optimizer — Worked Example - -Referenced from [SKILL.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/SKILL.md). - ---- - -## Worked Example - -**User**: "Find internal linking opportunities for my blog post about 'email marketing best practices'" - -**Output**: - -```markdown -## Internal Linking Opportunities - -**Page**: /blog/email-marketing-best-practices/ -**Current Internal Links**: 2 - -### Recommended Links to Add - -| Section | Text to Link | Target Page | Anchor | -|---------|--------------|-------------|--------| -| Para 2 | "building your email list" | /blog/grow-email-list/ | "building your email list" | -| Para 5 | "subject lines" | /blog/email-subject-lines/ | "write compelling subject lines" | -| Section on segmentation | "audience segments" | /blog/email-segmentation-guide/ | "segment your audience" | -| CTA section | "marketing automation" | /services/email-automation/ | "email automation services" | -| Conclusion | "email marketing tools" | /blog/best-email-tools/ | "top email marketing tools" | - -### Pages That Should Link TO This Article - -| Source Page | Location | Anchor Text | -|-------------|----------|-------------| -| /blog/digital-marketing-guide/ | Email section | "email marketing best practices" | -| /services/marketing-services/ | Related content | "email marketing strategies" | -| /blog/lead-generation-tips/ | Email mention | "email marketing techniques" | - -### Priority Actions - -1. Add 5 outbound internal links (listed above) -2. Request 3 inbound links from related pages -3. Add to "Marketing" category page -``` diff --git a/.agents/skills/internal-linking-optimizer/references/linking-templates.md b/.agents/skills/internal-linking-optimizer/references/linking-templates.md deleted file mode 100644 index ce5e1e4c40..0000000000 --- a/.agents/skills/internal-linking-optimizer/references/linking-templates.md +++ /dev/null @@ -1,219 +0,0 @@ -# Internal Linking Optimizer — Output Templates - -Detailed output templates for internal-linking-optimizer steps 4-7. Referenced from [SKILL.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/optimize/internal-linking-optimizer/SKILL.md). - ---- - -## Step 4: Create Topic Cluster Link Strategy - -```markdown -## Topic Cluster Internal Linking - -### Cluster: [Main Topic] - -**Pillar Page**: [URL] -**Cluster Articles**: [X] - -### Current Link Map - -``` -[Pillar Page] - ├── [Cluster Article 1] ←→ [linked?] - ├── [Cluster Article 2] ←→ [linked?] - ├── [Cluster Article 3] ←→ [linked?] - └── [Cluster Article 4] ←→ [linked?] -``` - -### Recommended Link Structure - -``` -[Pillar Page] - ├── Links TO all cluster articles ✅ - │ - ├── [Cluster Article 1] - │ ├── Link TO pillar ✅ - │ └── Link TO related cluster articles - │ - ├── [Cluster Article 2] - │ ├── Link TO pillar ✅ - │ └── Link TO related cluster articles - │ - └── [etc.] -``` - -### Links to Add - -| From Page | To Page | Anchor Text | Location | -|-----------|---------|-------------|----------| -| [URL 1] | [URL 2] | "[anchor]" | [paragraph/section] | -| [URL 2] | [URL 3] | "[anchor]" | [paragraph/section] | -| [Pillar] | [Cluster 1] | "[anchor]" | [section] | -``` - ---- - -## Step 5: Find Contextual Link Opportunities - -```markdown -## Contextual Link Opportunities - -### Link Opportunity Analysis - -For each page, find relevant pages to link to based on: -- Topic relevance -- Keyword overlap -- User journey logic -- Authority distribution needs - -### Opportunities Found - -**Page: [URL 1]** -**Topic**: [topic] -**Current internal links**: [X] - -| Opportunity | Target Page | Anchor Text | Why Link | -|-------------|-------------|-------------|----------| -| Paragraph 2 mentions "[topic]" | [URL] | "[topic phrase]" | Topic match | -| Section on "[subject]" | [URL] | "[anchor]" | Related guide | -| CTA at end | [URL] | "[anchor]" | User journey | - -**Page: [URL 2]** -[Continue for each page...] - -### Priority Link Additions - -**High Impact Links** (add these first): - -1. **From**: [Source URL] - **To**: [Target URL] - **Anchor**: "[anchor text]" - **Why**: [reason - e.g., "Target page needs authority boost"] - **Where to add**: [specific location in content] - -2. **From**: [Source URL] - **To**: [Target URL] - [etc.] -``` - ---- - -## Step 6: Optimize Navigation and Footer Links - -```markdown -## Site-Wide Link Optimization - -### Current Navigation Analysis - -**Main Navigation**: -- Links present: [list] -- Missing important pages: [list] -- Too many links: [Yes/No] - -**Footer Navigation**: -- Links present: [list] -- SEO value: [assessment] - -### Navigation Recommendations - -| Element | Current | Recommended | Reason | -|---------|---------|-------------|--------| -| Main nav | [X] links | [Y] links | [reason] | -| Footer | [X] links | [Y] links | [reason] | -| Sidebar | [status] | [recommendation] | [reason] | -| Breadcrumbs | [status] | [recommendation] | [reason] | - -### Pages to Add to Navigation - -1. [Page] - Add to [location] because [reason] -2. [Page] - Add to [location] because [reason] - -### Pages to Remove from Navigation - -1. [Page] - Move to [footer/remove] because [reason] -``` - ---- - -## Step 7: Generate Link Implementation Plan - -```markdown -# Internal Linking Optimization Plan - -**Site**: [domain] -**Analysis Date**: [date] - -## Executive Summary - -- Total link opportunities found: [X] -- Orphan pages to fix: [X] -- Estimated traffic impact: [+X%] -- Priority actions: [X] - -## Current State - -| Metric | Current | Target | Gap | -|--------|---------|--------|-----| -| Avg links per page | [X] | [X] | [X] | -| Orphan pages | [X] | 0 | [X] | -| Over-optimized anchors | [X]% | <10% | [X]% | -| Topic cluster coverage | [X]% | 100% | [X]% | - -## Priority Actions - -### Phase 1: Critical Fixes (Week 1) - -**Fix Orphan Pages**: -- [ ] [URL] - Add links from [X] pages -- [ ] [URL] - Add links from [X] pages - -**High-Value Link Additions**: -- [ ] Link [Page A] to [Page B] with "[anchor]" -- [ ] Link [Page A] to [Page C] with "[anchor]" - -### Phase 2: Topic Clusters (Week 2-3) - -**Cluster 1: [Topic]** -- [ ] Ensure pillar links to all [X] cluster articles -- [ ] Add [X] cross-links between cluster articles - -**Cluster 2: [Topic]** -- [ ] [Tasks] - -### Phase 3: Optimization (Week 4+) - -**Anchor Text Diversity**: -- [ ] Vary anchors for [Page] - currently [X]% exact match -- [ ] [Additional tasks] - -**Navigation Updates**: -- [ ] Add [Page] to main navigation -- [ ] Update footer links - -## Implementation Guide - -### Adding Internal Links - -Best practices: -1. Add links contextually within content -2. Use descriptive anchor text (not "click here") -3. Link to relevant, helpful pages -4. Aim for 3-10 internal links per 1,000 words -5. Vary anchor text for the same target - -### Anchor Text Guidelines - -| Type | Example | Usage | -|------|---------|-------| -| Exact match | "keyword research" | 10-20% | -| Partial match | "tips for keyword research" | 30-40% | -| Branded | "Brand's guide to..." | 10-20% | -| Natural | "this article", "learn more" | 20-30% | - -## Tracking Success - -Monitor these metrics weekly: -- [ ] Rankings for target keywords -- [ ] Traffic to previously orphan pages -- [ ] Crawl stats in ~~search console -- [ ] Internal link distribution changes -``` diff --git a/.agents/skills/keyword-research/SKILL.md b/.agents/skills/keyword-research/SKILL.md deleted file mode 100644 index fdbb208b11..0000000000 --- a/.agents/skills/keyword-research/SKILL.md +++ /dev/null @@ -1,435 +0,0 @@ ---- -name: keyword-research -description: 'Find high-value SEO keywords: search volume, difficulty, intent classification, topic clusters. 关键词研究/内容选题' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when starting keyword research for a new page, topic, or campaign. Also when the user asks about search volume, keyword difficulty, topic clusters, long-tail keywords, or what to write about." -argument-hint: " [market/language]" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "medium" - tags: - - seo - - geo - - keywords - - keyword-research - - search-volume - - keyword-difficulty - - topic-clusters - - long-tail-keywords - - search-intent - - content-calendar - - ahrefs - - semrush - - google-keyword-planner - - 关键词研究 - - SEO关键词 - - キーワード調査 - - 키워드분석 - - palabras-clave - triggers: - # EN-formal - - "keyword research" - - "find keywords" - - "keyword analysis" - - "keyword discovery" - - "search volume analysis" - - "keyword difficulty" - - "topic research" - - "identify ranking opportunities" - # EN-casual - - "what should I write about" - - "what are people searching for" - - "what are people googling" - - "find me topics to write" - - "give me keyword ideas" - - "which keywords should I target" - - "why is my traffic low" - - "I need content ideas" - # EN-question - - "how do I find good keywords" - - "what keywords should I target" - - "how competitive is this keyword" - # EN-competitor - - "Ahrefs keyword explorer alternative" - - "Semrush keyword magic tool" - - "Google Keyword Planner alternative" - - "Ubersuggest alternative" - # ZH-pro - - "关键词研究" - - "关键词分析" - - "搜索量查询" - - "关键词难度" - - "SEO关键词" - - "长尾关键词" - - "词库整理" - - "关键词布局" - - "关键词挖掘" - # ZH-casual - - "写什么内容好" - - "找选题" - - "帮我挖词" - - "不知道写什么" - - "查关键词" - - "选词" - - "帮我找词" - # JA - - "キーワード調査" - - "キーワードリサーチ" - - "SEOキーワード分析" - - "検索ボリューム" - - "ロングテールキーワード" - - "検索意図分析" - # KO - - "키워드 리서치" - - "키워드 분석" - - "검색량 분석" - - "키워드 어떻게 찾아요?" - - "검색어 분석" - - "경쟁도 낮은 키워드는?" - # ES - - "investigación de palabras clave" - - "análisis de palabras clave" - - "volumen de búsqueda" - - "posicionamiento web" - - "cómo encontrar palabras clave" - # PT - - "pesquisa de palavras-chave" - # Misspellings - - "keywrod research" - - "keywork research" ---- - -# Keyword Research - - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This research skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -Discovers, analyzes, and prioritizes keywords for SEO and GEO content strategies. Identifies high-value opportunities based on search volume, competition, intent, and business relevance. - -**System role**: Research layer skill. It turns market signals into reusable strategic inputs for the rest of the library. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs reusable market intelligence that should influence strategy, not just an ad hoc answer. - -- Starting a new content strategy or campaign -- Expanding into new topics or markets -- Finding keywords for a specific product or service -- Identifying long-tail keyword opportunities -- Understanding search intent for your industry -- Planning content calendars -- Researching keywords for GEO optimization - -## What This Skill Does - -1. **Keyword Discovery**: Generates comprehensive keyword lists from seed terms -2. **Intent Classification**: Categorizes keywords by user intent (informational, navigational, commercial, transactional) -3. **Difficulty Assessment**: Evaluates competition level and ranking difficulty -4. **Opportunity Scoring**: Prioritizes keywords by potential ROI -5. **Clustering**: Groups related keywords into topic clusters -6. **GEO Relevance**: Identifies keywords likely to trigger AI responses - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Basic Keyword Research - -``` -Research keywords for [topic/product/service] -``` - -``` -Find keyword opportunities for a [industry] business targeting [audience] -``` - -### With Specific Goals - -``` -Find low-competition keywords for [topic] with commercial intent -``` - -``` -Identify question-based keywords for [topic] that AI systems might answer -``` - -### Competitive Research - -``` -What keywords is [competitor URL] ranking for that I should target? -``` - -## Skill Contract - -**Expected output**: a prioritized research brief, evidence-backed findings, and a short handoff summary ready for `memory/research/`. - -- **Reads**: user goals, target market inputs, available tool data, and prior strategy from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing research deliverable plus a reusable summary that can be stored under `memory/research/`. -- **Promotes**: durable keyword priorities, competitor facts, entity candidates, and strategic decisions to `CLAUDE.md`, `memory/decisions.md`, and `memory/research/`; hand canonical entity work to `entity-optimizer`. -- **Next handoff**: use the `Next Best Skill` below when the findings are ready to drive action. - -## Data Sources - -> **Note:** All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected. - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~SEO tool + ~~search console connected:** -Automatically pull historical search volume data, keyword difficulty scores, SERP analysis, current rankings from ~~search console, and competitor keyword overlap. The skill will fetch seed keyword metrics, related keyword suggestions, and search trend data. - -**With manual data only:** -Ask the user to provide: -1. Seed keywords or topic description -2. Target audience and geographic location -3. Business goals (traffic, leads, sales) -4. Current domain authority (if known) or site age -5. Any known keyword performance data or search volume estimates - -Proceed with the full analysis using provided data. Note in the output which metrics are from automated collection vs. user-provided data. - -## Instructions - -When a user requests keyword research: - -At the start of each phase, announce: **[Phase X/8: Name]** so the user can track progress. - -### Phase 1/8: Scope - - Ask clarifying questions if not provided: - - What is your product/service/topic? - - Who is your target audience? - - What is your business goal? (traffic, leads, sales) - - What is your current domain authority? (new site, established, etc.) - - Any specific geographic targeting? - - Preferred language? - -### Phase 2/8: Discover - - Start with: - - Core product/service terms - - Problem-focused keywords (what issues do you solve?) - - Solution-focused keywords (how do you help?) - - Audience-specific terms - - Industry terminology - -### Phase 3/8: Variations - - For each seed keyword, generate variations: - - ```markdown - ## Keyword Expansion Patterns - - ### Modifiers - - Best [keyword] - - Top [keyword] - - [keyword] for [audience] - - [keyword] near me - - [keyword] [year] - - How to [keyword] - - What is [keyword] - - [keyword] vs [alternative] - - [keyword] examples - - [keyword] tools - - ### Long-tail Variations - - [keyword] for beginners - - [keyword] for small business - - Free [keyword] - - [keyword] software/tool/service - - [keyword] template - - [keyword] checklist - - [keyword] guide - ``` - -### Phase 4/8: Classify - - Categorize each keyword: - - | Intent | Signals | Example | Content Type | - |--------|---------|---------|--------------| - | Informational | what, how, why, guide, learn | "what is SEO" | Blog posts, guides | - | Navigational | brand names, specific sites | "google analytics login" | Homepage, product pages | - | Commercial | best, review, vs, compare | "best SEO tools [current year]" | Comparison posts, reviews | - | Transactional | buy, price, discount, order | "buy SEO software" | Product pages, pricing | - -### Phase 5/8: Score - - Score each keyword (1-100 scale): - - ```markdown - ### Difficulty Factors - - **High Difficulty (70-100)** - - Major brands ranking - - High domain authority competitors - - Established content (1000+ backlinks) - - Paid ads dominating SERP - - **Medium Difficulty (40-69)** - - Mix of authority and niche sites - - Some opportunities for quality content - - Moderate backlink requirements - - **Low Difficulty (1-39)** - - Few authoritative competitors - - Thin or outdated content ranking - - Long-tail variations - - New or emerging topics - ``` - -#### Opportunity Score - - Formula: `Opportunity = (Volume × Intent Value) / Difficulty` - - **Intent Value** assigns a numeric weight by search intent: - - Informational = 1 - - Navigational = 1 - - Commercial = 2 - - Transactional = 3 - - ```markdown - ### Opportunity Matrix - - | Scenario | Volume | Difficulty | Intent | Priority | - |----------|--------|------------|--------|----------| - | Quick Win | Low-Med | Low | High | ⭐⭐⭐⭐⭐ | - | Growth | High | Medium | High | ⭐⭐⭐⭐ | - | Long-term | High | High | High | ⭐⭐⭐ | - | Research | Low | Low | Low | ⭐⭐ | - ``` - -### Phase 6/8: GEO-Check — AI Answer Overlap - - Keywords likely to trigger AI responses: - - ```markdown - ### GEO-Relevant Keywords - - **High GEO Potential** - - Question formats: "What is...", "How does...", "Why is..." - - Definition queries: "[term] meaning", "[term] definition" - - Comparison queries: "[A] vs [B]", "difference between..." - - List queries: "best [category]", "top [number] [items]" - - How-to queries: "how to [action]", "steps to [goal]" - - **AI Answer Indicators** - - Query is factual/definitional - - Answer can be summarized concisely - - Topic is well-documented online - - Low commercial intent - ``` - -### Phase 7/8: Cluster - - Group keywords into content clusters: - - ```markdown - ## Topic Cluster: [Main Topic] - - **Pillar Content**: [Primary keyword] - - Search volume: [X] - - Difficulty: [X] - - Content type: Comprehensive guide - - **Cluster Content**: - - ### Sub-topic 1: [Secondary keyword] - - Volume: [X] - - Difficulty: [X] - - Links to: Pillar - - Content type: [Blog post/Tutorial/etc.] - - ### Sub-topic 2: [Secondary keyword] - - Volume: [X] - - Difficulty: [X] - - Links to: Pillar + Sub-topic 1 - - Content type: [Blog post/Tutorial/etc.] - - [Continue for all cluster keywords...] - ``` - -### Phase 8/8: Deliver - - Produce a report containing: Executive Summary, Top Keyword Opportunities (Quick Wins, Growth, GEO), Topic Clusters, Content Calendar, and Next Steps. - - **Quality bar** — every recommendation must include at least one specific number. If it reads like the left column, rewrite it before including. - - | ❌ Generic (rewrite before including) | ✅ Actionable | - |---|---| - | "Target long-tail keywords for better results" | "Target 'project management for nonprofits' (vol: 320, KD: 22) — no DR>40 sites in top 10" | - | "This keyword has good potential" | "Opportunity 8.4: vol 4,800, KD 28, transactional intent — gap analysis shows no content updated since 2023 in top 5" | - | "Consider creating content around this topic" | "Write '[Tool A] vs [Tool B] for small teams' — 1,200/mo searches, current #1 is a 2022 article with 12 backlinks" | - | "Optimize your page for this keyword" | "Add primary keyword to H1 (currently missing), write a 40-word direct answer in paragraph 1, add 3 internal links from your /blog/ cluster" | - - > **Reference**: See [references/example-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/references/example-report.md) for the full report template and example. - -## Validation Checkpoints - -### Input Validation -- [ ] Seed keywords or topic description clearly provided -- [ ] Target audience and business goals specified -- [ ] Geographic and language targeting confirmed -- [ ] Domain authority or site maturity level established - -### Output Validation -- [ ] Every recommendation cites specific data points (not generic advice) -- [ ] Search volume and difficulty scores included for each keyword -- [ ] Keywords grouped by intent and mapped to content types -- [ ] Topic clusters show clear pillar-to-cluster relationships -- [ ] Source of each data point clearly stated (~~SEO tool data, user-provided, or estimated) - -## Example - -> **Reference**: See [references/example-report.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/references/example-report.md) for a complete example report for "project management software for small businesses". - -### Advanced Usage - -- **Intent Mapping**: `Map all keywords for [topic] by search intent and funnel stage` -- **Seasonal Analysis**: `Identify seasonal keyword trends for [industry]` -- **Competitor Gap**: `What keywords do [competitor 1], [competitor 2] rank for that I'm missing?` -- **Local Keywords**: `Research local keywords for [business type] in [city/region]` - -## Tips for Success - -1. **Start with seed keywords** that describe your core offering -2. **Don't ignore long-tail** - they often have highest conversion rates -3. **Match content to intent** - informational queries need guides, not sales pages -4. **Group into clusters** for topical authority -5. **Prioritize quick wins** to build momentum and credibility -6. **Include GEO keywords** in your strategy for AI visibility -7. **Review quarterly** - keyword dynamics change over time - - - -### Save Results - -After delivering findings to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/research/keyword-research/YYYY-MM-DD-.md` containing: -- One-line headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - -## Reference Materials - -- [Keyword Intent Taxonomy](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/references/keyword-intent-taxonomy.md) — Complete intent classification with signal words and content strategies -- [Topic Cluster Templates](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/references/topic-cluster-templates.md) — Hub-and-spoke architecture templates for pillar and cluster content -- [Keyword Prioritization Framework](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/references/keyword-prioritization-framework.md) — Priority scoring matrix, categories, and seasonal keyword patterns -- [Example Report](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/references/example-report.md) — Complete example keyword research report for project management software - -## Next Best Skill - -- **Primary**: [competitor-analysis](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/competitor-analysis/SKILL.md) — turn keyword opportunities into a competitive benchmark. diff --git a/.agents/skills/keyword-research/references/example-report.md b/.agents/skills/keyword-research/references/example-report.md deleted file mode 100644 index 3c0a0f495a..0000000000 --- a/.agents/skills/keyword-research/references/example-report.md +++ /dev/null @@ -1,96 +0,0 @@ -# Keyword Research — Example Report - -This is a complete example output showing the expected format for keyword research deliverables. - -## Prompt - -**User**: "Research keywords for a project management software company targeting small businesses" - -## Example Output - -```markdown -# Keyword Research Report: Project Management Software - -**Generated**: [current month and year] -**Target Audience**: Small business owners and teams -**Business Goal**: Software signups and trials - -## Executive Summary - -- Total keywords analyzed: 150+ -- High-priority opportunities: 23 -- Estimated traffic potential: 45,000/month -- Recommended focus areas: - - Task management workflows - - Team collaboration - - Small business productivity - -## Top Keyword Opportunities - -### Quick Wins (Priority: Immediate) - -| Keyword | Volume | Difficulty | Intent | Score | -|---------|--------|------------|--------|-------| -| project management for small teams | 1,200 | 28 | Commercial | 92 | -| simple task management software | 890 | 25 | Commercial | 89 | -| best free project management tool | 2,400 | 35 | Commercial | 85 | -| how to manage remote team projects | 720 | 22 | Informational | 82 | -| project tracking spreadsheet alternative | 480 | 18 | Commercial | 80 | - -### Growth Keywords (Priority: 3-6 months) - -| Keyword | Volume | Difficulty | Intent | Score | -|---------|--------|------------|--------|-------| -| project management software | 18,000 | 72 | Commercial | 65 | -| best project management tools [current year] | 8,500 | 65 | Commercial | 62 | -| project management app | 12,000 | 68 | Commercial | 58 | - -### GEO Opportunities (AI-citation potential) - -| Keyword | Type | AI Potential | Recommended Format | -|---------|------|--------------|-------------------| -| what is project management | Definition | ⭐⭐⭐⭐⭐ | Clear definition + methodology | -| agile vs waterfall | Comparison | ⭐⭐⭐⭐⭐ | Side-by-side comparison table | -| project management methodologies | List | ⭐⭐⭐⭐ | Comprehensive list with pros/cons | -| how to create a project plan | How-to | ⭐⭐⭐⭐ | Step-by-step guide | -| project management best practices | List | ⭐⭐⭐⭐ | Numbered best practices | - -## Topic Clusters - -### Cluster 1: Project Management Fundamentals - -**Pillar**: "Complete Guide to Project Management" (8,500 volume) - -Cluster articles: -1. What is project management? (2,200 volume) -2. Project management methodologies explained (1,800 volume) -3. How to create a project plan (1,400 volume) -4. Project management best practices (1,200 volume) -5. Project management roles and responsibilities (890 volume) - -### Cluster 2: Team Collaboration - -**Pillar**: "Team Collaboration Tools Guide" (4,200 volume) - -Cluster articles: -1. How to improve team communication (1,600 volume) -2. Remote team management tips (1,400 volume) -3. Best practices for distributed teams (920 volume) -4. Team productivity tools comparison (780 volume) - -## Content Calendar Recommendations - -| Month | Content | Target Keyword | Type | -|-------|---------|----------------|------| -| Week 1 | Simple Task Management Guide | simple task management software | Blog + Demo | -| Week 2 | Project Management for Small Teams | project management for small teams | Pillar Page | -| Week 3 | Agile vs Waterfall: Complete Comparison | agile vs waterfall | Comparison | -| Week 4 | Free PM Tools Roundup | best free project management tool | Listicle | - -## Next Steps - -1. **Immediate**: Create landing pages for top 5 quick-win keywords -2. **Week 1-2**: Write pillar content for "Project Management Fundamentals" -3. **Week 3-4**: Build out cluster content with internal linking -4. **Ongoing**: Track rankings and adjust strategy based on performance -``` diff --git a/.agents/skills/keyword-research/references/keyword-intent-taxonomy.md b/.agents/skills/keyword-research/references/keyword-intent-taxonomy.md deleted file mode 100644 index 7e5f362506..0000000000 --- a/.agents/skills/keyword-research/references/keyword-intent-taxonomy.md +++ /dev/null @@ -1,324 +0,0 @@ -# Keyword Intent Taxonomy - -A comprehensive classification system for understanding search intent, mapping keywords to content strategies, and aligning with conversion funnels. - -## Overview - -Search intent (also called user intent or query intent) is the underlying goal a user has when typing a query into a search engine or AI system. Correctly classifying intent determines content format, tone, depth, and conversion strategy. - -This taxonomy defines **4 primary intent types**, each with **3-4 sub-categories**, along with signal words, SERP feature correlations, and content format recommendations. - ---- - -## Primary Intent Types - -### 1. Informational Intent - -The user wants to **learn something**. They are seeking knowledge, not a product. - -#### Sub-categories - -| Sub-category | Description | Example Queries | -|-------------|------------|----------------| -| **Educational** | Broad learning about a topic | "what is SEO", "content marketing explained", "how search engines work" | -| **Instructional** | Step-by-step guidance on completing a task | "how to set up Google Analytics", "how to write a meta description", "how to do keyword research" | -| **Exploratory** | Open-ended research to understand options | "types of SEO strategies", "content marketing trends", "link building techniques" | -| **Troubleshooting** | Diagnosing and fixing a problem | "why is my site not ranking", "Google indexing issues", "404 error fix" | - -#### Signal Words and Modifiers - -**Strong informational signals:** -- what, what is, what are, what does -- how, how to, how does, how do I -- why, why is, why does, why do -- when, when to, when should -- who, who is, who does -- where, where to, where can I -- guide, tutorial, learn, explain -- examples, tips, ideas, ways -- definition, meaning, difference between -- history of, basics of, introduction to -- pros and cons, advantages, disadvantages -- best practices, checklist, steps - -**Weak informational signals (may overlap with commercial):** -- overview, comparison, review (can be commercial) -- list of, types of, methods - -#### SERP Feature Correlation - -| SERP Feature | Frequency for Informational | Notes | -|-------------|---------------------------|-------| -| Featured Snippet | Very High | Especially for "what is" and "how to" queries | -| People Also Ask | Very High | Almost always present for informational queries | -| Knowledge Panel | High | For entity-related informational queries | -| AI Overview | Very High | AI systems prioritize informational answers | -| Video Carousel | Medium-High | Especially for instructional queries | -| Image Pack | Medium | For visual/diagram-seeking queries | -| Shopping Results | Very Low | Rarely appears for pure informational | - -#### Content Format Recommendations - -| Sub-category | Best Formats | Target Length | Key Elements | -|-------------|-------------|--------------|-------------| -| Educational | Definitive guides, explainers, glossary entries | 1,500-3,000 words | Clear definitions, examples, visuals | -| Instructional | Step-by-step tutorials, how-to guides, video walkthroughs | 2,000-4,000 words | Numbered steps, screenshots, code samples | -| Exploratory | Listicles, roundups, trend reports, comparison tables | 2,000-3,500 words | Categories, pros/cons, use cases | -| Troubleshooting | FAQ pages, debug guides, knowledge base articles | 800-2,000 words | Problem-solution pairs, error codes, screenshots | - ---- - -### 2. Navigational Intent - -The user wants to **find a specific website, page, or resource**. They already know where they want to go. - -#### Sub-categories - -| Sub-category | Description | Example Queries | -|-------------|------------|----------------| -| **Brand Search** | Looking for a specific company or product | "Ahrefs", "HubSpot CRM", "Moz Pro" | -| **Product/Feature Search** | Looking for a specific feature or tool within a known brand | "Ahrefs keyword explorer", "Google Search Console coverage report" | -| **Login/Access** | Trying to access an account or dashboard | "Ahrefs login", "Google Analytics sign in", "SEMrush dashboard" | -| **Support/Docs** | Seeking help documentation for a known product | "Ahrefs API documentation", "Google Search Console help" | - -#### Signal Words and Modifiers - -**Strong navigational signals:** -- [brand name] (standalone) -- [brand name] login, sign in, sign up -- [brand name] pricing, plans -- [brand name] support, help, docs -- [brand name] [feature name] -- [brand name] download, app -- official, website, site -- [brand name] alternatives (borderline commercial) - -#### SERP Feature Correlation - -| SERP Feature | Frequency for Navigational | Notes | -|-------------|---------------------------|-------| -| Sitelinks | Very High | Brand queries almost always show sitelinks | -| Knowledge Panel | High | For established brands with Google entity | -| Featured Snippet | Low | Rarely for pure navigational | -| People Also Ask | Medium | Often brand-related questions | -| AI Overview | Low | AI usually defers to direct navigation | - -#### Content Format Recommendations - -| Sub-category | Best Formats | Key Elements | -|-------------|-------------|-------------| -| Brand Search | Homepage, about page, landing pages | Clear branding, value prop, navigation | -| Product/Feature Search | Product pages, feature pages | Feature details, CTAs, screenshots | -| Login/Access | Login page, getting started guide | Fast load, clear login form, password reset | -| Support/Docs | Knowledge base, documentation hub | Search functionality, categorized articles | - ---- - -### 3. Commercial Investigation Intent - -The user is **researching before making a purchase decision**. They are comparing options, reading reviews, and evaluating alternatives. - -#### Sub-categories - -| Sub-category | Description | Example Queries | -|-------------|------------|----------------| -| **Comparison** | Evaluating two or more specific options | "Ahrefs vs SEMrush", "WordPress vs Squarespace for SEO" | -| **Review-seeking** | Looking for opinions and evaluations | "Ahrefs review 2026", "best SEO tool reviews", "is Moz worth it" | -| **Best-of Research** | Seeking curated top options in a category | "best SEO tools", "top keyword research tools", "best free backlink checkers" | -| **Evaluation** | Assessing fit for a specific need | "SEO tools for small business", "enterprise SEO platform features", "SEO tool for agencies" | - -#### Signal Words and Modifiers - -**Strong commercial signals:** -- best, top, leading -- vs, versus, compared to, comparison -- review, reviews, rating, ratings -- alternative, alternatives to -- like [brand], similar to -- for [audience], for [use case] -- pros and cons (when about products) -- worth it, is [product] good -- features, capabilities, integrations -- pricing, cost, how much -- free vs paid, premium - -**Seasonal/temporal commercial signals:** -- [product] [year] (e.g., "best SEO tools 2026") -- new, latest, updated - -#### SERP Feature Correlation - -| SERP Feature | Frequency for Commercial | Notes | -|-------------|------------------------|-------| -| People Also Ask | Very High | Comparison and evaluation questions | -| Featured Snippet | High | Especially for "best" and comparison queries | -| AI Overview | High | AI often synthesizes comparisons | -| Shopping Results | Medium | When products have direct purchase path | -| Video Carousel | Medium | Review and comparison videos | -| Ads (top) | Very High | Advertisers target commercial intent heavily | - -#### Content Format Recommendations - -| Sub-category | Best Formats | Target Length | Key Elements | -|-------------|-------------|--------------|-------------| -| Comparison | Side-by-side comparison pages, "X vs Y" articles | 2,500-4,000 words | Feature tables, pricing tables, verdict | -| Review-seeking | In-depth reviews, user testimonials, case studies | 2,000-3,500 words | Honest pros/cons, screenshots, scoring | -| Best-of Research | Ranked listicles, category roundups | 3,000-5,000 words | Criteria explanation, mini-reviews, comparison table | -| Evaluation | Buying guides, use-case guides, ROI calculators | 2,000-3,500 words | Decision criteria, audience segmentation, recommendations | - ---- - -### 4. Transactional Intent - -The user wants to **complete a specific action**, typically a purchase, signup, or download. - -#### Sub-categories - -| Sub-category | Description | Example Queries | -|-------------|------------|----------------| -| **Purchase** | Ready to buy a product or service | "buy Ahrefs subscription", "SEMrush pricing plans", "order SEO audit" | -| **Signup/Trial** | Ready to create an account or start a trial | "Ahrefs free trial", "sign up for Moz", "create Google Search Console account" | -| **Download** | Wants to download software, template, or resource | "download Screaming Frog", "SEO checklist PDF download", "free keyword template" | -| **Hire/Engage** | Ready to hire a professional or agency | "hire SEO consultant", "SEO agency near me", "freelance SEO expert" | - -#### Signal Words and Modifiers - -**Strong transactional signals:** -- buy, purchase, order, subscribe -- price, pricing, cost, discount, coupon, deal -- free trial, demo, signup, sign up, register -- download, install, get -- hire, book, schedule, request quote -- near me, in [location] -- cheap, affordable, budget -- premium, pro, enterprise -- [product] discount code, [product] coupon - -#### SERP Feature Correlation - -| SERP Feature | Frequency for Transactional | Notes | -|-------------|---------------------------|-------| -| Shopping Results | Very High | Dominant for product purchase queries | -| Ads (top + bottom) | Very High | Maximum ad coverage | -| Local Pack | High | For service-based transactional queries | -| Sitelinks | Medium | For brand-specific transactional | -| Featured Snippet | Low | Rarely for pure transactional | -| AI Overview | Low-Medium | Sometimes for pricing/comparison | - -#### Content Format Recommendations - -| Sub-category | Best Formats | Key Elements | -|-------------|-------------|-------------| -| Purchase | Product pages, pricing pages, checkout | Clear pricing, CTAs, trust signals, reviews | -| Signup/Trial | Landing pages, signup forms, onboarding | Minimal friction, benefits, social proof | -| Download | Resource landing pages, gated content | Preview, form, instant delivery | -| Hire/Engage | Service pages, contact forms, portfolios | Credentials, case studies, clear next step | - ---- - -## Intent Mapping to Conversion Funnel - -### Full Funnel Alignment - -``` -FUNNEL STAGE INTENT TYPE CONTENT GOAL -───────────────────────────────────────────────────────────── -Awareness → Informational Attract & educate - (Educational, Exploratory) - -Interest → Informational Deepen engagement - (Instructional) - -Consideration → Commercial Investigation Build preference - (Best-of, Comparison) - -Evaluation → Commercial Investigation Overcome objections - (Review, Evaluation) - -Decision → Transactional Convert - (Purchase, Signup, Hire) - -Retention → Navigational Support & retain - (Support/Docs, Login) - -Advocacy → Informational Enable sharing - (Educational — about you) -``` - -### Conversion Potential by Intent - -| Intent Type | Avg. Conversion Rate | Typical CTA | Nurture Length | -|------------|---------------------|-------------|---------------| -| Informational | 0.5-2% | Newsletter signup, content download | Long (weeks-months) | -| Navigational | N/A (already engaged) | Login, access feature | N/A | -| Commercial Investigation | 2-5% | Free trial, demo request, comparison download | Medium (days-weeks) | -| Transactional | 5-15% | Buy now, subscribe, hire | Short (immediate-days) | - ---- - -## Mixed Intent Queries - -Many real-world queries blend multiple intent types. Recognize and address the dominant + secondary intent. - -### Common Mixed Intent Patterns - -| Query Pattern | Primary Intent | Secondary Intent | Content Strategy | -|--------------|---------------|-----------------|-----------------| -| "best [product] for [use case]" | Commercial | Informational | Buying guide with educational context | -| "how to [task] with [product]" | Instructional | Navigational | Tutorial featuring the product | -| "[product] review and pricing" | Commercial | Transactional | Review with clear pricing CTAs | -| "what is [concept] tools" | Informational | Commercial | Explainer that transitions to tool recommendations | -| "[product] free download" | Transactional | Navigational | Landing page with download + product info | - -### Handling Mixed Intent in Content - -1. **Lead with the primary intent** -- answer the dominant question first -2. **Bridge to the secondary intent** -- naturally transition to the secondary need -3. **Match SERP format** -- if SERPs show mixed results, your content should too -4. **Segment CTAs** -- provide different CTAs for different intent segments within the page - ---- - -## SEO-Specific Keyword Intent Examples - -### SEO Industry Keyword Classification - -| Keyword | Intent | Sub-category | Recommended Content | -|---------|--------|-------------|-------------------| -| what is SEO | Informational | Educational | Definitive guide with visuals | -| how to do keyword research | Informational | Instructional | Step-by-step tutorial | -| best SEO tools 2026 | Commercial | Best-of Research | Ranked listicle with mini-reviews | -| Ahrefs vs SEMrush | Commercial | Comparison | Side-by-side feature comparison | -| buy Ahrefs subscription | Transactional | Purchase | Pricing/product page | -| Google Search Console login | Navigational | Login/Access | Login page or redirect | -| why is my site not indexing | Informational | Troubleshooting | Debug guide with solutions | -| SEO agency for ecommerce | Transactional | Hire/Engage | Service page with portfolio | -| is Moz worth it | Commercial | Review-seeking | Honest review with alternatives | -| SEO checklist template | Transactional | Download | Gated resource landing page | -| types of backlinks | Informational | Exploratory | Categorized explainer | -| SEMrush pricing | Commercial | Evaluation | Pricing breakdown page | - ---- - -## Applying Intent Taxonomy in Keyword Research - -### Step-by-Step Classification Process - -1. **Read the query literally** -- what is the user asking for? -2. **Check for signal words** -- match against the signal word tables above -3. **Verify against SERP** -- what does Google actually show? The SERP is the ground truth -4. **Identify sub-category** -- narrow from primary intent to sub-category -5. **Check for mixed intent** -- does the query blend two types? -6. **Map to content format** -- select format based on intent + sub-category -7. **Assign funnel stage** -- determine where this fits in the buyer journey -8. **Set conversion expectation** -- align CTA and goals with intent type - -### Common Classification Mistakes - -| Mistake | Example | Why It's Wrong | Correct Classification | -|---------|---------|---------------|----------------------| -| Treating all "best" queries as informational | "best CRM software" | User is comparing products to buy | Commercial Investigation | -| Treating all questions as informational | "how much does Ahrefs cost" | User is evaluating a purchase | Commercial / Transactional | -| Ignoring local intent | "SEO services" | May have strong local intent in many contexts | Transactional (local) | -| Missing navigational intent | "HubSpot blog" | User wants a specific page | Navigational | -| Assuming single intent | "SEO tools" | Could be informational OR commercial | Mixed -- check SERP | diff --git a/.agents/skills/keyword-research/references/keyword-prioritization-framework.md b/.agents/skills/keyword-research/references/keyword-prioritization-framework.md deleted file mode 100644 index e32b7dda99..0000000000 --- a/.agents/skills/keyword-research/references/keyword-prioritization-framework.md +++ /dev/null @@ -1,41 +0,0 @@ -# Keyword Prioritization Framework - -Systematic scoring methodology for ranking keywords by strategic value. - -## Relationship to Opportunity Score - -**Which score to use**: Use the Priority Score (below) for initial keyword triage and shortlisting. Use the Opportunity Score (Step 6 in the main workflow) for final content calendar prioritization, where the additional GEO and competitive factors provide more nuanced ranking. - -## Priority Scoring Matrix - -Score each keyword 1-5 on these factors, then calculate weighted total: - -| Factor | Weight | Score 1 (Low) | Score 5 (High) | -|--------|--------|---------------|----------------| -| Search Volume | 20% | <100/mo | >10,000/mo | -| Keyword Difficulty | 25% | KD >80 (hard) | KD <20 (easy) | -| Business Relevance | 30% | Tangential to offering | Core to offering | -| Search Intent Match | 15% | Informational only | Transactional/commercial | -| Trend Direction | 10% | Declining | Growing | - -**Priority Score** = Σ(Factor Weight × Score) / 5 - -## Priority Categories - -| Priority | Score Range | Action | -|----------|------------|--------| -| P0 — Must Target | 4.0-5.0 | Create content immediately | -| P1 — High Value | 3.0-3.9 | Queue for next content sprint | -| P2 — Opportunity | 2.0-2.9 | Plan for future content calendar | -| P3 — Monitor | 1.0-1.9 | Track but don't prioritize | - -## Seasonal Keyword Patterns - -### Seasonal Analysis Framework - -| Season Trigger | Example Keywords | Planning Lead Time | Content Strategy | -|---------------|-----------------|-------------------|-----------------| -| Calendar events | "Black Friday SEO", "New Year marketing plan" | 3-4 months ahead | Publish 6-8 weeks before peak | -| Industry events | "[Conference] takeaways", "Google algorithm update" | 1-2 months / reactive | Pre-plan templates, react quickly | -| Budget cycles | "marketing budget template Q1", "SEO ROI report" | 2-3 months ahead | Target planning season (Oct-Dec) | -| Seasonal demand | "summer marketing ideas", "holiday email campaigns" | 2-3 months ahead | Refresh annually with new data | diff --git a/.agents/skills/keyword-research/references/topic-cluster-templates.md b/.agents/skills/keyword-research/references/topic-cluster-templates.md deleted file mode 100644 index da6ec594d2..0000000000 --- a/.agents/skills/keyword-research/references/topic-cluster-templates.md +++ /dev/null @@ -1,286 +0,0 @@ -# Topic Cluster Templates - -Planning worksheets, architecture patterns, and measurement frameworks for building topic clusters that establish topical authority and drive organic traffic. - -## Overview - -A topic cluster is a group of interlinked content pieces organized around a central pillar page. The pillar covers a broad topic comprehensively, while cluster pages dive deep into specific subtopics. Internal links bind the cluster together, signaling topical authority to search engines and AI systems. - ---- - -## Topic Cluster Planning Worksheet - -### Step 1: Define the Pillar Topic - -Use this template to evaluate whether a topic deserves a full cluster: - -| Field | Your Input | -|-------|-----------| -| **Pillar Topic** | [Broad topic name] | -| **Pillar Keyword** | [Head keyword, typically 1-3 words] | -| **Monthly Search Volume** | [Volume] | -| **Keyword Difficulty** | [KD score] | -| **Business Relevance** | [1-5 scale: how core is this to your product/service?] | -| **Current Ranking** | [Your current position, or "Not ranking"] | -| **Competitor Coverage** | [How many competitors have pillar content on this?] | -| **Estimated Cluster Size** | [How many subtopics can you identify?] | -| **Content Assets Available** | [Existing content you can repurpose or link] | - -**Pillar viability checklist:** -- [ ] At least 8-12 subtopics can be identified -- [ ] Combined cluster keyword volume exceeds 5,000/month -- [ ] Topic is directly relevant to your product or service -- [ ] You can provide unique expertise or data on this topic -- [ ] Competitors have not yet built a dominant cluster - -### Step 2: Map Cluster Subtopics - -| # | Subtopic | Target Keyword | Volume | KD | Intent | Content Format | Status | -|---|---------|---------------|--------|-----|--------|---------------|--------| -| 1 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 2 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 3 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 4 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 5 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 6 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 7 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | -| 8 | [Subtopic name] | [Long-tail keyword] | [Vol] | [KD] | [I/N/C/T] | [Guide/Tutorial/List/etc.] | [Idea/Draft/Published] | - -### Step 3: Define Internal Linking Map - -| Source Page | Links To | Anchor Text Strategy | -|------------|---------|---------------------| -| Pillar | Cluster 1, 2, 3... (all) | Descriptive, keyword-relevant anchors | -| Cluster 1 | Pillar + Cluster 2, 3 | Natural contextual links | -| Cluster 2 | Pillar + Cluster 1, 4 | Natural contextual links | -| Cluster 3 | Pillar + Cluster 1 | Natural contextual links | - -**Linking rules:** -- Every cluster page MUST link to the pillar page -- The pillar page MUST link to every cluster page -- Cluster pages SHOULD link to 2-3 related cluster pages where contextually relevant -- Use descriptive anchor text (not "click here" or bare URLs) -- Link placement should be within body content, not just in a footer list - ---- - -## Hub-and-Spoke Architecture Patterns - -### Pattern 1: Classic Hub-Spoke (Best for Educational Topics) - -``` - ┌──────────────────┐ - ┌────│ What is [Topic]? │ - │ └──────────────────┘ - │ ┌──────────────────┐ - ├────│ [Topic] Benefits │ - │ └──────────────────┘ -┌───────────────┐ │ ┌──────────────────┐ -│ PILLAR: │───┼────│ [Topic] Examples │ -│ Complete │ │ └──────────────────┘ -│ Guide to │ │ ┌──────────────────┐ -│ [Topic] │ ├────│ [Topic] Tools │ -└───────────────┘ │ └──────────────────┘ - │ ┌──────────────────┐ - ├────│ [Topic] Mistakes │ - │ └──────────────────┘ - │ ┌──────────────────┐ - └────│ [Topic] Checklist │ - └──────────────────┘ -``` - -**Best for:** Broad educational topics where subtopics don't overlap much. -**Example:** "Content Marketing" pillar with spokes for strategy, types, examples, tools, metrics, mistakes. - -### Pattern 2: Layered Cluster (Best for Technical Topics) - -``` - ┌─────────────────────────────┐ - ┌────│ BEGINNER CLUSTER │ - │ │ - Getting Started │ - │ │ - Basic Concepts │ - │ │ - First Steps Tutorial │ - │ └─────────────────────────────┘ -┌──────────┐ │ ┌─────────────────────────────┐ -│ PILLAR: │───┼────│ INTERMEDIATE CLUSTER │ -│ Complete │ │ │ - Advanced Techniques │ -│ Guide │ │ │ - Common Patterns │ -└──────────┘ │ │ - Optimization Strategies │ - │ └─────────────────────────────┘ - │ ┌─────────────────────────────┐ - └────│ ADVANCED CLUSTER │ - │ - Expert Strategies │ - │ - Edge Cases │ - │ - Integration Guides │ - └─────────────────────────────┘ -``` - -**Best for:** Topics with clear skill progressions (programming, technical SEO, data analysis). -**Example:** "Technical SEO" pillar with beginner (crawling basics), intermediate (JavaScript rendering), advanced (log file analysis) layers. - -### Pattern 3: Use-Case Cluster (Best for Product/Service Topics) - -``` - ┌──────────────────────┐ - ┌────│ [Topic] for SMBs │ - │ └──────────────────────┘ - │ ┌──────────────────────┐ -┌──────────┐ ├────│ [Topic] for Enterprise│ -│ PILLAR: │───┤ └──────────────────────┘ -│ [Topic] │ │ ┌──────────────────────┐ -│ Guide │ ├────│ [Topic] for Agencies │ -└──────────┘ │ └──────────────────────┘ - │ ┌──────────────────────┐ - ├────│ [Topic] for Ecommerce │ - │ └──────────────────────┘ - │ ┌──────────────────────┐ - └────│ [Topic] for SaaS │ - └──────────────────────┘ -``` - -**Best for:** Products/services with distinct audience segments. -**Example:** "SEO Strategy" pillar with spokes for different business types (ecommerce SEO, SaaS SEO, local SEO, B2B SEO). - -### Pattern 4: Process Cluster (Best for How-To Topics) - -``` -┌──────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ -│ PILLAR: │────→│ Step 1: │────→│ Step 2: │────→│ Step 3: │────→│ Step 4: │ -│ How to │ │ Research │ │ Plan │ │ Execute │ │ Measure │ -│ [Process]│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ -└──────────┘ │ │ │ │ - ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ - │Sub-guide │ │Template │ │Tutorial │ │Dashboard │ - │ 1a │ │ 2a │ │ 3a │ │ Guide │ - └─────────┘ └─────────┘ └─────────┘ └─────────┘ -``` - -**Best for:** Multi-step processes where each step is complex enough for its own article. -**Example:** "Link Building" pillar with sequential steps (prospecting, outreach, content creation, tracking). - ---- - -## Internal Linking Patterns Within Clusters - -### Linking Density Guidelines - -| Cluster Size | Min Links Per Cluster Page | Max Links Per Cluster Page | Pillar Link Density | -|-------------|--------------------------|--------------------------|-------------------| -| 5-8 pages | 2-3 internal links | 5-6 internal links | Link to every cluster page | -| 9-15 pages | 3-4 internal links | 6-8 internal links | Link to every cluster page | -| 16+ pages | 4-5 internal links | 8-10 internal links | Link to top cluster pages, categorize rest | - -### Anchor Text Strategy - -| Link Type | Anchor Text Approach | Example | -|-----------|---------------------|---------| -| Cluster → Pillar | Broad keyword or branded | "our complete guide to keyword research" | -| Pillar → Cluster | Specific keyword for that cluster page | "learn about long-tail keyword strategies" | -| Cluster → Cluster | Contextual, conversational | "this connects to how you assess keyword difficulty" | - -### Linking Anti-Patterns to Avoid - -| Anti-Pattern | Why It's Bad | Fix | -|-------------|-------------|-----| -| Footer-only links to cluster pages | Low link equity, poor UX | Move links into body content | -| Generic anchors ("click here", "read more") | No keyword signal | Use descriptive, keyword-relevant anchors | -| Orphan cluster pages (no inbound links) | Search engines can't discover/value them | Add contextual links from pillar and related clusters | -| Over-linking (20+ internal links per page) | Dilutes link equity, feels spammy | Keep to 5-10 relevant internal links | -| Linking only to pillar (ignoring sibling clusters) | Misses cross-cluster relevance | Link to 2-3 related sibling pages | - ---- - -## Content Calendar Integration - -### Cluster Build Sequence - -The order you publish cluster content matters. Follow this sequence for maximum impact: - -| Phase | What to Publish | Why This Order | -|-------|----------------|---------------| -| **Phase 1: Pillar** | Publish the pillar page first | Establishes the hub; cluster pages need something to link to | -| **Phase 2: Quick-win clusters** | Publish 3-4 lowest-difficulty cluster pages | Build early traffic and indexing momentum | -| **Phase 3: High-value clusters** | Publish highest-volume cluster pages | Leverage pillar authority for competitive terms | -| **Phase 4: Long-tail clusters** | Publish remaining niche subtopics | Fill coverage gaps, capture long-tail traffic | -| **Phase 5: Update cycle** | Refresh pillar with new links; update outdated clusters | Maintain freshness signals | - -### Sample Content Calendar for One Cluster - -| Week | Content Piece | Type | Target Keyword | Word Count | Dependencies | -|------|--------------|------|---------------|-----------|-------------| -| 1 | Complete Guide to [Pillar Topic] | Pillar | [Head keyword] | 3,500-5,000 | None | -| 2 | What is [Subtopic A]? | Cluster | [Long-tail A] | 1,500-2,000 | Pillar published | -| 3 | How to [Subtopic B] | Cluster | [Long-tail B] | 2,000-2,500 | Pillar published | -| 4 | [Subtopic C] vs [Subtopic D] | Cluster | [Long-tail C] | 2,000-2,500 | Pillar published | -| 5 | Best [Subtopic E] Tools | Cluster | [Long-tail E] | 2,500-3,000 | Pillar published | -| 6 | [Subtopic F] for Beginners | Cluster | [Long-tail F] | 1,500-2,000 | Pillar published | -| 7 | [Subtopic G] Checklist | Cluster | [Long-tail G] | 1,000-1,500 | Pillar published | -| 8 | Update Pillar + add all internal links | Update | -- | -- | All clusters published | - -### Publishing Cadence Recommendations - -| Team Size | Cluster Build Time | Recommended Cadence | -|-----------|-------------------|-------------------| -| Solo content creator | 6-8 weeks per cluster | 1 cluster per quarter | -| Small team (2-3 writers) | 3-4 weeks per cluster | 1 cluster per month | -| Content team (4-6 writers) | 2-3 weeks per cluster | 2 clusters per month | -| Large team (7+ writers) | 1-2 weeks per cluster | 1 cluster per week | - ---- - -## Cluster Performance Measurement Framework - -### Key Metrics by Level - -#### Cluster-Level Metrics - -| Metric | What It Measures | Target | How to Track | -|--------|-----------------|--------|-------------| -| Total cluster traffic | Aggregate organic visits to all pages in cluster | Growing month-over-month | Analytics: filter by URL folder/tag | -| Keyword coverage | Number of keywords cluster ranks for | 50+ keywords per mature cluster | SEO tool: filter by cluster URLs | -| Average position | Mean ranking across all cluster keywords | Improving trend toward top 10 | SEO tool: average position report | -| Internal link equity | PageRank flow within cluster | Pillar has highest internal links | Site audit tool: internal link report | -| Cluster completeness | Percentage of planned subtopics published | 100% within planned timeframe | Content calendar tracking | - -#### Page-Level Metrics (Per Cluster Page) - -| Metric | Pillar Target | Cluster Page Target | -|--------|-------------|-------------------| -| Organic traffic | Highest in cluster | Proportional to keyword volume | -| Keywords ranking | 50-200+ | 10-50 | -| Backlinks | Attracts most links | Some organic links | -| Avg. time on page | 4-8 minutes | 2-5 minutes | -| Bounce rate | <60% | <70% | -| Internal CTR | High clicks to cluster pages | Clicks to pillar + sibling pages | - -### Performance Review Cadence - -| Timeframe | What to Review | Action If Underperforming | -|-----------|---------------|--------------------------| -| 2 weeks post-publish | Indexing status, initial impressions | Fix indexing issues, check for crawl errors | -| 1 month post-publish | Early ranking signals, traffic | Optimize titles/metas, add internal links | -| 3 months post-publish | Ranking positions, traffic trends | Content refresh, add missing subtopics, build links | -| 6 months post-publish | Full performance assessment | Major content update or strategic pivot | -| Quarterly (ongoing) | Cluster-level aggregate trends | Identify declining pages, plan refreshes | - -### Cluster Health Scorecard - -Rate each cluster quarterly on these dimensions: - -| Dimension | Score 1 (Poor) | Score 3 (Average) | Score 5 (Excellent) | -|-----------|---------------|-------------------|-------------------| -| Traffic growth | Declining | Flat | Growing 10%+ MoM | -| Keyword coverage | <20 keywords | 20-50 keywords | 50+ keywords | -| Top 10 rankings | 0 keywords in top 10 | 1-5 in top 10 | 5+ in top 10 | -| Content freshness | Not updated in 12+ months | Updated within 6 months | Updated within 3 months | -| Internal linking | Missing links, orphan pages | Basic linking in place | Full cross-linking with relevant anchors | -| Completeness | <50% of subtopics covered | 50-80% covered | 80-100% covered | - -**Cluster Health Score** = Average of all dimension scores - -| Score Range | Health Status | Action | -|------------|--------------|--------| -| 4.0-5.0 | Healthy | Maintain cadence, expand to adjacent clusters | -| 3.0-3.9 | Needs attention | Refresh outdated content, fill subtopic gaps | -| 2.0-2.9 | At risk | Major content update, link building campaign | -| 1.0-1.9 | Failing | Strategic review -- consider merging, rewriting, or retiring | diff --git a/.agents/skills/make-interfaces-feel-better/SKILL.md b/.agents/skills/make-interfaces-feel-better/SKILL.md deleted file mode 100644 index e47b87b369..0000000000 --- a/.agents/skills/make-interfaces-feel-better/SKILL.md +++ /dev/null @@ -1,187 +0,0 @@ ---- -name: make-interfaces-feel-better -description: >- - Design engineering principles for making interfaces feel polished. Use when building UI components, reviewing frontend code, implementing animations, hover states, shadows, borders, typography, icons, micro-interactions, enter/exit animations, or any visual detail work. Supports quick and full review modes. Triggers on UI polish, design details, "make it feel better", "feels off", stagger animations, border radius, optical alignment, font smoothing, tabular numbers, image outlines, box shadows, icons, icon stroke weight, icon states, motion restraint. ---- - -# Details that make interfaces feel better - -Great interfaces rarely come from a single thing. It's usually a collection of small details that compound into a great experience. Apply these principles when building or reviewing UI code. Before suggesting or writing a fix, identify the project's existing styling system and express the change in that system: Tailwind in a Tailwind project, plain CSS in a CSS project, or the established CSS-in-JS approach. Never introduce a second styling system just to apply a polish fix. - -When reviewing, slow the interface down: replay motion at 10% speed in the browser's Animations panel and walk every state: hover, focus, active, loading, empty. What feels off at 10% speed is what's subtly wrong at full speed. - -## Quick Reference - -| Category | When to Use | -| --- | --- | -| [Typography](typography.md) | Text wrapping, font smoothing, tabular numbers | -| [Surfaces](surfaces.md) | Border radius, optical alignment, shadows, image outlines, hit areas | -| [Animations](animations.md) | Interruptible animations, enter/exit transitions, icon animations, scale on press, motion restraint | -| [Icons](icons.md) | Icon stroke weight, states via `currentColor`, outline vs fill, sizing, RTL flipping | -| [Performance](performance.md) | Transition specificity, `will-change` usage | - -## Core Principles - -### 1. Concentric Border Radius - -Outer radius = inner radius + padding. Mismatched radii on nested elements is the most common thing that makes interfaces feel off. - -### 2. Optical Over Geometric Alignment - -When geometric centering looks off, align optically. Buttons with icons, play triangles, and asymmetric icons all need manual adjustment. - -### 3. Shadows for Elevation, Borders for Structure - -For buttons, cards, and containers whose border exists only to create depth, prefer layered transparent `box-shadow` values. Keep borders that communicate structure or state: dividers, layout separators, and selected or focus states. - -### 4. Interruptible Animations - -Use CSS transitions for interactive state changes — they can be interrupted mid-animation. Reserve keyframes for staged sequences that run once. - -### 5. Split and Stagger Enter Animations - -For an infrequent staged entrance where sequence helps communicate hierarchy, break content into semantic chunks and stagger them by ~100ms instead of animating one container. Do not stagger routine, high-frequency interactions. - -### 6. Subtle Exit Animations - -Use a small fixed `translateY` instead of full height. Exits should be softer than enters. Use `ease-out` for both enter and exit transitions. - -### 7. Contextual Icon Animations - -Animate icons with `opacity`, `scale`, and `blur` instead of toggling visibility. Use exactly these values: scale from `0.25` to `1`, opacity from `0` to `1`, blur from `4px` to `0px`. If the project has `motion` or `framer-motion` in `package.json`, match that package's import path (or the established nearby imports when both exist) and use `transition: { type: "spring", duration: 0.3, bounce: 0 }` — bounce must always be `0`. If no motion library is installed, keep both icons in the DOM (one absolute-positioned) and cross-fade with CSS transitions using `cubic-bezier(0.2, 0, 0, 1)` — this gives both enter and exit animations without any dependency. - -### 8. Font Smoothing - -Apply `-webkit-font-smoothing: antialiased` to the root layout on macOS for crisper text. - -### 9. Tabular Numbers - -Use `font-variant-numeric: tabular-nums` for any dynamically updating numbers to prevent layout shift. - -### 10. Text Wrapping - -Use `text-wrap: balance` on headings. Use `text-wrap: pretty` for body text to avoid orphans. - -### 11. Image Outlines - -Add a subtle `1px` outline with low opacity to images for consistent depth. The color must be pure black in light mode (`oklch(0 0 0 / 0.1)`) and pure white in dark mode (`oklch(1 0 0 / 0.1)`), never a near-black like slate, zinc, or any tinted neutral. A tinted outline picks up the surface color underneath it and reads as dirt on the image edge. - -### 12. Scale on Press - -A subtle `scale(0.96)` on click gives buttons tactile feedback. Always use `0.96`. Never use a value smaller than `0.95` — anything below feels exaggerated. Add a `static` prop to disable it when motion would be distracting. - -### 13. Skip Animation on Page Load - -Use `initial={false}` on `AnimatePresence` to prevent enter animations on first render. Verify it doesn't break intentional entrance animations. - -### 14. Never Use `transition: all` - -Always specify exact properties: `transition-property: scale, opacity`. Tailwind's `transition-transform` covers `transform, translate, scale, rotate`. - -### 15. Use `will-change` Sparingly - -Only for `transform`, `opacity`, `filter` — properties the GPU can composite. Never use `will-change: all`. Only add when you notice first-frame stutter. - -### 16. Minimum Hit Area - -Interactive elements should prefer a 44×44px hit area for touch or mobile contexts. In dense desktop interfaces, use at least 40×40px. Extend with a pseudo-element if the visible element is smaller. Never let hit areas of two elements overlap. - -### 17. Match Icon Stroke to Text Weight - -An icon next to text carries the text's optical weight: `1.5px` stroke beside regular (400) text, `2px` beside semibold (600). One stroke weight per icon set; never mix libraries on one surface. - -### 18. One SVG, Recolored per State - -Icons use `currentColor` and get their states (hover, selected, disabled) from CSS color and opacity, never from separate assets. Outline variant is the default; fill variant marks the active state. - -### 19. Motion Restraint - -No custom animation on high-frequency interactions: the attention cost repeats on every trigger. Motion is never the only feedback channel; every animated state change also needs a static cue such as color, icon, or label. - -## Common Mistakes - -| Mistake | Fix | -| --- | --- | -| Same border radius on parent and child | Calculate `outerRadius = innerRadius + padding` | -| Icons look off-center | Adjust optically with padding or fix SVG directly | -| Border used only to fake elevation | Use layered `box-shadow` with transparency; keep structural and state borders | -| Jarring staged entrance or contextual exit | Stagger infrequent entrances and keep context-preserving exits subtle | -| Numbers cause layout shift | Apply `tabular-nums` | -| Heavy text on macOS | Apply `antialiased` to root | -| Animation plays on page load | Add `initial={false}` to `AnimatePresence` | -| `transition: all` on elements | Specify exact properties | -| First-frame animation stutter | Add `will-change: transform` (sparingly) | -| Tiny hit areas on small controls | Extend with a pseudo-element to 44×44px for touch/mobile, or at least 40×40px in dense desktop UI | -| Hairline icon beside bold text | Match the stroke width to the text weight | -| Separate icon assets per state | One `currentColor` SVG, states via CSS | -| Filled icons everywhere | Outline as default, fill only for the active state | -| Entrance animation on every hover or keystroke | Instant feedback or ≤150ms opacity/color transition | - -## Review Output Format - -Use `full` when no review mode is supplied. - -| Mode | Coverage | Finding cap | -| --- | --- | --- | -| `quick` | Primary user path and highest-traffic states; report only `HIGH` and `MEDIUM` issues | 5 | -| `full` | Entire requested scope across typography, surfaces, animations, icons, and performance | 15 | - -### Scope and Coverage - -State the mode, exact scope, framework, styling conventions, and any review boundary. Show what was actually inspected: - -| Category | Evidence inspected | Result | -| --- | --- | --- | -| Typography | Files, components, states, or checks | Findings count, `Clear`, or `Not reviewed` with a reason | - -Include all five Quick Reference categories. Never imply an uninspected surface was reviewed. - -### Findings - -Group findings by principle. Use a markdown table with **Severity**, **Location**, **Before**, **After**, and **Why** columns. Include every change made or proposed, not a subset. Never use separate "Before:" / "After:" lines. - -- **Severity**: `HIGH` makes an interaction inaccessible, misleading, unreadable, or repeatedly disruptive; `MEDIUM` creates a noticeable usability or consistency problem; `LOW` is isolated polish and appears only in `full` mode. -- **Location**: cite `path/to/file:line`. If the artifact has no source files, cite the exact screen and component instead. -- **Before / After**: show the current implementation and an actionable replacement. -- **Why**: name the violated principle and explain its user impact. - -Consolidate a repeated systemic issue into one row and list every affected location. Omit principles with no findings and never pad the report to reach the cap. - -### Example - -#### Concentric border radius -| Severity | Location | Before | After | Why | -| --- | --- | --- | --- | --- | -| LOW | `src/Card.tsx:28` | `rounded-xl` on card + `rounded-xl` on inner button (`p-2`) | `rounded-2xl` on card (`8 + 8 = 16`), `rounded-lg` on inner button | Nested corners should be concentric | -| LOW | `src/card.css:11` | `border-radius: 16px` on both nested surfaces | Outer `24px`, inner `16px` with `8px` padding | Equal nested radii make the inner surface look pinched | - -#### Tabular numbers -| Severity | Location | Before | After | Why | -| --- | --- | --- | --- | --- | -| MEDIUM | `src/Counter.tsx:17` | `{count}` | `{count}` | Proportional digits cause changing values to shift | -| LOW | `src/timer.css:8` | Default numerals on a timer | Add `font-variant-numeric: tabular-nums` to the timer | Equal-width digits keep the timer stable | - -#### Scale on press -| Severity | Location | Before | After | Why | -| --- | --- | --- | --- | --- | -| LOW | `src/Button.tsx:19` | ` - - - ); -} -``` - -### CSS-Only Stagger - -```css -.stagger-item { - opacity: 0; - transform: translateY(12px); - filter: blur(4px); - animation: fadeInUp 400ms ease-out forwards; -} - -.stagger-item:nth-child(1) { animation-delay: 0ms; } -.stagger-item:nth-child(2) { animation-delay: 100ms; } -.stagger-item:nth-child(3) { animation-delay: 200ms; } - -@keyframes fadeInUp { - to { - opacity: 1; - transform: translateY(0); - filter: blur(0); - } -} -``` - -## Exit Animations - -Exit animations should be softer and less attention-grabbing than enter animations. The user's focus is moving to the next thing — don't fight for attention. - -### Subtle Exit (Recommended) - -```tsx -// Small fixed translateY — indicates direction without drama - - {content} - -``` - -### Full Exit (When Context Matters) - -```tsx -// Slide fully out — use when spatial context is important -// (e.g., a card returning to a list, a drawer closing) - - {content} - -``` - -### Good vs. Bad - -```css -/* Good — subtle exit */ -.item-exit { - opacity: 0; - transform: translateY(-12px); - transition: opacity 150ms ease-out, transform 150ms ease-out; -} - -/* Bad — dramatic exit that steals focus */ -.item-exit { - opacity: 0; - transform: translateY(-100%) scale(0.5); - transition: all 400ms ease-out; -} - -/* Sometimes correct — remove immediately when motion adds no context */ -.item-exit { - display: none; -} -``` - -**Key points:** -- Use a small fixed `translateY` (e.g., `-12px`) instead of the full container height -- Keep some directional movement to indicate where the element went -- Exit duration should be shorter than enter duration (150ms vs 300ms) -- Use a subtle exit when it preserves spatial context. Remove immediately when motion adds no information, the interaction repeats frequently, or reduced motion is requested. - -## Contextual Icon Animations - -When icons appear or disappear contextually (on hover, on state change), animate them with `opacity`, `scale`, and `blur` rather than just toggling visibility. - -### Motion Example - -This example uses the `motion` package. If the project instead has `framer-motion`, import the same APIs from `"framer-motion"`; never mix an installed package with the other package's import path. - -```tsx -import { AnimatePresence, motion } from "motion/react"; - -function IconButton({ isActive, icon: Icon }) { - return ( - - ); -} -``` - -### CSS Transition Approach (No Motion) - -If the project doesn't use Motion (Framer Motion), keep both icons in the DOM and cross-fade them with CSS transitions. Because neither icon unmounts, both enter and exit animate smoothly. - -The trick: one icon is absolutely positioned on top of the other. Toggling state cross-fades them — the entering icon scales up from `0.25` while the exiting icon scales down to `0.25`, both with opacity and blur. - -```tsx -function IconButton({ isActive, ActiveIcon, InactiveIcon }) { - return ( - - ); -} -``` - -The non-absolute icon (InactiveIcon) defines the layout size. The absolute icon (ActiveIcon) overlays it without affecting flow. - -### Choosing Between Motion and CSS - -| | Motion (Framer Motion) | CSS transitions (both icons in DOM) | -| --- | --- | --- | -| **Enter animation** | Yes | Yes | -| **Exit animation** | Yes (via `AnimatePresence`) | Yes (cross-fade — icon never unmounts) | -| **Spring physics** | Yes | No — use `cubic-bezier(0.2, 0, 0, 1)` as approximation | -| **When to use** | Project already uses `motion` or `framer-motion` | No motion dependency, or keeping bundle small | - -**Rule:** Check the project's `package.json`. Import from `"motion/react"` when `motion` is installed, or from `"framer-motion"` when `framer-motion` is installed. If both exist, follow the imports already used by the component or its nearest peers. If neither is present, use the CSS cross-fade pattern — don't add a dependency just for icon transitions. - -### When to Animate Icons - -| Animate | Don't animate | -| --- | --- | -| Icons that appear on hover (action buttons) | Static navigation icons | -| State change icons (play → pause, like → liked) | Decorative icons | -| Icons in contextual toolbars | Icons that are always visible | -| Loading/success state indicators | Icon labels (text next to icon) | - -**Important:** Always use exactly these values for contextual icon animations — do not deviate: -- `scale`: `0.25` → `1` (never use `0.5` or `0.6`) -- `opacity`: `0` → `1` -- `filter`: `"blur(4px)"` → `"blur(0px)"` -- `transition`: `{ type: "spring", duration: 0.3, bounce: 0 }` — **bounce must always be `0`**, never `0.1` or any other value - -## Scale on Press - -A subtle scale-down on click gives buttons tactile feedback. Always use `scale(0.96)`. Never use a value smaller than `0.95` — anything below feels exaggerated. Use CSS transitions for interruptibility — if the user releases mid-press, it should smoothly return. - -Not every button needs this. Add a `static` prop to your button component that disables the scale effect when the motion would be distracting. - -### CSS Example - -```css -.button { - transition-property: scale; - transition-duration: 150ms; - transition-timing-function: ease-out; -} - -.button:active { - scale: 0.96; -} -``` - -### Tailwind Example - -```tsx - -``` - -### Motion Example - -```tsx - - Click me - -``` - -### Static Prop Pattern - -Extract the scale class into a variable and conditionally apply it based on a `static` prop: - -```tsx -const tapScale = "active:not-disabled:scale-[0.96]"; - -function Button({ static: isStatic, className, children, ...props }) { - return ( - - ); -} - -// Usage - {/* scales on press */} - {/* no scale */} -``` - -## Skip Animation on Page Load - -Use `initial={false}` on `AnimatePresence` to prevent enter animations from firing on first render. Elements that are already in their default state shouldn't animate in on page load — only on subsequent state changes. - -### When It Works - -```tsx -// Good — icon doesn't animate in on mount, only on state change - - - - - -``` - -Works well for: icon swaps, toggles, tabs, segmented controls — anything that has a default state on page load. - -### When It Breaks - -Don't use `initial={false}` when the component relies on its `initial` prop to set up a first-time enter animation, like a staggered page hero or a loading state. In those cases, removing the initial animation skips the entire entrance. - -```tsx -// Bad — initial={false} would skip the staggered page enter entirely - - - ... - - -``` - -Verify the component still looks right on a full page refresh before applying this. - -## Motion Restraint - -Motion is a budget, not a garnish: - -- **No custom animation on high-frequency interactions.** Repeated interactions get instant feedback or a minimal `opacity` or `background-color` transition at ≤150ms. -- **Motion is never the only feedback channel.** Every animated state change also needs a static cue such as color, icon, or label. -- **Brief and precise beats prominent.** If a shorter, smaller animation communicates the same thing, use it. -- **Honor reduced-motion preferences.** Preserve the static cue and remove unnecessary movement. - -```css -/* Good: high-frequency hover gets a minimal transition */ -.row:hover { - background-color: var(--surface-hover); - transition: background-color 100ms ease-out; -} - -/* Bad: every hover replays a full entrance */ -.row:hover .row-icon { - animation: bounceIn 500ms; -} -``` diff --git a/.agents/skills/make-interfaces-feel-better/icons.md b/.agents/skills/make-interfaces-feel-better/icons.md deleted file mode 100644 index 6bdc0078c0..0000000000 --- a/.agents/skills/make-interfaces-feel-better/icons.md +++ /dev/null @@ -1,63 +0,0 @@ -# Icons - -Icon weight, states, sizing, and direction: the details that make icons sit naturally in an interface. - -## Match Icon Stroke to Text Weight - -An icon next to text should carry the same optical weight as the text. - -| Adjacent text | Icon stroke width (24px grid) | -| --- | --- | -| Regular (400), 14–16px | `1.5px` | -| Medium/Semibold (500–600) | `2px` | -| Bold (700), or emphasized standalone | `2.5px` | - -Use one stroke weight per icon set on a surface. Size inline icons relative to the text's cap height, typically `1em`–`1.25em`. - -## One SVG, Recolored per State - -Use one SVG drawn with `currentColor`; let CSS drive hover, selected, and disabled states. Strip hardcoded `fill` and `stroke` colors when importing icons. - -```html -… -``` - -```css -.icon-button { color: oklch(0.552 0.016 285.938); } -.icon-button:hover { color: oklch(0.21 0.006 285.885); } -.icon-button[aria-pressed="true"] { color: oklch(0.623 0.188 259.815); } -.icon-button:disabled { opacity: 0.4; } -``` - -## Outline Default, Fill Active - -| Variant | Use for | -| --- | --- | -| Outline | Default state: toolbars, list rows, inline with text | -| Fill | Selected or active state: active tab, toggled bookmark, liked heart | - -The swap between variants is a contextual icon animation; use the exact cross-fade values in [animations.md](animations.md). - -## Design at Render Size - -- Test every icon at the smallest size it will render, often `16px`. -- Prefer simplified glyphs for small contexts over scaled-down detailed artwork. -- Use the icon set's native grid sizes (`16`, `20`, `24`) rather than arbitrary fractional scales. -- Use SVG rather than raster assets. - -## Icons in RTL - -| Flip | Don't flip | -| --- | --- | -| Back/forward arrows, navigation chevrons | Logos and brand marks | -| Text alignment, lists, indent | Checkmarks | -| Directional send glyphs | Clocks, cups, pencils | -| Speaker waves tied to reading direction | Media playback controls | - -```css -[dir="rtl"] .icon-directional { - scale: -1 1; -} -``` - -Analyze composite icons part by part: an overlay may keep its position even when the base glyph flips. Give every icon-only control an accessible name and mark purely decorative icons hidden from assistive technology. diff --git a/.agents/skills/make-interfaces-feel-better/performance.md b/.agents/skills/make-interfaces-feel-better/performance.md deleted file mode 100644 index c12257a2ba..0000000000 --- a/.agents/skills/make-interfaces-feel-better/performance.md +++ /dev/null @@ -1,88 +0,0 @@ -# Performance - -Transition specificity and GPU compositing hints. - -## Transition Only What Changes - -Never use `transition: all` or Tailwind's `transition-all`. Always specify the exact properties that change. Tailwind's bare `transition` maps to a curated default list of colors, opacity, shadow, and transforms, not to `all`; still prefer naming exactly what changes. - -### Why - -- `transition: all` forces the browser to watch every property for changes -- Causes unexpected transitions on properties you didn't intend to animate (colors, padding, shadows) -- Prevents browser optimizations - -### CSS Example - -```css -/* Good — only transition what changes */ -.button { - transition-property: scale, background-color; - transition-duration: 150ms; - transition-timing-function: ease-out; -} - -/* Bad — transition everything */ -.button { - transition: all 150ms ease-out; -} -``` - -### Tailwind - -```tsx -// Good — explicit properties - -``` - -### Play Button Triangles - -Play icons are triangular and their geometric center is not their visual center. Shift slightly right: - -```css -/* Good — optically centered */ -.play-button svg { - margin-left: 2px; /* shift right to account for triangle shape */ -} - -/* Bad — geometrically centered but looks off */ -.play-button svg { - /* no adjustment */ -} -``` - -### Asymmetric Icons (Stars, Arrows, Carets) - -Some icons have uneven visual weight. The best fix is adjusting the SVG directly so no extra margin/padding is needed in the component code. - -```tsx -// Best — fix in the SVG itself -// Adjust the viewBox or path to visually center the icon - -// Fallback — adjust with margin - - - -``` - -## Shadows Instead of Borders - -For **buttons, cards, and containers** that use a border for depth or elevation, prefer replacing it with a subtle `box-shadow`. Shadows adapt to any background since they use transparency; solid borders don't. This also helps when using images or multiple colors as backgrounds — solid border colors don't work well on backgrounds other than the ones they were designed for. - -**Do not apply this to dividers** (`border-b`, `border-t`, side borders) or any border whose purpose is layout separation rather than element depth. Those should stay as borders. - -### Shadow as Border (Light Mode) - -The shadow is comprised of three layers. The first acts as a 1px border ring, the second adds subtle lift, and the third provides ambient depth: - -```css -:root { - --shadow-border: - 0px 0px 0px 1px oklch(0 0 0 / 0.06), - 0px 1px 2px -1px oklch(0 0 0 / 0.06), - 0px 2px 4px 0px oklch(0 0 0 / 0.04); - --shadow-border-hover: - 0px 0px 0px 1px oklch(0 0 0 / 0.08), - 0px 1px 2px -1px oklch(0 0 0 / 0.08), - 0px 2px 4px 0px oklch(0 0 0 / 0.06); -} -``` - -### Shadow as Border (Dark Mode) - -In dark mode, simplify to a single white ring — layered depth shadows aren't visible on dark backgrounds: - -```css -/* Dark mode — adapt to whatever setup the project uses - (prefers-color-scheme, class, data attribute, etc.) */ ---shadow-border: 0 0 0 1px oklch(1 0 0 / 0.08); ---shadow-border-hover: 0 0 0 1px oklch(1 0 0 / 0.13); -``` - -### Usage with Hover Transition - -Apply the variable and add `transition-[box-shadow]` for a smooth hover: - -```css -.card { - box-shadow: var(--shadow-border); - transition-property: box-shadow; - transition-duration: 150ms; - transition-timing-function: ease-out; -} - -.card:hover { - box-shadow: var(--shadow-border-hover); -} -``` - -### When to Use Shadows vs. Borders - -| Use shadows | Use borders | -| --- | --- | -| Cards, containers with depth | Dividers between list items | -| Buttons with bordered styles | Table cell boundaries | -| Elevated elements (dropdowns, modals) | Form input outlines (for accessibility) | -| Elements on varied backgrounds | Hairline separators in dense UI | -| Hover/focus states for lift effect | | - -## Image Outlines - -Add a subtle `1px` outline with low opacity to images. This creates consistent depth, especially in design systems where other elements use borders or shadows. - -### Color rules (non-negotiable) - -- **Light mode**: pure black, `oklch(0 0 0 / 0.1)`. -- **Dark mode**: pure white, `oklch(1 0 0 / 0.1)`. -- Never use a near-black or near-white from the project palette (e.g. slate-900, zinc-900, `#0a0a0a`, `#111827`, `#f5f5f7`). Tinted outlines pick up the surrounding surface color and read as dirt on the image edge. -- Never match the outline to the project's accent or ink color. The outline is a neutral separator, not a themed element. - -### Light Mode - -```css -img { - outline: 1px solid oklch(0 0 0 / 0.1); - outline-offset: -1px; /* inset so it doesn't add to layout */ -} -``` - -### Dark Mode - -```css -img { - outline: 1px solid oklch(1 0 0 / 0.1); - outline-offset: -1px; -} -``` - -### Tailwind with Dark Mode - -```tsx -{alt} -``` - -Use `outline-black/10` and `outline-white/10` specifically — not `outline-slate-*`, `outline-zinc-*`, `outline-neutral-*`, or any tinted scale. - -**Why outline instead of border?** `outline` doesn't affect layout (no added width/height), and `outline-offset: -1px` keeps it inset so images stay their intended size. - -## Minimum Hit Area - -Interactive elements should prefer a 44×44px hit area for touch or mobile contexts. In dense desktop interfaces, use at least 40×40px. If the visible element is smaller (e.g., a 20×20 checkbox), extend the hit area with a pseudo-element. - -### CSS Example - -```css -/* Small checkbox with expanded 44px hit area */ -.checkbox { - position: relative; - width: 20px; - height: 20px; -} - -.checkbox::after { - content: ""; - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - width: 44px; - height: 44px; -} -``` - -### Tailwind Example - -```tsx - -``` - -### Collision Rule - -If the extended hit area overlaps another interactive element, shrink the pseudo-element — but make it as large as possible without colliding. Two interactive elements should never have overlapping hit areas. diff --git a/.agents/skills/make-interfaces-feel-better/typography.md b/.agents/skills/make-interfaces-feel-better/typography.md deleted file mode 100644 index a950535942..0000000000 --- a/.agents/skills/make-interfaces-feel-better/typography.md +++ /dev/null @@ -1,157 +0,0 @@ -# Typography - -Typography rendering details that make interfaces feel better. - -## Text Wrapping - -### text-wrap: balance - -Distributes text evenly across lines, preventing orphaned words on headings and short text blocks. **Only works on blocks of 6 lines or fewer** (Chromium) or 10 lines or fewer (Firefox) — the balancing algorithm is computationally expensive, so browsers limit it to short text. - -```css -/* Good — even line lengths on short text */ -h1, h2, h3 { - text-wrap: balance; -} -``` - -```css -/* Bad — default wrapping leaves orphans */ -h1 { - /* no text-wrap rule → "Read our - blog" instead of balanced lines */ -} -``` - -```css -/* Bad — balance on long paragraphs (silently ignored, wastes intent) */ -.article-body p { - text-wrap: balance; -} -``` - -**Tailwind:** `text-balance` - -### text-wrap: pretty - -Prevents orphaned words (a single word dangling on the last line) by adjusting line breaks throughout the paragraph. Unlike `balance`, it doesn't try to equalize line lengths — it just ensures the last line isn't embarrassingly short. Works on text of any length with no line-count limit. - -This should be your **default for short-to-medium text** — paragraphs, descriptions, captions, list items, card text. For very long text (10+ lines), skip both `pretty` and `balance` — the browser's default wrapping is fine and you avoid unnecessary layout cost. - -```css -/* Good — descriptions, captions, short paragraphs */ -p, li, figcaption, blockquote { - text-wrap: pretty; -} -``` - -```tsx -// Tailwind -

- A short paragraph that won't leave an orphan on the last line. -

-``` - -**Tailwind:** `text-pretty` - -### When to Use Which - -| Scenario | Use | -| --- | --- | -| Headings, titles where even distribution matters | `text-wrap: balance` | -| Short-to-medium text — paragraphs, descriptions, captions, UI text | `text-wrap: pretty` | -| Long text (10+ lines), code blocks, pre-formatted text | Neither — leave default | - -## Font Smoothing (macOS) - -On macOS, text renders heavier than intended by default. Apply antialiased smoothing to the root layout so all text renders crisper and thinner. - -```css -/* CSS */ -html { - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} -``` - -```tsx -// Tailwind — apply to root layout - -``` - -### Good vs. Bad - -```css -/* Good — applied once at the root */ -html { - -webkit-font-smoothing: antialiased; -} - -/* Bad — applied per-element, inconsistent */ -.heading { - -webkit-font-smoothing: antialiased; -} -.body { - /* no smoothing → heavier than heading */ -} -``` - -**Note:** This only affects macOS rendering. Other platforms ignore these properties, so it's safe to apply universally. - -## Font Family Scope - -This skill does not require a specific font family. Do not introduce a paid or proprietary typeface just to satisfy the polish checklist. - -Use the product's existing type system unless the task explicitly asks for a type change. If the design calls for a system-native macOS feel, use the system font stack. If the design calls for a commercial face such as Helvetica Now, treat it as an optional brand decision and keep a practical fallback stack. - -```css -/* System-native macOS/iOS feel */ -html { - font-family: system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; -} -``` - -```css -/* Commercial brand face with safe fallbacks */ -html { - font-family: "Helvetica Now", "Helvetica Neue", Arial, sans-serif; -} -``` - -**Rule:** font smoothing, text wrapping, and tabular numbers are rendering details. They do not override the project's chosen font family. - -## Tabular Numbers - -When numbers update dynamically (counters, prices, timers, table columns), use tabular-nums to make all digits equal width. This prevents layout shift as values change. - -```css -/* CSS */ -.counter { - font-variant-numeric: tabular-nums; -} -``` - -```tsx -// Tailwind -{count} -``` - -### When to Use - -| Use tabular-nums | Don't use tabular-nums | -| --- | --- | -| Counters and timers | Static display numbers | -| Prices that update | Decorative large numbers | -| Table columns with numbers | Phone numbers, zip codes | -| Animated number transitions | Version numbers (v2.1.0) | -| Scoreboards, dashboards | | - -### Caveat - -Some fonts (like Inter) change the visual appearance of numerals with this property — specifically, the digit `1` becomes wider and centered. This is expected behavior and usually desirable for alignment, but verify it looks right in your specific font. - -```css -/* With Inter font: - Default: 1234 → proportional, "1" is narrow - Tabular: 1234 → all digits equal width, "1" centered */ -``` diff --git a/.agents/skills/memory-management/SKILL.md b/.agents/skills/memory-management/SKILL.md deleted file mode 100644 index c9ec3af3b5..0000000000 --- a/.agents/skills/memory-management/SKILL.md +++ /dev/null @@ -1,342 +0,0 @@ ---- -name: memory-management -description: 'Persist SEO/GEO campaign context across Claude sessions with automatic hot-list, active work, and archive tiers. 项目记忆/跨会话' -version: "7.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when reviewing, archiving, or cleaning up campaign memory. Also when the user asks to check saved findings, manage hot cache, or archive old data." -argument-hint: "[review|archive|cleanup]" -metadata: - author: aaron-he-zhu - version: "7.0.0" - geo-relevance: "low" - tags: - - seo - - geo - - project-memory - - context-management - - campaign-tracking - - session-context - - hot-cache - - 项目记忆 - - プロジェクト記憶 - - 프로젝트메모리 - - memoria-proyecto - triggers: - # EN-formal - - "remember project context" - - "save SEO data" - - "track campaign progress" - - "store keyword data" - - "manage project memory" - - "project context" - - "refresh wiki index" - - "build wiki index" - - "wiki lint" - # EN-casual - - "remember this for next time" - - "save my keyword data" - - "keep track of this campaign" - - "what did we decide last time" - - "what do we know so far" - - "project status" - # EN-question - - "how to save project progress" - # ZH-pro - - "项目记忆管理" - - "SEO数据保存" - - "跨会话记忆" - - "刷新wiki索引" - - "项目状况" - # ZH-casual - - "保存进度" - - "上次说了什么" - - "记住这个" - # JA - - "プロジェクト記憶" - - "SEOデータ保存" - # KO - - "프로젝트 메모리" - - "데이터 저장" - # ES - - "memoria del proyecto" - - "guardar progreso" - # PT - - "memória do projeto" ---- - -# Memory Management - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This cross-cutting skill is part of the protocol layer and follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - -This skill implements a three-tier memory system (HOT/WARM/COLD) for SEO and GEO projects. HOT memory (80 lines max) loads automatically every session via the SessionStart hook. WARM memory loads on demand per skill. COLD memory is archived data queried only when explicitly requested. The skill manages the full lifecycle: capture, promote, demote, and archive. - -**System role**: Campaign Memory Loop. It defines how project context is captured, promoted, archived, and handed off across sessions. It is the sole executor of WARM-to-COLD archival and the aggregator for cross-skill project status queries. - -## When This Must Trigger - -Use this whenever project state should survive the current session — even if the user doesn't use memory terminology: - -- User says "remember this", "save this", "keep track of this" -- User asks "what did we decide", "what do we know", "project status" -- Setting up memory structure for a new SEO project -- After completing audits, ranking checks, or performance reports (Stop hook reminds automatically) -- When project context needs updating (new keywords, competitors, priorities) -- When you need to look up historical data or project-specific terminology -- After 30+ days of work to clean up and archive stale data -- When open-loops.md has items older than 7 days (SessionStart hook reminds automatically) - -## What This Skill Does - -1. **HOT Cache Management**: Maintains `memory/hot-cache.md` (80 lines max) — loaded automatically every session by SessionStart hook -2. **WARM Storage**: Organizes dated findings in `memory/` subdirectories — loaded on demand by relevant skills -3. **COLD Archive**: Moves stale data (90+ days unreferenced) to `memory/archive/` with date prefix -4. **Promotion**: Elevates frequently-referenced findings from WARM to HOT (3+ refs in 7 days, or 2+ skill refs) -5. **Demotion**: Moves unreferenced HOT items to WARM (30 days), WARM to COLD (90 days) -6. **Cross-Skill Aggregation**: When user asks "what do we know", aggregates from all `memory/` subdirectories -7. **Open Loop Tracking**: Maintains `memory/open-loops.md`, reminds user of stale items via SessionStart hook -8. **Wiki Index Maintenance**: Compiles `memory/wiki/index.md` — a structured, auto-refreshed index of all WARM files with precise fields (score, 健康度, status, next_action, mtime) and best-effort summaries. Supports project isolation via `memory/wiki//index.md`. Auto-refreshed on PostToolUse; user confirmation not required (index is a fully rebuildable derived artifact). Delete `memory/wiki/` at any time to revert to pre-wiki behavior. -9. **Wiki Compiled Pages** (Phase 2): Generates interlinked entity, keyword, and topic pages from WARM files with source hash tracking, contradiction detection, and confidence-labeled reconciliation. Requires user confirmation before writing. -10. **Wiki Lint** (Phase 2): Detects contradictions, orphan pages, stale claims, missing pages, and source hash mismatches across wiki and WARM files via `/seo:wiki-lint`. -11. **WARM Retirement Preview** (Phase 3): `wiki-lint --retire-preview` lists WARM files fully covered by wiki compiled pages as retirement candidates. Actual archival to COLD requires explicit user confirmation. - -## Quick Start - -Start with one of these prompts. Finish with a hot-cache update plan and a handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Initialize Wiki Index - -``` -Refresh wiki index -``` - -``` -Build wiki index for [project name] -``` - -Generates `memory/wiki/index.md` from existing WARM files. Required once to enable wiki features; subsequent refreshes happen automatically. - -### Initialize Memory Structure - -``` -Set up SEO memory for [project name] -``` - -``` -Initialize memory structure for a new [industry] website optimization project -``` - -### Update After Analysis - -``` -Update memory after ranking check for [keyword group] -``` - -``` -Refresh hot cache with latest competitor analysis findings -``` - -### Query Stored Context - -``` -What are our hero keywords? -``` - -``` -Show me the last ranking update date for [keyword category] -``` - -``` -Look up our primary competitors and their domain authority -``` - -### Promotion and Demotion - -``` -Promote [keyword] to hot cache -``` - -``` -Archive stale data that hasn't been referenced in 30+ days -``` - -### Glossary Management - -``` -Add [term] to project glossary: [definition] -``` - -``` -What does [internal jargon] mean in this project? -``` - -## Skill Contract - -**Expected output**: a memory update plan, hot-cache changes, and a short handoff summary. - -- **Reads**: current campaign facts, new findings from other skills, approved decisions, and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). -- **Writes**: updates to `memory/hot-cache.md`, `memory/open-loops.md`, `memory/decisions.md`, and related `memory/` folders. Manages WARM-to-COLD archival in `memory/archive/`. Compiles `memory/wiki/index.md` (auto-refreshed) and wiki compiled pages (user-confirmed). -- **Promotes**: durable strategy, blockers, terminology, entity candidates, and major deltas. Applies temperature lifecycle rules: promote to HOT on high reference frequency, demote on staleness. -- **Next handoff**: use the `Next Best Skill` below when the project memory baseline is ready for active work. - -### Temperature Lifecycle Rules - -> See [references/promotion-demotion-rules.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/references/promotion-demotion-rules.md) for the full promotion/demotion table and action procedures. - -### Hook Integration - -This skill's behavior is reinforced by the library's prompt-based hooks: -- **SessionStart**: loads `memory/hot-cache.md`, reminds of stale open loops; loads `memory/wiki//index.md` (or global `index.md`) if it exists; provides light-user guidance based on Quick Status when `next_action` items are available -- **PostToolUse**: after any WARM file write, silently refreshes `memory/wiki/index.md` (Phase 1); prompts to update compiled pages (Phase 2) -- **Stop**: prompts to save session findings, auto-saves veto issues to hot-cache; appends changelog entry to index.md bottom - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~SEO tool + ~~analytics + ~~search console connected:** -Automatically populate memory from historical data: keyword rankings over time, competitor domain authority changes, traffic metrics, conversion data, backlink profile evolution. The skill will fetch current rankings, alert on significant changes, and update both hot cache and cold storage. - -**With manual data only:** -Ask the user to provide: -1. Current target keywords with priority levels -2. Primary competitors (3-5 domains) -3. Key performance metrics and last update date -4. Active campaigns and their status -5. Any project-specific terminology or abbreviations - -Proceed with memory structure creation using provided data. Note in CLAUDE.md which data requires manual updates vs. automated refresh. - -## Instructions - -When a user requests SEO memory management: - -### 1. Initialize Memory Structure - -For new projects, create the directory structure defined in the [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). Key directories: `memory/` (decisions, open-loops, glossary, entities, research, content, audits, monitoring) plus `memory/wiki/` (auto-managed compiled index with optional per-project subdirectories). - -> **Templates**: [hot-cache-template.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/references/hot-cache-template.md) · [glossary-template.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/references/glossary-template.md) · [Wiki spec](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/proposal-wiki-layer-v3.md) - -### 2. Context Lookup Flow - -When a user references something unclear, follow this lookup sequence: - -**Step 1: Check CLAUDE.md (Hot Cache)** -- Is it in active keywords? -- Is it in primary competitors? -- Is it in current priorities or campaigns? - -**Step 2: Check Wiki Index** (`memory/wiki/index.md` or project-level) — locate relevant WARM files - -**Step 3: Check memory/glossary.md** -- Is it defined as project terminology? -- Is it a custom segment or shorthand? - -**Step 4: Check Cold Storage** -- Search memory/research/keywords/ for historical keyword context -- Search memory/research/competitors/ for past analyses -- Search memory/monitoring/reports/ for archived mentions - -**Step 5: Ask User** -- If not found in any layer, ask for clarification -- Log the new term in glossary if it's project-specific - -Example lookup: - -```markdown -User: "Update rankings for our hero KWs" - -Step 1: Check CLAUDE.md → Found "Hero Keywords (Priority 1)" section -Step 2: Extract keyword list from hot cache -Step 3: Execute ranking check -Step 4: Update both CLAUDE.md and memory/monitoring/rank-history/YYYY-MM-DD-ranks.csv -``` - -### 3. Promotion & Demotion Logic - -> **Reference**: See [references/promotion-demotion-rules.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/references/promotion-demotion-rules.md) for detailed promotion/demotion triggers (keywords, competitors, metrics, campaigns) and the action procedures for each. - -### 4. Update Triggers, Archive Management & Cross-Skill Integration - -> **Reference**: See [references/update-triggers-integration.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/references/update-triggers-integration.md) for the complete update procedures after ranking checks, competitor analyses, audits, and reports; monthly/quarterly archive routines; and integration points with all 8 connected skills (keyword-research, rank-tracker, competitor-analysis, content-gap-analysis, seo-content-writer, content-quality-auditor, domain-authority-auditor). - -### 5. Memory Hygiene Checks - -When invoked for review or cleanup: - -1. **Line count check**: Count lines in `memory/hot-cache.md`. If >80, list oldest entries for archival. -2. **Byte check**: If hot-cache exceeds 25KB, warn and recommend trimming long entries. -3. **Staleness scan**: List memory files older than 30 days that have not been referenced. Recommend archival for files >90 days. -4. **Frontmatter audit**: Check that all memory files (except hot-cache.md) have `name`, `description`, and `type` in their frontmatter. Report any missing fields. - -### 6. Save Results - -After delivering any memory update or aggregation to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to the appropriate `memory/` path using filename `YYYY-MM-DD-.md` containing: -- One-line verdict or headline finding -- Top 3-5 actionable items -- Open loops or blockers -- Source data references - -If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to `memory/hot-cache.md` without asking. - -## Validation Checkpoints - -### Structure Validation -- [ ] memory/hot-cache.md exists and is under 80 lines -- [ ] memory/ directory structure matches the shared state model -- [ ] glossary.md exists and is populated with project basics -- [ ] All historical data files include timestamps in filename or metadata - -### Content Validation -- [ ] CLAUDE.md "Last Updated" date is current -- [ ] Every keyword in hot cache has current rank, target rank, and status -- [ ] Every competitor has domain authority and position assessment -- [ ] Every active campaign has status percentage and expected completion date -- [ ] Key Metrics Snapshot shows "Previous" values for comparison - -### Lookup Validation -- [ ] Test lookup flow: reference a term → verify it finds it in correct layer -- [ ] Test promotion: manually promote item → verify it appears in CLAUDE.md -- [ ] Test demotion: manually archive item → verify removed from CLAUDE.md -- [ ] Glossary contains all custom segments and shorthand used in CLAUDE.md - -### Update Validation -- [ ] After ranking check, `memory/monitoring/rank-history/` has a dated snapshot or export -- [ ] After competitor analysis, `memory/research/competitors/` has a dated file -- [ ] After audit, top action items appear in CLAUDE.md priorities -- [ ] After monthly report, metrics snapshot reflects new data - -## Examples - -> **Reference**: See [references/examples.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/references/examples.md) for three complete examples: (1) updating hero keyword rankings with memory refresh, (2) glossary lookup flow, and (3) initializing memory for a new e-commerce project. - -## Advanced Features - -- **Smart Context Loading**: `Load full context for [campaign name]` — retrieves hot cache + all cold storage files for a campaign -- **Memory Health Check**: `Run memory health check` — finds orphaned files, missing timestamps, stale items, broken references -- **Bulk Promotion/Demotion**: `Promote all keywords ranking in top 10 to hot cache` / `Demote all completed campaigns from Q3` -- **Memory Snapshot**: `Create memory snapshot for [date/milestone]` — point-in-time copy for major milestones -- **Cross-Project Memory**: `Compare memory with [other project]` — keyword overlaps, competitor intersections across projects -- **Wiki Lint**: `/seo:wiki-lint [--fix] [--project name] [--retire-preview]` — contradictions, orphans, stale claims, hash mismatches. See [commands/wiki-lint.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/commands/wiki-lint.md) - -## Practical Limitations - -- **Concurrent access**: Use timestamped filenames to avoid overwrites from parallel sessions. -- **Cold storage retrieval**: WARM/COLD files only load on demand. Hot cache is the primary cross-session mechanism. -- **Data freshness**: Stale data (>90 days) should be flagged for refresh. Wiki index `mtime` field helps detect staleness. -- **Wiki compilation**: Index is best-effort for summaries; precise fields (score, status, mtime) are deterministic. Delete `memory/wiki/` anytime to revert. - -## Reference Materials - -- [CORE-EEAT Content Benchmark](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md) — Content quality scoring stored in memory -- [CITE Domain Rating](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/cite-domain-rating.md) — Domain authority scoring stored in memory - -## Next Best Skill - -- **Primary**: [keyword-research](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/research/keyword-research/SKILL.md) — seed or refresh campaign strategy with current demand signals. diff --git a/.agents/skills/memory-management/references/examples.md b/.agents/skills/memory-management/references/examples.md deleted file mode 100644 index c114b5ea53..0000000000 --- a/.agents/skills/memory-management/references/examples.md +++ /dev/null @@ -1,138 +0,0 @@ -# Memory Management Examples - -Detailed examples showing memory management in action. - ---- - -## Example 1: Update Rankings for Hero Keywords - -**User**: "Update rankings for our hero keywords" - -**Assistant**: - -```markdown -I'll update rankings for your hero keywords. Let me check the hot cache for the list. - -[Reads CLAUDE.md, finds Hero Keywords section:] -- keyword research tools (currently #15, target #5) -- best seo software (currently #23, target #10) -- free backlink checker (currently #8, target #3) -- content optimization guide (currently #12, target #5) -- technical seo audit (currently #18, target #8) - -[If ~~SEO tool connected: runs automated ranking check] -[If manual: asks user for current rankings] - ---- - -## Ranking Update - December 15, 2024 - -| Keyword | Previous | Current | Change | Target | Status | -|---------|----------|---------|--------|--------|--------| -| keyword research tools | #15 | #12 | up 3 | #5 | Improving | -| best seo software | #23 | #24 | down 1 | #10 | Stable | -| free backlink checker | #8 | #6 | up 2 | #3 | Near target | -| content optimization guide | #12 | #11 | up 1 | #5 | Improving | -| technical seo audit | #18 | #16 | up 2 | #8 | Improving | - -### Notable Changes -- **free backlink checker** moved to #6 (only 3 positions from target!) -- **keyword research tools** gained 3 positions (strong movement) -- All other keywords showing stable or positive movement - -### Updated Files -1. CLAUDE.md -> Hero Keywords table updated with current ranks -2. memory/monitoring/rank-history/2024-12-15-ranks.csv -> New row added: - `2024-12-15,keyword research tools,12,best seo software,24,free backlink checker,6,...` - -### Recommendations -1. Push additional content/backlinks to "free backlink checker" pages to hit #3 target -2. Investigate why "keyword research tools" suddenly gained 3 positions (replicate success) -3. Monitor "best seo software" for any negative trends - -Hot cache refreshed. Next ranking check recommended: December 22, 2024. -``` - ---- - -## Example 2: Glossary Lookup - -**User**: "What does 'quick wins' mean in our project?" - -**Assistant**: - -```markdown -Let me look that up in the project memory. - -[Step 1: Check CLAUDE.md -> not found] -[Step 2: Check memory/glossary.md -> found!] - -From your project glossary: - -**Quick Wins** -- **Definition**: Low-difficulty, high-value target keywords -- **Keyword Count**: 25 keywords -- **Context**: This segment includes keywords where you can achieve page 1 rankings within 30-60 days due to low competition, despite having decent search volume - -According to your memory files, your current quick wins are stored in: -`memory/research/keywords/long-tail-keywords.md` (tagged with "quick-win" label) - -Would you like me to show you the current list of quick win keywords? -``` - ---- - -## Example 3: Initialize Memory for New Project - -**User**: "Set up memory for a new e-commerce site selling outdoor gear" - -**Assistant**: - -```markdown -I'll set up the two-layer memory system for your outdoor gear e-commerce site. - -## Creating Directory Structure - -[Creates: memory/glossary.md, memory/research/, memory/content/, memory/monitoring/, etc.] - -## CLAUDE.md (Hot Cache) - Initial Setup - -I'll need some information to populate the hot cache: - -1. **Target Keywords**: What are your top 10 hero keywords? - (e.g., "hiking backpacks", "camping tents", "outdoor clothing") - -2. **Primary Competitors**: Who are your top 3-5 competitors? - (e.g., rei.com, backcountry.com, moosejaw.com) - -3. **Current Metrics** (if available): - - Domain authority? - - Current organic traffic? - - Any existing rankings? - -4. **Active Initiatives**: - - Are you launching a new category? - - Running a content campaign? - - Focusing on any specific product line? - -5. **Project Terminology**: - - Any internal product names or abbreviations? - - How do you segment your keyword strategy? - -Please provide what you have, and I'll create your initial CLAUDE.md and glossary. We can start with basics and enrich as we gather more data. - ---- - -Once you provide this information, I'll generate: -- CLAUDE.md with your initial hot cache -- memory/glossary.md with project terminology -- memory/research/keywords/ structure with placeholder files -- memory/research/competitors/ with initial competitor list -- memory/content/calendar/active-calendar.md for Q1 2025 - -After setup, you can use commands like: -- "Update rankings for hero keywords" -- "Add new competitor [domain]" -- "Promote [keyword] to hot cache" -- "Archive campaigns from Q3 2024" -``` diff --git a/.agents/skills/memory-management/references/glossary-template.md b/.agents/skills/memory-management/references/glossary-template.md deleted file mode 100644 index 2e3edd309d..0000000000 --- a/.agents/skills/memory-management/references/glossary-template.md +++ /dev/null @@ -1,64 +0,0 @@ -# Glossary Template - -Use this template to create the project glossary at memory/glossary.md. - -### 3. Glossary Structure (memory/glossary.md) - -```markdown -# [Project Name] - SEO Glossary - -**Purpose**: Project-specific terminology, brand terms, internal jargon, and abbreviations. - -**Last Updated**: [Date] - -## Brand Terms - -| Term | Full Name | Context | -|------|-----------|---------| -| [abbreviation] | [full name] | [when/how it's used] | - -## Product Names - -| Internal Name | Public Name | Category | -|---------------|-------------|----------| -| [code name] | [market name] | [product type] | - -## Custom Segments - -| Segment Name | Definition | Keyword Count | -|--------------|------------|---------------| -| Hero KWs | Top 10 revenue-driving keywords | 10 | -| Quick Wins | Low-difficulty, high-value targets | 25 | -| Brand Defense | Brand + modifier combinations | 15 | - -## Competitor Shorthand - -| Shorthand | Full Domain | Notes | -|-----------|-------------|-------| -| Comp A | competitor-a.com | Main competitor, [industry] | -| Comp B | competitor-b.com | Niche competitor, [specialty] | - -## Project Acronyms - -| Acronym | Full Term | Definition | -|---------|-----------|------------| -| [ABC] | [Full term] | [What it means in this project] | - -## Internal Jargon - -| Phrase | Meaning | Example Usage | -|--------|---------|---------------| -| [phrase] | [definition] | "Check rankings for [phrase]" | - -## Historical Context - -- **Project started**: [Date] -- **Major algorithm updates affecting us**: [List with dates] -- **Site migrations/redesigns**: [List with dates] -- **Key milestones**: [List with dates] - ---- - -_This glossary helps decode project shorthand and maintain consistency across reports and communications._ -``` - diff --git a/.agents/skills/memory-management/references/hot-cache-template.md b/.agents/skills/memory-management/references/hot-cache-template.md deleted file mode 100644 index a57a46bd55..0000000000 --- a/.agents/skills/memory-management/references/hot-cache-template.md +++ /dev/null @@ -1,96 +0,0 @@ -# Hot Cache (CLAUDE.md) Template - -Use this template when initializing memory for a new SEO project. - -### 2. Hot Cache (CLAUDE.md) Structure - -Create or update CLAUDE.md with this template: - -```markdown -# [Project Name] - SEO Memory (Hot Cache) - -**Last Updated**: [Date] -**Project Status**: [Active/Maintenance/Growth] - -## Active Target Keywords (Top 10-20) - -### Hero Keywords (Priority 1) -| Keyword | Current Rank | Target Rank | Volume | Status | -|---------|--------------|-------------|--------|--------| -| [keyword 1] | #[X] | #[Y] | [vol] | [tracking/optimizing/achieved] | -| [keyword 2] | #[X] | #[Y] | [vol] | [tracking/optimizing/achieved] | - -### Secondary Keywords (Priority 2) -| Keyword | Current Rank | Target Rank | Volume | Status | -|---------|--------------|-------------|--------|--------| -| [keyword 1] | #[X] | #[Y] | [vol] | [tracking/optimizing/achieved] | - -_Full keyword research: memory/research/keywords/_ - -## Primary Competitors (Top 3-5) - -1. **[Competitor 1]** (DA: [X]) - - Main strengths: [brief] - - Our position vs. them: [ahead/behind/competitive] - -2. **[Competitor 2]** (DA: [X]) - - Main strengths: [brief] - - Our position vs. them: [ahead/behind/competitive] - -_Detailed analyses: memory/research/competitors/_ - -## Current Optimization Priorities - -1. **[Priority 1]** - [Brief description] - - Status: [not started/in progress/completed] - - Expected impact: [high/medium/low] - - Deadline: [date] - -2. **[Priority 2]** - [Brief description] - - Status: [not started/in progress/completed] - - Expected impact: [high/medium/low] - - Deadline: [date] - -## Key Metrics Snapshot - -**Last Metrics Update**: [Date] - -| Metric | Current | Previous | Change | Target | -|--------|---------|----------|--------|--------| -| Organic Traffic | [X] | [X] | [+/-X%] | [target] | -| Avg. Position | [X] | [X] | [+/-X] | [target] | -| Total Keywords Ranking | [X] | [X] | [+/-X] | [target] | -| Page 1 Rankings | [X] | [X] | [+/-X] | [target] | -| Domain Authority | [X] | [X] | [+/-X] | [target] | -| CITE Score | [X] | [X] | [+/-X] | [target] | -| Last Content Audit Score | [score]/100 | ([rating]) | — [date] | [page audited] | -| Total Backlinks | [X] | [X] | [+/-X%] | [target] | - -_Historical data: memory/monitoring/reports/ and memory/monitoring/rank-history/_ - -## Active Campaigns - -### [Campaign Name 1] -- **Duration**: [Start date] - [End date] -- **Goal**: [Specific goal] -- **Status**: [planning/active/completed] -- **Progress**: [X]% -- **Key activities**: [brief list] - -### [Campaign Name 2] -- **Duration**: [Start date] - [End date] -- **Goal**: [Specific goal] -- **Status**: [planning/active/completed] -- **Progress**: [X]% -- **Key activities**: [brief list] - -_Campaign plans and archives: memory/content/calendar/_ - -## Quick Reference Notes - -- [Important note 1] -- [Important note 2] -- [Important note 3] - -_For project terminology, see: memory/glossary.md_ -``` diff --git a/.agents/skills/memory-management/references/promotion-demotion-rules.md b/.agents/skills/memory-management/references/promotion-demotion-rules.md deleted file mode 100644 index 01332cb1b7..0000000000 --- a/.agents/skills/memory-management/references/promotion-demotion-rules.md +++ /dev/null @@ -1,58 +0,0 @@ -# Promotion and Demotion Rules - -Detailed triggers and actions for moving items between the hot cache (CLAUDE.md) and cold storage (memory/ subdirectories). - -## Promotion Logic - -**Promote to Hot Cache when:** - -### Keyword promotion triggers: -- Keyword referenced in 3+ conversations within 7 days -- Keyword showing significant movement (5+ position change) -- Keyword targeted in new active campaign -- Keyword suddenly driving traffic spike - -### Competitor promotion triggers: -- Competitor mentioned in 3+ recent analyses -- Competitor showing aggressive SEO activity -- Competitor launching competing content - -### Metric promotion triggers: -- Metric requested frequently for reporting -- Metric showing anomalous behavior -- Metric tied to active priority/campaign - -**Promotion action:** -```markdown -1. Add item to relevant section in CLAUDE.md -2. Add note: "Promoted [date] - [reason]" -3. Keep detailed data in cold storage -4. Set reminder to review in 30 days -``` - -## Demotion Logic - -**Demote to Cold Storage when:** - -### Keyword demotion triggers: -- Not referenced in 30+ days -- Target rank achieved and stable for 60+ days -- Keyword no longer in active strategy -- Keyword replaced by higher-priority target - -### Competitor demotion triggers: -- No significant activity in 60+ days -- Fell out of top 5 competitors by relevance -- No longer targeting same keywords - -### Campaign demotion triggers: -- Campaign completed 30+ days ago -- Campaign cancelled or postponed indefinitely - -**Demotion action:** -```markdown -1. Remove from CLAUDE.md -2. Archive full data in memory/[category]/archive/ -3. Add line to CLAUDE.md: "Last reviewed [category]: [date]" -4. Keep 1-line summary if historically significant -``` diff --git a/.agents/skills/memory-management/references/update-triggers-integration.md b/.agents/skills/memory-management/references/update-triggers-integration.md deleted file mode 100644 index 00faa54c43..0000000000 --- a/.agents/skills/memory-management/references/update-triggers-integration.md +++ /dev/null @@ -1,102 +0,0 @@ -# Update Triggers & Cross-Skill Integration - -Systematic memory update procedures triggered by specific events, plus integration points with other SEO skills. - -## Update Triggers - -After specific events, update memory systematically: - -### After Ranking Check -```markdown -1. Update CLAUDE.md -> Hero Keywords table (current ranks) -2. Save a dated snapshot to memory/monitoring/rank-history/YYYY-MM-DD-ranks.csv -3. Note any keywords with significant movement -4. Update "Last Metrics Update" date in CLAUDE.md -5. If hero keyword moves +/-5 positions, create alert note -``` - -### After Competitor Analysis -```markdown -1. Update CLAUDE.md -> Primary Competitors section (DA, position vs. them) -2. Save full report to memory/research/competitors/YYYY-MM-DD-analysis.md -3. Update competitor overview notes in memory/research/competitors/ -4. Note new competitor strategies in hot cache -``` - -### After Audit (Technical/Content/Backlink) -```markdown -1. Save full report to memory/audits/[type]/YYYY-MM-DD-[audit-name].md -2. Extract top 3-5 action items -> CLAUDE.md Current Optimization Priorities -3. Update Key Metrics Snapshot if audit includes metrics -4. Create campaign entry if audit spawns new initiative -``` - -### After Monthly/Quarterly Report -```markdown -1. Save report to memory/monitoring/reports/[period]/YYYY-MM-report.md -2. Update all metrics in CLAUDE.md Key Metrics Snapshot -3. Review hot cache -> demote stale items -4. Update campaign statuses -5. Archive completed campaigns -``` - -## Archive Management - -### Monthly Archive Routine -```markdown -1. Review CLAUDE.md for items not referenced in 30 days -2. Move stale items to appropriate cold storage -3. Create monthly snapshot: memory/monitoring/snapshots/YYYY-MM-CLAUDE.md -4. Compress old rank-history exports (keep recent snapshots easiest to access) -5. Update glossary with new terms from the month -``` - -### Quarterly Archive Routine -```markdown -1. Review entire cold storage structure -2. Compress files older than 6 months -3. Create quarterly summary report -4. Update project timeline in glossary -5. Audit all active campaigns -> archive completed ones -``` - -## Cross-Skill Memory Integration - -This skill coordinates with other SEO skills: - -### When keyword-research runs: -- Add discovered keywords to memory/research/keywords/ -- Promote top opportunities to CLAUDE.md if high-value -- Update glossary if new terminology emerges - -### When rank-tracker runs: -- Update memory/monitoring/rank-history/ -- Refresh CLAUDE.md Hero Keywords table -- Flag significant movements for hot cache notes - -### When competitor-analysis runs: -- Update competitor files in memory/research/competitors/ -- Refresh CLAUDE.md Primary Competitors section -- Add new competitors if they outrank current top 5 - -### When content-gap-analysis runs: -- Store full findings in memory/research/content-gaps/ -- Promote gap opportunities to CLAUDE.md priorities -- Update memory/content/calendar/ with recommended topics - -### When seo-content-writer produces content: -- Log to memory/content/published/YYYY-MM-DD-[slug].md -- Track target keyword and publish date -- Set reminder to check performance in 30 days - -### When content-quality-auditor runs: -- Save full report to `memory/audits/content/YYYY-MM-DD-core-eeat-[page-slug].md` -- Update CLAUDE.md Key Metrics with latest score -- If score < 60 (Poor/Low), flag in Active Campaigns section -- Track dimension scores for trend analysis - -### When domain-authority-auditor runs: -- Save full report to memory/audits/domain/YYYY-MM-DD-cite-audit.md -- Update CITE Score in CLAUDE.md Key Metrics Snapshot -- Note veto item status and dimension scores -- Compare against previous CITE audit if available diff --git a/.agents/skills/meta-tags-optimizer/SKILL.md b/.agents/skills/meta-tags-optimizer/SKILL.md deleted file mode 100644 index 1e3a81c9f9..0000000000 --- a/.agents/skills/meta-tags-optimizer/SKILL.md +++ /dev/null @@ -1,417 +0,0 @@ ---- -name: meta-tags-optimizer -description: 'Optimize title tags, meta descriptions, Open Graph, Twitter cards for maximum CTR with A/B variations. 标题优化/元描述/CTR' -version: "6.0.0" -license: Apache-2.0 -compatibility: "Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations." -homepage: "https://github.com/aaron-he-zhu/seo-geo-claude-skills" -when_to_use: "Use when optimizing title tags, meta descriptions, Open Graph tags, or Twitter Cards for a page." -argument-hint: "" -metadata: - author: aaron-he-zhu - version: "6.0.0" - geo-relevance: "low" - tags: - - seo - - meta-tags - - title-tag - - meta-description - - open-graph - - twitter-card - - ctr-optimization - - social-sharing - - 标题优化 - - 元描述 - - メタタグ - - 메타태그 - - meta-tags-seo - triggers: - # EN-formal - - "optimize title tag" - - "write meta description" - - "improve CTR" - - "Open Graph tags" - - "social media preview" - - "title optimization" - - "meta tags" - - "Twitter cards" - # EN-casual - - "my title tag needs work" - - "low click-through rate" - - "fix my meta tags" - - "OG tags not showing" - - "social preview looks wrong" - - "my click rate is low" - # EN-question - - "how to write a good title tag" - - "how to improve click-through rate" - - "what is a good meta description" - # EN-competitor - - "Yoast SEO title tool" - - "RankMath title optimizer" - # ZH-pro - - "标题标签优化" - - "元描述优化" - - "OG标签" - - "点击率提升" - - "社交预览" - - "TDK优化" - # ZH-casual - - "标题不好" - - "点击率太低" - - "社交分享预览不对" - - "标题怎么写" - - "TDK怎么写" - # JA - - "メタタグ最適化" - - "タイトルタグ" - - "CTR改善" - # KO - - "메타 태그 최적화" - - "제목 태그" - - "클릭률 개선" - # ES - - "optimizar meta tags" - - "mejorar CTR" - - "etiquetas Open Graph" - # PT - - "otimizar meta tags" - # Misspellings - - "meta discription" - - "tittle tag" ---- - -# Meta Tags Optimizer - -> **[SEO & GEO Skills Library](https://github.com/aaron-he-zhu/seo-geo-claude-skills)** · 20 skills for SEO + GEO · [ClawHub](https://clawhub.ai/u/aaron-he-zhu) · [skills.sh](https://skills.sh/aaron-he-zhu/seo-geo-claude-skills) -> **System Mode**: This build skill follows the shared [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md) and [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md). - - -This skill creates compelling, optimized meta tags that improve click-through rates from search results and enhance social media sharing. It covers title tags, meta descriptions, and social meta tags. - -**System role**: Build layer skill. It turns briefs and signals into assets that other skills can review, publish, and monitor. - -## When This Must Trigger - -Use this when the conversation involves any of these situations — even if the user does not use SEO terminology: - -Use this whenever the task needs a shippable asset or transformation that should feed directly into quality review, deployment, or monitoring. - -- Creating meta tags for new pages -- Optimizing existing meta tags for better CTR -- Preparing pages for social media sharing -- Fixing duplicate or missing meta tags -- A/B testing title and description variations -- Optimizing for specific SERP features -- Creating meta tags for different page types - -## What This Skill Does - -1. **Title Tag Creation**: Writes compelling, keyword-optimized titles -2. **Meta Description Writing**: Creates click-worthy descriptions -3. **Open Graph Optimization**: Prepares pages for social sharing -4. **Twitter Card Setup**: Optimizes Twitter-specific meta tags -5. **CTR Analysis**: Suggests improvements for better click rates -6. **Character Counting**: Ensures proper length for SERP display -7. **A/B Test Suggestions**: Provides variations for testing - -## Quick Start - -Start with one of these prompts. Finish with a short handoff summary using the repository format in [Skill Contract](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). - -### Create Meta Tags - -``` -Create meta tags for a page about [topic] targeting [keyword] -``` - -``` -Write title and meta description for this content: [content/URL] -``` - -### Optimize Existing Tags - -``` -Improve these meta tags for better CTR: [current tags] -``` - -### Social Media Tags - -``` -Create Open Graph and Twitter card tags for [page/URL] -``` - -## Skill Contract - -**Expected output**: a ready-to-use asset or implementation-ready transformation plus a short handoff summary ready for `memory/content/`. - -- **Reads**: the brief, target keywords, entity inputs, quality constraints, and prior decisions from [CLAUDE.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) when available. -- **Writes**: a user-facing content, metadata, or schema deliverable plus a reusable summary that can be stored under `memory/content/`. -- **Promotes**: approved angles, messaging choices, missing evidence, and publish blockers to `CLAUDE.md`, `memory/decisions.md`, and `memory/open-loops.md`. -- **Next handoff**: use the `Next Best Skill` below when the asset is ready for review or deployment. - -## Data Sources - -> See [CONNECTORS.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CONNECTORS.md) for tool category placeholders. - -**With ~~search console + ~~SEO tool connected:** -Automatically pull current meta tags, CTR data by query, competitor title/description patterns, SERP preview data, and impression/click metrics to identify optimization opportunities. - -**With manual data only:** -Ask the user to provide: -1. Current title and meta description (if optimizing existing) -2. Target primary keyword and 2-3 secondary keywords -3. Page URL and main content/value proposition -4. Competitor URLs or examples of well-performing titles in the SERP - -Proceed with the full workflow using provided data. Note in the output which metrics are from automated collection vs. user-provided data. - -## Instructions - -When a user requests meta tag optimization: - -1. **Gather Page Information** - - ```markdown - ### Page Analysis - - **Page URL**: [URL] - **Page Type**: [blog/product/landing/service/homepage] - **Primary Keyword**: [keyword] - **Secondary Keywords**: [keywords] - **Target Audience**: [audience] - **Primary CTA**: [action you want users to take] - **Unique Value Prop**: [what makes this page special] - ``` - -2. **Create Optimized Title Tag** - - ```markdown - ### Title Tag Optimization - - **Requirements**: - - Length: 50-60 characters (displays fully in SERP) - - Include primary keyword (preferably near front) - - Make it compelling and click-worthy - - Match search intent - - Include brand name if appropriate - - **Title Tag Formula Options**: - - 1. **Keyword | Benefit | Brand** - "[Primary Keyword]: [Benefit] | [Brand Name]" - - 2. **Number + Keyword + Promise** - "[Number] [Keyword] That [Promise/Result]" - - 3. **How-to Format** - "How to [Keyword]: [Benefit/Result]" - - 4. **Question Format** - "What is [Keyword]? [Brief Answer/Hook]" - - 5. **Year + Keyword** - "[Keyword] in [Year]: [Hook/Update]" - - **Generated Title Options**: - - | Option | Title | Length | Power Words | Keyword Position | - |--------|-------|--------|-------------|------------------| - | 1 | [Title] | [X] chars | [words] | [Front/Middle] | - | 2 | [Title] | [X] chars | [words] | [Front/Middle] | - | 3 | [Title] | [X] chars | [words] | [Front/Middle] | - - **Recommended**: Option [X] - **Reasoning**: [Why this option is best] - - **Title Tag Code**: - ```html - [Selected Title] - ``` - ``` - -3. **Write Meta Description** - - ```markdown - ### Meta Description Optimization - - **Requirements**: - - Length: 150-160 characters (displays fully in SERP) - - Include primary keyword naturally - - Include clear call-to-action - - Match page content accurately - - Create urgency or curiosity - - Avoid duplicate descriptions - - **Meta Description Formula**: - - [What the page offers] + [Benefit to user] + [Call-to-action] - - **Power Elements to Include**: - - Numbers and statistics - - Current year - - Emotional triggers - - Action verbs - - Unique value proposition - - **Generated Description Options**: - - | Option | Description | Length | CTA | Emotional Trigger | - |--------|-------------|--------|-----|-------------------| - | 1 | [Description] | [X] chars | [CTA] | [Trigger] | - | 2 | [Description] | [X] chars | [CTA] | [Trigger] | - | 3 | [Description] | [X] chars | [CTA] | [Trigger] | - - **Recommended**: Option [X] - **Reasoning**: [Why this option is best] - - **Meta Description Code**: - ```html - - ``` - ``` - -4. **Create Open Graph, Twitter Card, and Additional Meta Tags** - - Generate OG tags (og:type, og:url, og:title, og:description, og:image), Twitter Card tags, canonical URL, robots, viewport, author, and article-specific tags. Then combine into a complete meta tag block. - - > **Reference**: See [references/meta-tag-code-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/meta-tags-optimizer/references/meta-tag-code-templates.md) for OG type selection guide, Twitter card type selection, all HTML code templates, and the complete meta tag block template. - -5. **CORE-EEAT Alignment Check** - - Verify meta tags align with content quality standards. Reference: [CORE-EEAT Benchmark](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/core-eeat-benchmark.md) - - ```markdown - ### CORE-EEAT Meta Tag Alignment - - | Check | Status | Notes | - |-------|--------|-------| - | **C01 Intent Alignment**: Title promise matches actual content delivery | ✅/⚠️/❌ | [Does the title accurately represent what the page delivers?] | - | **C02 Direct Answer**: Meta description reflects the core answer available in first 150 words | ✅/⚠️/❌ | [Does the description preview the direct answer?] | - - **If C01 fails**: Title is misleading — rewrite to match actual content. - **If C02 fails**: Content may need restructuring to front-load the answer, or description should better reflect available content. - ``` - -9. **Provide CTR Optimization Tips** - - ```markdown - ## CTR Optimization Analysis - - ### Power Words Used - - [Word 1] - Creates [emotion/action] - - [Word 2] - Creates [emotion/action] - - ### CTR Boosting Elements - - | Element | Present | Impact | - |---------|---------|--------| - | Numbers | Yes/No | +20-30% CTR | - | Current Year | Yes/No | +15-20% CTR | - | Power Words | Yes/No | +10-15% CTR | - | Question | Yes/No | +10-15% CTR | - | Brackets | Yes/No | +10% CTR | - - ### A/B Test Suggestions - - Test these variations: - - **Version A** (Current): - - Title: [Title] - - Description: [Description] - - **Version B** (Test): - - Title: [Alternative title] - - Description: [Alternative description] - - Hypothesis: [Why this might perform better] - ``` - -## Validation Checkpoints - -### Input Validation -- [ ] Primary keyword confirmed and matches page content -- [ ] Page type identified (blog/product/landing/service/homepage) -- [ ] Target audience and search intent clearly defined -- [ ] Unique value proposition articulated - -### Output Validation -- [ ] Title length 50-60 characters (displays fully in SERP) -- [ ] Meta description length 150-160 characters -- [ ] Primary keyword appears in both title and description -- [ ] Open Graph image specified (1200x630px recommended) -- [ ] All HTML syntax valid (no unclosed quotes or tags) -- [ ] Source of each data point clearly stated (~~search console CTR data, ~~SEO tool competitor data, user-provided, or estimated) - -## Example - -**User**: "Create meta tags for a blog post about 'how to start a podcast in [current year]'" - -**Output**: - -```markdown -## Meta Tags: How to Start a Podcast ([current year]) - -### Title Tag -```html -How to Start a Podcast in [current year]: Complete Beginner's Guide -``` -**Length**: ~55 characters ✅ -**Keyword**: "how to start a podcast" at front ✅ -**Power Words**: "Complete", "Beginner's" ✅ - -### Meta Description -```html - -``` -**Length**: ~163 characters ✅ -**Keyword**: Included naturally ✅ -**CTA**: "Start podcasting today!" ✅ - -_Complete meta tag block (with OG, Twitter, Article tags) generated using template from [references/meta-tag-code-templates.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/meta-tags-optimizer/references/meta-tag-code-templates.md)._ - -### A/B Test Variations - -**Title Variation B**: -"Start a Podcast in [current year]: Step-by-Step Guide (+ Free Checklist)" - -**Title Variation C**: -"How to Start a Podcast: [current year] Guide [Equipment + Software + Tips]" - -**Description Variation B**: -"Want to start a podcast in [current year]? This guide covers everything: equipment ($100 budget option), best hosting platforms, recording tips, and how to get your first 1,000 listeners." -``` - -## Tips for Success - -1. **Front-load keywords** - Put important terms at the beginning -2. **Match intent** - Description should preview what page delivers -3. **Be specific** - Vague descriptions get ignored -4. **Test variations** - Small changes can significantly impact CTR -5. **Update regularly** - Add current year, refresh messaging -6. **Check competitors** - See what's working in your SERP - - -### Save Results - -After delivering content or optimization output to the user, ask: - -> "Save these results for future sessions?" - -If yes, write a dated summary to `memory/content/YYYY-MM-DD-.md` containing: -- One-line description of what was created -- Target keyword and content type -- Open loops or items needing review -- Source data references - -**Gate check recommended**: Run content-quality-auditor before publishing (PostToolUse hook will remind automatically). - -If any findings should influence ongoing strategy, recommend promoting key conclusions to `memory/hot-cache.md`. - -## Reference Materials - -- [Meta Tag Formulas](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/meta-tags-optimizer/references/meta-tag-formulas.md) — Proven title and description formulas -- [CTR and Social Reference](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/meta-tags-optimizer/references/ctr-and-social-reference.md) — Page-type templates, CTR data, OG best practices - -## Next Best Skill - -- **Primary**: [schema-markup-generator](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/schema-markup-generator/SKILL.md) — complete the SERP packaging with structured data. diff --git a/.agents/skills/meta-tags-optimizer/references/ctr-and-social-reference.md b/.agents/skills/meta-tags-optimizer/references/ctr-and-social-reference.md deleted file mode 100644 index 8fc5205c80..0000000000 --- a/.agents/skills/meta-tags-optimizer/references/ctr-and-social-reference.md +++ /dev/null @@ -1,149 +0,0 @@ -# CTR Optimization, Social Tags, and Page-Type Templates - -Reference tables for click-through rate optimization, Open Graph best practices, meta description copywriting frameworks, and page-type meta tag templates. - ---- - -## Page-Type Meta Tag Templates - -### Homepage - -```html -[Brand Name] - [Primary Value Proposition] - -``` - -### Product Page - -```html -[Product Name] - [Key Benefit] | [Brand] - -``` - -### Blog Post - -```html -[How to/What is/Number] [Keyword] [Benefit/Year] - -``` - -### Service Page - -```html -[Service] in [Location] - [Brand] | [Differentiator] - -``` - ---- - -## Title Tag Formula Patterns - -### Proven Title Formulas - -| Formula | Template | Example | Best For | -|---------|----------|---------|---------| -| How-To | How to [Achieve Result] ([Year]) | How to Improve SEO Rankings (2026) | Informational guides | -| Number List | [Number] [Adjective] [Topic] for [Audience] | 15 Proven SEO Strategies for E-commerce | Listicles | -| Question | [Question]? Here's [Answer Preview] | What Is Technical SEO? Complete Guide | Definition content | -| Comparison | [Option A] vs [Option B]: [Differentiator] | Ahrefs vs SEMrush: Which SEO Tool Wins? | Comparison pages | -| Year-Based | Best [Topic] in [Year] ([Tested/Ranked]) | Best SEO Tools in 2026 (Expert Tested) | Evergreen + fresh | -| Benefit-Driven | [Benefit]: [How to Achieve It] | Rank #1 on Google: 10-Step SEO Checklist | High-CTR pages | -| Parenthetical | [Topic] ([Modifier]) | Link Building Guide (With Templates) | Adding value signal | -| Negative | [Number] [Topic] Mistakes [Consequence] | 7 SEO Mistakes That Kill Your Rankings | Problem-awareness content | - -### Title Tag Power Words - -| Category | Power Words | Use Sparingly | -|----------|-----------|---------------| -| Urgency | Now, Today, Quick, Fast, Instant, 2026 | Don't overuse; pair with substance | -| Value | Free, Proven, Complete, Essential, Ultimate | Avoid hyperbole without backing | -| Specificity | [Exact number], Step-by-Step, Checklist, Template | More specific = more clicks | -| Curiosity | Secret, Little-Known, Surprising, Actually | Must deliver on the promise | -| Authority | Expert, Research-Backed, Data-Driven, Tested | Only use when genuinely expert content | -| Emotional | Best, Worst, Mistakes, Warning, Powerful | Balance emotion with credibility | - -### Title Tag Length Optimization - -| Length Range | SERP Behavior | Recommendation | -|-------------|--------------|----------------| -| <30 characters | May appear thin | Expand with modifier or brand | -| 30-50 characters | Fully displayed on all devices | Ideal for mobile-first | -| 50-60 characters | Full display on desktop, may truncate mobile | Sweet spot for desktop | -| 60-65 characters | Truncated on some devices | Front-load keywords | -| >65 characters | Truncated everywhere | Avoid — key info gets cut | - ---- - -## Meta Description Copywriting Frameworks - -### AIDA Framework - -| Element | What It Does | Example | -|---------|-------------|---------| -| **A**ttention | Hook with a bold claim or question | "Want to double your organic traffic?" | -| **I**nterest | Build relevance | "This guide covers 15 proven strategies..." | -| **D**esire | Show benefit | "...used by top-ranking sites to increase traffic by 200%." | -| **A**ction | CTA | "Read the full guide now." | - -**Full Example**: "Want to double your organic traffic? This guide covers 15 proven SEO strategies used by top-ranking sites to increase traffic by 200%. Read the full guide now." (158 chars) - -### PAS Framework - -| Element | What It Does | Example | -|---------|-------------|---------| -| **P**roblem | Identify pain point | "Struggling to rank on Google?" | -| **A**gitate | Amplify the problem | "Most SEO guides are outdated and miss key ranking factors." | -| **S**olution | Offer the solution | "Our 2026 guide covers what actually works. Read now." | - -### Benefit-Proof-CTA Framework - -| Element | Example | -|---------|---------| -| Benefit | "Learn the exact SEO process that grew our traffic 5x" | -| Proof | "— with real data from 200+ audits." | -| CTA | "Get the free checklist." | - ---- - -## CTR Optimization Data - -### Factors That Increase Organic CTR - -| Factor | CTR Impact | Implementation | -|--------|-----------|----------------| -| Number in title | +20-30% | "7 Ways", "15 Tips", "2026" | -| Question in title | +14% | Start with How, What, Why | -| Emotional words | +7% | "Proven", "Essential", "Mistake" | -| Brackets/parentheses | +38% | "[Guide]", "(With Examples)" | -| Current year | +10-15% | "Best SEO Tools 2026" | -| Power words | +12% | See Power Words table above | -| Match search intent | +15-25% | Align title with SERP expectations | -| Rich results (schema) | +30% | FAQ, HowTo, Review stars | - ---- - -## Open Graph Best Practices by Platform - -### Platform-Specific OG Optimization - -| Platform | Image Size | Title Length | Description Length | Special Tags | -|----------|-----------|-------------|-------------------|-------------| -| Facebook | 1200x630px | 40-60 chars | 125-155 chars | og:type, og:locale | -| Twitter/X | 1200x600px | 70 chars max | 200 chars | twitter:card, twitter:site | -| LinkedIn | 1200x627px | 70 chars | 150 chars | Standard OG tags | -| Pinterest | 1000x1500px (2:3) | 100 chars | 500 chars | og:type=article preferred | -| Slack | 500x500px min | Full title | First ~300 chars | Standard OG tags | - -### OG Tag Checklist - -| Tag | Required? | Notes | -|-----|----------|-------| -| og:title | Yes | Can differ from HTML title; optimize for social sharing | -| og:description | Yes | Social-optimized; can differ from meta description | -| og:image | Yes | Must be absolute URL; minimum 200x200px, ideal 1200x630px | -| og:url | Yes | Canonical URL | -| og:type | Yes | "website" for homepage, "article" for blog posts | -| og:site_name | Recommended | Your brand name | -| og:locale | Recommended | e.g., "en_US" | -| twitter:card | Recommended | "summary_large_image" for articles | -| twitter:site | Recommended | @yourbrand | diff --git a/.agents/skills/meta-tags-optimizer/references/meta-tag-code-templates.md b/.agents/skills/meta-tags-optimizer/references/meta-tag-code-templates.md deleted file mode 100644 index 88d5b62867..0000000000 --- a/.agents/skills/meta-tags-optimizer/references/meta-tag-code-templates.md +++ /dev/null @@ -1,153 +0,0 @@ -# Meta Tag Code Templates - -HTML code templates for Open Graph tags, Twitter cards, additional meta tags, and complete meta tag blocks. - ---- - -## Open Graph Tags (Facebook, LinkedIn, etc.) - -**Required OG Tags**: - -```html - - - - - - - - - - -``` - -**OG Type Selection Guide**: - -| Page Type | og:type | -|-----------|---------| -| Blog post | article | -| Homepage | website | -| Product | product | -| Video | video.other | -| Profile | profile | - -**OG Title Considerations**: -- Can be different from title tag -- Optimize for social sharing context -- More conversational tone acceptable -- Up to 60 characters ideal - -**OG Description Considerations**: -- Can be longer than meta description (up to 200 chars) -- Focus on shareability -- What would make someone click when shared? - -**OG Image Requirements**: -- Recommended size: 1200x630 pixels -- Minimum size: 600x315 pixels -- Format: JPG or PNG -- Keep text to less than 20% of image -- Include branding subtly - ---- - -## Twitter Card Tags - -**Card Type Selection**: - -| Card Type | Best For | Image Size | -|-----------|----------|------------| -| summary | Articles, blogs | 144x144 min | -| summary_large_image | Visual content | 300x157 min | -| player | Video/audio | 640x360 min | -| app | Mobile apps | 800x418 | - -**Twitter Card Code**: - -```html - - - - - - - - -``` - -**Twitter-Specific Considerations**: -- Shorter titles work better (under 70 chars) -- Include @mentions if relevant -- Hashtag-relevant terms can help discovery -- Test with Twitter Card Validator - ---- - -## Additional Recommended Meta Tags - -**Canonical URL** (Prevent duplicates): -```html - -``` - -**Robots Tag** (Indexing control): -```html - -``` - -**Viewport** (Mobile optimization): -```html - -``` - -**Author** (For articles): -```html - -``` - -**Language**: -```html - -``` - -**Article-Specific** (For blog posts): -```html - - - - - -``` - ---- - -## Complete Meta Tag Block Template - -Copy and paste this complete meta tag block: - -```html - -[Optimized Title] - - - - - - - - - - - - - - - - - - - - - - -``` diff --git a/.agents/skills/meta-tags-optimizer/references/meta-tag-formulas.md b/.agents/skills/meta-tags-optimizer/references/meta-tag-formulas.md deleted file mode 100644 index dd1399f2be..0000000000 --- a/.agents/skills/meta-tags-optimizer/references/meta-tag-formulas.md +++ /dev/null @@ -1,355 +0,0 @@ -# Meta Tag Formulas Reference - -Extended title tag formulas, meta description templates, A/B testing methodology, seasonal patterns, CTR benchmarks, and multi-language optimization guidance. - ---- - -## Title Tag Formulas by Content Type - -### Informational Content - -| # | Formula | Template | Example | -|---|---------|----------|---------| -| 1 | How-To + Year | How to [Action] in [Year] | How to Build Backlinks in 2026 | -| 2 | How-To + Result | How to [Action] ([Result]) | How to Write Meta Tags (Rank Higher) | -| 3 | Definitive Guide | The Definitive Guide to [Topic] | The Definitive Guide to Technical SEO | -| 4 | Complete Guide | [Topic]: The Complete Guide ([Year]) | Schema Markup: The Complete Guide (2026) | -| 5 | Beginner's Guide | [Topic] for Beginners: [Promise] | SEO for Beginners: Rank in 30 Days | -| 6 | What Is | What Is [Topic]? [Short Clarifier] | What Is E-E-A-T? Google's Quality Standard | -| 7 | Everything You Need | Everything You Need to Know About [Topic] | Everything You Need to Know About Core Web Vitals | -| 8 | Step-by-Step | [Topic]: A Step-by-Step Guide | Link Building: A Step-by-Step Guide | - -### Listicle Content - -| # | Formula | Template | Example | -|---|---------|----------|---------| -| 9 | Numbered Tips | [N] [Topic] Tips That [Result] | 12 On-Page SEO Tips That Boost Rankings | -| 10 | Best Of | [N] Best [Items] in [Year] ([Qualifier]) | 9 Best SEO Tools in 2026 (Expert Tested) | -| 11 | Ways To | [N] Ways to [Achieve Goal] [Timeframe] | 7 Ways to Increase Organic Traffic This Month | -| 12 | Strategies | [N] [Adjective] [Topic] Strategies for [Audience] | 15 Proven Content Strategies for SaaS | -| 13 | Examples | [N] [Topic] Examples [Value Add] | 20 Meta Description Examples (+ Templates) | -| 14 | Mistakes | [N] [Topic] Mistakes [Negative Consequence] | 10 Title Tag Mistakes That Hurt Your CTR | -| 15 | Secrets | [N] [Topic] Secrets [Experts/Pros] Use | 5 SEO Secrets Top Agencies Use | -| 16 | Reasons | [N] Reasons [Why/To] [Statement] | 8 Reasons Your Site Isn't Ranking | - -### Comparison Content - -| # | Formula | Template | Example | -|---|---------|----------|---------| -| 17 | A vs B | [A] vs [B]: [Differentiator] | Ahrefs vs SEMrush: Which Tool Is Better? | -| 18 | A vs B + Year | [A] vs [B] ([Year]): [Angle] | WordPress vs Webflow (2026): Honest Comparison | -| 19 | A vs B + Winner | [A] vs [B]: Which [Category] Wins? | Yoast vs Rank Math: Which SEO Plugin Wins? | -| 20 | Alternatives | [N] Best [Product] Alternatives in [Year] | 7 Best Ahrefs Alternatives in 2026 | - -### Commercial / Transactional Content - -| # | Formula | Template | Example | -|---|---------|----------|---------| -| 21 | Product + Benefit | [Product] - [Primary Benefit] \| [Brand] | SEO Audit Tool - Find Issues in Minutes \| SiteChecker | -| 22 | Pricing | [Product] Pricing: [Value Statement] | Semrush Pricing: Plans from $119/mo (2026) | -| 23 | Review | [Product] Review ([Year]): [Verdict] | Surfer SEO Review (2026): Worth the Price? | -| 24 | Free Tool | Free [Tool Type]: [What It Does] | Free Meta Tag Generator: Preview Your SERP Listing | - -### Local SEO Content - -| # | Formula | Template | Example | -|---|---------|----------|---------| -| 25 | Service + Location | [Service] in [City] - [Brand] \| [Differentiator] | Plumber in Austin - FastFix \| Same-Day Service | -| 26 | Best + Location | Best [Service] in [City] ([Year]) | Best Coffee Shops in Portland (2026) | -| 27 | Near Me Style | [Service] Near [Location]: [Value] | SEO Agency Near Chicago: Free Site Audit | -| 28 | Location Guide | [Topic] in [Location]: [Qualifier] Guide | Restaurants in Brooklyn: The Local's Guide | - -### Problem-Awareness Content - -| # | Formula | Template | Example | -|---|---------|----------|---------| -| 29 | Warning | Warning: [Problem] [Consequence] | Warning: These SEO Tactics Will Get You Penalized | -| 30 | Why + Problem | Why [Problem Exists] (And How to Fix It) | Why Your Meta Descriptions Are Ignored (And How to Fix It) | -| 31 | Stop Doing | Stop [Bad Practice]: [Better Alternative] | Stop Keyword Stuffing: What to Do Instead | -| 32 | Truth About | The Truth About [Topic] [Revelation] | The Truth About SEO in 2026: What Changed | - ---- - -## Meta Description Templates - -### By Content Type - -#### Blog Posts / Articles - -| # | Template | Character Count | -|---|----------|-----------------| -| 1 | Learn [topic] with our [qualifier] guide. Covers [point 1], [point 2], and [point 3]. [CTA]. | ~140-155 | -| 2 | [Question]? This [year] guide explains [what], [why], and [how]. Get actionable tips now. | ~130-150 | -| 3 | Discover [N] [adjective] [topic] strategies that [result]. Backed by [proof element]. Read the full guide. | ~145-160 | -| 4 | [Bold claim about topic]. We tested [N] [methods/tools] and found what works. See the results. | ~130-150 | - -#### Product / Service Pages - -| # | Template | Character Count | -|---|----------|-----------------| -| 5 | [Product] helps you [benefit]. [Feature 1], [Feature 2], [Feature 3]. [Price/offer]. [CTA]. | ~140-155 | -| 6 | Looking for [solution]? [Product] [key differentiator]. Trusted by [social proof]. [CTA]. | ~130-150 | -| 7 | [Product] - [primary benefit] in [timeframe]. [Star rating] from [N]+ reviews. [CTA]. | ~130-145 | - -#### E-commerce / Category Pages - -| # | Template | Character Count | -|---|----------|-----------------| -| 8 | Shop [category] from [Brand]. [N]+ [items] starting at $[price]. Free shipping on orders over $[amount]. | ~145-160 | -| 9 | Browse our [adjective] collection of [products]. [Unique selling point]. [Guarantee/offer]. Order today. | ~140-155 | - -#### Comparison Pages - -| # | Template | Character Count | -|---|----------|-----------------| -| 10 | [A] vs [B]: which is better for [use case]? We compared [criteria]. See the winner + detailed breakdown. | ~145-160 | -| 11 | Comparing [A] and [B] on [criteria 1], [criteria 2], and price. Our [year] verdict inside. | ~130-145 | - -#### Local Business Pages - -| # | Template | Character Count | -|---|----------|-----------------| -| 12 | [Service] in [City] by [Brand]. [Experience] years of experience. [N]-star rated. [CTA] — call [phone] today. | ~150-160 | -| 13 | Need [service] in [location]? [Brand] offers [differentiator]. [Guarantee]. Book your [offer] now. | ~140-155 | - ---- - -## A/B Testing Methodology for Titles and Descriptions - -### Pre-Test Preparation - -1. **Baseline metrics** — Record current CTR, average position, impressions, and clicks for the target page (minimum 30 days of data from ~~search console). -2. **Hypothesis** — State what you expect to change and why: "Adding a number to the title will increase CTR by X% because listicle titles outperform generic ones at this SERP position." -3. **Single variable** — Change only ONE element per test (title OR description, not both). -4. **Minimum sample** — Wait until the page has accumulated at least 1,000 impressions in the test period before drawing conclusions. - -### Test Execution Steps - -| Step | Action | Duration | -|------|--------|----------| -| 1 | Record baseline CTR for 30 days | 30 days | -| 2 | Implement title/description change | Day 0 | -| 3 | Allow Google to re-crawl and update SERP | 3-7 days | -| 4 | Monitor new CTR (exclude first 7 days) | 30+ days | -| 5 | Compare new CTR vs. baseline at same avg. position | — | -| 6 | Decide: keep, revert, or iterate | — | - -### What to Test (Priority Order) - -| Priority | Element | Test Variable | Expected Impact | -|----------|---------|--------------|-----------------| -| 1 | Title tag | Add/remove number | +15-25% CTR | -| 2 | Title tag | Add/remove year | +10-15% CTR | -| 3 | Title tag | Add brackets/parentheses | +10-38% CTR | -| 4 | Title tag | Change power word | +5-12% CTR | -| 5 | Title tag | Reorder keyword placement | +5-10% CTR | -| 6 | Meta description | Add CTA | +5-10% CTR | -| 7 | Meta description | Add numbers/statistics | +5-15% CTR | -| 8 | Meta description | Change emotional tone | +3-8% CTR | - -### Interpreting Results - -| Scenario | Interpretation | Action | -|----------|---------------|--------| -| CTR up, position stable | Title/description change is working | Keep the change | -| CTR up, position up | Improved engagement signals helping rankings | Keep and document | -| CTR down, position stable | Change was negative | Revert immediately | -| CTR flat, position stable | Change had no impact | Try a bolder variation | -| CTR up, position down | Higher CTR but lower impressions | Evaluate net click change | - -### Statistical Significance Checklist - -- [ ] Minimum 1,000 impressions in test period -- [ ] Test ran for at least 30 days (after re-crawl) -- [ ] Average position did not shift more than 2 positions -- [ ] No major algorithm updates during test period -- [ ] No seasonal bias (compare similar months year-over-year) -- [ ] CTR change exceeds 10% relative difference - ---- - -## Seasonal Title Patterns - -### Calendar-Driven Updates - -| Season/Event | Title Modifier | Example | -|-------------|---------------|---------| -| New Year (Jan) | [Year] Edition, Updated for [Year] | SEO Checklist: 2026 Edition | -| Q1 Planning | [Year] Strategy, Q1 [Year] | Content Strategy for Q1 2026 | -| Spring | Spring [Year], Fresh | Spring 2026 SEO Audit Checklist | -| Back to School (Aug-Sep) | Fall [Year], New Season | Fall 2026 Marketing Trends | -| Black Friday (Nov) | Black Friday [Year], Holiday | Black Friday SEO Prep Guide (2026) | -| End of Year (Dec) | [Year] Wrap-Up, [Next Year] Preview | 2026 SEO Wrap-Up + 2027 Predictions | -| Any month | [Month] [Year] | Best SEO Tools (February 2026) | - -### Evergreen + Fresh Hybrid Strategy - -Keep the core title evergreen but append a freshness signal: - -| Base Title (Evergreen) | Freshness Signal | Combined | -|-----------------------|------------------|----------| -| How to Start a Blog | (Updated Feb 2026) | How to Start a Blog (Updated Feb 2026) | -| Best CRM Software | [2026 Rankings] | Best CRM Software [2026 Rankings] | -| SEO Beginner's Guide | - Latest Edition | SEO Beginner's Guide - Latest Edition | - -**Update cadence**: Refresh the year/date modifier every 3-6 months for pages targeting "best", "top", or year-based queries. - ---- - -## CTR Benchmarks by Industry - -### Average Organic CTR by SERP Position (All Industries) - -| Position | Desktop CTR | Mobile CTR | Combined Avg | -|----------|------------|------------|-------------| -| 1 | 31.7% | 26.9% | 29.3% | -| 2 | 17.5% | 15.1% | 16.3% | -| 3 | 11.3% | 10.2% | 10.8% | -| 4 | 7.9% | 7.1% | 7.5% | -| 5 | 5.8% | 5.2% | 5.5% | -| 6 | 4.4% | 3.9% | 4.2% | -| 7 | 3.5% | 3.1% | 3.3% | -| 8 | 2.9% | 2.5% | 2.7% | -| 9 | 2.5% | 2.1% | 2.3% | -| 10 | 2.2% | 1.8% | 2.0% | - -### CTR Variation by Industry Vertical - -| Industry | Avg CTR (Pos 1) | Avg CTR (Pos 1-3) | Notes | -|----------|----------------|-------------------|-------| -| Technology / SaaS | 25-30% | 50-55% | High competition; rich results common | -| E-commerce | 20-25% | 40-45% | Shopping results reduce organic CTR | -| Healthcare | 28-35% | 55-60% | Users seek authoritative sources | -| Finance | 22-28% | 45-50% | Featured snippets capture clicks | -| Travel | 18-24% | 38-44% | Maps and hotel packs reduce CTR | -| Legal | 30-38% | 58-65% | High intent; fewer SERP features | -| Education | 26-32% | 52-58% | Informational queries; high trust needed | -| Real Estate | 20-26% | 42-48% | Local packs reduce organic CTR | -| Food / Recipes | 15-22% | 35-42% | Recipe carousels dominate top positions | -| B2B Services | 28-34% | 55-62% | Less SERP competition; niche queries | - -### When Your CTR Is Below Benchmark - -| CTR vs. Benchmark | Likely Cause | Recommended Action | -|-------------------|-------------|-------------------| -| 50%+ below | Title does not match intent | Rewrite title to align with query intent | -| 25-50% below | Weak title or strong SERP features | Add power words, numbers, or brackets | -| 10-25% below | Competitors have better titles | Analyze top 3 competitor titles and differentiate | -| At benchmark | Performing as expected | Test incremental improvements | -| Above benchmark | Strong title; optimize further | Protect current title; test description instead | - ---- - -## Multi-Language Title Optimization - -### General Principles - -1. **Do not translate literally** -- Adapt the title formula to the target language's natural phrasing and cultural norms. -2. **Character limits vary** -- CJK characters are wider; effective SERP display length is shorter by character count. -3. **Keyword research per locale** -- Search volume and intent differ across languages. Always do locale-specific keyword research. -4. **Brand name placement** -- Some markets prefer brand-first; others prefer keyword-first. - -### Language-Specific Considerations - -| Language | Max Chars (SERP) | Key Differences | Example Adaptation | -|----------|-----------------|-----------------|-------------------| -| English | 55-60 | Standard baseline | How to Learn SEO (2026 Guide) | -| Spanish | 55-60 | Longer phrases; use abbreviations | Como Aprender SEO: Guia Completa 2026 | -| French | 55-60 | Accented characters count same | Guide SEO Complet : Comment se Classer en 2026 | -| German | 50-55 | Compound words are long | SEO-Leitfaden 2026: Schritt-fur-Schritt | -| Japanese | 30-35 | CJK characters take ~2x width | SEO対策ガイド2026【初心者向け】 | -| Chinese (Simplified) | 28-32 | CJK characters; brackets common | SEO优化指南（2026完整版） | -| Korean | 30-35 | CJK width; particles add length | SEO 최적화 가이드 2026 [초보자용] | -| Arabic | 50-55 | RTL display; test SERP rendering | دليل تحسين محركات البحث 2026 | -| Portuguese (BR) | 55-60 | Similar to Spanish | Guia de SEO 2026: Como Ranquear no Google | -| Hindi | 45-50 | Devanagari takes more width | SEO कैसे करें: 2026 गाइड | - -### Multi-Language Meta Description Tips - -| Principle | Detail | -|-----------|--------| -| Localize the CTA | "Learn more" may not translate well; use culturally appropriate CTAs | -| Adjust character targets | For CJK, aim for 70-80 characters; for Latin-script languages, 150-160 | -| Use local proof points | Reference local statistics, regulations, or market leaders | -| Test SERP rendering | Use Google's SERP preview in the target locale to check truncation | -| Separate hreflang tags | Ensure each language version has the correct hreflang annotation | - ---- - -## Title Tag Modifier Library - -### Modifiers by Purpose - -| Purpose | Modifiers | -|---------|----------| -| Freshness | 2026, Updated, Latest, New, This Month, This Week | -| Comprehensiveness | Complete, Ultimate, Definitive, A-to-Z, Full, In-Depth | -| Ease | Easy, Simple, Quick, Beginner, No-Code, DIY | -| Speed | Fast, Instant, [N]-Minute, Today, Quick-Start | -| Authority | Expert, Pro, Advanced, Research-Backed, Data-Driven | -| Value | Free, Cheap, Budget, Affordable, Open-Source | -| Format | Guide, Checklist, Template, Cheat Sheet, Playbook, Toolkit | -| Exclusivity | Insider, Members-Only, Exclusive, First Look, Early Access | -| Social Proof | Trusted, [N]+ Users, Award-Winning, #1 Rated | -| Scarcity | Limited, Last Chance, Ending Soon, Only [N] Left | - -### Modifier Placement Rules - -| Position | When to Use | Example | -|----------|------------|---------| -| Front | When modifier IS the keyword ("Free", "Best") | Free SEO Audit Tool for Small Business | -| Middle | When clarifying the topic | SEO Tools (Free) for Small Business | -| End (parenthetical) | When adding value signal | SEO Tools for Small Business (Free) | -| End (pipe/dash) | When adding brand | SEO Tools for Small Business \| Brand | - ---- - -## Common Title Tag Mistakes - -| Mistake | Why It Hurts | Fix | -|---------|-------------|-----| -| Keyword stuffing | Google may rewrite your title | Use primary keyword once; vary phrasing | -| Too generic ("Home", "Welcome") | Zero CTR differentiation | Write a benefit-driven title | -| All caps | Looks spammy; may be rewritten | Use title case or sentence case | -| Missing primary keyword | Misses ranking signal | Include target keyword naturally | -| Duplicate titles across pages | Cannibalization; Google may pick wrong page | Unique title per page | -| Brand name first (non-branded query) | Wastes prime keyword space | Put brand at end after pipe | -| No emotional hook | Blends in with competitors | Add one power word or number | -| Exceeding 60 characters | Key info truncated in SERP | Front-load; keep under 60 chars | -| Misleading clickbait | High bounce rate hurts rankings | Title must match content delivery | -| Ignoring SERP context | Title doesn't stand out among competitors | Check live SERP before finalizing | - ---- - -## Quick-Reference: Title Formula Selection Flowchart - -``` -START: What type of content is this? -| -+-- Informational (how-to, guide, tutorial) -| +-- Is it step-by-step? --> Use Formula #2 or #8 -| +-- Is it a definition? --> Use Formula #6 -| +-- Is it comprehensive? --> Use Formula #3 or #4 -| +-- Is it for beginners? --> Use Formula #5 -| -+-- Listicle (tips, tools, examples) -| +-- Is it "best of"? --> Use Formula #10 -| +-- Is it mistakes/warnings? --> Use Formula #14 or #29 -| +-- Is it tips/strategies? --> Use Formula #9 or #12 -| +-- Is it examples? --> Use Formula #13 -| -+-- Comparison -| +-- Two products/tools? --> Use Formula #17 or #18 -| +-- Multiple alternatives? --> Use Formula #20 -| -+-- Commercial / Product -| +-- Product page? --> Use Formula #21 -| +-- Pricing page? --> Use Formula #22 -| +-- Review? --> Use Formula #23 -| +-- Free tool? --> Use Formula #24 -| -+-- Local -| +-- Service page? --> Use Formula #25 -| +-- Best-of local? --> Use Formula #26 -| +-- Near me? --> Use Formula #27 -``` diff --git a/.agents/skills/migrate-radix-to-base/SKILL.md b/.agents/skills/migrate-radix-to-base/SKILL.md deleted file mode 100644 index 5eb5dc5700..0000000000 --- a/.agents/skills/migrate-radix-to-base/SKILL.md +++ /dev/null @@ -1,173 +0,0 @@ ---- -name: migrate-radix-to-base -description: Migrates React projects and components from Radix UI to Base UI. Use when asked to migrate from radix, move to base-ui, convert radix primitives, or switch a shadcn project's base library. Handles single components ("migrate accordion") and whole projects. ---- - -# Radix UI -> Base UI migration - -You migrate shadcn wrappers, hand-rolled radix compositions, and their -consumers to `@base-ui/react`, keeping the project buildable at every step. -Be precise; never guess a mapping. When a prop or part is not in these -reference files, check `node_modules/@base-ui/react/**/*.d.ts` before -transforming, and record gaps in the report. - -## Preflight (always) - -1. `npx shadcn@latest info --json` (or the project's runner): gives the - current base, STYLE (e.g. `radix-lyra`), tailwind version, aliases, - installed components, and package manager. Trust it over inference. -2. Detect the package manager (packageManager field / lockfile: - pnpm-lock.yaml, bun.lock, yarn.lock, package-lock.json) and use IT for - every install. Never leave a stale lockfile. -3. Require a clean git tree; work on a branch; one commit per component. -4. Baseline check BEFORE touching dependencies: run the project's - typecheck/build so pre-existing failures are never attributed to you. -5. Install `@base-ui/react` alongside radix. Radix packages are removed only - after the LAST component is migrated (both coexist fine). - -## Strategy: golden pair first, transformation engine second - -- **Golden pair via the CLI (preferred).** If the project is shadcn with a - known style (`radix- - - -
-

${escapeHtml(title)}

-

${escapeHtml(message)}

-
- -`; -} - -export function writeOAuthPage( - res: http.ServerResponse, - statusCode: number, - title: string, - message: string, - _tone?: 'success' | 'error' | 'neutral', -): void { - res - .writeHead(statusCode, { 'content-type': 'text/html; charset=utf-8' }) - .end(renderOAuthPage(title, message)); -} diff --git a/surfsense_desktop/src/modules/oauth.ts b/surfsense_desktop/src/modules/oauth.ts deleted file mode 100644 index 65b1b207b0..0000000000 --- a/surfsense_desktop/src/modules/oauth.ts +++ /dev/null @@ -1,155 +0,0 @@ -import { shell } from 'electron'; -import crypto from 'node:crypto'; -import http from 'node:http'; -import { writeOAuthPage } from './oauth-page'; - -export interface DesktopAuthTokens { - access_token: string; - refresh_token: string; -} - -const OAUTH_TIMEOUT_MS = 5 * 60 * 1000; -const OAUTH_CALLBACK_PATH = '/callback'; - -function base64Url(buffer: Buffer): string { - return buffer.toString('base64').replace(/\+/g, '-').replace(/\//g, '_').replace(/=+$/, ''); -} - -function randomUrlSafe(bytes = 32): string { - return base64Url(crypto.randomBytes(bytes)); -} - -function sha256(value: string): string { - return base64Url(crypto.createHash('sha256').update(value).digest()); -} - -function getGoogleDesktopClientId(): string { - const clientId = (process.env.GOOGLE_DESKTOP_CLIENT_ID || '').trim(); - if (!clientId) { - throw new Error('Google desktop OAuth client ID is not configured'); - } - return clientId; -} - -export async function startGoogleOAuth(backendUrl: string): Promise { - const clientId = getGoogleDesktopClientId(); - const state = randomUrlSafe(); - const codeVerifier = randomUrlSafe(64); - const codeChallenge = sha256(codeVerifier); - - return new Promise((resolve, reject) => { - let settled = false; - let port: number | null = null; - let timeout: NodeJS.Timeout | null = null; - - const cleanup = () => { - if (timeout) { - clearTimeout(timeout); - timeout = null; - } - if (server.listening) { - server.close(); - } - }; - - const fail = (error: Error) => { - if (settled) return; - settled = true; - cleanup(); - reject(error); - }; - - const succeed = (tokens: DesktopAuthTokens) => { - if (settled) return; - settled = true; - cleanup(); - resolve(tokens); - }; - - const server = http.createServer(async (req, res) => { - try { - const url = new URL(req.url || '/', 'http://127.0.0.1'); - if (url.pathname !== OAUTH_CALLBACK_PATH) { - writeOAuthPage(res, 404, 'Not found', 'This OAuth callback endpoint is only used by SurfSense.'); - return; - } - - const oauthError = url.searchParams.get('error'); - if (oauthError) { - const description = url.searchParams.get('error_description'); - writeOAuthPage(res, 400, 'Authentication failed', 'You can close this window and return to SurfSense.', 'error'); - fail(new Error(description || `Google OAuth failed: ${oauthError}`)); - return; - } - - const code = url.searchParams.get('code'); - const returnedState = url.searchParams.get('state'); - if (!code || returnedState !== state) { - writeOAuthPage(res, 400, 'Authentication failed', 'You can close this window and return to SurfSense.', 'error'); - fail(new Error('Invalid OAuth callback')); - return; - } - - if (!port) { - writeOAuthPage(res, 500, 'Authentication failed', 'You can close this window and return to SurfSense.', 'error'); - fail(new Error('OAuth loopback server was not ready')); - return; - } - - const redirectUri = `http://127.0.0.1:${port}${OAUTH_CALLBACK_PATH}`; - const response = await fetch(`${backendUrl}/auth/desktop/session`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ code, code_verifier: codeVerifier, redirect_uri: redirectUri }), - }); - if (!response.ok) { - let detail = 'Desktop session exchange failed'; - try { - const error = (await response.json()) as { detail?: string }; - detail = error.detail || detail; - } catch { - // Keep the generic exchange error if the backend did not return JSON. - } - writeOAuthPage(res, 401, 'Authentication failed', 'You can close this window and return to SurfSense.', 'error'); - fail(new Error(detail)); - return; - } - const tokens = (await response.json()) as DesktopAuthTokens; - writeOAuthPage(res, 200, 'Authentication complete', 'You can close this window and return to SurfSense.', 'success'); - succeed(tokens); - } catch (error) { - fail(error instanceof Error ? error : new Error('Google OAuth failed')); - } - }); - - server.listen(0, '127.0.0.1', () => { - const addressInfo = server.address(); - if (!addressInfo || typeof addressInfo === 'string') { - fail(new Error('Unable to bind loopback OAuth server')); - return; - } - port = addressInfo.port; - timeout = setTimeout(() => { - fail(new Error('Google OAuth timed out')); - }, OAUTH_TIMEOUT_MS); - - const redirectUri = `http://127.0.0.1:${port}${OAUTH_CALLBACK_PATH}`; - const authUrl = new URL('https://accounts.google.com/o/oauth2/v2/auth'); - authUrl.searchParams.set('client_id', clientId); - authUrl.searchParams.set('redirect_uri', redirectUri); - authUrl.searchParams.set('response_type', 'code'); - authUrl.searchParams.set('scope', 'openid email profile'); - authUrl.searchParams.set('state', state); - authUrl.searchParams.set('code_challenge', codeChallenge); - authUrl.searchParams.set('code_challenge_method', 'S256'); - - shell.openExternal(authUrl.toString()).catch((error) => { - fail(error instanceof Error ? error : new Error('Unable to open browser for Google OAuth')); - }); - }); - - server.on('error', (error) => { - fail(error); - }); - }); -} diff --git a/surfsense_desktop/src/modules/permissions.ts b/surfsense_desktop/src/modules/permissions.ts deleted file mode 100644 index 02786113e9..0000000000 --- a/surfsense_desktop/src/modules/permissions.ts +++ /dev/null @@ -1,51 +0,0 @@ -import { app } from 'electron'; - -type PermissionStatus = 'authorized' | 'denied' | 'not determined' | 'restricted' | 'limited'; - -export interface PermissionsStatus { - accessibility: PermissionStatus; - screenRecording: PermissionStatus; -} - -function isMac(): boolean { - return process.platform === 'darwin'; -} - -function getNodeMacPermissions() { - return require('node-mac-permissions'); -} - -export function getPermissionsStatus(): PermissionsStatus { - if (!isMac()) { - return { accessibility: 'authorized', screenRecording: 'authorized' }; - } - - const perms = getNodeMacPermissions(); - return { - accessibility: perms.getAuthStatus('accessibility'), - screenRecording: perms.getAuthStatus('screen'), - }; -} - -export function requestAccessibility(): void { - if (!isMac()) return; - const perms = getNodeMacPermissions(); - perms.askForAccessibilityAccess(); -} - -export function hasScreenRecordingPermission(): boolean { - if (!isMac()) return true; - const perms = getNodeMacPermissions(); - return perms.getAuthStatus('screen') === 'authorized'; -} - -export function requestScreenRecording(): void { - if (!isMac()) return; - const perms = getNodeMacPermissions(); - perms.askForScreenCaptureAccess(); -} - -export function restartApp(): void { - app.relaunch(); - app.exit(0); -} diff --git a/surfsense_desktop/src/modules/platform.ts b/surfsense_desktop/src/modules/platform.ts deleted file mode 100644 index 2b4d1f4a1d..0000000000 --- a/surfsense_desktop/src/modules/platform.ts +++ /dev/null @@ -1,76 +0,0 @@ -import { execSync } from 'child_process'; -import { systemPreferences } from 'electron'; - -const EXEC_OPTS = { windowsHide: true } as const; - -export function getFrontmostApp(): string { - try { - if (process.platform === 'darwin') { - return execSync( - 'osascript -e \'tell application "System Events" to get name of first application process whose frontmost is true\'', - EXEC_OPTS, - ).toString().trim(); - } - if (process.platform === 'win32') { - return execSync( - 'powershell -NoProfile -NonInteractive -command "Add-Type \'using System; using System.Runtime.InteropServices; public class W { [DllImport(\\\"user32.dll\\\")] public static extern IntPtr GetForegroundWindow(); }\'; (Get-Process | Where-Object { $_.MainWindowHandle -eq [W]::GetForegroundWindow() }).ProcessName"', - EXEC_OPTS, - ).toString().trim(); - } - } catch { - return ''; - } - return ''; -} - -export function simulatePaste(): void { - if (process.platform === 'darwin') { - execSync('osascript -e \'tell application "System Events" to keystroke "v" using command down\'', EXEC_OPTS); - } else if (process.platform === 'win32') { - execSync('powershell -NoProfile -NonInteractive -command "Add-Type -AssemblyName System.Windows.Forms; [System.Windows.Forms.SendKeys]::SendWait(\'^v\')"', EXEC_OPTS); - } -} - -export function simulateCopy(): boolean { - try { - if (process.platform === 'darwin') { - execSync('osascript -e \'tell application "System Events" to keystroke "c" using command down\'', EXEC_OPTS); - } else if (process.platform === 'win32') { - execSync('powershell -NoProfile -NonInteractive -command "Add-Type -AssemblyName System.Windows.Forms; [System.Windows.Forms.SendKeys]::SendWait(\'^c\')"', EXEC_OPTS); - } - return true; - } catch (err) { - console.error('[simulateCopy] Failed:', err); - return false; - } -} - -export function checkAccessibilityPermission(): boolean { - if (process.platform !== 'darwin') return true; - return systemPreferences.isTrustedAccessibilityClient(true); -} - -export function getWindowTitle(): string { - try { - if (process.platform === 'darwin') { - return execSync( - 'osascript -e \'tell application "System Events" to get title of front window of first application process whose frontmost is true\'', - EXEC_OPTS, - ).toString().trim(); - } - if (process.platform === 'win32') { - return execSync( - 'powershell -NoProfile -NonInteractive -command "(Get-Process | Where-Object { $_.MainWindowHandle -eq (Add-Type -MemberDefinition \'[DllImport(\\\"user32.dll\\\")] public static extern IntPtr GetForegroundWindow();\' -Name W -PassThru)::GetForegroundWindow() }).MainWindowTitle"', - EXEC_OPTS, - ).toString().trim(); - } - } catch { - return ''; - } - return ''; -} - -export function hasAccessibilityPermission(): boolean { - if (process.platform !== 'darwin') return true; - return systemPreferences.isTrustedAccessibilityClient(false); -} diff --git a/surfsense_desktop/src/modules/quick-ask.ts b/surfsense_desktop/src/modules/quick-ask.ts deleted file mode 100644 index 4b48a3d19e..0000000000 --- a/surfsense_desktop/src/modules/quick-ask.ts +++ /dev/null @@ -1,193 +0,0 @@ -import { app, BrowserWindow, clipboard, globalShortcut, ipcMain, screen, shell } from 'electron'; -import path from 'path'; -import { IPC_CHANNELS } from '../ipc/channels'; -import { checkAccessibilityPermission, getFrontmostApp, simulateCopy, simulatePaste } from './platform'; -import { getServerOrigin } from './server'; -import { getShortcuts } from './shortcuts'; -import { getActiveWorkspaceId } from './active-workspace'; -import { trackEvent } from './analytics'; - -let currentShortcut = ''; -let quickAskWindow: BrowserWindow | null = null; -let pendingText = ''; -let pendingMode = ''; -let pendingWorkspaceId: string | null = null; -let sourceApp = ''; -let savedClipboard = ''; - -function destroyQuickAsk(): void { - if (quickAskWindow && !quickAskWindow.isDestroyed()) { - quickAskWindow.close(); - } - quickAskWindow = null; - pendingMode = ''; -} - -function clampToScreen(x: number, y: number, w: number, h: number): { x: number; y: number } { - const display = screen.getDisplayNearestPoint({ x, y }); - const { x: dx, y: dy, width: dw, height: dh } = display.workArea; - return { - x: Math.max(dx, Math.min(x, dx + dw - w)), - y: Math.max(dy, Math.min(y, dy + dh - h)), - }; -} - -function createQuickAskWindow(x: number, y: number): BrowserWindow { - destroyQuickAsk(); - - quickAskWindow = new BrowserWindow({ - width: 450, - height: 750, - x, - y, - ...(process.platform === 'darwin' - ? { type: 'panel' as const } - : { type: 'toolbar' as const, alwaysOnTop: true }), - resizable: true, - fullscreenable: false, - maximizable: false, - webPreferences: { - preload: path.join(__dirname, 'preload.js'), - contextIsolation: true, - nodeIntegration: false, - sandbox: true, - devTools: !app.isPackaged, - }, - show: false, - skipTaskbar: true, - }); - - const spaceId = pendingWorkspaceId; - const route = spaceId ? `/dashboard/${spaceId}/new-chat` : '/dashboard'; - quickAskWindow.loadURL(`${getServerOrigin()}${route}?quickAssist=true`); - - quickAskWindow.once('ready-to-show', () => { - quickAskWindow?.show(); - }); - - quickAskWindow.webContents.on('before-input-event', (_event, input) => { - if (input.key === 'Escape') destroyQuickAsk(); - }); - - quickAskWindow.webContents.setWindowOpenHandler(({ url }) => { - if (url.startsWith(getServerOrigin())) { - return { action: 'allow' }; - } - shell.openExternal(url); - return { action: 'deny' }; - }); - - quickAskWindow.on('closed', () => { - quickAskWindow = null; - }); - - return quickAskWindow; -} - -async function openQuickAsk(text: string): Promise { - pendingText = text; - pendingMode = 'quick-assist'; - pendingWorkspaceId = await getActiveWorkspaceId(); - const cursor = screen.getCursorScreenPoint(); - const pos = clampToScreen(cursor.x, cursor.y, 450, 750); - createQuickAskWindow(pos.x, pos.y); -} - -async function quickAskHandler(): Promise { - console.log('[quick-ask] Handler triggered'); - - if (quickAskWindow && !quickAskWindow.isDestroyed()) { - console.log('[quick-ask] Window already open, closing'); - destroyQuickAsk(); - return; - } - - if (!checkAccessibilityPermission()) { - console.log('[quick-ask] Accessibility permission denied'); - return; - } - - savedClipboard = clipboard.readText(); - console.log('[quick-ask] Saved clipboard length:', savedClipboard.length); - - const copyOk = simulateCopy(); - console.log('[quick-ask] simulateCopy result:', copyOk); - - await new Promise((r) => setTimeout(r, 300)); - - const afterCopy = clipboard.readText(); - const selected = afterCopy.trim(); - console.log('[quick-ask] Clipboard after copy length:', afterCopy.length, 'changed:', afterCopy !== savedClipboard); - - const text = selected || savedClipboard.trim(); - - sourceApp = getFrontmostApp(); - console.log('[quick-ask] Source app:', sourceApp, '| Opening Quick Assist with', text.length, 'chars', selected ? '(selected)' : text ? '(clipboard fallback)' : '(empty)'); - trackEvent('desktop_quick_ask_opened', { has_selected_text: !!selected }); - openQuickAsk(text); -} - -let ipcRegistered = false; - -function registerIpcHandlers(): void { - if (ipcRegistered) return; - ipcRegistered = true; - - ipcMain.handle(IPC_CHANNELS.QUICK_ASK_TEXT, () => { - const text = pendingText; - pendingText = ''; - return text; - }); - - ipcMain.handle(IPC_CHANNELS.SET_QUICK_ASK_MODE, (_event, mode: string) => { - pendingMode = mode; - }); - - ipcMain.handle(IPC_CHANNELS.GET_QUICK_ASK_MODE, (event) => { - if (quickAskWindow && !quickAskWindow.isDestroyed() && event.sender.id === quickAskWindow.webContents.id) { - return pendingMode; - } - return ''; - }); - - ipcMain.handle(IPC_CHANNELS.REPLACE_TEXT, async (_event, text: string) => { - if (!sourceApp) return; - - if (!checkAccessibilityPermission()) return; - - trackEvent('desktop_quick_ask_replaced'); - clipboard.writeText(text); - destroyQuickAsk(); - - try { - await new Promise((r) => setTimeout(r, 50)); - simulatePaste(); - await new Promise((r) => setTimeout(r, 100)); - clipboard.writeText(savedClipboard); - } catch { - clipboard.writeText(savedClipboard); - } - }); -} - -async function registerShortcut(): Promise { - const shortcuts = await getShortcuts(); - currentShortcut = shortcuts.quickAsk; - - const ok = globalShortcut.register(currentShortcut, () => { quickAskHandler(); }); - console.log(`[quick-ask] Register ${currentShortcut}: ${ok ? 'OK' : 'FAILED'}`); -} - -export async function registerQuickAsk(): Promise { - registerIpcHandlers(); - await registerShortcut(); -} - -export function unregisterQuickAsk(): void { - if (currentShortcut) globalShortcut.unregister(currentShortcut); -} - -export async function reregisterQuickAsk(): Promise { - unregisterQuickAsk(); - await registerShortcut(); -} diff --git a/surfsense_desktop/src/modules/screen-capture/index.ts b/surfsense_desktop/src/modules/screen-capture/index.ts deleted file mode 100644 index 6c1c75509f..0000000000 --- a/surfsense_desktop/src/modules/screen-capture/index.ts +++ /dev/null @@ -1,7 +0,0 @@ -/** - * Window capture for Screenshot Assist and chat fullscreen: single-session - * desktopCapturer, region overlay, and shortcut entry point. - */ -export { pickOpenWindowCapture, type PickedWindowResult } from './window-picker'; -export { pickScreenRegion, captureCurrentDisplayDataUrl } from './screen-region-picker'; -export { runScreenshotAssistShortcut } from './screenshot-assist'; diff --git a/surfsense_desktop/src/modules/screen-capture/screen-region-picker.ts b/surfsense_desktop/src/modules/screen-capture/screen-region-picker.ts deleted file mode 100644 index 0cfc922978..0000000000 --- a/surfsense_desktop/src/modules/screen-capture/screen-region-picker.ts +++ /dev/null @@ -1,336 +0,0 @@ -import { app, BrowserWindow, desktopCapturer, nativeImage, screen } from 'electron'; -import path from 'path'; -import { IPC_CHANNELS } from '../../ipc/channels'; -function fitNativeImageToWorkArea(img: Electron.NativeImage, display: Electron.Display): Electron.NativeImage { - const wa = display.workArea; - const { width: iw, height: ih } = img.getSize(); - const scale = Math.min(1, wa.width / iw, wa.height / ih); - if (scale >= 1) return img; - return img.resize({ - width: Math.max(1, Math.floor(iw * scale)), - height: Math.max(1, Math.floor(ih * scale)), - quality: 'best', - }); -} - -// One getSources per pick; overlay and final crop share that bitmap (avoids a second portal session, e.g. Wayland). - -let pickInProgress = false; - -type DisplayCaptureSnapshot = { - dataUrl: string; - width: number; - height: number; -}; - -async function captureDisplaySnapshot(display: Electron.Display): Promise { - try { - const sf = display.scaleFactor || 1; - const tw = Math.max(1, Math.round(display.size.width * sf)); - const th = Math.max(1, Math.round(display.size.height * sf)); - const sources = await desktopCapturer.getSources({ - types: ['screen'], - thumbnailSize: { width: tw, height: th }, - }); - if (!sources.length) return null; - const idStr = String(display.id); - let chosen = - sources.find((s) => s.display_id === idStr) || - sources.find((s) => s.display_id && s.display_id === idStr) || - null; - if (!chosen && screen.getPrimaryDisplay().id === display.id) { - chosen = sources[0]; - } - if (!chosen) chosen = sources[0]; - const dataUrl = chosen.thumbnail.toDataURL(); - const { width, height } = chosen.thumbnail.getSize(); - return { dataUrl, width, height }; - } catch { - return null; - } -} - -export async function captureCurrentDisplayDataUrl(): Promise { - const display = screen.getDisplayNearestPoint(screen.getCursorScreenPoint()); - const snapshot = await captureDisplaySnapshot(display); - return snapshot?.dataUrl ?? null; -} - -function buildInjectScript(dataUrl: string, iw: number, ih: number): string { - return `(() => { - const api = window.surfsenseScreenRegion; - if (!api) return; - const dataUrl = ${JSON.stringify(dataUrl)}; - const iw = ${iw}; - const ih = ${ih}; - document.body.style.margin = '0'; - document.body.style.overflow = 'hidden'; - document.body.style.background = '#000'; - const img = document.createElement('img'); - img.draggable = false; - img.src = dataUrl; - img.style.cssText = 'position:fixed;inset:0;width:100vw;height:100vh;object-fit:fill;user-select:none;pointer-events:none;'; - const veil = document.createElement('div'); - veil.style.cssText = 'position:fixed;inset:0;cursor:crosshair;background:rgba(0,0,0,0.15);'; - const sel = document.createElement('div'); - sel.style.cssText = 'position:fixed;border:2px solid #38bdf8;box-shadow:0 0 0 9999px rgba(0,0,0,0.45);display:none;pointer-events:none;z-index:2;'; - document.body.appendChild(img); - document.body.appendChild(veil); - document.body.appendChild(sel); - let ax = 0, ay = 0, dragging = false; - function show(x0, y0, x1, y1) { - const l = Math.min(x0, x1), t = Math.min(y0, y1); - const w = Math.abs(x1 - x0), h = Math.abs(y1 - y0); - if (w < 2 || h < 2) { sel.style.display = 'none'; return; } - sel.style.display = 'block'; - sel.style.left = l + 'px'; - sel.style.top = t + 'px'; - sel.style.width = w + 'px'; - sel.style.height = h + 'px'; - } - function mapRect(l, t, w, h) { - const vw = window.innerWidth, vh = window.innerHeight; - const sx = Math.round((l / vw) * iw); - const sy = Math.round((t / vh) * ih); - const sw = Math.max(1, Math.round((w / vw) * iw)); - const sh = Math.max(1, Math.round((h / vh) * ih)); - const cx = Math.min(Math.max(0, sx), iw - 1); - const cy = Math.min(Math.max(0, sy), ih - 1); - const cw = Math.min(sw, iw - cx); - const ch = Math.min(sh, ih - cy); - return { x: cx, y: cy, width: cw, height: ch }; - } - function endDrag(clientX, clientY, pointerId) { - if (!dragging) return; - dragging = false; - if (typeof pointerId === 'number' && pointerId >= 0) { - try { veil.releasePointerCapture(pointerId); } catch (_) {} - } - const l = Math.min(ax, clientX), t = Math.min(ay, clientY); - const w = Math.abs(clientX - ax), h = Math.abs(clientY - ay); - if (w < 4 || h < 4) { sel.style.display = 'none'; return; } - api.submit(mapRect(l, t, w, h)); - } - veil.addEventListener('pointerdown', (e) => { - if (e.button !== 0) return; - try { veil.setPointerCapture(e.pointerId); } catch (_) {} - dragging = true; - ax = e.clientX; ay = e.clientY; - show(ax, ay, ax, ay); - }); - veil.addEventListener('pointermove', (e) => { - if (!dragging) return; - show(ax, ay, e.clientX, e.clientY); - }); - veil.addEventListener('pointerup', (e) => { - endDrag(e.clientX, e.clientY, e.pointerId); - }); - window.addEventListener('pointerup', (e) => { - endDrag(e.clientX, e.clientY, e.pointerId); - }); - document.addEventListener( - 'mouseup', - (e) => { - endDrag(e.clientX, e.clientY, -1); - }, - true - ); - veil.addEventListener('pointercancel', (e) => { - if (!dragging) return; - dragging = false; - try { veil.releasePointerCapture(e.pointerId); } catch (_) {} - sel.style.display = 'none'; - }); - window.addEventListener('keydown', (e) => { - if (e.key === 'Escape') { api.cancel(); return; } - if (e.key === 'Enter' && sel.style.display === 'block') { - const l = parseFloat(sel.style.left), t = parseFloat(sel.style.top); - const w = parseFloat(sel.style.width), h = parseFloat(sel.style.height); - if (w >= 4 && h >= 4) api.submit(mapRect(l, t, w, h)); - } - }); - })();`; -} - -export function pickScreenRegion(opts?: { windowDataUrl?: string }): Promise { - if (pickInProgress) return Promise.resolve(null); - pickInProgress = true; - - return new Promise((resolve) => { - const display = screen.getDisplayNearestPoint(screen.getCursorScreenPoint()); - let settled = false; - let overlay: BrowserWindow | null = null; - /** webContents for listener removal after `BrowserWindow` may already be destroyed. */ - let overlayWc: Electron.WebContents | null = null; - - const cleanupListeners = () => { - const wc = overlayWc; - overlayWc = null; - if (!wc || wc.isDestroyed()) return; - wc.removeListener('before-input-event', onBeforeInput); - wc.ipc.removeListener(IPC_CHANNELS.SCREEN_REGION_SUBMIT, onSubmit); - wc.ipc.removeListener(IPC_CHANNELS.SCREEN_REGION_CANCEL, onCancel); - }; - - const finish = (result: string | null) => { - if (settled) return; - settled = true; - pickInProgress = false; - cleanupListeners(); - if (overlay && !overlay.isDestroyed()) { - overlay.removeAllListeners('closed'); - overlay.close(); - } - overlay = null; - resolve(result); - }; - - let snapshot: DisplayCaptureSnapshot | null = null; - let cropSource: Electron.NativeImage | null = null; - - const onSubmit = ( - _event: Electron.IpcMainEvent, - rect: { x: number; y: number; width: number; height: number } - ) => { - if (settled || !overlay || overlay.isDestroyed()) return; - if (!rect || rect.width < 1 || rect.height < 1) { - finish(null); - return; - } - if (!snapshot || !cropSource) { - finish(null); - return; - } - try { - const iw = snapshot.width; - const ih = snapshot.height; - const { width: cw, height: ch } = cropSource.getSize(); - const scaleX = cw / iw; - const scaleY = ch / ih; - const ox = Math.floor(rect.x * scaleX); - const oy = Math.floor(rect.y * scaleY); - const ow = Math.min(Math.floor(rect.width * scaleX), cw - ox); - const oh = Math.min(Math.floor(rect.height * scaleY), ch - oy); - const cropped = cropSource.crop({ - x: ox, - y: oy, - width: Math.max(1, ow), - height: Math.max(1, oh), - }); - finish(cropped.toDataURL()); - } catch { - finish(null); - } - }; - - const onCancel = (_event: Electron.IpcMainEvent) => { - if (settled || !overlay || overlay.isDestroyed()) return; - finish(null); - }; - - const onBeforeInput = (_event: Electron.Event, input: Electron.Input) => { - if (input.type === 'keyDown' && input.key === 'Escape') { - finish(null); - } - }; - - const openOverlay = ( - cap: DisplayCaptureSnapshot, - crop: Electron.NativeImage, - bounds: { x: number; y: number; width: number; height: number } - ) => { - snapshot = cap; - cropSource = crop; - - overlay = new BrowserWindow({ - x: bounds.x, - y: bounds.y, - width: bounds.width, - height: bounds.height, - frame: false, - transparent: true, - fullscreenable: false, - skipTaskbar: true, - alwaysOnTop: true, - focusable: true, - show: false, - autoHideMenuBar: true, - backgroundColor: '#00000000', - webPreferences: { - preload: path.join(__dirname, 'modules', 'screen-capture', 'screen-region-preload.js'), - contextIsolation: true, - nodeIntegration: false, - sandbox: true, - devTools: !app.isPackaged, - }, - }); - - overlayWc = overlay.webContents; - overlayWc.on('before-input-event', onBeforeInput); - overlayWc.ipc.on(IPC_CHANNELS.SCREEN_REGION_SUBMIT, onSubmit); - overlayWc.ipc.on(IPC_CHANNELS.SCREEN_REGION_CANCEL, onCancel); - - overlay.setIgnoreMouseEvents(false); - overlay.loadURL( - 'data:text/html;charset=utf-8,' + - encodeURIComponent('') - ); - - overlay.on('closed', () => { - if (!settled) finish(null); - }); - - overlay.webContents.once('did-finish-load', () => { - if (!overlay || overlay.isDestroyed()) return; - overlay.webContents - .executeJavaScript(buildInjectScript(cap.dataUrl, cap.width, cap.height), true) - .then(() => { - overlay?.show(); - overlay?.focus(); - }) - .catch(() => { - finish(null); - }); - }); - }; - - void (async () => { - try { - if (opts?.windowDataUrl) { - const fullRes = nativeImage.createFromDataURL(opts.windowDataUrl); - if (fullRes.isEmpty()) { - finish(null); - return; - } - const fitted = fitNativeImageToWorkArea(fullRes, display); - const fw = fitted.getSize().width; - const fh = fitted.getSize().height; - const wa = display.workArea; - const x = wa.x + Math.floor((wa.width - fw) / 2); - const y = wa.y + Math.floor((wa.height - fh) / 2); - openOverlay( - { dataUrl: fitted.toDataURL(), width: fw, height: fh }, - fullRes, - { x, y, width: fw, height: fh } - ); - return; - } - - const cap = await captureDisplaySnapshot(display); - if (!cap) { - finish(null); - return; - } - const crop = nativeImage.createFromDataURL(cap.dataUrl); - openOverlay(cap, crop, { - x: display.bounds.x, - y: display.bounds.y, - width: display.bounds.width, - height: display.bounds.height, - }); - } catch { - finish(null); - } - })(); - }); -} diff --git a/surfsense_desktop/src/modules/screen-capture/screen-region-preload.ts b/surfsense_desktop/src/modules/screen-capture/screen-region-preload.ts deleted file mode 100644 index 4263e0f6e1..0000000000 --- a/surfsense_desktop/src/modules/screen-capture/screen-region-preload.ts +++ /dev/null @@ -1,11 +0,0 @@ -import { contextBridge, ipcRenderer } from 'electron'; -import { IPC_CHANNELS } from '../../ipc/channels'; - -contextBridge.exposeInMainWorld('surfsenseScreenRegion', { - submit: (rect: { x: number; y: number; width: number; height: number }) => { - ipcRenderer.send(IPC_CHANNELS.SCREEN_REGION_SUBMIT, rect); - }, - cancel: () => { - ipcRenderer.send(IPC_CHANNELS.SCREEN_REGION_CANCEL); - }, -}); diff --git a/surfsense_desktop/src/modules/screen-capture/screenshot-assist.ts b/surfsense_desktop/src/modules/screen-capture/screenshot-assist.ts deleted file mode 100644 index 171b98a579..0000000000 --- a/surfsense_desktop/src/modules/screen-capture/screenshot-assist.ts +++ /dev/null @@ -1,26 +0,0 @@ -import { IPC_CHANNELS } from '../../ipc/channels'; -import { trackEvent } from '../analytics'; -import { pickScreenRegion } from './screen-region-picker'; -import { pickOpenWindowCapture } from './window-picker'; -import { getMainWindow, showMainWindow } from '../window'; -import { hasScreenRecordingPermission, requestScreenRecording } from '../permissions'; - -export async function runScreenshotAssistShortcut(): Promise { - if (!hasScreenRecordingPermission()) { - requestScreenRecording(); - return; - } - - const picked = await pickOpenWindowCapture(); - if (!picked) return; - - const url = await pickScreenRegion({ windowDataUrl: picked.dataUrl }); - if (!url) return; - - showMainWindow('shortcut'); - const mw = getMainWindow(); - if (mw && !mw.isDestroyed()) { - mw.webContents.send(IPC_CHANNELS.CHAT_SCREEN_CAPTURE, url); - trackEvent('desktop_screenshot_assist_region_to_chat', {}); - } -} diff --git a/surfsense_desktop/src/modules/screen-capture/window-picker-preload.ts b/surfsense_desktop/src/modules/screen-capture/window-picker-preload.ts deleted file mode 100644 index dd0acd81ea..0000000000 --- a/surfsense_desktop/src/modules/screen-capture/window-picker-preload.ts +++ /dev/null @@ -1,15 +0,0 @@ -import { contextBridge, ipcRenderer } from 'electron'; -import { IPC_CHANNELS } from '../../ipc/channels'; - -contextBridge.exposeInMainWorld('surfsenseWindowPick', { - list: () => - ipcRenderer.invoke(IPC_CHANNELS.WINDOW_PICK_LIST) as Promise< - { id: string; name: string; thumbDataUrl: string }[] - >, - submit: (sourceId: string) => { - ipcRenderer.send(IPC_CHANNELS.WINDOW_PICK_SUBMIT, sourceId); - }, - cancel: () => { - ipcRenderer.send(IPC_CHANNELS.WINDOW_PICK_CANCEL); - }, -}); diff --git a/surfsense_desktop/src/modules/screen-capture/window-picker.ts b/surfsense_desktop/src/modules/screen-capture/window-picker.ts deleted file mode 100644 index 1abafd51c8..0000000000 --- a/surfsense_desktop/src/modules/screen-capture/window-picker.ts +++ /dev/null @@ -1,245 +0,0 @@ -import { app, BrowserWindow, desktopCapturer, ipcMain, screen } from 'electron'; -import path from 'path'; -import { IPC_CHANNELS } from '../../ipc/channels'; - -let pickInProgress = false; - -const PREVIEW_THUMB = { width: 280, height: 180 } as const; - -function maxCaptureThumbSize(): { width: number; height: number } { - const d = screen.getPrimaryDisplay(); - const sf = d.scaleFactor || 1; - const w = Math.min(3840, Math.max(1280, Math.round(d.size.width * sf))); - const h = Math.min(2160, Math.max(720, Math.round(d.size.height * sf))); - return { width: w, height: h }; -} - -function isDesktopWindowSourceId(s: string): boolean { - return typeof s === 'string' && s.startsWith('window:'); -} - -export type PickedWindowResult = { - sourceId: string; - /** Same pixels as the one `desktopCapturer` snapshot (max thumbnail size). */ - dataUrl: string; -}; - -function buildPickerInjectScript(): string { - return `(async function () { - const api = window.surfsenseWindowPick; - if (!api) return; - const items = await api.list(); - document.body.style.cssText = - 'margin:0;font-family:system-ui,-apple-system,sans-serif;background:#0f172a;color:#e2e8f0;min-height:100vh;padding:16px;box-sizing:border-box;'; - const top = document.createElement('div'); - top.style.cssText = - 'display:flex;justify-content:space-between;align-items:center;margin-bottom:12px;flex-wrap:wrap;gap:8px;'; - const t = document.createElement('strong'); - t.textContent = 'Open windows'; - const hint = document.createElement('span'); - hint.style.cssText = 'opacity:0.75;font-size:13px;'; - hint.textContent = 'Click a window · Esc to cancel'; - top.appendChild(t); - top.appendChild(hint); - document.body.appendChild(top); - if (!items || !items.length) { - const p = document.createElement('p'); - p.style.cssText = 'line-height:1.5;max-width:42rem;'; - p.textContent = - 'No windows were returned by the system. On Linux, allow screen capture when prompted. If other apps are open, try again.'; - document.body.appendChild(p); - return; - } - const grid = document.createElement('div'); - grid.style.cssText = - 'display:grid;grid-template-columns:repeat(auto-fill,minmax(200px,1fr));gap:12px;max-height:calc(100vh - 88px);overflow:auto;padding-bottom:8px;'; - for (const it of items) { - const card = document.createElement('button'); - card.type = 'button'; - card.style.cssText = - 'text-align:left;background:#1e293b;border:1px solid #334155;border-radius:8px;padding:8px;cursor:pointer;color:inherit;'; - card.addEventListener('mouseenter', function () { - card.style.borderColor = '#38bdf8'; - }); - card.addEventListener('mouseleave', function () { - card.style.borderColor = '#334155'; - }); - const img = document.createElement('img'); - img.alt = ''; - img.src = - it.thumbDataUrl || - 'data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///ywAAAAAAQABAAACAUwAOw=='; - img.style.cssText = - 'width:100%;height:100px;object-fit:cover;border-radius:4px;background:#000;display:block;'; - const cap = document.createElement('div'); - cap.textContent = it.name || '(untitled)'; - cap.style.cssText = - 'margin-top:6px;font-size:12px;line-height:1.35;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical;'; - card.appendChild(img); - card.appendChild(cap); - card.addEventListener('click', function () { - api.submit(it.id); - }); - grid.appendChild(card); - } - document.body.appendChild(grid); - window.addEventListener('keydown', function (e) { - if (e.key === 'Escape') api.cancel(); - }); - })();`; -} - -/** - * One OS / Chromium capture session: `getSources` runs once (important on Wayland / - * PipeWire so the portal is not opened again for the same flow). Opens our grid to - * choose a window; resolves with the chosen snapshot for region or full-frame use. - */ -export function pickOpenWindowCapture(): Promise { - if (pickInProgress) return Promise.resolve(null); - pickInProgress = true; - - return new Promise((resolve) => { - let settled = false; - let picker: BrowserWindow | null = null; - let pickerWc: Electron.WebContents | null = null; - /** Filled once before the grid runs — reused for list + final image (no second getSources). */ - let sessionSources: Electron.DesktopCapturerSource[] = []; - - const finish = (result: PickedWindowResult | null) => { - if (settled) return; - settled = true; - pickInProgress = false; - ipcMain.removeHandler(IPC_CHANNELS.WINDOW_PICK_LIST); - const wc = pickerWc; - pickerWc = null; - if (wc && !wc.isDestroyed()) { - wc.removeListener('before-input-event', onBeforeInput); - wc.ipc.removeListener(IPC_CHANNELS.WINDOW_PICK_SUBMIT, onSubmit); - wc.ipc.removeListener(IPC_CHANNELS.WINDOW_PICK_CANCEL, onCancel); - } - if (picker && !picker.isDestroyed()) { - picker.removeAllListeners('closed'); - picker.close(); - } - picker = null; - resolve(result); - }; - - const onSubmit = (_event: Electron.IpcMainEvent, sourceId: string) => { - if (settled || !picker || picker.isDestroyed()) return; - if (!isDesktopWindowSourceId(sourceId)) { - finish(null); - return; - } - const hit = sessionSources.find((s) => s.id === sourceId); - if (!hit || hit.thumbnail.isEmpty()) { - finish(null); - return; - } - finish({ sourceId, dataUrl: hit.thumbnail.toDataURL() }); - }; - - const onCancel = () => { - if (settled || !picker || picker.isDestroyed()) return; - finish(null); - }; - - const onBeforeInput = (_event: Electron.Event, input: Electron.Input) => { - if (input.type === 'keyDown' && input.key === 'Escape') { - finish(null); - } - }; - - ipcMain.handle(IPC_CHANNELS.WINDOW_PICK_LIST, async () => { - return sessionSources.map((s, i) => { - let thumbDataUrl = ''; - if (!s.thumbnail.isEmpty()) { - try { - const sm = s.thumbnail.resize({ - width: PREVIEW_THUMB.width, - height: PREVIEW_THUMB.height, - quality: 'good', - }); - thumbDataUrl = sm.toDataURL(); - } catch { - thumbDataUrl = s.thumbnail.toDataURL(); - } - } - return { - id: s.id, - name: (s.name || '').trim() || `Window ${i + 1}`, - thumbDataUrl, - }; - }); - }); - - picker = new BrowserWindow({ - width: 760, - height: 560, - show: false, - center: true, - autoHideMenuBar: true, - title: 'SurfSense — choose window', - webPreferences: { - preload: path.join(__dirname, 'modules', 'screen-capture', 'window-picker-preload.js'), - contextIsolation: true, - nodeIntegration: false, - sandbox: true, - devTools: !app.isPackaged, - }, - }); - - pickerWc = picker.webContents; - - pickerWc.on('before-input-event', onBeforeInput); - pickerWc.ipc.on(IPC_CHANNELS.WINDOW_PICK_SUBMIT, onSubmit); - pickerWc.ipc.on(IPC_CHANNELS.WINDOW_PICK_CANCEL, onCancel); - - picker.on('closed', () => { - if (!settled) finish(null); - }); - - picker - .loadURL( - 'data:text/html;charset=utf-8,' + - encodeURIComponent('') - ) - .catch(() => finish(null)); - - picker.webContents.once('did-finish-load', () => { - void (async () => { - if (!picker || picker.isDestroyed()) return; - let selfId = ''; - try { - selfId = picker.getMediaSourceId(); - } catch { - selfId = ''; - } - try { - const { width, height } = maxCaptureThumbSize(); - const sources = await desktopCapturer.getSources({ - types: ['window'], - thumbnailSize: { width, height }, - fetchWindowIcons: false, - }); - sessionSources = sources.filter((s) => !(selfId && s.id === selfId)); - } catch { - sessionSources = []; - } - if (sessionSources.length === 1) { - const only = sessionSources[0]; - if (!only.thumbnail.isEmpty()) { - finish({ sourceId: only.id, dataUrl: only.thumbnail.toDataURL() }); - return; - } - } - try { - await picker.webContents.executeJavaScript(buildPickerInjectScript(), true); - if (!picker.isDestroyed()) picker.show(); - } catch { - finish(null); - } - })(); - }); - }); -} diff --git a/surfsense_desktop/src/modules/secret-store.ts b/surfsense_desktop/src/modules/secret-store.ts deleted file mode 100644 index 28a1cfc4b7..0000000000 --- a/surfsense_desktop/src/modules/secret-store.ts +++ /dev/null @@ -1,86 +0,0 @@ -import { app, safeStorage } from 'electron'; -import fs from 'node:fs/promises'; -import path from 'node:path'; - -export interface SecretStore { - set(key: string, value: string): Promise; - get(key: string): Promise; - clear(key: string): Promise; - isHardwareBacked(): Promise; -} - -const memoryStore = new Map(); -const storePath = path.join(app.getPath('userData'), 'secrets.enc.json'); - -async function readDiskStore(): Promise> { - try { - const raw = await fs.readFile(storePath, 'utf8'); - return JSON.parse(raw) as Record; - } catch { - return {}; - } -} - -async function writeDiskStore(data: Record): Promise { - await fs.mkdir(path.dirname(storePath), { recursive: true }); - await fs.writeFile(storePath, JSON.stringify(data), { encoding: 'utf8', mode: 0o600 }); -} - -async function canPersistEncryptedSecrets(): Promise { - try { - if (safeStorage.getSelectedStorageBackend?.() === 'basic_text') { - return false; - } - return await safeStorage.isAsyncEncryptionAvailable(); - } catch { - return false; - } -} - -export const secretStore: SecretStore = { - async set(key, value) { - if (!(await canPersistEncryptedSecrets())) { - memoryStore.set(key, value); - return; - } - - const encrypted = await safeStorage.encryptStringAsync(value); - const data = await readDiskStore(); - data[key] = encrypted.toString('base64'); - await writeDiskStore(data); - }, - - async get(key) { - if (!(await canPersistEncryptedSecrets())) { - return memoryStore.get(key) ?? null; - } - - const data = await readDiskStore(); - const encoded = data[key]; - if (!encoded) return null; - - try { - const decrypted = await safeStorage.decryptStringAsync(Buffer.from(encoded, 'base64')); - if (decrypted.shouldReEncrypt) { - await this.set(key, decrypted.result); - } - return decrypted.result; - } catch { - await this.clear(key); - return null; - } - }, - - async clear(key) { - memoryStore.delete(key); - const data = await readDiskStore(); - if (key in data) { - delete data[key]; - await writeDiskStore(data); - } - }, - - async isHardwareBacked() { - return canPersistEncryptedSecrets(); - }, -}; diff --git a/surfsense_desktop/src/modules/server.ts b/surfsense_desktop/src/modules/server.ts deleted file mode 100644 index d7274ad9c5..0000000000 --- a/surfsense_desktop/src/modules/server.ts +++ /dev/null @@ -1,100 +0,0 @@ -import path from 'path'; -import { app, utilityProcess } from 'electron'; -import { getPort } from 'get-port-please'; - -const isDev = !app.isPackaged; -const SERVER_HOST = '127.0.0.1'; -let serverPort = 3000; -let nextServerProcess: ReturnType | null = null; - -export function getServerPort(): number { - return serverPort; -} - -export function getServerOrigin(): string { - return `http://${SERVER_HOST}:${serverPort}`; -} - -function getStandalonePath(): string { - if (isDev) { - return path.join(__dirname, '..', '..', 'surfsense_web', '.next', 'standalone', 'surfsense_web'); - } - return path.join(process.resourcesPath, 'standalone'); -} - -async function waitForServer(url: string, maxRetries = 60): Promise { - for (let i = 0; i < maxRetries; i++) { - try { - const res = await fetch(url); - if (res.ok || res.status === 404 || res.status === 500) return true; - } catch { - // not ready yet - } - await new Promise((r) => setTimeout(r, 500)); - } - return false; -} - -export async function startNextServer(): Promise { - if (isDev) return; - - serverPort = await getPort({ port: 3000, portRange: [30_011, 50_000] }); - console.log(`Selected port ${serverPort}`); - - const standalonePath = getStandalonePath(); - const serverScript = path.join(standalonePath, 'server.js'); - const backendInternalUrl = process.env.SURFSENSE_BACKEND_INTERNAL_URL || process.env.HOSTED_BACKEND_URL; - - const child = utilityProcess.fork(serverScript, [], { - cwd: standalonePath, - env: { - ...process.env, - ...(backendInternalUrl ? { SURFSENSE_BACKEND_INTERNAL_URL: backendInternalUrl } : {}), - PORT: String(serverPort), - // Loopback bind: avoids 0.0.0.0 leaking into request.url and redirect origins. - HOSTNAME: SERVER_HOST, - NODE_ENV: 'production', - }, - serviceName: 'SurfSense Next Server', - stdio: 'pipe', - }); - nextServerProcess = child; - - child.stdout?.on('data', (chunk) => { - process.stdout.write(chunk); - }); - child.stderr?.on('data', (chunk) => { - process.stderr.write(chunk); - }); - - const handleExit = (code: number) => { - if (nextServerProcess === child) { - nextServerProcess = null; - } - console.error(`Next.js server exited with code ${code}`); - }; - child.on('exit', handleExit); - - let startupExitHandler: ((code: number) => void) | null = null; - const exited = new Promise((_resolve, reject) => { - startupExitHandler = (code: number) => { - reject(new Error(`Next.js server exited before startup completed with code ${code}`)); - }; - child.once('exit', startupExitHandler); - }); - - const ready = await Promise.race([waitForServer(getServerOrigin()), exited]); - if (startupExitHandler) { - child.removeListener('exit', startupExitHandler); - } - if (!ready) { - stopNextServer(); - throw new Error('Next.js server failed to start within 30 s'); - } - console.log(`Next.js server ready on port ${serverPort}`); -} - -export function stopNextServer(): void { - nextServerProcess?.kill(); - nextServerProcess = null; -} diff --git a/surfsense_desktop/src/modules/shortcuts.ts b/surfsense_desktop/src/modules/shortcuts.ts deleted file mode 100644 index 64687f7db9..0000000000 --- a/surfsense_desktop/src/modules/shortcuts.ts +++ /dev/null @@ -1,60 +0,0 @@ -export interface ShortcutConfig { - generalAssist: string; - quickAsk: string; - screenshotAssist: string; -} - -const DEFAULTS: ShortcutConfig = { - generalAssist: 'CommandOrControl+Shift+S', - quickAsk: 'CommandOrControl+Alt+S', - screenshotAssist: 'CommandOrControl+Shift+Space', -}; - -const STORE_KEY = 'shortcuts'; -// eslint-disable-next-line @typescript-eslint/no-explicit-any -- lazily imported ESM module; matches folder-watcher.ts pattern -let store: any = null; - -async function getStore() { - if (!store) { - const { default: Store } = await import('electron-store'); - store = new Store({ - name: 'keyboard-shortcuts', - defaults: { [STORE_KEY]: DEFAULTS }, - }); - } - return store; -} - -export async function getShortcuts(): Promise { - const s = await getStore(); - const raw = (s.get(STORE_KEY) as Record | undefined) ?? {}; - const legacyAutocomplete = raw.autocomplete; - const { autocomplete: _drop, ...rest } = raw; - let merged: ShortcutConfig = { ...DEFAULTS, ...rest }; - if ( - typeof legacyAutocomplete === 'string' && - legacyAutocomplete.length > 0 && - !('screenshotAssist' in raw) - ) { - merged = { ...merged, screenshotAssist: legacyAutocomplete }; - s.set(STORE_KEY, { - generalAssist: merged.generalAssist, - quickAsk: merged.quickAsk, - screenshotAssist: merged.screenshotAssist, - }); - } - return merged; -} - -export async function setShortcuts(config: Partial): Promise { - const s = await getStore(); - const raw = (s.get(STORE_KEY) as Record | undefined) ?? {}; - const { autocomplete: _drop, ...current } = raw; - const merged = { ...DEFAULTS, ...current, ...config }; - s.set(STORE_KEY, merged); - return merged; -} - -export function getDefaults(): ShortcutConfig { - return { ...DEFAULTS }; -} diff --git a/surfsense_desktop/src/modules/sunset.ts b/surfsense_desktop/src/modules/sunset.ts deleted file mode 100644 index f57a379bf8..0000000000 --- a/surfsense_desktop/src/modules/sunset.ts +++ /dev/null @@ -1,24 +0,0 @@ -const DEFAULT_SUNSET_URL = 'https://surfsense.com/sunset'; -const TIMEOUT_MS = 3000; - -let sunsetUrl: string | null = null; - -export async function checkSunset(): Promise { - const backendUrl = process.env.SURFSENSE_BACKEND_INTERNAL_URL || process.env.HOSTED_BACKEND_URL; - if (!backendUrl) return; - try { - const res = await fetch(`${backendUrl.replace(/\/$/, '')}/health`, { - signal: AbortSignal.timeout(TIMEOUT_MS), - }); - if (!res.ok) return; - const body = (await res.json()) as { sunset?: unknown; sunset_url?: unknown }; - if (body.sunset !== true) return; - sunsetUrl = typeof body.sunset_url === 'string' && body.sunset_url ? body.sunset_url : DEFAULT_SUNSET_URL; - } catch { - // fail open - } -} - -export function getSunsetUrl(): string | null { - return sunsetUrl; -} diff --git a/surfsense_desktop/src/modules/tray.ts b/surfsense_desktop/src/modules/tray.ts deleted file mode 100644 index e71168f6e2..0000000000 --- a/surfsense_desktop/src/modules/tray.ts +++ /dev/null @@ -1,136 +0,0 @@ -import { app, globalShortcut, Menu, nativeImage, Tray, type NativeImage } from 'electron'; -import path from 'path'; -import { runGeneralAssistShortcut } from './general-assist'; -import { runScreenshotAssistShortcut } from './screen-capture'; -import { showMainWindow } from './window'; -import { getShortcuts } from './shortcuts'; -import { trackEvent } from './analytics'; - -let tray: Tray | null = null; -let registeredGeneralAssist: string | null = null; -let registeredScreenshotAssist: string | null = null; - -function buildContextMenu(screenshotAccelerator: string): Menu { - return Menu.buildFromTemplate([ - { label: 'Open SurfSense', click: () => showMainWindow('tray_menu') }, - { - label: 'Take Screenshot\u2026', - accelerator: screenshotAccelerator || undefined, - click: () => { - trackEvent('desktop_tray_screenshot_clicked'); - void Promise.resolve(runScreenshotAssistShortcut()).catch((err) => { - console.error('[tray] Screenshot Assist failed:', err); - }); - }, - }, - { type: 'separator' }, - { - label: 'Quit', - click: () => { - trackEvent('desktop_tray_quit_clicked'); - app.exit(0); - }, - }, - ]); -} - -function getTrayIcon(): NativeImage { - const iconName = - process.platform === 'darwin' - ? 'iconTemplate.png' - : process.platform === 'win32' - ? 'icon.ico' - : 'icon.png'; - const iconPath = app.isPackaged - ? path.join(process.resourcesPath, 'assets', iconName) - : path.join(__dirname, '..', 'assets', iconName); - const img = nativeImage.createFromPath(iconPath); - if (process.platform === 'darwin') { - img.setTemplateImage(true); - return img; - } - return img.resize({ width: 16, height: 16 }); -} - -function registerOne( - previous: string | null, - accelerator: string, - onFire: () => void | Promise, - label: string -): string | null { - if (previous) { - globalShortcut.unregister(previous); - } - if (!accelerator) return null; - try { - const ok = globalShortcut.register(accelerator, () => { - void Promise.resolve(onFire()); - }); - if (ok) { - console.log(`[hotkeys] Register ${label} ${accelerator}: OK`); - return accelerator; - } - console.warn(`[hotkeys] Register ${label} ${accelerator}: FAILED (OS or another app may own this chord)`); - } catch (err) { - console.error(`[tray] Error registering ${label} shortcut:`, err); - } - return null; -} - -export async function createTray(): Promise { - if (tray) return; - - tray = new Tray(getTrayIcon()); - tray.setToolTip('SurfSense'); - - const shortcuts = await getShortcuts(); - tray.setContextMenu(buildContextMenu(shortcuts.screenshotAssist)); - tray.on('double-click', () => showMainWindow('tray_click')); - - registeredGeneralAssist = registerOne( - null, - shortcuts.generalAssist, - runGeneralAssistShortcut, - 'General Assist' - ); - registeredScreenshotAssist = registerOne( - null, - shortcuts.screenshotAssist, - runScreenshotAssistShortcut, - 'Screenshot Assist' - ); -} - -export async function reregisterGeneralAssist(): Promise { - const shortcuts = await getShortcuts(); - registeredGeneralAssist = registerOne( - registeredGeneralAssist, - shortcuts.generalAssist, - runGeneralAssistShortcut, - 'General Assist' - ); -} - -export async function reregisterScreenshotAssist(): Promise { - const shortcuts = await getShortcuts(); - registeredScreenshotAssist = registerOne( - registeredScreenshotAssist, - shortcuts.screenshotAssist, - runScreenshotAssistShortcut, - 'Screenshot Assist' - ); - tray?.setContextMenu(buildContextMenu(shortcuts.screenshotAssist)); -} - -export function destroyTray(): void { - if (registeredGeneralAssist) { - globalShortcut.unregister(registeredGeneralAssist); - registeredGeneralAssist = null; - } - if (registeredScreenshotAssist) { - globalShortcut.unregister(registeredScreenshotAssist); - registeredScreenshotAssist = null; - } - tray?.destroy(); - tray = null; -} diff --git a/surfsense_desktop/src/modules/window.ts b/surfsense_desktop/src/modules/window.ts deleted file mode 100644 index e918a15667..0000000000 --- a/surfsense_desktop/src/modules/window.ts +++ /dev/null @@ -1,185 +0,0 @@ -import { app, BrowserWindow, shell, session } from 'electron'; -import path from 'path'; -import { trackEvent } from './analytics'; -import { showErrorDialog } from './errors'; -import { getServerOrigin, getServerPort } from './server'; -import { getSunsetUrl } from './sunset'; -import { setActiveWorkspaceId } from './active-workspace'; - -const isDev = !app.isPackaged; -const isMac = process.platform === 'darwin'; -const WINDOW_TITLE = 'SurfSense'; - -function getHostedFrontendUrl(): string { - return ( - process.env.SURFSENSE_HOSTED_FRONTEND_URL_OVERRIDE || - process.env.HOSTED_FRONTEND_URL || - 'https://surfsense.com' - ); -} - -function getHostedFrontendHosts(): string[] { - try { - const host = new URL(getHostedFrontendUrl()).host; - const sibling = host.startsWith('www.') ? host.slice(4) : `www.${host}`; - return Array.from(new Set([host, sibling])); - } catch { - return []; - } -} - -let mainWindow: BrowserWindow | null = null; -let isQuitting = false; - -export function getMainWindow(): BrowserWindow | null { - return mainWindow; -} - -// Called from main.ts on `before-quit` so the close-to-tray handler knows -// to actually let the window die instead of hiding it. -export function markQuitting(): void { - isQuitting = true; -} - -export function createMainWindow(initialPath = '/dashboard'): BrowserWindow { - mainWindow = new BrowserWindow({ - title: WINDOW_TITLE, - width: 1280, - height: 800, - minWidth: 800, - minHeight: 600, - webPreferences: { - preload: path.join(__dirname, 'preload.js'), - contextIsolation: true, - nodeIntegration: false, - sandbox: true, - webviewTag: false, - devTools: !app.isPackaged, - }, - show: false, - ...(isMac - ? { - titleBarStyle: 'hidden' as const, - trafficLightPosition: { x: 12, y: 10 }, - } - : {}), - }); - - mainWindow.once('ready-to-show', () => { - mainWindow?.maximize(); - mainWindow?.show(); - }); - - mainWindow.webContents.on('page-title-updated', (event) => { - event.preventDefault(); - mainWindow?.setTitle(WINDOW_TITLE); - }); - mainWindow.webContents.on('did-finish-load', () => { - mainWindow?.setTitle(WINDOW_TITLE); - }); - - mainWindow.loadURL(getSunsetUrl() ?? `${getServerOrigin()}${initialPath}`); - - mainWindow.webContents.setWindowOpenHandler(({ url }) => { - if (url.startsWith(getServerOrigin())) { - return { action: 'allow' }; - } - shell.openExternal(url); - return { action: 'deny' }; - }); - - const hostedHosts = getHostedFrontendHosts(); - const rewriteFilter = { - urls: hostedHosts.flatMap((h) => [`http://${h}/*`, `https://${h}/*`]), - }; - if (rewriteFilter.urls.length > 0) { - session.defaultSession.webRequest.onBeforeRequest(rewriteFilter, (details, callback) => { - try { - const u = new URL(details.url); - if (!u.pathname.includes('/connectors/callback')) { - callback({}); - return; - } - const originalHost = u.host; - const local = new URL(getServerOrigin()); - u.protocol = local.protocol; - u.host = local.host; - trackEvent('desktop_oauth_redirect_intercepted', { - host: originalHost, - path: u.pathname, - rewritten_to_port: getServerPort(), - }); - callback({ redirectURL: u.toString() }); - } catch { - callback({}); - } - }); - } - - // Diagnostic: connector callback landing somewhere other than localhost - // means the rewrite missed and the user is stranded off-app. - session.defaultSession.webRequest.onCompleted( - { urls: ['*://*/dashboard/*/connectors/callback*'] }, - (details) => { - try { - const u = new URL(details.url); - if (u.hostname === 'localhost' || u.hostname === '127.0.0.1') return; - trackEvent('desktop_oauth_redirect_missed', { - host: u.host, - path: u.pathname, - status_code: details.statusCode, - }); - } catch { - // ignore malformed URLs - } - } - ); - - mainWindow.webContents.on('did-fail-load', (_event, errorCode, errorDescription, validatedURL) => { - console.error(`Failed to load ${validatedURL}: ${errorDescription} (${errorCode})`); - if (errorCode === -3) return; - showErrorDialog('Page failed to load', new Error(`${errorDescription} (${errorCode})\n${validatedURL}`)); - }); - - // Auto-sync active search space from URL navigation - const syncWorkspace = (url: string) => { - const match = url.match(/\/dashboard\/(\d+)/); - if (match) { - setActiveWorkspaceId(match[1]); - } - }; - mainWindow.webContents.on('did-navigate', (_event, url) => syncWorkspace(url)); - mainWindow.webContents.on('did-navigate-in-page', (_event, url) => syncWorkspace(url)); - - if (isDev) { - mainWindow.webContents.openDevTools(); - } - - // Hide-to-tray on close (don't actually destroy the window unless the - // user really is quitting). Applies to every instance — including the one - // created lazily after a launch-at-login boot. - mainWindow.on('close', (e) => { - if (!isQuitting && mainWindow) { - e.preventDefault(); - mainWindow.hide(); - } - }); - - mainWindow.on('closed', () => { - mainWindow = null; - }); - - return mainWindow; -} - -export function showMainWindow(source: 'tray_click' | 'tray_menu' | 'shortcut' = 'tray_click'): void { - const existing = getMainWindow(); - const reopened = !existing || existing.isDestroyed(); - if (reopened) { - createMainWindow('/dashboard'); - } else { - existing.show(); - existing.focus(); - } - trackEvent('desktop_main_window_shown', { source, reopened }); -} diff --git a/surfsense_desktop/src/preload.ts b/surfsense_desktop/src/preload.ts deleted file mode 100644 index 96079d2413..0000000000 --- a/surfsense_desktop/src/preload.ts +++ /dev/null @@ -1,169 +0,0 @@ -const { contextBridge, ipcRenderer } = require('electron'); -const { IPC_CHANNELS } = require('./ipc/channels'); - -contextBridge.exposeInMainWorld('electronAPI', { - versions: { - electron: process.versions.electron, - node: process.versions.node, - chrome: process.versions.chrome, - platform: process.platform, - }, - openExternal: (url: string) => ipcRenderer.send(IPC_CHANNELS.OPEN_EXTERNAL, url), - getAppVersion: () => ipcRenderer.invoke(IPC_CHANNELS.GET_APP_VERSION), - onUpdateDownloaded: (callback: (data: { version: string }) => void) => { - const listener = (_event: unknown, data: { version: string }) => callback(data); - ipcRenderer.on(IPC_CHANNELS.UPDATE_DOWNLOADED, listener); - return () => { - ipcRenderer.removeListener(IPC_CHANNELS.UPDATE_DOWNLOADED, listener); - }; - }, - installUpdateNow: () => ipcRenderer.invoke(IPC_CHANNELS.UPDATE_INSTALL_NOW), - onDeepLink: (callback: (url: string) => void) => { - const listener = (_event: unknown, url: string) => callback(url); - ipcRenderer.on(IPC_CHANNELS.DEEP_LINK, listener); - return () => { - ipcRenderer.removeListener(IPC_CHANNELS.DEEP_LINK, listener); - }; - }, - onChatScreenCapture: (callback: (dataUrl: string) => void) => { - const listener = (_event: unknown, dataUrl: string) => callback(dataUrl); - ipcRenderer.on(IPC_CHANNELS.CHAT_SCREEN_CAPTURE, listener); - return () => { - ipcRenderer.removeListener(IPC_CHANNELS.CHAT_SCREEN_CAPTURE, listener); - }; - }, - getQuickAskText: () => ipcRenderer.invoke(IPC_CHANNELS.QUICK_ASK_TEXT), - setQuickAskMode: (mode: string) => ipcRenderer.invoke(IPC_CHANNELS.SET_QUICK_ASK_MODE, mode), - getQuickAskMode: () => ipcRenderer.invoke(IPC_CHANNELS.GET_QUICK_ASK_MODE), - replaceText: (text: string) => ipcRenderer.invoke(IPC_CHANNELS.REPLACE_TEXT, text), - // Permissions - getPermissionsStatus: () => ipcRenderer.invoke(IPC_CHANNELS.GET_PERMISSIONS_STATUS), - requestAccessibility: () => ipcRenderer.invoke(IPC_CHANNELS.REQUEST_ACCESSIBILITY), - requestScreenRecording: () => ipcRenderer.invoke(IPC_CHANNELS.REQUEST_SCREEN_RECORDING), - captureFullScreen: () => ipcRenderer.invoke(IPC_CHANNELS.CAPTURE_FULL_SCREEN), - restartApp: () => ipcRenderer.invoke(IPC_CHANNELS.RESTART_APP), - // Folder sync - selectFolder: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_SELECT_FOLDER), - addWatchedFolder: (config: any) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_ADD_FOLDER, config), - removeWatchedFolder: (folderPath: string) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_REMOVE_FOLDER, folderPath), - getWatchedFolders: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_GET_FOLDERS), - getWatcherStatus: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_GET_STATUS), - onFileChanged: (callback: (data: any) => void) => { - const listener = (_event: unknown, data: any) => callback(data); - ipcRenderer.on(IPC_CHANNELS.FOLDER_SYNC_FILE_CHANGED, listener); - return () => { - ipcRenderer.removeListener(IPC_CHANNELS.FOLDER_SYNC_FILE_CHANGED, listener); - }; - }, - onWatcherReady: (callback: (data: any) => void) => { - const listener = (_event: unknown, data: any) => callback(data); - ipcRenderer.on(IPC_CHANNELS.FOLDER_SYNC_WATCHER_READY, listener); - return () => { - ipcRenderer.removeListener(IPC_CHANNELS.FOLDER_SYNC_WATCHER_READY, listener); - }; - }, - pauseWatcher: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_PAUSE), - resumeWatcher: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_RESUME), - signalRendererReady: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_RENDERER_READY), - getPendingFileEvents: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_GET_PENDING_EVENTS), - acknowledgeFileEvents: (eventIds: string[]) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_ACK_EVENTS, eventIds), - listFolderFiles: (config: any) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_LIST_FILES, config), - seedFolderMtimes: (folderPath: string, mtimes: Record) => - ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_SEED_MTIMES, folderPath, mtimes), - - // Browse files via native dialog - browseFiles: () => ipcRenderer.invoke(IPC_CHANNELS.BROWSE_FILES), - readLocalFiles: (paths: string[]) => ipcRenderer.invoke(IPC_CHANNELS.READ_LOCAL_FILES, paths), - readAgentLocalFileText: (virtualPath: string, workspaceId?: number | null) => - ipcRenderer.invoke(IPC_CHANNELS.READ_AGENT_LOCAL_FILE_TEXT, virtualPath, workspaceId), - writeAgentLocalFileText: (virtualPath: string, content: string, workspaceId?: number | null) => - ipcRenderer.invoke(IPC_CHANNELS.WRITE_AGENT_LOCAL_FILE_TEXT, virtualPath, content, workspaceId), - - // Auth token sync across windows - getAccessToken: () => ipcRenderer.invoke(IPC_CHANNELS.GET_ACCESS_TOKEN), - refreshAccessToken: () => ipcRenderer.invoke(IPC_CHANNELS.REFRESH_ACCESS_TOKEN), - logout: () => ipcRenderer.invoke(IPC_CHANNELS.LOGOUT), - startGoogleOAuth: () => ipcRenderer.invoke(IPC_CHANNELS.AUTH_START_GOOGLE), - loginPassword: (email: string, password: string) => - ipcRenderer.invoke(IPC_CHANNELS.AUTH_LOGIN_PASSWORD, { email, password }), - onAuthChanged: (callback: (payload: { authed: boolean; accessToken: string | null }) => void) => { - const listener = (_event: Electron.IpcRendererEvent, payload: { authed: boolean; accessToken: string | null }) => - callback(payload); - ipcRenderer.on(IPC_CHANNELS.AUTH_CHANGED, listener); - return () => ipcRenderer.removeListener(IPC_CHANNELS.AUTH_CHANGED, listener); - }, - - // Keyboard shortcut configuration - getShortcuts: () => ipcRenderer.invoke(IPC_CHANNELS.GET_SHORTCUTS), - setShortcuts: (config: Record) => - ipcRenderer.invoke(IPC_CHANNELS.SET_SHORTCUTS, config), - - // Launch on system startup - getAutoLaunch: () => ipcRenderer.invoke(IPC_CHANNELS.GET_AUTO_LAUNCH), - setAutoLaunch: (enabled: boolean, openAsHidden?: boolean) => - ipcRenderer.invoke(IPC_CHANNELS.SET_AUTO_LAUNCH, { enabled, openAsHidden }), - - // Active search space - getActiveWorkspace: () => ipcRenderer.invoke(IPC_CHANNELS.GET_ACTIVE_WORKSPACE), - setActiveWorkspace: (id: string) => - ipcRenderer.invoke(IPC_CHANNELS.SET_ACTIVE_WORKSPACE, id), - - // Analytics bridge — lets posthog-js running inside the Next.js renderer - // mirror identify/reset/capture into the Electron main-process PostHog - // client so desktop-only events are attributed to the logged-in user. - analyticsIdentify: (userId: string, properties?: Record) => - ipcRenderer.invoke(IPC_CHANNELS.ANALYTICS_IDENTIFY, { userId, properties }), - analyticsReset: () => ipcRenderer.invoke(IPC_CHANNELS.ANALYTICS_RESET), - analyticsCapture: (event: string, properties?: Record) => - ipcRenderer.invoke(IPC_CHANNELS.ANALYTICS_CAPTURE, { event, properties }), - getAnalyticsContext: () => ipcRenderer.invoke(IPC_CHANNELS.ANALYTICS_GET_CONTEXT), - // Agent filesystem mode - getAgentFilesystemSettings: (workspaceId?: number | null) => - ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_GET_SETTINGS, workspaceId), - getAgentFilesystemMounts: (workspaceId?: number | null) => - ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_GET_MOUNTS, workspaceId), - listAgentFilesystemFiles: (options: { - rootPath: string; - workspaceId?: number | null; - excludePatterns?: string[] | null; - fileExtensions?: string[] | null; - }) => ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_LIST_FILES, options), - startAgentFilesystemTreeWatch: (options: { - workspaceId?: number | null; - rootPaths: string[]; - excludePatterns?: string[] | null; - fileExtensions?: string[] | null; - }) => ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_TREE_WATCH_START, options), - stopAgentFilesystemTreeWatch: (workspaceId?: number | null) => - ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_TREE_WATCH_STOP, workspaceId), - onAgentFilesystemTreeDirty: ( - callback: (data: { - workspaceId: number | null; - reason: 'watcher_event' | 'safety_poll'; - rootPath: string; - changedPath: string | null; - timestamp: number; - }) => void - ) => { - const listener = ( - _event: unknown, - data: { - workspaceId: number | null; - reason: 'watcher_event' | 'safety_poll'; - rootPath: string; - changedPath: string | null; - timestamp: number; - } - ) => callback(data); - ipcRenderer.on(IPC_CHANNELS.AGENT_FILESYSTEM_TREE_DIRTY, listener); - return () => { - ipcRenderer.removeListener(IPC_CHANNELS.AGENT_FILESYSTEM_TREE_DIRTY, listener); - }; - }, - setAgentFilesystemSettings: (settings: { - mode?: "cloud" | "desktop_local_folder"; - localRootPaths?: string[] | null; - }, workspaceId?: number | null) => - ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_SET_SETTINGS, { workspaceId, settings }), - pickAgentFilesystemRoot: () => ipcRenderer.invoke(IPC_CHANNELS.AGENT_FILESYSTEM_PICK_ROOT), -}); diff --git a/surfsense_desktop/tsconfig.json b/surfsense_desktop/tsconfig.json deleted file mode 100644 index 4315c75719..0000000000 --- a/surfsense_desktop/tsconfig.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "compilerOptions": { - "target": "ES2022", - "module": "commonjs", - "lib": ["ES2022"], - "outDir": "./dist", - "rootDir": "./src", - "strict": true, - "esModuleInterop": true, - "skipLibCheck": true, - "resolveJsonModule": true, - "noEmit": true - }, - "include": ["src/**/*.ts"], - "exclude": ["node_modules", "dist", "scripts", "src/**/*.test.ts"] -} diff --git a/surfsense_evals/.env.example b/surfsense_evals/.env.example deleted file mode 100644 index aa716063c7..0000000000 --- a/surfsense_evals/.env.example +++ /dev/null @@ -1,83 +0,0 @@ -# surfsense_evals — environment template. -# -# Copy this file to `.env` (in the surfsense_evals/ project root or your -# CWD) and fill in the values. `python-dotenv` loads it automatically -# the first time `core.config` is imported, so every CLI subcommand -# (`setup`, `ingest`, `run`, `report`, `teardown`, `models list`, …) -# will pick the values up. -# -# cp .env.example .env -# # then edit .env with your values -# -# `.env` is gitignored — never commit real secrets. - -# --------------------------------------------------------------------------- -# 1. Backend target — REQUIRED (default works for a local dev backend) -# --------------------------------------------------------------------------- -SURFSENSE_API_BASE=http://localhost:8000 - -# --------------------------------------------------------------------------- -# 2. OpenRouter — REQUIRED for any `run` invocation -# --------------------------------------------------------------------------- -# The `native_pdf` arm calls OpenRouter directly; the `surfsense` arm -# routes through SurfSense which uses the same key under the hood. -OPENROUTER_API_KEY=sk-or-... - -# Override only if you proxy OpenRouter through a private gateway: -# OPENROUTER_BASE_URL=https://openrouter.ai/api/v1 - -# Multimodal benchmarks (medxpertqa, mmlongbench) require a vision-capable -# slug. Recommended (verify in your catalog with `models list --grep ...`): -# anthropic/claude-sonnet-4.5 (default recommendation) -# anthropic/claude-opus-4.7 (strongest) -# openai/gpt-5 (top-tier vision) -# google/gemini-2.5-pro (1M-token context, best for long PDFs) -# DO NOT use openai/gpt-5.4-mini for image-bearing benchmarks — it's -# text-only on PDF content and the runner emits a warning if pinned. - -# --------------------------------------------------------------------------- -# 3. Auth — pick EXACTLY ONE of the two modes below -# --------------------------------------------------------------------------- - -# --- Mode A: LOCAL (backend started with AUTH_TYPE=LOCAL) -# The harness POSTs these to /auth/jwt/login automatically. -# SURFSENSE_USER_EMAIL=you@example.com -# SURFSENSE_USER_PASSWORD=... - -# --- Mode B: GOOGLE OAuth (or any pre-issued JWT) -# Open the SurfSense web UI in your browser, log in via Google, then in -# DevTools → Application → Local Storage copy: -# surfsense_bearer_token → SURFSENSE_JWT -# surfsense_refresh_token → SURFSENSE_REFRESH_TOKEN (optional, enables -# auto-refresh on 401) -# SURFSENSE_JWT=eyJhbGciOi... -# SURFSENSE_REFRESH_TOKEN=eyJhbGciOi... - -# --------------------------------------------------------------------------- -# 4. Filesystem paths — OPTIONAL (defaults below) -# --------------------------------------------------------------------------- -# Where datasets, rendered PDFs, ingestion id maps, run outputs, and -# state.json live. Default: /data/ -# EVAL_DATA_DIR=./data - -# Where generated reports (summary.md / summary.json) get written. -# Default: /reports/ -# EVAL_REPORTS_DIR=./reports - -# --------------------------------------------------------------------------- -# 5. Parser SDKs — REQUIRED for the multimodal_doc / parser_compare suite -# --------------------------------------------------------------------------- -# parser_compare calls Azure Document Intelligence and LlamaParse SDKs -# directly from the eval harness so each (basic / premium) extraction -# is a clean A/B test independent of the SurfSense backend's ETL routing. -# -# Azure Document Intelligence — used for the `azure_basic_lc` (prebuilt-read) -# and `azure_premium_lc` (prebuilt-layout) arms. Get an endpoint + key from -# https://portal.azure.com (Document Intelligence resource, F0 / S0 tier). -# AZURE_DI_ENDPOINT=https://.cognitiveservices.azure.com/ -# AZURE_DI_KEY= -# -# LlamaCloud (LlamaParse) — used for `llamacloud_basic_lc` (parse_page_with_llm) -# and `llamacloud_premium_lc` (parse_page_with_agent). Get a key from -# https://cloud.llamaindex.ai/api-key. -# LLAMA_CLOUD_API_KEY=llx-... diff --git a/surfsense_evals/.gitignore b/surfsense_evals/.gitignore deleted file mode 100644 index fd6e505c2e..0000000000 --- a/surfsense_evals/.gitignore +++ /dev/null @@ -1,35 +0,0 @@ -# Python bytecode + caches -__pycache__/ -*.py[cod] -*.pyo - -# Editable-install / build artifacts -*.egg-info/ -build/ -dist/ -.eggs/ - -# Virtual envs (uv venv default + common alternates) -.venv/ -venv/ -env/ - -# Tooling caches -.pytest_cache/ -.ruff_cache/ -.mypy_cache/ -.coverage -.coverage.* -htmlcov/ - -# Local secrets — keep `.env.example` tracked, never the real `.env`. -.env -.env.local -.env.*.local -!.env.example - -# Run / debug logs — keep ephemeral, structured artifacts in -# `data/...//` are the citation surface, not these. -*.log -logs_*.txt -retry_run.log diff --git a/surfsense_evals/README.md b/surfsense_evals/README.md deleted file mode 100644 index e6fc52ca17..0000000000 --- a/surfsense_evals/README.md +++ /dev/null @@ -1,227 +0,0 @@ -# SurfSense Evals - -Domain-agnostic eval harness for SurfSense. Each benchmark is a Python subpackage under `suites///` that self-registers with the CLI; `core/` is the shared infrastructure (HTTP clients, arms, parsers, metrics, report writer, registry). The harness talks to SurfSense over HTTP only — it does **not** import any backend Python module — so it ships in its own venv and never bloats the FastAPI runtime image. - -## Benchmarks - -| Benchmark | Shape | Vision required? | Default ingest | -|---------------------------------|--------------------------------------------------|------------------|----------------------------| -| `medical/medxpertqa` (headline) | Native PDF vs SurfSense head-to-head, MCQ | yes | `vision=on, mode=basic` | -| `medical/mirage` | SurfSense single-arm, MCQ | no | `vision=off, mode=basic` | -| `medical/cure` | SurfSense single-arm retrieval (Recall/MRR/nDCG) | no | `vision=off, mode=basic` | -| `multimodal_doc/mmlongbench` | Native PDF vs SurfSense head-to-head, open-ended | yes | `vision=on, mode=basic` | - -Future domains (`legal/`, `finance/`, `code/`, `scientific/`) drop into `suites/` without touching `core/` or the CLI. - -## Install + auth - -```bash -uv pip install -e ./surfsense_evals -cp surfsense_evals/.env.example surfsense_evals/.env -# Edit .env: SURFSENSE_API_BASE, OPENROUTER_API_KEY, and ONE of: -# LOCAL → SURFSENSE_USER_EMAIL + SURFSENSE_USER_PASSWORD -# GOOGLE → SURFSENSE_JWT (+ optional SURFSENSE_REFRESH_TOKEN) -# (lift both from browser localStorage after a normal Google login) -``` - -## Step-by-step: run all four benchmarks - -The medical and multimodal_doc suites each get their own SearchSpace and pinned model, so they're independent — run them in any order. Both head-to-head benchmarks (`medxpertqa`, `mmlongbench`) require a **vision-capable** OpenRouter slug; pinning a text-only one (e.g. `openai/gpt-5.4-mini`) silently drops images and the runner emits a warning. - -Recommended vision slugs (use `models list --grep ` to confirm one): `anthropic/claude-sonnet-4.5` (balanced cost), `anthropic/claude-opus-4.7` (strongest reasoning), `openai/gpt-5` (top-tier vision), `google/gemini-2.5-pro` (best for long PDFs, 1M-token context). - -```bash -# 0. (optional) discover what's registered -python -m surfsense_evals suites list -python -m surfsense_evals benchmarks list - -# 1. MEDICAL SUITE — one SearchSpace, three benchmarks -python -m surfsense_evals setup --suite medical --provider-model anthropic/claude-sonnet-4.5 - -# 1a. headline head-to-head: Native PDF (vision) vs SurfSense (vision RAG) -# Downloads dev+test JSONL + images.zip, renders one PDF per question -# (case + table + images + 5 options), uploads with use_vision_llm=True. -python -m surfsense_evals ingest medical medxpertqa --split test -python -m surfsense_evals run medical medxpertqa --concurrency 4 - -# 1b. MIRAGE — single-arm SurfSense MCQ accuracy -# (MMLU-Med / MedQA-US / MedMCQA / PubMedQA / BioASQ) -python -m surfsense_evals ingest medical mirage -python -m surfsense_evals run medical mirage - -# 1c. CUREv1 — single-arm SurfSense retrieval (Recall@k / MRR / nDCG@10) -python -m surfsense_evals ingest medical cure --lang en -python -m surfsense_evals run medical cure --lang en - -# 1d. write reports/medical//summary.{md,json} -python -m surfsense_evals report --suite medical - -# 2. MULTIMODAL_DOC SUITE — long PDFs with embedded images, charts, tables -python -m surfsense_evals setup --suite multimodal_doc --provider-model google/gemini-2.5-pro -python -m surfsense_evals ingest multimodal_doc mmlongbench # ~660MB, resumable -python -m surfsense_evals run multimodal_doc mmlongbench --concurrency 4 -python -m surfsense_evals report --suite multimodal_doc - -# 3. CLEANUP — soft-deletes the SearchSpaces; rendered PDFs stay cached -python -m surfsense_evals teardown --suite medical -python -m surfsense_evals teardown --suite multimodal_doc -``` - -## Asymmetric scenarios — the "vision-extract once, answer cheap" play - -The walkthrough above is `--scenario head-to-head` (default): both arms answer with the same vision-capable slug. SurfSense's actual architectural value-prop is that the **ingestion-time vision LLM and the runtime LLM are completely independent** — you can pay a vision LLM *once*, at ingest, to convert every embedded image into text (per-image OCR **and** semantic description, inlined where the image actually appears in the document — see [What `--use-vision-llm` produces](#what---use-vision-llm-produces) below). Then every query is served by a cheap text-only model that sees that extracted text natively. Two extra scenarios make this explicit: - -| `--scenario` | Native arm answers with | SurfSense arm answers with | Question being measured | -|--------------------|----------------------------------------|--------------------------------|------------------------------------------------------------------------------------------| -| `head-to-head` | `--provider-model` (vision) | `--provider-model` (vision) | Pure RAG quality at parity. (Default.) | -| `symmetric-cheap` | `--provider-model` (cheap, text-only) | `--provider-model` (same) | Does pre-extracted image context let a non-vision LLM reason over image-heavy docs? | -| `cost-arbitrage` | `--native-arm-model` (vision) | `--provider-model` (cheap) | How close does SurfSense get to a vision-native baseline at a fraction of per-query cost?| - -In all three modes the **ingest-time** vision LLM is set on the SearchSpace's `vision_model_id` (auto-picked from the strongest registered global OpenRouter vision-capable model — `claude-sonnet-4.5` > `claude-opus-4.7` > `gpt-5` > `gemini-2.5-pro`, override with `--vision-llm `). What changes is which slug the *answering* models hit per arm. - -### Ingest with vision, evaluate with a non-vision LLM (`symmetric-cheap`) - -This is the answer to *"does SurfSense give a non-vision LLM enough context to reason over image-heavy docs?"*. Both arms hit the same cheap text-only slug. The native arm is structurally blind to images (text-only LLM + raw PDFs). The SurfSense arm reads chunks that already contain the per-image OCR and visual descriptions, written there by the vision LLM at ingest time. - -```bash -python -m surfsense_evals setup --suite medical \ - --scenario symmetric-cheap \ - --provider-model openai/gpt-5.4-mini - # vision LLM at ingest = auto-picked (claude-sonnet-4.5 by default) - # answer LLM for BOTH arms = openai/gpt-5.4-mini (text-only) - -python -m surfsense_evals ingest medical medxpertqa --split test # vision=on by default -python -m surfsense_evals run medical medxpertqa --concurrency 4 -python -m surfsense_evals report --suite medical -# Δ accuracy on image-required MCQs is the headline number; native arm -# baseline is "what a text-only LLM gets without seeing the images". -``` - -### Cheap SurfSense vs vision-native baseline (`cost-arbitrage`) - -```bash -python -m surfsense_evals setup --suite medical \ - --scenario cost-arbitrage \ - --provider-model openai/gpt-5.4-mini \ - --native-arm-model anthropic/claude-sonnet-4.5 - # vision LLM at ingest = auto-picked claude-sonnet-4.5 - # native arm = sonnet (vision); SurfSense arm = gpt-5.4-mini (text-only) - -python -m surfsense_evals ingest medical medxpertqa --split test -python -m surfsense_evals run medical medxpertqa --concurrency 4 -python -m surfsense_evals report --suite medical -# Report header reads: -# Scenario: cost-arbitrage — native arm answers with `anthropic/claude-sonnet-4.5` -# (vision); SurfSense answers with `openai/gpt-5.4-mini` over chunks vision-extracted -# at ingest by `anthropic/claude-sonnet-4.5`. -``` - -Notes: -- `cost-arbitrage` requires both `--provider-model` (the cheap SurfSense slug) AND `--native-arm-model `. -- `--vision-llm ` is optional; if omitted the harness queries `GET /api/v1/model-connections/global` and auto-picks the strongest registered vision-capable model. Pass `--no-vision-llm-setup` if you want to keep whatever vision model is already attached to the SearchSpace. -- The runner's "looks text-only" warning is suppressed (or relabelled as informational) for `symmetric-cheap` so intentional asymmetry doesn't read as a misconfiguration. -- All three scenario fields (`scenario`, `provider_model`, `native_arm_model`, `vision_provider_model`) are persisted to `state.json` and recorded in `run_artifact.extra` + the report header — no need to retrace what was set. - -## Per-benchmark useful flags - -`medical/medxpertqa` (`run`): -- `--split {test,dev,all}` — pick a subset (default `test`) -- `--task "Diagnosis"` / `--body-system "Cardiovascular"` — slice the report -- `--require-images` — drop rare rows where every image filename failed to resolve -- `--n 100` — quick smoke run -- `--no-mentions` — let SurfSense retrieve unscoped ("did the @-mention matter?") - -`multimodal_doc/mmlongbench`: -- `--max-docs N` (ingest) — cap downloads at the first N unique PDFs -- `--format {str,int,float,list,none}` (run) — slice by answer format; `none` = the ~22% intentionally unanswerable hallucination probes -- `--skip-unanswerable` (run) — drop unanswerable questions -- `--docs ,` (run) — scope to specific docs - -## Ingestion knobs (vision LLM, processing mode) - -The harness exposes `POST /api/v1/documents/fileupload`'s ingest knobs on every `ingest` subcommand: - -| Flag pair | Effect | -|--------------------------------------------|-----------------------------------------------------------------------------------------| -| `--use-vision-llm` / `--no-vision-llm` | Walk every embedded image in the PDF and inline image-derived text at the image's position (see below). | -| `--processing-mode {basic,premium}` | `premium` carries a 10× page multiplier and routes to a stronger ETL (e.g. LlamaCloud). | - -The "Default ingest" column in the benchmarks table is what runs if you don't pass any flag. Whatever was actually used is recorded as a `__settings__` header in the doc map (`data//maps/_*_map.jsonl`) and as `extra.ingest_settings` in `run_artifact.json`, then surfaced in the report — no need to hunt through CLI history. - -> The backend's `ETL_SERVICE` env var (`DOCLING` | `UNSTRUCTURED` | `LLAMACLOUD`) is **not** per-upload. Restart the backend with a different `ETL_SERVICE` and re-ingest to compare ETLs (route through `--processing-mode premium` if your backend uses that mode for the stronger ETL). - -### What `--use-vision-llm` produces - -When vision is on, the backend's ETL pipeline (`app/etl_pipeline/picture_describer.py`) does, **per embedded image** in the PDF: - -1. Extract the raw image bytes via `pypdf` (deduped by sha256, size-capped to match the vision LLM's per-image limit). -2. **Per-image OCR** — re-feed the image as a standalone upload through the configured ETL service (Docling / Azure DI / LlamaCloud) with `vision_llm=None`, so the ETL's OCR engine extracts the literal text-in-image. -3. **Visual description** — call the vision LLM on the image with a description-only prompt (it's explicitly told *not* to transcribe text — that's OCR's job). Steps 2 and 3 run in parallel per image. -4. Splice a horizontal-rule-delimited section **at the image's original position** in the parser markdown (replacing Docling's `` placeholder + caption, or the bare `Image: ` caption a stripped-image parser leaves behind): - - ```markdown - --- - - **Embedded image:** `MM-130-a.jpeg` - - **OCR text:** - Slice 24 / 60 - L R - - **Visual description:** - - - Axial contrast-enhanced CT showing a large cystic mass in the left upper quadrant. - - Mass effect on the adjacent stomach; left kidney displaced inferiorly. - - --- - ``` - -This is what makes `--scenario symmetric-cheap` and `--scenario cost-arbitrage` work: a non-vision LLM reading SurfSense's chunks sees the image's text and semantic content as plain markdown, alongside the surrounding case text, in the same retrieved chunk. Without it the cheap LLM would have nothing extra to read. - -### A/B testing the same corpus with different settings - -SurfSense dedupes uploads by `(filename, search_space_id)` — **not** by content hash and **not** by ingestion settings. Re-uploading the same filename to the same SearchSpace with a different `--use-vision-llm` flag silently skips re-processing. Give each variant its own SearchSpace: - -```bash -# Baseline arm (vision off) -python -m surfsense_evals setup --suite medical --provider-model anthropic/claude-sonnet-4.5 -python -m surfsense_evals ingest medical medxpertqa --no-vision-llm -python -m surfsense_evals run medical medxpertqa --n 100 -python -m surfsense_evals teardown --suite medical - -# Vision arm (the benchmark default) -python -m surfsense_evals setup --suite medical --provider-model anthropic/claude-sonnet-4.5 -python -m surfsense_evals ingest medical medxpertqa -python -m surfsense_evals run medical medxpertqa --n 100 -python -m surfsense_evals report --suite medical -``` - -Both runs land in `data/medical/runs//medxpertqa/` with their settings recorded; rendered PDFs stay cached under `data/medical/medxpertqa/pdfs/` so the second `ingest` is upload-only. - -## Environment variables - -- `SURFSENSE_API_BASE` (default `http://localhost:8000`) -- `OPENROUTER_API_KEY` — required for the `native_pdf` arm and for `models list` -- One of `SURFSENSE_USER_EMAIL` + `SURFSENSE_USER_PASSWORD` (LOCAL), **or** `SURFSENSE_JWT` (+ optional `SURFSENSE_REFRESH_TOKEN`) for GOOGLE/pre-issued JWT -- `EVAL_DATA_DIR` (default `/data`) — datasets, rendered PDFs, ingestion id maps, run outputs, `state.json` -- `EVAL_REPORTS_DIR` (default `/reports`) -- `OPENROUTER_BASE_URL` (default `https://openrouter.ai/api/v1`) — only if you proxy OpenRouter - -## Adding a new domain suite - -1. Create `surfsense_evals/src/surfsense_evals/suites///` with `__init__.py`, `ingest.py`, `runner.py`, optional `prompt.py`. -2. Implement a `Benchmark` subclass (see `core/registry.py`); compose with `core.clients.*`, `core.arms.*`, `core.parse.*`, `core.metrics.*`. -3. Call `register(MyBenchmark())` at the bottom of `/__init__.py`. Auto-discovery picks it up; `setup --suite ` and `ingest/run ` work immediately. - -Each suite gets its own SearchSpace (`eval--`), `state.json` slot, data dir, reports dir, and pinned LLM. Suites never share a SearchSpace. - -## Out of scope (follow-up PRs) - -- Docker service for `docker compose run evals run medical medxpertqa`. -- Multi-model sweeps (one slug per `setup` for now; aggregate reports come later). -- A long-context-stuffing arm (give the model the same retrieved chunks SurfSense saw). -- LLM-judge grader for MMLongBench-Doc (paper uses GPT-4 as judge; we ship a deterministic rule-based grader). -- MedXpertQA-MM accuracy by image modality — dataset doesn't tag modality directly; we slice by `medical_task` and `body_system`. -- A `--slot ` flag that decouples the state-slot key from the benchmark registry's `suite` attribute, so parallel SearchSpaces with different ingestion settings can coexist on the same benchmark without `teardown` between A/B arms. - -See `c:/Users/91882/.cursor/plans/medical_rag_evals_(mirage_+_curev1)_e797a324.plan.md` for the full design rationale. diff --git a/surfsense_evals/data/.gitignore b/surfsense_evals/data/.gitignore deleted file mode 100644 index 529223f581..0000000000 --- a/surfsense_evals/data/.gitignore +++ /dev/null @@ -1,22 +0,0 @@ -# Default: don't track anything under data/ (large benchmarks, PDFs, -# extracted markdown, ingestion caches, per-run artifacts can balloon). -* - -# Always keep this gitignore file -!.gitignore - -# Whitelist the artifacts the n=171 multimodal_doc / parser_compare blog -# report (`reports/blog/multimodal_doc_parser_compare_n171_report.md`) -# cites by path. These are the verifiable numbers source for the report. -# -# Path components have to be unblocked one level at a time because git -# does not look into a directory whose parent is ignored. -!multimodal_doc/ -!multimodal_doc/runs/ -!multimodal_doc/runs/2026-05-14T00-53-19Z/ -!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/ -!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl -!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl -!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json -!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl -!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl deleted file mode 100644 index d91e2e530e..0000000000 --- a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl +++ /dev/null @@ -1,1026 +0,0 @@ -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults\n- 36% rated the ethical standards as \"poor\" (from the table showing \"% who say that the ethical standards of top Trump administration officials are...\")\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 6925, "output_tokens": 125, "cost_micros": 22650, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults (from the Methodology section on page 14)\n- According to the chart on page 2, 36% of the total rated the ethical standards as \"poor\"\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9417, "output_tokens": 128, "cost_micros": 30171, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document, I can find:\n1. The total sample size: 1,503 adults (from the Methodology section)\n2. The percentage who rated ethical standards as \"poor\": 36% (from the table \"Most rate top Trump administration officials' ethics as 'not good' or 'poor'\")\n\nTo calculate the number of adults:\n1,503 \u00d7 0.36 = 541.08\n\nSince we need an integer and this represents people, I should round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 7774, "output_tokens": 158, "cost_micros": 25692, "latency_ms": 6405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump's government ethical standards as poor.\n\nFrom the document:\n- The survey was conducted April 25-May 1, 2018 among 1,503 adults (from the Methodology section on page 14)\n- 36% rated the ethical standards of top Trump administration officials as \"poor\" (from the table on page 2)\n\nCalculation:\n1,503 adults \u00d7 36% = 1,503 \u00d7 0.36 = 541.08\n\nSince we need to report the number of adults, I'll round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9517, "output_tokens": 145, "cost_micros": 30726, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question about the survey conducted April 25 - May 1, 2018.Let me search for the specific data about ethical standards ratings. I can see the document mentions ethical standards. Let me look for the detailed breakdown.I can see from the grep results that there's a table showing ethical standards ratings. Let me read the specific section more carefully:I can see a table showing percentages for ethical standards of Trump administration officials. However, I need to understand the table structure better. The table shows:\n- Total: 58, 36, 9, 39\n- These appear to be percentages for different categories\n\nLet me look at the context more carefully to understand what \"58\" represents:Now I understand the table structure. Looking at the figure:\n- The categories are listed as: Poor, Not good, Good, Excellent\n- For \"Total\" (all adults): The values appear to be 58, 36, 9, 39\n\nHowever, the text says \"58% rate them as not good or poor\" which suggests 58% is the combined total of \"not good\" and \"poor\". Let me look more carefully at how to read this table. Looking at lines 248-249: \"39% say the administration's ethical standards are excellent or good, while 58% rate them as not good or poor.\"\n\nThis indicates:\n- 39% = Excellent + Good (combined)\n- 58% = Not good + Poor (combined)\n\nLooking at the table values for Total: 58, 36, 9, 39\n\nThe layout suggests:\n- Row 1 (Poor): 58\n- Row 2 (Not good): 36 \n- Row 3 (Good): 9\n- Row 4 (Excellent): 39\n\nBut this doesn't make sense because 58 + 36 would be 94%, not matching the text's 58% for \"not good or poor\" combined.\n\nLet me reconsider the table structure. Perhaps it's arranged differently - the numbers might be in columns rather than associating each number with the label above it. Let me look for more context about the survey sample size:The survey was conducted among 1,503 adults. Now, looking back at the text on line 248: \"58% rate them as not good or poor\"\n\nIf 58% of 1,503 adults rated Trump's ethical standards as \"not good or poor\", then:\n58% \u00d7 1,503 = 0.58 \u00d7 1,503 = 871.74 \u2248 872 adults\n\nBut the question specifically asks for those who rated it as \"poor\", not \"not good or poor\" combined.\n\nLet me re-examine the table structure. The table shows categories and then rows for Total, Rep/Lean Rep, and Dem/Lean Dem. Looking at the numbers again:\n\nFor the \"Total\" row, I see: 58, 36, 9, 39\n\nGiven that the text says 58% rated as \"not good or poor\" combined, and the categories listed are Poor, Not good, Good, Excellent - perhaps the table is showing these in a different order or the numbers represent something else.\n\nActually, looking at line 289-295 more carefully, let me reconsider: perhaps the numbers don't align sequentially with the labels above. The figure might have a more complex layout. Let me look at the Republican row: 22, 7, 19, 75. And Democrat row: 86, 61, 2, 12.\n\nFor Republicans: The text (line 257) says \"75% giving the administration high marks\" - this matches the \"75\" in the data.\nFor Democrats: The text (line 258) says \"86% of Democrats rating its ethical standards negatively\" - this matches the \"86\" in the data.\n\nSo for the Total row showing 58, 36, 9, 39:\n- 58 + 36 = 94 (doesn't match the 58% mentioned)\n- 36 + 58 = 94 \n- 9 + 39 = 48 (doesn't match the 39% for excellent/good)\n\nWait - perhaps I'm misreading the structure. Let me think about this differently. The text says:\n- 39% say excellent or good\n- 58% say not good or poor\n\nThese add to 97%, with 3% presumably \"don't know\" (which the note says is not shown).\n\nLooking at the table structure, maybe it's:\n- Poor: 36\n- Not good: 22 (so 36 + 22 = 58% for \"poor or not good\")\n- Good: ? \n- Excellent: ?\n\nActually, rereading the visual structure between lines 273-315, the checkboxes (\u2610) appear before each category label, and then the numbers follow. Let me parse this more carefully as a table where columns might represent the categories.\n\nLooking at the pattern, it seems like the table might have columns for each rating category. If the first number in each row represents \"Poor\":\n- Total: 58 (but this would be just \"Poor\", not \"Poor + Not good\")\n\nBut the text clearly states 58% is the combined \"not good or poor\". So if:\n- Poor alone = X%\n- Not good alone = Y% \n- X + Y = 58%\n\nAnd looking at potential individual values, perhaps:\n- Poor = 36%\n- Not good = 22%\n- 36 + 22 = 58% \u2713\n\nThis would make sense! So in the Total row (58, 36, 9, 39), these might be arranged as:\n- Combined negative: 58%\n- Poor: 36%\n- Good: 9%\n- Combined positive: 39% (should be Good 9% + Excellent, but listed as 39%)\n\nWait, that still doesn't work cleanly. Let me look for another table or clarification in the document:Perfect! I found a more detailed table starting at line 875. This table clearly shows the structure with labels on lines 880-885:\n- Poor\n- Not good\n- Good\n- Excellent\n\nAnd then for \"Total\" (lines 890-898):\n- 58\n- 36\n- 9\n- 39\n\nBut wait - this arrangement still seems odd. Let me look at the actual layout more carefully. Lines 890-898 show:\n```\nTotal\n58\n36\n9\n39\n```\n\nLooking at the structure and comparing with the text, I think the table columns might be:\n- Column 1: Row label (Total, Rep/Lean Rep, etc.)\n- Column 2: Combined \"Not good or Poor\" (58 for Total)\n- Column 3: Poor specifically (36 for Total)\n- Column 4: Good specifically (9 for Total)\n- Column 5: Combined \"Good or Excellent\" (39 for Total)\n\nThis would make sense because:\n- 58% = \"not good or poor\" combined (matches the text on line 248)\n- 36% = \"poor\" alone\n- 9% = \"good\" alone\n- 39% = \"good or excellent\" combined (matches the text on line 247)\n\nSo the answer to \"how many adults rated Trump's government ethical standards as poor\" would be:\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08 \u2248 541 adults\n\nActually, let me double-check by reading the column headers more carefully in this second table:Looking at the table header (lines 880-885), the categories are listed as:\n- Poor\n- Not good\n- Good \n- Excellent\n\nAnd for Total (lines 892-898): 58, 36, 9, 39\n\nGiven the table structure and that these are percentages, I now believe the columns represent:\n1. First column after \"Total\" = one rating category\n2. Second column = another rating category\n3. Third column = another rating category\n4. Fourth column = another rating category\n\nBut there are only 4 numbers (58, 36, 9, 39) for 4 categories (Poor, Not good, Good, Excellent).\n\nSo the mapping should be:\n- Poor: 58%? or 36%?\n- Not good: 36%? or 9%?\n- Good: 9%? or 39%?\n- Excellent: 39%? or something else?\n\nLet me check against the text constraints:\n- Text says \"39% say excellent or good\" (line 247-248) \u2192 Good + Excellent = 39%\n- Text says \"58% rate them as not good or poor\" (line 248-249) \u2192 Not good + Poor = 58%\n\nIf the four values are 58, 36, 9, 39:\n- 58 + 36 + 9 + 39 = 142% (impossible for percentages)\n\nSo these must not all be individual category percentages. Some must be combined values. Given the text:\n- 58% = Not good + Poor (combined)\n- 39% = Good + Excellent (combined)\n- 9% = one individual category\n- 36% = one individual category\n\nSince 58% + 39% = 97% (with 3% don't know), and we have two individual values (36 and 9), these must be subcategories within the combined values:\n- 36% = Poor (part of the 58% \"not good or poor\")\n- Therefore Not good = 58% - 36% = 22%\n- 9% = Good (part of the 39% \"good or excellent\") \n- Therefore Excellent = 39% - 9% = 30%\n\nThis makes sense! So the table is showing:\n- Column 1: Not good or Poor combined (58%)\n- Column 2: Poor alone (36%)\n- Column 3: Good alone (9%)\n- Column 4: Good or Excellent combined (39%)\n\nTherefore, 36% of adults rated Trump's ethical standards as \"poor\".\n\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest integer: 541 adults\n\nAnswer: 541", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94875, "error": null, "extra": {"thread_id": 1904, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7e2e86436614d5c96d6bd9dd3a561c5", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 977, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the domains where adults are \"very confident\" and \"not at all confident\" in Donald Trump, and identify which has the highest percentage for each category.\n\nLooking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the breakdown of confidence levels (Very, Somewhat, Not too, Not at all) for various policy areas.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 19%\n- Make good appointments to the federal courts: 21%\n- Manage the executive branch effectively: 21%\n- Make wise decisions about immigration policy: 16%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 30%\n\nThe highest \"Very confident\" is \"Work effectively with Congress\" at 30%.\n\nFor \"Not at all confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 26%\n- Make good decisions about economic policy: 17%\n- Use military force wisely: 32%\n- Make good appointments to the federal courts: 32%\n- Manage the executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nThe highest \"Not at all confident\" is \"Make wise decisions about immigration policy\" at 39%.\n\nIn alphabetical order:\n1. Make wise decisions about immigration policy\n2. Work effectively with Congress\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 6973, "output_tokens": 374, "cost_micros": 26529, "latency_ms": 9500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to find:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table data:\n\n**Very confident (highest):**\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (HIGHEST)\n\n**Not at all confident (highest):**\n- Make wise decisions about immigration policy: 39% (HIGHEST)\n- Handle an international crisis: 35%\n- Manage the executive branch effectively: 34%\n\nThe two domains are:\n1. \"Make good decisions about economic policy\" (highest very confident at 31%)\n2. \"Make wise decisions about immigration policy\" (highest not at all confident at 39%)\n\nIn alphabetical order:\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9465, "output_tokens": 216, "cost_micros": 31635, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table \"Public's confidence in Trump to handle several policy areas mixed\" on page 4 of the document, I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table:\n\n**Very confident percentages:**\n- Negotiate favorable trade agreements: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 20%\n- Make good appointments to federal courts: 21%\n- Manage executive branch effectively: 24%\n- Make wise decisions about immigration policy: 17%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 21%\n\nHighest \"Very\" confident: **Negotiate favorable trade agreements** and **Manage the executive branch effectively** (both at 24%)\n\n**Not at all confident percentages:**\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: 29%\n- Use military force wisely: 32%\n- Make good appointments to federal courts: 32%\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nHighest \"Not at all\" confident: **Make wise decisions about immigration policy** (39%)\n\nIn alphabetical order:\n1. Manage the executive branch effectively\n2. Make wise decisions about immigration policy\n3. Negotiate favorable trade agreements with other countries\n\nAnswer: Manage the executive branch effectively, Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 7822, "output_tokens": 372, "cost_micros": 29046, "latency_ms": 8530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, manage executive branch effectively, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 3 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table, examining the \"Very\" column:\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (highest)\n- Use military force wisely: 26%\n- Make good appointments to federal courts: 25%\n- Manage executive branch effectively: 21%\n- Make wise decisions about immigration policy: 26%\n- Handle an international crisis: 25%\n- Work effectively with Congress: 13%\n\nFrom the \"Not at all\" column:\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: (not shown separately)\n- Use military force wisely: 32%\n- Make good appointments to federal courts: (not shown separately)\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39% (highest)\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9565, "output_tokens": 285, "cost_micros": 32970, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the survey data from April 25 - May 1, 2018.Let me continue reading to find the specific data about confidence levels in different domains.I need to find the specific data showing \"very confident\" and \"not at all confident\" percentages for different domains. Let me search for that data.Perfect! I found the table with confidence levels. Now I need to identify which columns represent \"Very confident\" and \"Not at all confident\". Let me re-read this section more carefully.Now I can see the table structure clearly. The columns are:\n- Column 1: \"Not at all\" (confidence)\n- Column 2: \"Not too\" (confidence) \n- Column 3: \"Somewhat\" (confident)\n- Column 4: \"Very\" (confident)\n\nHowever, I notice the last column shows combined percentages (e.g., 54 for trade agreements). Let me look more carefully at the structure. The header says \"Not at all Not too\" (colspan=2) and \"Somewhat Very\" (colspan=2), but then there are 4 data columns per row.\n\nLooking at the data pattern:\n- Row 1 (Trade): 44, 26, 30, 54\n- The 54 appears to be a combined \"Very/Somewhat\" total\n\nLet me reconsider. Looking at the structure more carefully:\n- Column 1: Combined \"Not at all/Not too\" total\n- Column 2: \"Not at all\" alone\n- Column 3: \"Somewhat\" alone \n- Column 4: Combined \"Somewhat/Very\" total\n\nActually, examining the pattern again with the header \"Not at all Not too\" and \"Somewhat Very\", the most logical interpretation is:\n- Total negative (44) | Not at all (26) | Somewhat (30) | Total positive (54)\n\nBut that doesn't match typical survey formats. Let me look for clarification in the surrounding text or try a different interpretation.\n\nGiven the colspan=2 for each pair and 4 columns of data, the structure should be:\n- Not at all: Column 2 (26, 29, 32, 32, 34, 39, 35, 33)\n- Not too: (would be calculated from total minus \"not at all\")\n- Somewhat: Column 3 (30, 31, 26, 25, 21, 26, 25, 13)\n- Very: (would be calculated from total minus \"somewhat\")\n\nSo \"Not at all confident\" values are: 26, 29, 32, 32, 34, 39, 35, 33\nAnd \"Very confident\" values need to be derived. Looking at the totals in column 4 (54, 53, 46, 46, 45, 43, 43, 43) and subtracting column 3 values would give \"Very\":\n- Trade: 54 - 30 = 24\n- Economic: 53 - 31 = 22\n- Military: 46 - 26 = 20\n- Courts: 46 - 25 = 21\n- Executive branch: 45 - 21 = 24\n- Immigration: 43 - 26 = 17\n- International crisis: 43 - 25 = 18\n- Congress: 43 - 13 = 30\n\nWait, that doesn't seem right either. Let me look at this more systematically. The first column (44, 46, 51, etc.) appears to be totals for \"not confident\" responses.\n\nActually, re-reading the header structure with colspan, I believe:\n- Columns are: [Total Not confident] [Not at all] [Somewhat] [Total confident]\n- So Column 2 is \"Not at all confident\": 26, 29, 32, 32, 34, 39, 35, 33\n- And I need to find \"Very confident\"\n\nThe most likely interpretation given standard survey formats:\nThe rightmost number in each row (54, 53, 46, 46, 45, 43, 43, 43) represents the TOTAL for somewhat + very confident.\nThe second-to-right number (30, 31, 26, 25, 21, 26, 25, 13) represents \"somewhat\" confident.\n\nTherefore \"Very confident\" = Total confident - Somewhat:\n- Trade: 54 - 30 = 24%\n- Economic: 53 - 31 = 22%\n- Military: 46 - 26 = 20%\n- Courts: 46 - 25 = 21%\n- Executive: 45 - 21 = 24%\n- Immigration: 43 - 26 = 17%\n- International: 43 - 25 = 18%\n- Congress: 43 - 13 = 30%\n\nNow finding the highest percentages:\n- \"Very confident\": Congress (30%) is highest\n- \"Not at all confident\": Immigration (39%) is highest\n\nLet me verify by checking if there are ties or other high values:\n- Very confident: 30% (Congress), 24% (Trade and Executive - tied for 2nd)\n- Not at all confident: 39% (Immigration), 35% (International crisis), 34% (Executive)\n\nSo the answers are:\n- Highest \"Very confident\": Work effectively with Congress\n- Highest \"Not at all confident\": Make wise decisions about immigration policy\n\nIn alphabetical order with the format requested:\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61000, "error": null, "extra": {"thread_id": 1905, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_63c71f31681e41ee869fe1c754c29c5b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party holds the highest total percentage of \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news \"very closely.\"\n\nFrom page 11, I can see the chart \"More view GOP as 'too extreme' than say this about Democratic Party\" which shows:\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections \"very closely.\"\n\nFrom page 13, in the chart \"Young people far less likely than older adults to follow news about midterms,\" I can see:\n- Dem/Lean Dem: Very closely = 21%\n\nAnswer: 21", "citations": [], "input_tokens": 33920, "output_tokens": 235, "cost_micros": 105285, "latency_ms": 12219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Good policy ideas:**\n- Republican Party: 50%\n- Democratic Party: 50%\n\n**High ethical standards:**\n- Republican Party: 41%\n- Democratic Party: 42%\n\n**Total percentages:**\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections very closely.\n\nFrom the table on page 12 titled \"Young people far less likely than older adults to follow news about midterms\":\n\nUnder \"Dem/Lean Dem\": 21% followed news very closely\n\nAnswer: 21", "citations": [], "input_tokens": 6951, "output_tokens": 210, "cost_micros": 24003, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest combined percentage for \"good policy ideas\" and \"high ethical standards,\" then determine what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party: Has good policy ideas (50%), Has high ethical standards (41%)\n- Democratic Party: Has good policy ideas (50%), Has high ethical standards (42%)\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**News following by party (page 11):**\nLooking at the table showing who followed news about congressional elections very closely:\n- Rep/Lean Rep: 18%\n- Dem/Lean Dem: 21%\n\nSince the Democratic Party has the highest combined percentage for good policy ideas and high ethical standards (92%), and Democrats/Democratic leaners followed news very closely at 21%, the answer is 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9443, "output_tokens": 235, "cost_micros": 31854, "latency_ms": 6125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage for both \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news very closely.\n\nFrom the document:\n\n**Party traits (page 9-10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\n**Following congressional election news very closely (page 11):**\nLooking at the table showing \"% who say they have followed news about congressional elections in their state or district...\"\n\nFor \"Dem/Lean Dem\" (Democratic Party/Lean Democratic):\n- Very closely: 21%\n\nAnswer: 21", "citations": [], "input_tokens": 7800, "output_tokens": 214, "cost_micros": 26610, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news about congressional elections very closely.\n\nFrom the document:\n\n**Policy Ideas and Ethical Standards (page 9):**\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**Following News About Congressional Elections (page 11):**\nLooking at the table \"Young people far less likely than older adults to follow news about midterms\"\n\nFor \"Dem/Lean Dem\" (Democrats/Democratic leaners):\n- Very closely: 21%\n- Fairly closely: 31%\n- NET: 52%\n\nThe percentage who followed very closely is 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 9543, "output_tokens": 241, "cost_micros": 32244, "latency_ms": 6844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the provided document to answer your question.Let me continue reading to find information about which party holds the highest total percentage of good policy ideas and high ethical standards.Let me search for information about which party has good policy ideas and high ethical standards.Let me search for information about party policy ideas and ethical standards.Perfect! Now I need to find which party has the highest TOTAL percentage of both \"good policy ideas\" and \"high ethical standards\", then find the news following data for that party.\n\nFrom the document:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the higher total (92%). Now I need to find what percentage of Democrats followed congressional election news very closely.Perfect! I found the data. Looking at the table in the document:\n\nFrom lines 1327-1353, I can see:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the highest total percentage (92%).\n\nFrom lines 1600-1604, I can see:\n- Dem/Lean Dem: 21% followed news about congressional elections very closely\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69875, "error": null, "extra": {"thread_id": 1906, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bb4e12b774cf4c398f45bc409de06fef", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 386, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has higher combined ratings for \"good policy ideas\" and \"high ethical standards,\" and who also closely follow congressional elections.\n\nFrom page 11 (page 9 of content):\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal scores:\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nFrom page 14 (page 12 of content), looking at registered voters who support the Democratic candidate:\n- Total registered voters supporting Democratic candidate: 48%\n\nFrom page 13 (page 11 of content), looking at those who follow congressional elections \"very closely\":\n- Among Dem/Lean Dem: 21% follow very closely\n\nHowever, I need to find the intersection of:\n1. Registered voters who support the Democratic candidate\n2. Those who closely follow congressional elections\n\nFrom page 14, among all registered voters: 48% support Democratic candidate\nFrom page 13, among Dem/Lean Dem: 21% follow very closely, and 52% total follow very or fairly closely\n\nThe question asks for those who support the Democratic candidate AND closely follow elections. Looking at page 13, among Dem/Lean Dem, 52% follow closely (very or fairly).\n\nSince the Democratic Party has the higher combined score (92% vs 91%), and 48% of registered voters support the Democratic candidate, but I need those who also follow closely.\n\nFrom the data shown, among Democratic supporters who are registered voters, approximately 52% follow closely. However, the specific cross-tabulation isn't directly provided.\n\nLooking more carefully at page 14, the 48% represents all registered voters supporting Democrats. The closely following percentage among this group would need to be calculated, but the most direct answer from the available data for Democratic-leaning registered voters who follow closely would be derived from the 52% net figure.\n\nAnswer: 48", "citations": [], "input_tokens": 33928, "output_tokens": 480, "cost_micros": 108984, "latency_ms": 27639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher total percentage of \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate AND closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n - Total: 50% + 41% = 91%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n - Total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Congressional vote preferences (page 13):**\nThe table shows \"% of registered voters who say they support or lean toward the _ candidate for Congress in their district\"\n- Democratic: 48%\n- Republican: 43%\n\nHowever, I need to find those who BOTH:\n1. Support the Democratic candidate\n2. Closely follow congressional elections\n\nLooking at page 12 for those who follow elections \"very closely\" or \"fairly closely\":\n- The document shows 19% follow \"very closely\" and the NET (very + fairly closely) is shown in various demographic breakdowns\n- For \"Dem/Lean Dem\": 21% follow very closely, 52% follow very or fairly closely\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections. The document doesn't provide a cross-tabulation of vote preference by attention to elections.\n\nWait, let me re-read the question. It asks for \"registered voters who support or lean toward the candidate from the party with the higher total percentage... and closely follow congressional elections.\"\n\nLooking at the data again, I need to find if there's a cross-tab. The document shows on page 13 that 48% of registered voters support the Democratic candidate, but it doesn't break this down by whether they closely follow elections.\n\nThe document provides separate information:\n- 48% of registered voters support Democratic candidate (page 13)\n- Various percentages follow elections closely (page 12)\n\nWithout a cross-tabulation, I cannot determine the exact percentage who meet both criteria. However, re-reading more carefully - the question might be asking simply for the percentage who support the Democratic candidate, which is 48.\n\nAnswer", "citations": [], "input_tokens": 6959, "output_tokens": 512, "cost_micros": 28557, "latency_ms": 10796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher combined percentages for \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate and closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party:\n - Has good policy ideas: 50%\n - Has high ethical standards: 41%\n - Total: 91%\n\n- Democratic Party:\n - Has good policy ideas: 50%\n - Has high ethical standards: 42%\n - Total: 92%\n\nThe Democratic Party has the higher combined total (92% vs 91%).\n\n**Congressional vote preferences (page 12):**\nLooking at registered voters who support the Democratic candidate: 48%\n\nHowever, the question asks specifically for those who \"closely follow congressional elections in their district.\"\n\n**Following congressional elections (page 11):**\nThe question asks about following news \"very closely\" or \"fairly closely\"\n- Total following very closely: 19%\n- Total following fairly closely: 32%\n- NET (very or fairly closely): Not explicitly stated for total, but can be calculated\n\nLooking at the breakdown by party affiliation (page 11):\n- Dem/Lean Dem following very closely: 21%\n- The \"NET\" column shows 52% for Dem/Lean Dem\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections.\n\nLooking more carefully at page 12, the congressional vote preferences show 48% of registered voters favor the Democratic candidate.\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (48% of registered voters)\n2. Follow elections closely\n\nThis specific cross-tabulation is not provided in the document. The document shows these as separate statistics but doesn't provide the intersection.\n\nGiven the data available, if I must provide an answer based on what's shown, the percentage of registered voters who support the Democratic candidate is 48%.\n\nAnswer: 48", "citations": [], "input_tokens": 9451, "output_tokens": 446, "cost_micros": 35043, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find:\n1. Which party has higher total percentage of \"good policy ideas\" and \"high ethical standards\"\n2. The percentage of registered voters who support that party's candidate AND closely follow congressional elections\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91%\nTotal for Democratic Party: 50 + 42 = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Following congressional elections closely (page 11):**\nThe document shows percentages who follow \"very closely\" or \"fairly closely\" by party:\n- Dem/Lean Dem: 21% very closely, NET = 52%\n\n**Congressional vote preference (page 12):**\nThe table shows registered voters who support/lean toward:\n- Democratic candidate: 48%\n- Republican candidate: 43%\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (the party with higher total)\n2. Follow elections closely\n\nThe document doesn't provide a cross-tabulation of vote preference by how closely people follow elections. Looking at page 12, it only shows vote preference by demographics, not by attention to elections.\n\nSince the question asks for registered voters who support the Democratic candidate AND closely follow elections, but this specific cross-tab is not provided in the document, I cannot determine this exact percentage from the available data.\n\nHowever, re-reading the question, it may be asking simply for the percentage who support the Democratic candidate (48%), as that's the party with the higher combined score.\n\nAnswer: 48", "citations": [], "input_tokens": 7808, "output_tokens": 403, "cost_micros": 29469, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has both higher \"good policy ideas\" and \"high ethical standards\" ratings, and who closely follow congressional elections.\n\nFirst, let me identify which party has higher ratings on both metrics:\n\nFrom the document (page 9):\n- Republican Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 41%\n- Democratic Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 42%\n\nThe Democratic Party has a higher percentage on \"high ethical standards\" (42% vs 41%), while both parties tie on \"good policy ideas\" (50% each). Since the question asks for the party with \"higher total percentage,\" I need to add them:\n- Republican Party total: 50% + 41% = 91%\n- Democratic Party total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nNow I need to find registered voters who:\n1. Support or lean toward the Democratic candidate\n2. Closely follow congressional elections in their district\n\nFrom page 12, among all registered voters: 48% support or lean toward the Democratic candidate.\n\nHowever, I need to find those who ALSO closely follow elections. The document doesn't provide a cross-tabulation of vote preference by how closely people follow elections.\n\nLooking at page 11, it shows that 19% of all adults follow \"very closely\" and 31% follow \"fairly closely\" (total 50% follow at least fairly closely).\n\nBut the question asks specifically for those who \"closely follow\" - which appears to mean \"very closely\" (19% of total).\n\nSince the document doesn't provide the specific cross-tabulation of Democratic voters who follow very closely, I cannot calculate this precisely from the data provided. However, re-reading the question, it may be asking simply for the percentage of registered voters who support the Democratic candidate, which is 48%.\n\nBut looking more carefully at page 11, the breakdown shows percentages of people following elections, not broken down by their vote preference.\n\nGiven the data available, the answer appears to be: 48\n\nAnswer: 48", "citations": [], "input_tokens": 9551, "output_tokens": 478, "cost_micros": 35823, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question.Let me continue reading to find information about party policy ideas, ethical standards, and congressional election following:Let me read more sections systematically to find the relevant data:Now let me search for information about congressional elections and following:Now I need to find data specifically about registered voters who closely follow congressional elections broken down by party. Let me search for more detailed tables:Let me look for a more detailed table that might break down voting preferences by how closely they follow elections:Let me search through the appendix or topline data which might contain the detailed crosstabs:Let me search for any tables that combine party support, policy ideas, ethical standards, and following elections. Let me look more carefully at the data I've already read. First, let me identify the key information:\n\n1. From lines 1327-1353, I see that both parties score 50% on \"Has good policy ideas\" and Democratic Party scores 42% on \"Has high ethical standards\" vs Republican Party at 41%.\n\n2. Since both parties tie on policy ideas (50%), I need to determine which party has higher combined score. Democratic Party has 42% ethical standards, Republican has 41%, so Democratic Party has a higher total (50+42=92) vs Republican (50+41=91).\n\n3. Now I need to find data about registered voters who support the Democratic candidate AND closely follow congressional elections.\n\nLet me search for more detailed crosstabs that might show vote preference by attention to elections:I need to look for tables that show voting preference crossed with how closely voters follow congressional elections. Let me search the document more systematically for all tables and figures related to this:Based on my thorough review of the document, I can now solve this step by step:\n\n**Step 1: Determine which party has the higher total percentage of good policy ideas AND high ethical standards**\n\nFrom lines 1327-1353 (chunk id='49127'):\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91\nTotal for Democratic Party: 50 + 42 = 92\n\n**The Democratic Party has the higher total (92 vs 91).**\n\n**Step 2: Find registered voters who support the Democratic candidate**\n\nFrom lines 1638-1643 and 1661-1676 (chunks id='49140' and id='49142'):\n\"Currently, 48% of registered voters say they would vote for the Democratic Party's candidate for Congress in their district\"\n\n**Step 3: Find the percentage who closely follow congressional elections**\n\nFrom lines 1494-1537 and 1582-1604 (chunks id='49135' and id='49138'):\n- Among \"Dem/Lean Dem\": 21% follow \"Very closely\"\n\nHowever, I need to find data specifically about registered voters (not all adults) who support the Democratic candidate AND closely follow congressional elections. The document shows that 21% of Dem/Lean Dem follow very closely (line 1602), and from the congressional vote table, 92% of Dem/Lean Dem registered voters support the Democratic candidate (line 1763).\n\nBut the question asks specifically for registered voters who support the Democratic candidate AND closely follow. The data shows:\n- Line 1602: Among Dem/Lean Dem, 21% follow very closely\n- Line 1763: Among registered voters who are Dem/Lean Dem, 92% support the Democratic candidate\n\nSince 92% of Dem/Lean Dem registered voters support the Democratic candidate, and 21% of Dem/Lean Dem closely follow congressional elections, and these are measuring the same group (Dem/Lean Dem), the answer is **21**.\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 103328, "error": null, "extra": {"thread_id": 1907, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e2603e2724c3411e98fe7264f3325bbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 739, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at page 10, I can see a chart titled \"Wide gender gap in approval of Trump\" which shows approval/disapproval ratings by various demographics.\n\nFor the \"Disapprove\" column (which shows \"Strongly\" and \"Not strongly\" combined):\n\nAges 18-29: 65% total disapproval\nAges 65+: 49% total disapproval\n\nHowever, I need to find specifically the \"Strongly\" disapprove percentages. Looking more carefully at the chart structure, the disapproval is broken down into \"Strongly\" and \"Not strongly\" components.\n\nFrom the chart on page 10:\n- Ages 18-29: The bar shows 65 total, with components that would indicate strongly disapprove\n- Ages 65+: The bar shows 49 total\n\nLet me look more carefully at the specific breakdown. The chart shows:\n- Total disapproval for 18-29: 65%\n- Total disapproval for 65+: 49%\n\nHowever, I need the \"Strongly\" disapprove component specifically. Looking at the total row, it shows 54% total disapproval with 42% strongly disapproving.\n\nWithout being able to see the exact breakdown of \"strongly\" vs \"not strongly\" for each age group in the data provided, I cannot calculate the precise gap for \"strongly disapprove\" specifically.\n\nLet me reconsider - if the overall pattern shows 42% strongly disapprove out of 54% total, and the youngest group (18-29) has 65% total disapproval while the oldest (65+) has 49% total disapproval, the gap in total disapproval is 16 percentage points.\n\nAnswer: 16.0", "citations": [], "input_tokens": 33935, "output_tokens": 416, "cost_micros": 108045, "latency_ms": 24000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "16.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of those who \"strongly disapprove\" of the way Trump is handling his job for the youngest and oldest age groups.\n\nLooking at the table \"Wide gender gap in approval of Trump\" on page 9, I can find the \"Disapprove Strongly\" column for different age groups:\n\nAges 18-29: 46% disapprove strongly\nAges 65+: 39% disapprove strongly\n\nThe gap between the youngest age group (18-29) and the oldest age group (65+) is:\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 6966, "output_tokens": 137, "cost_micros": 22953, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of people who \"strongly disapprove\" of Trump's job performance for the youngest and oldest age groups.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can see the breakdown by age groups with approval/disapproval data.\n\nThe columns show:\n- Disapprove (with \"Strongly\" and \"Not strongly\" subcategories)\n- Approve (with \"Strongly\" and \"Not strongly\" subcategories)\n\nFor age groups:\n- Ages 18-29: The \"Disapprove\" total is 65, with \"Strongly\" at 46\n- Ages 65+: The \"Disapprove\" total is 49, with \"Strongly\" at 39\n\nThe gap between the youngest age group (18-29) strongly disapproving at 46% and the oldest age group (65+) strongly disapproving at 39% is:\n\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 9458, "output_tokens": 227, "cost_micros": 31779, "latency_ms": 6702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap in strong disapproval between the youngest and oldest age groups for Trump's job handling.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 9, which shows \"% who __ of the way Donald Trump is handling his job as president\":\n\nFor age groups:\n- Ages 18-29: 46% strongly disapprove\n- Ages 65+: 39% strongly disapprove\n\nThe gap between the youngest (18-29) and oldest (65+) age groups in strong disapproval is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 7815, "output_tokens": 145, "cost_micros": 25620, "latency_ms": 4266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can find the \"Disapprove Strongly\" column for different age groups:\n\n- Ages 18-29: 46% disapprove strongly\n- Ages 65+: 39% disapprove strongly\n\nThe gap between the youngest (18-29) and oldest (65+) age groups is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 9558, "output_tokens": 137, "cost_micros": 30729, "latency_ms": 4405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the information about the survey conducted April 25-May 1, 2018, regarding approval ratings by age group.Let me continue searching for the age group data on approval ratings. I need to find the specific breakdown by age showing \"strongly disapprove\" percentages.Let me search for \"disapprove\" more broadly:Good! I can see references to approval ratings. Let me read that section more carefully:Perfect! I found the table with age group breakdowns. I can see:\n\n- Ages 18-29 (youngest): Strongly disapprove = 46%\n- Ages 65+ (oldest): Strongly disapprove = 39%\n\nNow I need to calculate the gap:\n46 - 39 = 7\n\nAnswer: 7.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44469, "error": null, "extra": {"thread_id": 1908, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_478b9a7833a5424882dc6822080399bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about a survey conducted April 25-May 1, 2019 regarding Democratic people's views on whether either party has high ethical standards.\n\nHowever, I notice that this document is from a survey conducted April 25-May 1, **2018**, not 2019 as mentioned in the question.\n\nLooking at page 12 (document page 13), I can see a chart titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nThe data shows:\n- Total: 17% say it describes both parties, 47% say one party not the other, 25% say neither party\n- Among Dem/Lean Dem: 18% say both parties, 55% say one party, **18%** say neither party\n\nSo among Democrats and Democratic leaners in this survey, 18% said neither party has high ethical standards.\n\nHowever, the question asks specifically about \"Democratic people\" not \"Dem/Lean Dem\". Let me check if there's more specific data.\n\nThe chart shows the breakdown is for \"Dem/Lean Dem\" which includes both Democrats and Democratic leaners, showing 18% said neither party has high ethical standards.\n\nAnswer: 18", "citations": [], "input_tokens": 33910, "output_tokens": 299, "cost_micros": 106215, "latency_ms": 13266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards.\n\nLooking at the document, I can see a table on page 11 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, describes one party not the other, or describes neither party.\n\nAccording to this table:\n- For \"Democrat\": 18% say it describes neither party\n\nHowever, the question asks for the NUMBER of people, not the percentage. I need to find the actual sample size.\n\nLooking at the methodology section on page 16, the survey was conducted April 25-May 1, 2018 (note: the question asks about 2019, but the document is from 2018). The table shows:\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nSo if 18% of the 710 Democrats/Democratic leaners said neither party has high ethical standards:\n710 \u00d7 0.18 = 127.8\n\nSince we need an integer, this would round to 128.\n\nHowever, I should note that the question asks about 2019, but this document is from a survey conducted in 2018. There is no 2019 data in this document.\n\nBased on the 2018 data available:\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 6941, "output_tokens": 329, "cost_micros": 25758, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the April 25-May 1, 2018 survey (note: the question says 2019 but the document is from 2018).\n\nLooking at the document, I find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes neither party.\n\nFor Democrats, the table shows:\n- Democrat: 18% say \"Describes neither party\"\n\nHowever, I need the actual number of people, not the percentage. \n\nLooking at the methodology section on page 14-15:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe table on page 10 shows that 18% of Democrats said \"high ethical standards\" describes neither party.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents actual people, this would be approximately 128 people.\n\nHowever, I should note that the methodology indicates these are unweighted numbers, and the actual weighted percentages are shown in the table. Without the weighted sample size for Democrats, I can only use the unweighted sample size provided.\n\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9433, "output_tokens": 322, "cost_micros": 33129, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the table titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" on page 10:\n\nThe table shows the percentage who say 'high ethical standards' \"Describes neither party\":\n- Democrat: 18%\n\nThe survey methodology section on page 14 states:\n- Total sample: 1,503 adults\n- The unweighted sample sizes table on page 15 shows:\n - Dem/Lean Dem: 710\n\nHowever, the table on page 10 shows percentages, not raw numbers. To calculate the number of Democrats who said neither party has high ethical standards:\n\n710 (Dem/Lean Dem sample) \u00d7 0.18 (18% who said neither party has high ethical standards) = 127.8\n\nSince we need an integer and this represents people, this would round to 128.\n\nHowever, I should note that 710 is the unweighted sample size, and the actual weighted number might be different. But based on the information provided and the unweighted sample size given, the calculation would be approximately 128.\n\nAnswer: 128", "citations": [], "input_tokens": 7790, "output_tokens": 304, "cost_micros": 27930, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the document, I can find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nAccording to this table:\n- Democrat: 18% say it describes neither party\n\nHowever, this is a percentage, not the actual number of people. To find the actual number, I need to know how many Democrats were in the survey.\n\nLooking at page 15, the methodology section shows:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe question asks specifically about \"Democratic people\" (which I interpret as Dem/Lean Dem), and from the table on page 10, 18% of Democrats said neither party has high ethical standards.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents people, I should round to 128.\n\nHowever, I need to be careful - the table on page 15 shows \"unweighted\" sample sizes, but the percentages in the analysis would typically be based on weighted data. The actual number would be the unweighted count.\n\nUsing the unweighted sample: 710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9533, "output_tokens": 370, "cost_micros": 34149, "latency_ms": 9297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to find the answer to this question.I need to search for information about the survey conducted April 25-May 1, 2019. Let me continue reading the document to find the relevant data.Let me search for \"ethical standards\" in the document:Good! I found the survey date \"April 25-May 1, 2018\" (not 2019 as stated in the question, but this appears to be the document). Let me read the section about ethical standards of the parties:Perfect! I found the table with the data. Looking at the table in lines 1405-1470, I can see the breakdown by party affiliation. The question asks specifically about \"Democratic people\" (Democrats).\n\nFrom the table:\n- Row \"Democrat\": 18% say neither party has high ethical standards (column \"Describes neither party\")\n\nHowever, I should note that the survey was conducted April 25-May 1, 2018, not 2019 as stated in the question. But this appears to be the relevant document based on the question content.\n\nThe table shows that 18% of Democrats said neither the Republican Party nor the Democratic Party has 'high ethical standards'.\n\nSince the question asks for the number of people (not percentage), I need to find the sample size. Let me search for that information:Let me read more to find the specific sample size for Democrats:Now I need to check if the table shows data for \"Democrat\" or \"Dem/Lean Dem\". Let me reread that section:Perfect! Now I have all the information:\n\nFrom the table:\n- \"Democrat\" row shows 18% said neither party has high ethical standards\n- The note states \"Independents include all those who do not identify as Republican or Democrat\"\n- This means \"Republican\", \"Democrat\", and \"Independent\" are separate categories\n- \"Lean Rep\" and \"Lean Dem\" are subcategories of Independents\n\nLooking at the methodology section, I see:\n- Total sample: 1,503 adults\n- Dem/Lean Dem (combined): 710 people\n\nHowever, the table shows \"Democrat\" separately from \"Lean Dem\". I need to find the sample size for just \"Democrat\" (not including leaners).\n\nLet me search for more details about the Democrat sample size:Unfortunately, the document doesn't provide the specific sample size for \"Democrat\" separate from \"Dem/Lean Dem\". The methodology only provides:\n- Total sample: 1,503\n- Rep/Lean Rep: 644\n- Dem/Lean Dem: 710\n\nGiven that:\n- 18% of Democrats said neither party has high ethical standards\n- The combined \"Dem/Lean Dem\" sample size is 710\n\nHowever, since the table shows separate rows for \"Democrat\" and \"Lean Dem\", and we only have the combined sample size, I cannot calculate the exact number of Democrat respondents (excluding leaners).\n\nBut wait - let me check if there's more information about the sample composition:The document states \"Sample sizes and sampling errors for other subgroups are available upon request\" but doesn't provide the specific breakdown for Democrats vs. Dem/Lean Dem.\n\nLooking back at the data, the question asks for \"Democratic people\" which most likely refers to \"Democrat\" (those who identify as Democrats, not including Democratic leaners who are technically independents).\n\nFrom the table:\n- 18% of Democrats said neither party has high ethical standards\n\nHowever, without the specific sample size for \"Democrat\" (as opposed to \"Dem/Lean Dem\"), I cannot provide the exact number of people.\n\nBut based on typical Pew Research methodology and the fact that the question specifically mentions \"Democratic people in the survey,\" it's most reasonable to interpret this as asking for the actual count rather than the percentage.\n\nSince I don't have the specific Democrat-only sample size, let me check if there's any other information in the document that might help. Let me look at whether the question might be asking about the year 2019 instead:The document I've been examining is from 2018 (April 25-May 1, 2018), not 2019. The question states \"April 25- May 1, 2019\" but based on the priority documents and the content match, this 2018 survey is the relevant document.\n\nGiven the information available:\n- 18% of Democrats said neither party has high ethical standards\n- The document only provides \"Dem/Lean Dem\" combined sample size of 710\n\nWithout the specific Democrat-only sample size, and given that the question asks for an integer representing the number of people (not percentage), I cannot provide a definitive answer based solely on the document provided.\n\nHowever, if I interpret \"Democratic people\" as \"Dem/Lean Dem\" (the broader Democratic coalition), then:\n710 \u00d7 0.18 = 127.8 \u2248 128 people\n\nBut if we look at the note in the table that says \"Independents include all those who do not identify as Republican or Democrat,\" and line 1483 says \"only about two-in-ten Republicans (19%) or Democrats (18%) say this,\" this confirms 18% is for Democrats specifically (not including leaners).\n\nSince the document doesn't provide the Democrat-only sample size, and given that this is supposed to be answered from the document alone, let me reconsider. The percentage (18%) is clearly stated in the document. The question asks \"How many\" which typically means a count, not a percentage. Without the specific sample size for Democrats (excluding leaners), I cannot calculate the exact number.\n\nHowever, given the constraint to answer with a single integer, and that 18 appears prominently as the percentage for Democrats in the table, the answer is:\n\nAnswer: 18", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84969, "error": null, "extra": {"thread_id": 1909, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_265d3866b1d34612a515602bee4564b0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 708, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the first figure on the second page of the document, I can see a photograph of a military officer in uniform with an American flag in the background.\n\nAccording to the text in the \"NMRC Commanding Officer's Message\" section on page 2, the message is signed by:\n\n\"John W. Sanders III, CAPT, MC, USN\"\n\nThis indicates that Captain John W. Sanders III is the NMRC Commanding Officer shown in that photograph.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 40393, "output_tokens": 116, "cost_micros": 122919, "latency_ms": 29766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, on page 2, the first figure shows a photo with a caption that reads:\n\n\"Dr. Xiaoyan \"Cathy\" Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, this caption does not identify a commanding officer. Looking at the text on page 2, under \"NMRC Commanding Officer's Message,\" it states:\n\n\"NMRC Commanding Officer sends, John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14490, "output_tokens": 161, "cost_micros": 45885, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains the \"NMRC Commanding Officer's Message\" section. At the bottom of that message, it states:\n\n\"NMRC Commanding Officer sends,\nJohn W. Sanders III, CAPT, MC, USN\"\n\nThere is also a figure/photo associated with this message showing the commanding officer.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 16140, "output_tokens": 103, "cost_micros": 49965, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains two sections: \"NMRC Commanding Officer's Message\" and \"NSMRL Commanding Officer's Message.\"\n\nThe first figure/photo on this page shows \"Dr. Xiaoyan 'Cathy' Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, the question asks about the commanding officer. Looking at the page headers, the NMRC Commanding Officer's Message is signed by:\n\nAnswer: John W. Sanders III, CAPT, MC, USN", "citations": [], "input_tokens": 12934, "output_tokens": 171, "cost_micros": 41367, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7272727272727273, "method": "str_norm", "normalised_pred": "john w sanders iii capt mc usn", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, the second page contains the \"NMRC Commanding Officer's Message\" section.\n\nThe message is signed by:\n\"John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14408, "output_tokens": 61, "cost_micros": 44139, "latency_ms": 3796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the first figure on the second page and identify the commanding officer.Let me continue reading to see the actual content and locate the second page with the first figure:Let me continue reading to find page 2 and the first figure on it:Now let me search for page 2 specifically to find the first figure on that page:I can see that page 2 starts at line 585 (marked with ``). The first figure on page 2 appears at lines 634-681. Let me read that section more carefully to identify the commanding officer:\n\nLooking at the content, I can see:\n\n1. Page 2 starts at line 585 with the page break marker\n2. The first figure on page 2 appears starting at line 634 (with the embedded image `Image76.jpg` at line 647)\n3. The visual description indicates this is \"a formal military portrait photograph\" with \"Gold sleeve stripes on both cuffs indicating officer rank (appears to be four stripes with additional device)\"\n\nLooking back at the context before this figure, at line 610-611, it mentions \"NAMRU-3 Commanding Officer, Capt. Buhari Oyofo\" and the caption at lines 686-687 states \"NAMRU-3 hosted nine Afghan trainees from the Central PublicHealth Laboratory in Kabul for a bacteriology training workshop.\"\n\nHowever, the portrait figure appears to be associated with NAMRU-3's commanding officer based on the context and positioning in the article about NAMRU-3.\n\nAnswer: Capt. Buhari Oyofo", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36202, "error": null, "extra": {"thread_id": 1910, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f4f5354a0c7b455995c184df235b687c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "capt buhari oyofo", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document from December 2012, I can find information about a visit on November 29, 2012 on page 1.\n\nThe article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 40404, "output_tokens": 160, "cost_micros": 123612, "latency_ms": 34093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for November 29, 2012...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14501, "output_tokens": 147, "cost_micros": 45708, "latency_ms": 4968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about a November 29, 2012 visit to the Naval Medical Research Center...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 16151, "output_tokens": 158, "cost_micros": 50823, "latency_ms": 4608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for a visit on November 29, 2012 to the Naval Medical Research Center (NMRC)...\n\nOn page 1, in the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative,\" it states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 12945, "output_tokens": 164, "cost_micros": 41295, "latency_ms": 4907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" on page 1:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29...\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14419, "output_tokens": 120, "cost_micros": 45057, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about who visited the U.S. Naval Medical Research Centre on November 29, 2012.Let me search for the specific date \"November 29, 2012\" in the document:Let me try different date formats:I found a reference to \"November 29\". Let me read the specific chunk that contains this information:Perfect! I found the answer. Let me verify by searching for \"2012\" to confirm the year:The document is from December 2012, and it mentions the visit on November 29 (which would be November 29, 2012). Based on the text I read, the visitor was Rear Adm. (Ret.) Tim Ziemer, who was the U.S. Global Malaria Coordinator for the President's Malaria Initiative.\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer, U.S. Global Malaria Coordinator, President's Malaria Initiative", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40875, "error": null, "extra": {"thread_id": 1911, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b589ac0b3255440294d5d9346c5a691a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 205, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.25, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer u s global malaria coordinator president s malaria initiative", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nOn page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 40420, "output_tokens": 181, "cost_micros": 123975, "latency_ms": 34391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about the NAMRU-3 team's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, under \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\":\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 14517, "output_tokens": 176, "cost_micros": 46191, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 16167, "output_tokens": 176, "cost_micros": 51141, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the section titled \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe document states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 12961, "output_tokens": 156, "cost_micros": 41223, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, specifically the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe text states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 14435, "output_tokens": 159, "cost_micros": 45690, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the NAMRU-3 team visit to Monrovia, Liberia in November 2012.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23750, "error": null, "extra": {"thread_id": 1912, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_83c19c542b6a41ceac941d5981d28799", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 42, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find information about namru 3 team visit to monrovia, liberia in november 2012", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine each image in the document to count those containing only one person.\n\nGoing through the document:\n\n1. Page 1: Image of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Image of Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4: Group photo of Internal Medicine Department staff - multiple people\n4. Page 5: Image of Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 5: Image of Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n6. Page 7: Group photo with Col. Vernon Graham - 5 people\n7. Page 7: Group photo with Dr. Walter T. Gwenigale - 5 people\n8. Page 8: Group photo of Kazakh scientists and staff - 9 people\n9. Page 9: Image of Lt. j.g. Michael Rucker treating a child - 2 people\n10. Page 9: Image of U.S. Marines and Sailors - multiple people\n11. Page 10: Group photo of Joint Combat Casualty Research Team - 9 people\n12. Page 11: Graphic illustration (not a photograph of people)\n13. Page 12: Multiple images from Dining Out event - multiple people in each\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14: Image of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n16. Page 15: Images of officers teaching - appears to show teachers with students (multiple people)\n\nCounting images with only one person:\n- Cmdr. Charmagne Beckett on flight deck\n- Petty Officer 1st Class Dennis Gonzales\n\nAnswer: 2", "citations": [], "input_tokens": 14484, "output_tokens": 453, "cost_micros": 50247, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture/photo in the document systematically to count those containing only one person.\n\n1. Page 1: Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n2. Page 2: Photo of Capt. John W. Sanders III - 1 person \u2713\n3. Page 2: Photo of Capt. Steven M. Wechsler - 1 person \u2713\n4. Page 3: Photo of Afghan trainees in laboratory - multiple people\n5. Page 4: Photo of Cmdr. Charmagne Beckett - 1 person \u2713\n6. Page 4: Group photo of Internal Medicine Department staff - multiple people\n7. Page 5: Photo of Petty Officer 1st Class Dennis Gonzales - 1 person \u2713\n8. Page 6: Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n9. Page 7: Photo with Col. Vernon Graham and team - multiple people\n10. Page 7: Photo with Dr. Gwenigale and team - multiple people\n11. Page 7: Photo of Pfc. Nadoris Nador (mentioned in text but appears to be with another person based on context)\n12. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n13. Page 9: Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n14. Page 9: Photo of U.S. Marines and Sailors - multiple people\n15. Page 10: Group photo of Joint Combat Casualty Research Team - multiple people\n16. Page 12: Group photos from Dining Out event - multiple people in each\n17. Page 13: Photo with Eileen Franke Villasante and others - multiple people\n18. Page 14: Photo of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n19. Page 14: Photo of scouts learning flag etiquette - multiple people\n20. Page 15: Two photos of officers at", "citations": [], "input_tokens": 16134, "output_tokens": 512, "cost_micros": 56082, "latency_ms": 10422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture in the document systematically to count how many contain only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 5: Petty Officer 1st Class Dennis Gonzales getting swabbed - 2 people\n4. Page 6: Lance Cpl. Kip Boker standing while Cpl. Jacob Echeverri collects sample - 2 people\n5. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n6. Page 9: Lt. j.g. Michael Rucker treating a girl - 2 people\n7. Page 10: JC2RT Team Members group photo - multiple people\n8. Page 11: Graphic illustration (not a photo of people)\n9. Page 12: Group photo at dining out - multiple people\n10. Page 14: Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n11. Page 15: Two photos - Cmdr. Charmagne Beckett talking with students (multiple people) and Lt. Cmdr. Janine Danko talking with students (multiple people)\n\nCounting the pictures with only one person:\n- Page 4: Cmdr. Charmagne Beckett on flight deck\n\nAnswer: 1", "citations": [], "input_tokens": 12928, "output_tokens": 360, "cost_micros": 44184, "latency_ms": 8217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each image in the document and count those containing only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou discussing research - 2 people visible\n2. Page 4 (top): Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4 (bottom): Pacific Partnership 2012 Internal Medicine Department staff - multiple people (10)\n4. Page 5: Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 6: Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n6. Page 7 (top): Group photo with Dr. Gwenigale - 5 people\n7. Page 7 (bottom): Group photo with Col. Graham - 5 people\n8. Page 8: Group photo of Kazakh scientists - 9 people\n9. Page 9 (top): U.S. Marines and Sailors in aircraft - multiple people\n10. Page 9 (bottom): Lt. j.g. Michael Rucker treating girl - 2 people\n11. Page 10: Joint Combat Casualty Research Team - 9 people\n12. Page 12 (top): Dining Out Event photo - multiple people at tables\n13. Page 12 (bottom): Group photo at Dining Out - 6 people\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14 (top): Cmdr. Szumlas with Rear Adm. Ziemer - 3 people\n16. Page 14 (middle): Dr. Andrea Keane-Myers at podium - 1 person\n17. Page 14 (bottom): NAMRU-3 staff with Cub Scouts - multiple people\n18. Page 15 (left): Cmdr. Charmagne Beckett with student - 2 people\n19. Page 15 (right): Lt. Cmdr. Janine Danko with students - 3 people\n20. Page 15 (bottom): Lt. Danett Bishop in classroom - 1 ", "citations": [], "input_tokens": 14402, "output_tokens": 512, "cost_micros": 50886, "latency_ms": 10640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine the document to count pictures containing only one person.Let me continue reading to see all images in the document.Let me read the rest of the document more systematically by checking remaining chunks.Now let me read the visual descriptions for each image systematically to count how many contain only one person. Let me read through the remaining images I haven't seen yet.Now let me read the final images:Now let me count all the images with only one person based on the visual descriptions I've read:\n\nBased on my review of all the images in the document, here are the ones that contain only ONE person:\n\n1. **Image75.jpg** - Formal military portrait photograph of a naval officer (one person)\n2. **Image76.jpg** - Formal military portrait photograph (one person in uniform)\n3. **Image83.jpg** - Person on naval vessel deck wearing Navy uniform (one person)\n4. **Image86.jpg** - Close-up photograph of a young male service member in desert camouflage (one person)\n5. **Image125.jpg** - Woman speaking at a podium at Johns Hopkins (one person)\n6. **Image133.jpg** - Person in military uniform standing and presenting with equipment (one person)\n\nImages with MULTIPLE people:\n- Image9.jpg - Banner header (no people, just background with multiple individuals in soft focus)\n- Image55.png - QR code (no people)\n- Image69.jpg - Laboratory setting with TWO individuals\n- Image79.jpg - Laboratory scene with approximately 6 people\n- Image82.jpg - Group of 10 individuals\n- Image89.jpg - Emblem/insignia (no people)\n- Image90.jpg - Three individuals visible\n- Image93.jpg - Five individuals\n- Image94.jpg - Five individuals\n- Image97.jpg - Group of approximately 10 individuals\n- Image100.jpg - Healthcare provider and pediatric patient (TWO people)\n- Image101.jpg - Military transport aircraft interior with many personnel\n- Image103.jpg - Nine military personnel in front of helicopter\n- Image109.jpg - Diagram (no people)\n- Image112.jpg - Banquet hall with many attendees\n- Image113.jpg - Five individuals\n- Image116.jpg - Four individuals\n- Image127.jpg - Group of 10-12 people\n- Image128.jpg - Three men in conversation\n- Image131.jpg - Two women in foreground\n- Image132.jpg - Three individuals\n- Image139.jpg - Emblem/logo (no people)\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57125, "error": null, "extra": {"thread_id": 1913, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8b24b6e5b403450996ab78f93d47888d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 381, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the images in the document to count the number of people in each.\n\nLet me go through each page:\n\nPage 1: Contains one photo showing two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Tim Ziemer)\n\nPage 2: Contains two photos - one of Capt. Sanders (1 person) and one of Capt. Wechsler (1 person)\n\nPage 3: Contains one photo showing multiple people in a laboratory setting - I can count approximately 9 people\n\nPage 4: Contains one photo showing a group of medical staff - I can count approximately 10 people in the Internal Medicine Department staff photo\n\nPage 5: Contains one photo showing one person (Petty Officer 1st Class Dennis Gonzales)\n\nPage 6: Contains two photos - one showing multiple people swabbing (2 people visible), and one showing a group photo with approximately 9 people\n\nPage 7: Contains two photos - both showing groups of approximately 4-5 people each\n\nPage 8: Contains one photo showing approximately 8 people (Kazakh scientists and NMRC staff)\n\nPage 9: Contains two photos - one showing many Marines/Sailors on aircraft (approximately 50+ people), and one showing 2 people\n\nPage 10: Contains one photo showing approximately 9 people (JC2RT team members)\n\nPage 11: Contains one image/diagram (not a photo of people)\n\nPage 12: Contains two photos showing dining out events with multiple people at tables\n\nPage 13: Contains one photo showing 4 people\n\nPage 14: Contains two photos showing 2-3 people in each\n\nPage 15: Contains two photos showing 2-3 people in each\n\nPage 16: Contains no photos of people\n\nThe page with the most people is page 9, which contains the photo of U.S. Marines and Sailors on the aircraft with approximately 50+ people visible.\n\nAnswer: 9", "citations": [], "input_tokens": 40400, "output_tokens": 450, "cost_micros": 127950, "latency_ms": 40312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine each page of the document to count the number of people visible in pictures.\n\nPage 1: Picture shows one person (Dr. Xiaoyan \"Cathy\" Zou) with another person (Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: Picture shows people in protective equipment - difficult to count exactly but appears to be a few people\n\nPage 4: Multiple pictures - one shows Cmdr. Charmagne Beckett (1 person), another group photo shows approximately 10 people (the Internal Medicine Department staff)\n\nPage 5: Two pictures - one shows a person treating a child (2 people), another shows Marines/Sailors (multiple people but distant)\n\nPage 6: Two pictures - one shows a person standing while another collects a sample (2 people), another shows people at a food event (several people visible)\n\nPage 7: Two pictures - one shows 5 people posing together, another shows 5 people in a meeting\n\nPage 8: Picture shows 9 people (Kazakh scientists and staff)\n\nPage 9: Two pictures - one shows medical treatment (2-3 people), another shows troops (multiple but distant)\n\nPage 10: Picture shows approximately 9 people in the JC2RT team photo\n\nPage 11: One diagram/illustration, not a photograph of real people\n\nPage 12: Two pictures - one shows a dining event with many people, another shows 6 people\n\nPage 13: Picture shows 4 people at Notre Dame\n\nPage 14: Two pictures - one shows 2 people in lab, another shows scouts with adults (approximately 6-8 people)\n\nPage 15: Two pictures at Elizabeth Seton High School showing interactions with students - multiple people in each\n\nPage 16: No pictures with people\n\nThe page with the most people appears to be page 12, which shows the NMRC Dining Out event with many attendees visible in the first picture.\n\nAnswer: 12", "citations": [], "input_tokens": 14497, "output_tokens": 449, "cost_micros": 50226, "latency_ms": 12717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people visible in each one to determine which page has the picture with the most people.\n\nLet me go through each page systematically:\n\n**Page 1:** Picture shows two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\n**Page 2:** Two headshot photos of commanding officers (single person each)\n\n**Page 3:** One picture showing multiple people in what appears to be a laboratory setting with people in protective gear\n\n**Page 4:** One picture showing Cmdr. Charmagne Beckett (single person), and one group photo with approximately 10 people (Internal Medicine Department staff physicians)\n\n**Page 5:** Two pictures - one showing a person getting a swab sample (2 people), another showing two people (one standing, one collecting sample)\n\n**Page 6:** Two pictures with small groups (3-5 people each)\n\n**Page 7:** Two pictures with small groups (3-5 people each)\n\n**Page 8:** One group photo showing approximately 9 people (Kazakh scientists and NMRC staff)\n\n**Page 9:** Two individual pictures (single subjects each)\n\n**Page 10:** No pictures with people visible in detail\n\n**Page 11:** One diagram/illustration (not a photograph of actual people)\n\n**Page 12:** Two pictures - one appears to be a group photo at a dining event, and another showing 6 people (smaller group photo with officers and spouses)\n\n**Page 13:** One picture showing 4 people\n\n**Page 14:** One picture showing people in what appears to be a Scout activity\n\n**Page 15:** Two pictures showing classroom/teaching scenes with multiple students\n\n**Page 16:** One picture showing a single person\n\nThe largest group photo appears to be on **page 4**, which shows the Internal Medicine Department staff physicians with approximately 10 people clearly visible in the photograph.\n\nAnswer: 4", "citations": [], "input_tokens": 16147, "output_tokens": 443, "cost_micros": 55086, "latency_ms": 11452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each one.\n\nLet me go through each page:\n\nPage 1: Picture of two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: No pictures with people\n\nPage 4: Picture of one person (Cmdr. Charmagne Beckett on flight deck)\n\nPage 5: Picture of one person (Petty Officer 1st Class Dennis Gonzales getting swabbed)\n\nPage 6: Picture of two people (Lance Cpl. Kip Boker and Cpl. Jacob Echeverri)\n\nPage 7: No pictures with people\n\nPage 8: Picture with 9 people (Kazakh scientists group photo - Back row has 7 people: Gulnar Omasheva, Alexey Andryuchshenko, Sarah Pisarcik, Heidi St. John, Dr. Kenneth Yeh, Dr. Allen Richards, and Talgat Nurmakhanov. Bottom row has 3 people: Dr. Ekaterine Adeishvili, Lyazzat Musralina, and Dr. Ju Jiang. Total: 10 people)\n\nPage 9: Picture of one person (Lt. j.g. Michael Rucker treating a patient)\n\nPage 10: Picture with 9 people (JC2RT Team Members - Back row: 5 people, Front row: 4 people)\n\nPage 11: Graphic illustration (no people photo)\n\nPage 12: Picture with 6 people (Rear Adm. Bruce Doll, NMRC Commanding Officer Capt. John Sanders, Dr. Leighann Sanders, NMRC Executive Officer Capt. Elizabeth Montcalm-Smith, and Dr. Chris Smith - actually appears to be 5 people based on the caption listing)\n\nPage 13: No pictures with people\n\nPage 14: Picture with 2 people (Cmdr. Daniel Szumlas and Rear Adm. Ziemer with one other person visible = 3 people total in the image)\n\nPage ", "citations": [], "input_tokens": 12941, "output_tokens": 512, "cost_micros": 46503, "latency_ms": 12328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each to determine which page has the picture with the most people.\n\nLet me go through the document page by page:\n\n**Page 1:** Picture of Dr. Xiaoyan \"Cathy\" Zou discussing research with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\n**Page 4:** Two pictures:\n- Cmdr. Charmagne Beckett on flight deck - 1 person\n- Pacific Partnership 2012 Internal Medicine Department staff - 10 people\n\n**Page 5:** No pictures with people clearly visible\n\n**Page 6:** Two pictures:\n- Petty Officer 1st Class Dennis Gonzales - 1 person\n- Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n\n**Page 7:** Two pictures:\n- Group with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Group with military personnel - 5 people\n\n**Page 8:** Group photo with Kazakh scientists - 9 people (Top row: 7 people, Bottom row: 3 people, total 10 people visible)\n\n**Page 9:** Two pictures:\n- U.S. Marines and Sailors in aircraft cargo hold - This appears to have many people seated in rows, likely 30+ people\n- Lt. j.g. Michael Rucker with young girl - 2 people\n\n**Page 10:** Group photo of Joint Combat Casualty Research Team - 9 people\n\n**Page 12:** Picture at Dining Out event - 6 people visible\n\n**Page 13:** Picture with 4 people outdoors\n\n**Page 14:** Two pictures:\n- Cmdr. Daniel Szumlas with visitors - 3 people\n- NAMRU-3 staff teaching Cub Scouts flag folding - approximately 8-10 people\n\n**Page 15:** Two pictures with 2-3 people each\n\nThe picture on page 9 showing \"U", "citations": [], "input_tokens": 14415, "output_tokens": 512, "cost_micros": 50925, "latency_ms": 12344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find all pictures and count the number of people in each.Let me continue reading the document to find all images and their descriptions:Now let me read each image section to count the people in each. Let me check the images systematically:Let me search for page numbers and images with people descriptions:Now let me systematically check each page for images with people. Let me read the sections around each page number:Good! This image on page 3 has \"approximately 6 people\". Let me continue checking other pages:Excellent! This image has \"10 individuals\". Let me continue checking:This image has 1 person. Let me check page 5:This image has 1 person. Let me check more pages:This is an emblem with 0 people. Let me continue:This image has \"Three individuals visible in foreground and midground\" plus \"Additional blurred figures visible in far background\". Let me read more:This image has \"Five individuals\". Let me continue checking more pages:This has 5 people. Let me check page 8:Good! This image has \"approximately 10 individuals\". Let me check page 9:This image has 2 people (healthcare provider and patient). Let me check page 9 more:This aircraft image has many people, but let me get a more specific count. Let me continue checking other pages:Excellent! This image has \"nine military personnel\". Let me check page 11:This dining hall image has many people seated. Let me count more carefully by reading the description again. It mentions \"Audience members\" and \"Three individuals in naval/formal uniforms standing at front\" plus \"Head table visible in background with seated guests\". This is a large gathering but not specified exactly.\n\nLet me check more pages systematically:This has 5 people. Let me check page 13:This has 1 person at the podium. Let me check page 14:Page 14 has an image with \"approximately 10-12 people\" (Image127.jpg) and another with 3 people (Image128.jpg). Let me continue:This has \"Two women standing in foreground center\" plus \"Multiple people in burgundy/maroon uniforms scattered throughout background\". Let me check page 15:This image has \"Three individuals posing together\" plus \"Additional person visible in background on right side\" = 4 people total. Let me check page 16:Page 16 has an image with 1 person (Image133.jpg) and an emblem (Image139.jpg) with 0 people.\n\nNow let me summarize what I found. Let me verify the counts more carefully by re-reading specific key images:\n\nBased on my review:\n- Page 1: Header image with \"Multiple individuals visible in soft focus\"\n- Page 2: Individual portraits (1 person each)\n- Page 3: Laboratory scene with \"approximately 6 people\" (Image79.jpg) AND group photo with \"10 individuals\" (Image82.jpg)\n- Page 4: 1 person\n- Page 5: 1 person\n- Page 6: 5 people (two images with 3 and 5 people)\n- Page 7: 5 people\n- Page 8: \"approximately 10 individuals\" (Image97.jpg)\n- Page 9: Aircraft with many people + medical scene with 2 people\n- Page 10: Helicopter group with \"nine military personnel\" (Image103.jpg)\n- Page 11: Diagram (0 people) and banquet hall with many people (Image112.jpg - but not exactly counted)\n- Page 12: 5 people\n- Page 13: 4 people and 1 person\n- Page 14: \"approximately 10-12 people\" and 3 people\n- Page 15: Multiple people but not clearly counted\n- Page 16: 1 person and emblem\n\nThe images with the most people appear to be:\n- Page 3: Image82.jpg with \"10 individuals\"\n- Page 8: Image97.jpg with \"approximately 10 individuals\" \n- Page 10: Image103.jpg with \"nine military personnel\"\n- Page 14: Image127.jpg with \"approximately 10-12 people\"\n- Page 11: Image112.jpg (banquet hall with many people)\n\nLet me check the banquet hall image more carefully:\n\nThe banquet hall description mentions \"Audience members\" seated at tables and \"Three individuals in naval/formal uniforms standing at front\" and \"Head table visible in background with seated guests\" but doesn't give an exact count. Given it's a formal dining out event, there are likely many more than 12 people, but without an exact count stated, I should go with the clearly stated counts.\n\nThe highest clearly stated count is **\"approximately 10-12 people\"** on **page 14**.\n\nAnswer: 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 167000, "error": null, "extra": {"thread_id": 1914, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a7ec370e12904bd3b5569447e3077906", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1160, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "9"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically page 2, I can find information about where Gestalt psychology was conceived.\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 24360, "output_tokens": 56, "cost_micros": 73920, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I can find information about Gestalt psychology in the section titled \"Gestalt Principles of Visual Perception.\"\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 5194, "output_tokens": 62, "cost_micros": 16512, "latency_ms": 3421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 6446, "output_tokens": 32, "cost_micros": 19818, "latency_ms": 2437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 4354, "output_tokens": 32, "cost_micros": 13542, "latency_ms": 3719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 9744, "output_tokens": 32, "cost_micros": 29712, "latency_ms": 3890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the relevant document to find information about where Gestalt psychology was conceived.Let me search for \"Gestalt\" to find the relevant section:Perfect! I found the answer. The document clearly states where Gestalt psychology was conceived.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28250, "error": null, "extra": {"thread_id": 1915, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a7b4cbe305b48eca21d964a816bfb01", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 102, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at page 14, I can see a chart titled \"Time spent on weekends\" comparing 2005 and 2010. The chart shows two pie charts/clock-like circular diagrams.\n\nFor the 2010 chart (the larger circle on the right), I can see a segment labeled \"with family and friends\" which shows 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 24371, "output_tokens": 109, "cost_micros": 74748, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a chart titled \"Time spent on weekends\" that compares 2005 and 2010 data.\n\nIn this chart, for 2010, I can see:\n- Shopping 10%\n- Fitness 6%\n- With family and friends 35%\n- Eating out 17%\n- Hobbies 4%\n- Net surfing 10%\n- Travelling 6%\n- Reading 4%\n- Watching films 22%\n\nWait, I see \"With family and friends\" appears to be listed as 35% for 2010.\n\nHowever, looking more carefully at the layout, the percentages seem to be:\n- With family and friends 21% (this appears to be for 2005)\n- With family and friends 35% (this appears to be for 2010)\n\nThe chart shows that in 2010, 35% of time on weekends was spent with family and friends.\n\nAnswer: 35", "citations": [], "input_tokens": 5205, "output_tokens": 251, "cost_micros": 19380, "latency_ms": 5953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the chart on page 14 titled \"Time spent on weekends\", I can see two pie charts comparing 2005 and 2010.\n\nIn the 2010 chart (on the right), the segment labeled \"With family and friends\" shows 35%.\n\nAnswer: 35", "citations": [], "input_tokens": 6457, "output_tokens": 67, "cost_micros": 20376, "latency_ms": 3467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking through the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nIn this table, the row \"With family and friends\" shows:\n- 2005: 21%\n- 2010: 17%\n\nAnswer: 17", "citations": [], "input_tokens": 4365, "output_tokens": 97, "cost_micros": 14550, "latency_ms": 3280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nThe table shows:\n| Activity | 2005 | 2010 |\n| ----------------------- | ---- | ---- |\n| With family and friends | 35 | 21 |\n\nAccording to this table, time spent with family and friends in 2010 was 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9755, "output_tokens": 132, "cost_micros": 31245, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the chart on page 14 and extract the information about time spent with family and friends in 2010.Let me search for page 14 content in the document:Let me search for content related to \"family and friends\" and \"2010\":Great! I found the relevant information. Let me read that specific chunk to get the exact details:Perfect! I can see from the OCR text that the chart shows time spent on weekends in 2005 and 2010. Looking at the data:\n\nFor 2010:\n- \"With family and friends 21%\" appears on line 2410\n- \"With family and friends 17%\" appears on line 2412-2413\n\nWait, let me read this more carefully. The structure shows:\n- Line 2403: \"2005\"\n- Line 2404: \"2010\"\n- Line 2410: \"With family and friends 21%\"\n- Line 2412-2413: \"With family and friends 17%\"\n\nIt appears that 21% is associated with 2005, and 17% is associated with 2010 based on the layout. Let me verify by reading more context:Looking at the OCR text structure, it shows two columns (2005 and 2010). The pattern suggests:\n- Shopping: 10% (2005) and 10% (2010)\n- Fitness: 5% (2005) and 6% (2010)\n- With family and friends: 21% (2005) and 17% (2010)\n\nSo for 2010, the time spent with family and friends is 17%.\n\nLet me verify this is from page 14 by looking for page markers:Let me search for \"14\" near this chart content:Let me look further back in the document to find page numbering:Let me look for page markers earlier in the document structure:Let me check the document structure at the beginning to understand how pages are marked:I can see there are PageBreak markers. Let me count the PageBreaks to find page 14. Let me search more systematically:I can see the PageBreaks. The one at line 2340 appears to be just before the \"Time spent on weekends\" chart. Let me count the PageBreaks:\n\n1. Line 135\n2. Line 153\n3. Line 360\n4. Line 564\n5. Line 830\n6. Line 1381\n7. Line 1630\n8. Line 1676\n9. Line 1882\n10. Line 1951\n11. Line 2012\n12. Line 2143\n13. Line 2340\n14. Line 2470\n\nThe PageBreak at line 2340 is the 13th PageBreak. If we count pages starting from page 1, then after the 13th PageBreak we would be on page 14. The chart \"Time spent on weekends\" starts at line 2344, which is right after the 13th PageBreak at line 2340, so this chart is indeed on page 14.\n\nBased on the OCR text I found earlier, the chart shows:\n- 2005 column and 2010 column\n- \"With family and friends 21%\" (for 2005)\n- \"With family and friends 17%\" (for 2010)\n\nAnswer: 17", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79187, "error": null, "extra": {"thread_id": 1916, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6038a83cdf13408480954ef7afd6b6b7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 590, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the chart that tracks the West Nile Virus and look for the legend showing what the red color represents for distance from the Mississippi River.\n\nLooking at page 10, I can see a chart titled \"Tracking the West Nile Virus\" which shows a 3D map of what appears to be the United States with different colored regions.\n\nThe legend on the right side shows \"Approximate Distance from the Mississippi River (miles)\" with the following color-coded ranges:\n- Red: 0 - 375\n- Orange: 375 - 750\n- Yellow: 750 - 1125\n- Light yellow/cream: 1125 - 1500\n\nThe red color represents the closest distance range to the Mississippi River.\n\nAnswer: 0 - 375 miles", "citations": [], "input_tokens": 24377, "output_tokens": 178, "cost_micros": 75801, "latency_ms": 9609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart tracking the West Nile Virus and identify what the red color represents.\n\nI can see the \"Tracking the West Nile Virus\" chart which shows:\n\n**Approximate Distance from the Mississippi River (miles)**\n\nThe legend shows four distance ranges with different colors:\n- 0-375\n- 375 - 750\n- 750 -1125\n- 1125 - 1500\n\nBased on the typical color coding in such charts where red usually represents the most extreme or furthest category, the red color would represent the range **1125 - 1500** miles.\n\nNote: The question mentions \"in Europe\" but the chart shows distances from the Mississippi River, which is in North America, not Europe. The chart appears to be tracking West Nile Virus in relation to the Mississippi River region.\n\nAnswer: 1125 - 1500 miles", "citations": [], "input_tokens": 5211, "output_tokens": 207, "cost_micros": 18738, "latency_ms": 6687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "1125 1500 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to look at the \"Tracking the West Nile Virus\" chart in the document.\n\nLooking at the chart, I can see a legend that shows \"Approximate Distance from the Mississippi River (miles)\" with different ranges:\n\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nThe legend shows different shading/colors corresponding to these ranges. Based on the visual representation in the legend, the red color corresponds to the range 0-375 miles.\n\nHowever, I should note that the question asks about tracking West Nile Virus \"in Europe,\" but the chart actually shows tracking relative to the Mississippi River, which is in the United States, not Europe.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 6463, "output_tokens": 170, "cost_micros": 21939, "latency_ms": 6297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the \"Tracking the West Nile Virus\" chart in the document, I can see a legend that shows height-based ranges for approximate distance from the Mississippi River in miles.\n\nThe legend shows:\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nHowever, the document does not specify which color (including red) corresponds to which distance range in this chart. Additionally, the chart tracks West Nile Virus in relation to the Mississippi River, which is in the United States, not Europe as stated in the question.\n\nAnswer: The color coding for distance ranges is not specified in the document, and the chart is about the U.S., not Europe.", "citations": [], "input_tokens": 4371, "output_tokens": 160, "cost_micros": 15513, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color coding for distance ranges is not specified in document and chart is about u s not europe", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart about West Nile Virus and check what the red color represents.\n\nOn the page titled \"Tracking the West Nile Virus,\" I can see a 3D map of the United States (not Europe) with a legend showing:\n\n\"Approximate Distance from the Mississippi River (miles)\n[red square] 0 - 375\"\n\nHowever, I should note that this chart tracks West Nile Virus in the United States, not Europe. There is no chart about West Nile Virus in Europe in this document.\n\nAnswer: The chart tracks the United States, not Europe; red represents 0-375 miles from the Mississippi River.", "citations": [], "input_tokens": 9761, "output_tokens": 157, "cost_micros": 31638, "latency_ms": 4453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.35294117647058826, "method": "str_norm", "normalised_pred": "chart tracks united states not europe red represents 0 375 miles from mississippi river", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the chart about West Nile Virus in Europe and identify what the red color represents for distance from the Mississippi River.Let me search for content related to West Nile Virus and charts showing distance from the Mississippi River.I found a reference to a red zone around the Mississippi River. Let me read that section more carefully:Let me look at the beginning of this section to find the title and legend information:Perfect! I found the chart. Now let me check which color corresponds to which distance range. The legend shows:\n\n- 0-375 miles\n- 375-750 miles\n- 750-1125 miles\n- 1125-1500 miles\n\nAnd the description mentions that the red zone is the highest concentration around the Mississippi River area. Let me verify by checking if there's more specific information about the color coding:Based on the document, I can see that:\n- The chart shows \"Tracking the West Nile Virus\" (not in Europe as the question states, but in the US)\n- The legend shows distance ranges from the Mississippi River: 0-375, 375-750, 750-1125, 1125-1500 miles\n- The red zone represents the highest concentration, which is in the Central US region, particularly around the Mississippi River area\n- The color gradient goes: Red \u2192 Orange \u2192 Yellow \u2192 Pale Yellow (indicating decreasing distance/intensity)\n\nSince red is the highest concentration around the Mississippi River area, red represents the closest distance range.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54702, "error": null, "extra": {"thread_id": 1917, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_78ac91a640ab4a709acb37a25bd11e6b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at page 11 of the document, the Law of Good Gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nThe document further explains that this law means individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 24362, "output_tokens": 109, "cost_micros": 74721, "latency_ms": 10577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for the definition of the law of good Gestalt...\n\nThe document states: \"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. This law implies that as individuals perceive the world, they eliminate complexity and unfamiliarity so they can observe a reality in its most simplistic form.\"\n\nAnswer: Elements are grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 5196, "output_tokens": 104, "cost_micros": 17148, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7878787878787878, "method": "str_norm", "normalised_pred": "elements are grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good Gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The document also explains that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 6448, "output_tokens": 102, "cost_micros": 20874, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The law implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form. It focuses on the idea of conciseness and implies a global regularity that is mentally prioritized over spatial relations.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 4356, "output_tokens": 111, "cost_micros": 14733, "latency_ms": 3484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document's section on \"Gestalt Principles of Visual Perception\" and specifically the \"Law of good Gestalt\":\n\nThe document defines it as: **Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.**\n\nThe document further explains this implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 9746, "output_tokens": 125, "cost_micros": 31113, "latency_ms": 4062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the definition of the law of good gestalt.Let me search for \"gestalt\" in this document to find the relevant section.Let me read the document content to find mentions of \"gestalt\":Now let me read the specific section around line 1965 where \"good gestalt\" is mentioned:Perfect! I found the definition. Let me read a bit more context to ensure I have the complete definition:Based on the document, the law of good gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36484, "error": null, "extra": {"thread_id": 1918, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d9af7588088a4b3184b7d88eec528363", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 192, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the chart on page 14 that shows \"Time spent on weekends\" comparing 2005 and 2010.\n\nLooking at the two pie charts:\n\n2005 (smaller chart):\n- With family and friends: 35%\n- Watching films: 20%\n- Reading: 10%\n- Travelling: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n- Eating out: 10%\n- Fitness: 5%\n- Shopping: 10%\n\n2010 (larger chart):\n- With family and friends: 21%\n- Watching films: 22%\n- Eating out: 17%\n- Shopping: 10%\n- Fitness: 6%\n- Hobbies: 4%\n- Net surfing: 4%\n- Travelling: 6%\n\nCalculating the changes:\n- With family and friends: 21% - 35% = -14% (decrease)\n- Watching films: 22% - 20% = +2% (increase)\n- Reading: appears to be gone or very small\n- Eating out: 17% - 10% = +7% (increase)\n- Shopping: 10% - 10% = 0%\n- Fitness: 6% - 5% = +1% (increase)\n- Hobbies: 4% - 2% = +2% (increase)\n- Net surfing: 4% - 3% = +1% (increase)\n- Travelling: 6% - 5% = +1% (increase)\n\nThe largest increase is \"Eating out\" with a 7 percentage point increase.\n\nAnswer: Eating out", "citations": [], "input_tokens": 24374, "output_tokens": 401, "cost_micros": 79137, "latency_ms": 13093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the table showing time spent on weekends comparing 2005 and 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" with data for 2005 and 2010:\n\n2005 \u2192 2010:\n- Shopping: 10% \u2192 10% (0% change)\n- Fitness: 5% \u2192 6% (1% increase)\n- With family and friends: 21% \u2192 35% (14% increase)\n- Eating out: 10% \u2192 17% (7% increase)\n- Hobbies: 2% \u2192 4% (2% increase)\n- Net surfing: 3% \u2192 6% (3% increase)\n- Travelling: 5% \u2192 10% (5% increase)\n- Reading: 10% \u2192 4% (6% decrease)\n- Watching films: 20% \u2192 22% (2% increase)\n\nThe largest increase is \"With family and friends\" which went from 21% to 35%, an increase of 14 percentage points.\n\nAnswer: With family and friends", "citations": [], "input_tokens": 5208, "output_tokens": 260, "cost_micros": 19524, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" chart comparing 2005 and 2010:\n\n2005:\n- With family and friends: 21%\n- Watching films: 20%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 10%\n- Travelling: 5%\n- Fitness: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n\n2010:\n- With family and friends: 35%\n- Watching films: 22%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 6%\n- Travelling: 4%\n- Fitness: 6%\n- Net surfing: 4%\n- Hobbies: 4%\n\nCalculating increases:\n- With family and friends: 35% - 21% = +14%\n- Watching films: 22% - 20% = +2%\n- Hobbies: 4% - 2% = +2%\n- Fitness: 6% - 5% = +1%\n- Net surfing: 4% - 3% = +1%\n\nAnswer: With family and friends", "citations": [], "input_tokens": 6460, "output_tokens": 284, "cost_micros": 23640, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" table comparing 2005 and 2010:\n\n- Shopping: 10% to 10% (no change)\n- Fitness: 5% to 6% (+1%)\n- With family and friends: 21% to 17% (-4%)\n- Eating out: 10% to [value not shown]\n- Hobbies: 2% to 4% (+2%)\n- Net surfing: 3% to 4% (+1%)\n- Travelling: 5% to [value not shown]\n- Reading: 10% to [value not shown]\n- Watching films: 20% to [value not shown]\n\nFrom the visible data, Hobbies shows the largest increase at 2 percentage points (from 2% to 4%).\n\nAnswer: Hobbies", "citations": [], "input_tokens": 4368, "output_tokens": 197, "cost_micros": 16059, "latency_ms": 5109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "hobbies", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the table \"Time spent on weekends\" comparing 2005 and 2010:\n\n- With family and friends: 35 \u2192 21 (decrease of 14)\n- Watching films: 20 \u2192 22 (increase of 2)\n- Reading: 10 \u2192 10 (no change)\n- Travelling: 5 \u2192 6 (increase of 1)\n- Net surfing: 3 \u2192 4 (increase of 1)\n- Hobbies: 2 \u2192 4 (increase of 2)\n- Eating out: 10 \u2192 17 (increase of 7)\n- Fitness: 5 \u2192 6 (increase of 1)\n- Shopping: 10 \u2192 10 (no change)\n\nThe category with the most increase is \"Eating out\" with an increase of 7.\n\nAnswer: Eating out", "citations": [], "input_tokens": 9758, "output_tokens": 213, "cost_micros": 32469, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about time spent on weekends from 2005 to 2010.Let me continue reading the document to find information about time spent on weekends:This doesn't seem to be the right content. Let me search through more of the document to find information about time spent on weekends:Great! I found a reference to \"Time spent on weekends\" at line 2446. Let me read that section:Perfect! I found the data. Let me analyze the percentages for each category from 2005 to 2010:\n\nFrom the OCR text (lines 2402-2424):\n\n**2005 \u2192 2010 comparisons:**\n- Shopping: 10% \u2192 10% (increase: 0%)\n- Fitness: 5% \u2192 6% (increase: 1%)\n- With family and friends: 21% \u2192 17% (decrease: -4%)\n- Eating out: 10% \u2192 10% (increase: 0%)\n- Hobbies: 2% \u2192 4% (increase: 2%)\n- Net surfing: 3% \u2192 6% (increase: 3%)\n- Travelling: 5% \u2192 10% (increase: 5%)\n- Reading: 10% \u2192 4% (decrease: -6%)\n- Watching films: 20% \u2192 35% (increase: 15%)\n\nThe category with the most increase from 2005 to 2010 is **Watching films** with an increase of 15 percentage points (from 20% to 35%).\n\nAnswer: Watching films", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42125, "error": null, "extra": {"thread_id": 1919, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5fc07b74eefd45dcbf475865f8a3bf94", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "watching films", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at page 13, I can see an infographic titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO (Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization).\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A world map with numbers indicating participants by region:\n - 43 (North America)\n - 5 (Central/South America)\n - 130 (Europe - shown as the largest red circle)\n - 80 (Asia/Middle East region)\n - 77 (Africa/Asia region)\n - 30 (Africa)\n - 25 (Asia-Pacific region)\n\nBased on the map visualization, Europe has the largest circle with 130 participants, which is the highest number shown.\n\nAnswer: Europe", "citations": [], "input_tokens": 24369, "output_tokens": 216, "cost_micros": 76347, "latency_ms": 10344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at the document, I can see a section titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO PREPARATORY COMMISSION.\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (NGO, Station Operator, Ministry of FA, etc.)\n- Regional breakdown showing numbers: 98, 55, 43, 80, 66, 20, 77, 28, 30, 80, 8, 1\n\nI can see there's a map visualization with numbers distributed across different regions. Looking at the geographic distribution:\n- The number 98 appears to be positioned over Asia\n- 80 appears twice in different locations\n- Other numbers are distributed across continents\n\nBased on the positioning on the world map in the infographic, the largest number (98) appears to be associated with the Asian region.\n\nAnswer: Asia", "citations": [], "input_tokens": 5203, "output_tokens": 229, "cost_micros": 19044, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants by continent for the CTBTO Advanced Science Course.\n\nLooking at the document, I can see a visualization titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO Preparatory Commission. It shows:\n\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (University, Research Institute, National Data Centre, etc.)\n\nHowever, I do not see any breakdown of participants by continent in this visualization or anywhere else in the document. The document shows the total number of participants (425) and their institutional affiliations, but does not provide geographical/continental distribution data.\n\nAnswer: Information not provided in the document", "citations": [], "input_tokens": 6455, "output_tokens": 152, "cost_micros": 21645, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "information not provided in document", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the CTBTO Advanced Science Course data on page 12, I can see a visualization showing participants from 43 countries with numbers:\n\n- 77\n- 130\n- 80\n- 55\n- 66\n\nThe chart shows \"registered institutional affiliation: 425\" total participants and \"participants from 43 countries.\"\n\nThe number 130 appears to be the largest among the values shown in the visualization, which represents participants by region/continent. Based on the positioning and context of the chart layout, this would represent Asia.\n\nAnswer: Asia", "citations": [], "input_tokens": 4363, "output_tokens": 125, "cost_micros": 14964, "latency_ms": 4905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the \"ADVANCED SCIENCE COURSE at a glance\" section in the document, I can find the \"Participants by Region\" table which shows:\n\n- North America: 43\n- Central America: 20\n- South America: 8\n- Europe: 130\n- Africa (West/Central): 77\n- Africa (South): 30\n- Middle East/Asia: 80\n- South East Asia/Oceania: 28\n\nEurope has the highest number with 130 registered participants.\n\nAnswer: Europe", "citations": [], "input_tokens": 9753, "output_tokens": 120, "cost_micros": 31059, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about CTBTO registered participants for advanced science courses by continent.Let me search for information about CTBTO and advanced science course participants by continent.Let me read the entire document to find charts or tables about participant registration by continent.Let me continue reading to find information about CTBTO:Let me search for \"participant\" to find relevant information:Let me look at later sections of the document:Let me search more specifically:Let me continue reading the document to find the CTBTO-related content:Great! I found the CTBTO content. Let me read that specific section more carefully:I can see the CTBTO Advanced Science Course infographic, but I need to see if there's a visualization showing participant numbers by continent. Let me look for the visual description of this image:That's a different image. Let me search for more info about the CTBTO figure. Let me look for text that mentions continents or regions in the numbers shown:The numbers I see in the CTBTO section are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These numbers appear to be on a chart/map. Let me check if there's a visual description for the CTBTO image. Let me search around that area:Let me look for an embedded image description that might be for the CTBTO figure. The figure ends at line 2239. Let me check if there's a visual description after that:Let me look at what comes before the CTBTO figure to find if there's an embedded image marker:I notice Image18.jpg is about Indonesia entrepreneurship, not CTBTO. Let me search for Image17:That's also not it. The CTBTO figure must be between Image17 and Image18. Let me look for any embedded image between lines 2000-2150:Now I see the CTBTO figure starts at line 2146. Based on what I've seen, the numbers shown are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These appear to be data points on what's likely a world map. Let me look for any visual description or search for these specific numbers in context. Let me search for \"world\" or \"map\":Looking at the numbers from the CTBTO figure more carefully:\n- 43\n- 130 (appears as \"A0 130\" which might be \"Africa 130\")\n- 80\n- 66\n- 20\n- 77\n- 28\n- 30\n- 8\n- 80\n- 98\n\nThe \"A0 130\" at line 2216 could potentially be \"Africa 130\". Let me check if there are continent names mixed in with these numbers. Let me search the exact context:Based on the data I can see from the CTBTO Advanced Science Course infographic, I have:\n- 425 registered participants from 105 countries\n- Numbers: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98\n\nThese numbers likely represent participants by geographic region/continent on a world map visualization. The largest number is 130, which likely corresponds to a continent. Without a clear visual description identifying which number belongs to which continent, I need to make an inference. Given that 130 is the highest value and appears with \"A0\" (possibly Africa or Asia), and considering typical patterns in international scientific courses, the highest participation often comes from either Europe or Asia.\n\nHowever, I cannot definitively determine from the OCR text alone which continent each number represents. Let me see if I can find more context by reading around these numbers more carefully:The OCR shows \"A0 130\" on line 2216. This could be \"Africa 130\" or \"Asia 130\" where the character wasn't recognized properly. Given that 130 is the highest number and that this appears to be showing registered participants by continent on a world map, I need to determine which continent this refers to.\n\nLooking at the pattern, if this is a world map with continental data, and 130 is preceded by what looks like \"A0\" (possibly \"Africa\" or \"Asia\" with poor OCR), I cannot make a definitive determination from the text alone.\n\nHowever, based on the structure and the fact that the highest number is 130, and without additional visual context to confirm which specific continent each number represents, I cannot provide a certain answer from this document.\n\nAnswer: Cannot be determined from the document text alone - the highest value is 130 but the continent labels are not clearly identifiable in the OCR text", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 144672, "error": null, "extra": {"thread_id": 1920, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b3899f4c01ab4b9084195ac54d3f04aa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1007, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document text alone highest value is 130 but continent labels are not clearly identifiable in ocr text", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that discusses the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking at page 8, I can see a slide titled \"Gestalt Principles of Visual Perception\" that includes information about Closure. The text states: \"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nBelow this text, there is an illustration showing incomplete shapes. Looking at the image, I can see:\n- An incomplete circle (dotted/dashed lines forming most of a circle)\n- An incomplete rectangle or square (dotted/dashed lines)\n- Another incomplete rectangular shape (dotted/dashed lines)\n\nThe shapes used to illustrate closure are geometric shapes with gaps or missing parts that our minds fill in to perceive complete shapes.\n\nAnswer: circle, rectangle, square", "citations": [], "input_tokens": 24379, "output_tokens": 216, "cost_micros": 76377, "latency_ms": 13483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "circle, rectangle, square", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the section about \"Closure\" in the Gestalt Principles of Visual Perception and identify what shapes are shown.\n\nLooking at the document, I can see the section on \"Gestalt Principles of Visual Perception\" that explains Closure:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nLooking at the visual examples provided in the Closure section of the slide, I can see incomplete shapes that our minds complete. The shapes shown appear to be:\n- A circle (incomplete/broken circle)\n- A square or rectangle (incomplete/broken square)\n- A triangle (incomplete/broken triangle)\n\nAnswer: circle, square, triangle", "citations": [], "input_tokens": 5213, "output_tokens": 183, "cost_micros": 18384, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "circle, square, triangle", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide about the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses Closure. The text states:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nThis section appears on a page that also discusses \"Continuity.\" The document indicates there is a figure on this page, but the actual visual content of the figure is not provided in the text transcription.\n\nHowever, based on the context and typical Gestalt closure demonstrations, and looking at the structure of the document, I cannot definitively identify the specific shapes used in the illustration from the provided text alone, as the figure content is not described in detail in the transcription.\n\nSince I cannot see the actual shapes in the figure that illustrates closure, I cannot provide an accurate answer based solely on what is visible in this document transcription.\n\nAnswer: Unable to determine from provided document", "citations": [], "input_tokens": 6465, "output_tokens": 251, "cost_micros": 23160, "latency_ms": 7265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided document", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that illustrates the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses \"Closure\". The text states:\n\n\"Closure: We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nHowever, I need to identify what shapes are actually shown in the visual illustration for this principle. Looking at the page layout, I can see there are visual examples accompanying the Gestalt principles, but the specific shapes used to illustrate closure are not clearly described in the extracted text.\n\nFrom the document structure, I can see that the Closure section appears on a page, but the specific shapes or visual elements used to demonstrate closure are not explicitly listed in the text content that was extracted.\n\nBased on typical Gestalt closure examples and what might be visible in such presentations, common shapes used include circles, triangles, or other geometric shapes that are incomplete but our minds perceive as complete. However, I cannot definitively identify the specific shapes from the text content provided.\n\nAnswer: I cannot determine the specific shapes from the provided document text", "citations": [], "input_tokens": 4373, "output_tokens": 284, "cost_micros": 17379, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i cannot determine specific shapes from provided document text", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for information about the Closure aspect of Gestalt Principle of Visual Perception...\n\nOn the page titled \"Gestalt Principles of Visual Perception\" that discusses Closure and Continuity, I can see the following description:\n\n\"The image illustrates the principle of **Closure** with a dashed outline of a circle and a dashed outline of a square, which the mind perceives as complete shapes despite the gaps.\"\n\nAnswer: circle, square", "citations": [], "input_tokens": 9763, "output_tokens": 105, "cost_micros": 30864, "latency_ms": 4812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "circle, square", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the Gestalt Principle of Visual Perception and the closure aspect.Let me search for content about Gestalt Principle and closure:Now let me read the specific section around line 1631-1640 where closure is mentioned:Let me continue reading to see if there are visual examples for the closure principle:This doesn't seem to be the right section. Let me search more systematically for visual examples of the closure principle. Let me look at earlier sections:Perfect! I can see that Image13.jpg appears right before the Gestalt Principles section and shows \"dashed circle outline\" and \"dashed rectangle outline\". This image is likely illustrating the closure principle. Let me verify by reading a bit more context before the Gestalt section:Based on my reading of the document, Image13.jpg appears immediately before the Gestalt Principles of Visual Perception section discussing closure. The visual description clearly states that this image shows:\n- Left shape: dashed circle outline\n- Right shape: dashed rectangle outline\n\nThese dashed outlines (incomplete shapes) are used to illustrate the closure principle, where we perceive complete shapes even when they are not fully drawn.\n\nAnswer: circle, rectangle", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46890, "error": null, "extra": {"thread_id": 1921, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_76cfee4a20ba492dba921a5a3cdf2231", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 294, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "circle, rectangle", "normalised_gold": "circle, rectangle"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about voters' perceptions of mudslinging in the 2016 election compared to past elections.\n\nLooking through the document, I find a chart on page 7 titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more mudslinging than in past elections from 1992 to 2016.\n\nThe chart shows:\n- 1992: 68%\n- 1996: 49%\n- 2000: 46%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 90784, "output_tokens": 179, "cost_micros": 275037, "latency_ms": 41734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the chart titled \"More see 'mud-slinging,' less focus on issues\" on page 33 of the document, I can see the data points for \"More mudslinging than usual\" across different election years:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe document also explicitly states on page 6: \"Fully 92% of all voters - including 87% of Clinton supporters and 60% of Trump backers - say they were surprised by Trump's victory\" and \"Nearly three-quarters (73%) 0f all voters say there was more 'mudslinging' or negative campaigning compared with previous contests...\"\n\nHowever, looking at page 33 more carefully, it states: \"About nine-in-ten (92%) say there was more mudslinging or negative campaigning compared with previous contests, up from 68% who said that in 2012, up 38 points from 2008 (54% more negative) and 20 points higher than the previous high of 72% in 2004.\"\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 19137, "output_tokens": 292, "cost_micros": 61791, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on \"mudslinging\" or negative campaigning:\n\nOn page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing the percentage of voters who say there was more mudslinging than in past elections:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24416, "output_tokens": 147, "cost_micros": 75453, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking at the document, I can find a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" on page 6.\n\nThe chart shows percentages for different election years:\n- 1996: 46%\n- 2000: 49%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 21637, "output_tokens": 142, "cost_micros": 67041, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking through the document for information about mudslinging:\n\nOn page 5, there's a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a table showing:\n\n% of voters who say there was ___ than in past elections\n\n| Year | More mudslinging | Less mudslinging |\n| ---- | ---------------- | ---------------- |\n| 1992 | 68 | 16 |\n| 1996 | 49 | 36 |\n| 2000 | 46 | 34 |\n| 2004 | 72 | 14 |\n| 2008 | 54 | 27 |\n| 2012 | 68 | 19 |\n| 2016 | 92 | 4 |\n\nThe text also states: \"Fully 92% say there was more \"mudslinging\" or negative campaigning than in past elections \u2013 which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24377, "output_tokens": 286, "cost_micros": 77421, "latency_ms": 7157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about voters' perceptions of mudslinging compared to past elections.Let me search for content related to mudslinging:Let me read through the document to find the specific data about mudslinging. Let me start by reading the beginning sections:Let me continue searching for information about mudslinging. Let me read more of the document:Great! I found the relevant information. Let me continue reading this section to get the complete data:Perfect! I found the data about mudslinging. From the document, I can see the chart showing \"% of voters who say there was more mudslinging than in past elections\" with the following values across different election years:\n\n- 1992: 36%\n- 1996: 34%\n- 2000: 27%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe text also confirms: \"Fully 92% say there was more 'mudslinging' or negative campaigning than in past elections - which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39625, "error": null, "extra": {"thread_id": 1922, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_252b1c025dac41a5b4c7b4fbfefc6f87", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 214, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about whether men or women are more likely to think a female president will be elected in their lifetime.\n\nLooking through the document, I find on page 10 (document page 8) a section titled \"Will the country elect a female president in your lifetime?\"\n\nThe data shows:\n- All voters: No 17%, Yes 79%\n- Men: No 16%, Yes 81%\n- Women: No 18%, Yes 78%\n- Trump voters: No 17%, Yes 78%\n- Clinton voters: No 17%, Yes 81%\n\nComparing men (81% Yes) to women (78% Yes), more men think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 90776, "output_tokens": 163, "cost_micros": 274773, "latency_ms": 43483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on expectations of a female president by gender...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\"\n\nThe table shows:\n- Men who say \"Yes\": 81%\n- Women who say \"Yes\": 78%\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 19129, "output_tokens": 80, "cost_micros": 58587, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the table on page 8 titled \"Will the country elect a female president in your lifetime?\", I can find the breakdown by gender:\n\n- Men: 81% say yes\n- Women: 78% say yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 24408, "output_tokens": 64, "cost_micros": 74184, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on whether men or women think a female president will be elected in their lifetime...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\" showing:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 21629, "output_tokens": 80, "cost_micros": 66087, "latency_ms": 4219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to find the data about whether men or women think a female president will be elected in their lifetime.\n\nOn page 8, there is a table titled \"Will the country elect a female president in your lifetime?\" that shows:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nSince 81% > 78%, more men than women think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 24369, "output_tokens": 102, "cost_micros": 74637, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find information about opinions on whether a female president will be elected in a lifetime.Let me search for content related to female president and lifetime:Let me read the specific section around line 1095 to get the complete data:The text states \"There are no significant differences in these opinions among men and women,\" meaning men and women have similar percentages. Let me search for any table or chart that might have the exact percentages broken down by gender:I can see a table starting. Let me read more to see if it has the gender breakdown:Perfect! I found the table with the gender breakdown. Looking at the \"Yes\" column (indicating those who think a female president will be elected in their lifetime):\n\n- Men: 81%\n- Women: 78%\n\nAnswer: Men (81% vs 78%)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46859, "error": null, "extra": {"thread_id": 1923, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_47a779db7256446daa01071f4e8824a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll carefully examine the document to count all line plots.\n\nGoing through the document page by page:\n\n**Page 3:** Contains a line plot showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages from 1988-2016.\n\n**Page 5:** Contains a line plot showing \"Record share of voters say there was more campaign 'mudslinging'\" with two lines (More mudslinging and Less mudslinging) from 1992-2016.\n\n**Page 9:** Contains a line plot showing \"Half say they are happy Trump was elected president\" with percentages from 1996-2016.\n\n**Page 21:** Contains a line plot showing \"Will relations between Republicans and Democrats improve?\" with three lines from 1992-2016.\n\n**Page 24:** Contains two separate line plots:\n- One for Republican/Rep-leaning voters (More moderate vs More conservative) from 2008-2016\n- One for Democratic/Dem-leaning voters (More moderate vs More liberal) from 2008-2016\n\n**Page 27:** Contains two separate line plots showing grades for:\n- The Republican Party (1988-2016)\n- The Democratic Party (1988-2016)\n\n**Page 29:** Contains two separate line plots showing grades for:\n- The press (1988-2016)\n- The pollsters (1988-2016)\n\n**Page 31:** Contains a line plot showing \"For the first time in eight elections, most are dissatisfied with vote choices\" from 1988-2016.\n\n**Page 32:** Contains a line plot showing \"More see 'mud-slinging,' less focus on issues\" with two sets of lines from 1992-2016.\n\n**Page 33:** Contains two separate line plots showing \"Learning about the candidates and issues\" from 1988-2016.\n\n**Page 36:** Contains a line plot showing \"Most voters are confident own vote, national votes counted accurately\" from 2004-2016.\n\n**Page 37:** Contains a line plot showing \"Similar shares of Trump, Clinton voters very confident in national vote count\" from 2004-2016.\n\nTotal count:\nPage 3: 1\nPage 5: 1\nPage 9: 1\nPage ", "citations": [], "input_tokens": 90760, "output_tokens": 512, "cost_micros": 279960, "latency_ms": 41093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the number of line plots in this document.\n\nLet me go through the document systematically to identify line plots:\n\n1. Page 1: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with two lines (Winning candidate and Losing candidate) from 1988 to 2016.\n\n2. Page 6: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with two lines (More mudslinging and Less mudslinging) from 1992 to 2016.\n\n3. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows a line plot with two lines (Very/Fairly satisfied and Not very/Not at all satisfied) from 1988 to 2016.\n\n4. Page 32: Below the first plot on page 31, there's another line plot showing \"% of voters who say they were very/fairly satisfied with choice of presidential candidates\" with data points from 1988 to 2016 for winning and losing candidates.\n\n5. Page 33: \"More see 'mud-slinging,' less focus on issues\" - This contains TWO separate line plots:\n - First plot: Shows \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992 to 2016\n - Second plot: Shows discussion of issues \"Less than usual\" vs \"More than usual\" from 1992 to 2016\n\n6. Page 34: \"Learning about the candidates and issues\" - This contains TWO separate line plots:\n - First plot: \"Learned enough\" vs \"Did not learn enough\" from 1988 to 2016\n - Second plot: Presidential debates helpfulness from 1988 to 2016\n\nLet me recount to be sure:\n- Page 1: 1 line plot\n- Page 6: 1 line plot \n- Page 31: 2 line plots (one for all voters, one comparing winning/losing candidate voters)\n- Page 33: 2 line plots (mudslinging and issues discussion)\n- Page 34: 2 line plots (learning about candidates and debates)\n\nTotal:", "citations": [], "input_tokens": 19113, "output_tokens": 512, "cost_micros": 65019, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document to count all line plots (graphs that show data points connected by lines over time or categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This appears to be a line plot showing grades from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This is a line plot showing data from 1992-2016.\n\n3. Page 9: \"Half say they are happy Trump was elected president\" - This is a line plot showing data from 1996-2016.\n\n4. Page 24: Two separate line plots:\n - \"Most Republicans continue to say GOP should be more conservative\" (showing data from 2008-2016)\n - \"As the share of Democrats who want their party to be more liberal grows\" (showing data from 2008-2016)\n\n5. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This is a line plot showing data from 1988-2016.\n\n6. Page 32: Two separate line plots:\n - One showing \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992-2016\n - One showing discussion of issues \"Less than usual\" vs \"More than usual\" from 1992-2016\n\n7. Page 33: Two separate line plots:\n - One showing \"Learned enough\" vs \"Did not learn enough\" from 1988-2016\n - One showing helpfulness of debates from 1988-2016\n\n8. Page 34: \"More say press had 'too much' influence on outcome of the election\" - This is a line plot showing data from 1992-2016.\n\nTotal count: 1 + 1 + 1 + 2 + 1 + 2 + 2 + 1 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 24392, "output_tokens": 466, "cost_micros": 80166, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades...\" - This appears to be a bar chart showing percentages, not a line plot.\n\n2. Page 4: \"Large share of Trump voters are confident...\" - Bar chart\n \"How Clinton voters feel about 'giving Trump a chance'\" - Bar chart\n\n3. Page 5: \"% of voters saying Democratic leaders should...\" - Bar charts comparing 2016 and 2008\n\n4. Page 6: \"% of voters saying Donald Trump ___ appoint Democrats...\" - Table format\n \"% of voters who said Barack Obama ___ appoint Republicans...\" - Table format\n\n5. Page 7: \"Election report card: Barely passing grades\" - Table format\n \"Record share of voters say there was more campaign 'mudslinging'\" - This appears to be a LINE PLOT showing trend over years 1996-2016\n\n6. Page 8: \"Top reactions to Trump's victory\" - Bar chart\n\n7. Page 9: \"Trump's victory in a word\" - Word cloud/frequency table\n\n8. Page 10: \"Will the country elect a female president in your lifetime?\" - Bar chart\n\n9. Throughout the rest of the document, I see mostly bar charts, tables, and no additional line plots.\n\nLet me recheck page 7 more carefully. The chart showing \"More mudslinging\" and \"Less mudslinging\" from 1992-2016 appears to be displayed as a line plot with two lines tracking these measures over time.\n\nAfter careful review, I can identify 1 line plot in this document on page 7.\n\nAnswer: 1", "citations": [], "input_tokens": 21613, "output_tokens": 393, "cost_micros": 70734, "latency_ms": 11437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines over time or across categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with percentages over years from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with \"More mudslinging\" and \"Less mudslinging\" lines from 1992-2016.\n\n3. Page 9: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" (repeated from earlier) - This is a table showing A or B grades, labeled as \"Note: Percent of 'A' or 'B' grades\" - This appears to be the same as #1.\n\n4. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows two line plots tracking satisfaction levels from 1988-2016.\n\n5. Page 32: \"More see 'mud-slinging,' less focus on issues\" - This shows two separate line plots, one for mudslinging and one for discussion of issues from 1992-2016.\n\nLet me recount more carefully by looking at actual visual line plots:\n\n- Page 3: One line plot showing grades over time\n- Page 5: One line plot showing mudslinging trends\n- Page 31: Two line plots (satisfaction trends)\n- Page 32: Two line plots (mudslinging and issues discussion)\n\nTotal: 1 + 1 + 2 + 2 = 6 line plots\n\nAnswer: 6", "citations": [], "input_tokens": 24353, "output_tokens": 397, "cost_micros": 79014, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the report.Let me continue reading the document to see all the content and identify line plots:Let me continue reading to see if there are more charts/figures:Let me search for all figures/charts in the document to count line plots specifically:Now I need to look at the actual figures to determine which ones are line plots. Let me examine the key figure sections more carefully. Based on what I've already read, I saw one chart around line 798-853 that appears to be about \"mudslinging\" over time. Let me check the visual descriptions and content more carefully:This appears to be a line plot showing data over time (years 92, 96, 00, 04, 08, 12, 16). Let me check another figure:This appears to be another line plot showing data over time from 1996 to 2016. Let me check the first figure to see if it's a line plot:This first figure shows data for winning and losing candidates across years from 1988 to 2016. This appears to be a line plot with two lines (winning candidate and losing candidate). Let me check if there are any other potential line plots by examining more figures. Let me look at the figure around line 2240-2290:This appears to be a bar chart, not a line plot. Let me continue scanning through the document to identify all line plots. Let me check around line 2333:This appears to be a bar chart comparing 2016 and 2008 data, not a line plot. Based on my review of the document so far, I've identified:\n\n1. **Line plot 1** (lines 264-360): Shows grades for winning and losing candidates over time from 1988 to 2016 (two lines)\n2. **Line plot 2** (lines 798-848): Shows \"mudslinging\" over time with two lines (More mudslinging and Less mudslinging) from 1992 to 2016\n3. **Line plot 3** (lines 1209-1241): Shows happiness with election results from 1996 to 2016\n\nThese all show data trends over multiple time points, which is characteristic of line plots. Most other figures in the document appear to be bar charts showing comparisons between groups. Let me verify by checking if there are any other time-series charts I might have missed. Let me do a final check of the document to ensure I haven't missed any line plots.\n\nBased on my thorough review of the document, I found three distinct line plots that show data trends over time:\n\n1. A line plot showing grades for winning and losing candidates from 1988-2016\n2. A line plot showing perceptions of campaign mudslinging from 1992-2016 \n3. A line plot showing voter happiness with election results from 1996-2016\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 65172, "error": null, "extra": {"thread_id": 1924, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dcd15be6dac34256a7ae1a01a0998a5a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 476, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades information...\n\nOn page 3, there is a chart showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages of \"A\" or \"B\" grades on an A, B, C, D, F scale.\n\nThe chart shows:\n- Trump (2016): 30% gave A or B grades\n- Clinton (2016): 43% gave A or B grades\n\nThe document also states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nAdditionally, on page 26, there is a table showing average grades:\n- Trump: C- (average grade)\n- Clinton: C (average grade)\n\nSince C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 90768, "output_tokens": 246, "cost_micros": 275994, "latency_ms": 41342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the campaign grades data in the document:\n\nFor **Trump**:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor **Clinton**:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nTo find the median, I need to determine which grade represents the 50th percentile:\n\nFor Trump: 30% (A/B) + 19% (C) = 49%, then adding the next 15% (D) brings us to 64%. The 50th percentile falls in the D range.\n\nFor Clinton: 43% (A/B) + 20% (C) = 63%. The 50th percentile falls in the C range.\n\nThe document also explicitly states Trump receives a \"C-\" average grade while Clinton receives a \"C\" average grade, which is consistent with this analysis.\n\nAnswer: Clinton", "citations": [], "input_tokens": 19121, "output_tokens": 227, "cost_micros": 60768, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades:\n\nOn page 26, there is a table titled \"Election report card: Barely passing grades\" showing:\n- Trump: 30% gave A or B, Average grade: C-\n- Clinton: 43% gave A or B, Average grade: C\n\nThe document states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nClinton received an average grade of C, while Trump received an average grade of C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24400, "output_tokens": 176, "cost_micros": 75840, "latency_ms": 6358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Let me find the campaign grades for Trump and Clinton from the document.\n\nFrom the section \"Voters give Trump worse grades than they have for any winning candidate in recent decades\":\n\nFor Trump:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor Clinton:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nThe document also shows average grades:\n- Trump: C-\n- Clinton: C\n\nTo determine the median, I need to consider the distribution. With:\n- Trump: 30% (A/B), 19% (C), 15% (D), 35% (F)\n- Clinton: 43% (A/B), 20% (C), 16% (D), 21% (F)\n\nFor Trump, the cumulative percentages are: 30% at B or above, 49% at C or above, 64% at D or above. The median falls around the C grade.\n\nFor Clinton, the cumulative percentages are: 43% at B or above, 63% at C or above, 79% at D or above. The median falls around the C grade.\n\nHowever, since Clinton has a higher percentage (43%) receiving A or B compared to Trump (30%), and a lower percentage receiving F (21% vs 35%), Clinton's distribution is shifted toward higher grades. The average grades confirm this: Clinton received C while Trump received C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 21621, "output_tokens": 349, "cost_micros": 70098, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the campaign grades for Trump and Clinton.\n\nOn page 26-27, there is a table showing \"Voters grade the parties, press and pollsters quite negatively\" with the percentage of voters who give each a grade of A, B, C, D, or F:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n- Average grade: C-\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n- Average grade: C\n\nThe document explicitly states that Trump receives an average grade of C- and Clinton receives an average grade of C. Since C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24361, "output_tokens": 195, "cost_micros": 76008, "latency_ms": 5969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about campaign grades for Trump and Clinton.Let me search for \"campaign grade\" in the document to find relevant information.Let me search for \"grade\" to find relevant sections.Now let me read the specific sections that mention grades for Trump and Clinton. Let me look at the chunks around line 260 and 2800-2900.Good! Now let me look for more specific information about median grades. Let me check the section around line 2800-2900 where grades are discussed in detail.Perfect! I found the information. Let me look at the table that shows the grade distribution to calculate the median. From lines 2820-2838, I can see:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n\nThe document also explicitly states:\n- Trump receives a C- grade on average (line 2801-2802)\n- Clinton's average grade is a C (line 2897)\n\nTo find the median, I need to look at where the 50th percentile falls:\n\n**Trump:**\n- 0-30%: A or B\n- 31-49%: C\n- 50-64%: D (the 50th percentile falls here)\n- 65-100%: F\n\n**Clinton:**\n- 0-43%: A or B\n- 44-63%: C (the 50th percentile falls here)\n- 64-79%: D\n- 80-100%: F\n\nSo Trump's median grade is D, and Clinton's median grade is C.\n\nAnswer: Clinton", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45171, "error": null, "extra": {"thread_id": 1925, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5b7e95c94e72489983c39189c2251166", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 287, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to search through the document for charts related to mudslinging.\n\nLooking through the document, I can find references to mudslinging in the following locations:\n\n1. On page 5 (document page 7), there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more or less mudslinging than in past elections from 1992 to 2016.\n\n2. On page 32 (document page 34), there is another chart with the same title \"More see 'mud-slinging,' less focus on issues\" which shows similar data about mudslinging perceptions.\n\nLet me verify these are distinct charts:\n- The first chart on page 5 shows data points: 68, 49, 34, 72, 54, 68, 92 for \"More mudslinging\" \n- The second chart on page 32 shows the same data\n\nThese appear to be the same chart repeated in different sections of the document.\n\nTherefore, there is 1 unique chart related to mudslinging (though it appears twice in the document).\n\nAnswer: 1", "citations": [], "input_tokens": 90760, "output_tokens": 261, "cost_micros": 276195, "latency_ms": 37187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages from 1992-2016 of voters who say there was more or less mudslinging than in past elections.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows the same data about mudslinging over time (1992-2016) plus a separate section about discussion of issues.\n\nLet me verify if these are two separate charts or if one is a repeat:\n- Page 6 shows the mudslinging chart in the context of the executive summary\n- Page 33 shows the same mudslinging data but combined with another chart about discussion of issues in a single figure\n\nThese appear to be presenting the same mudslinging data, but in different contexts. The page 33 version combines it with the issues discussion data.\n\nCounting distinct charts that contain mudslinging data:\n1. The chart on page 6 (standalone mudslinging chart)\n2. The chart on page 33 (mudslinging combined with issues discussion)\n\nSince the question asks about charts \"related to mudslinging,\" both of these count as they both contain mudslinging data.\n\nAnswer: 2", "citations": [], "input_tokens": 19113, "output_tokens": 323, "cost_micros": 62184, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 5, there is a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a chart showing \"% of voters who say there was ___ than in past elections\" with \"More mudslinging\" and \"Less mudslinging\" data from years 92, 96, 00, 04, 08, 12, 16.\n\n2. On page 32, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" showing \"% of voters who say there was ... More mudslinging than usual\" and \"Less mudslinging than usual\" from years 92, 96, 00, 04, 08, 12, 16.\n\nThese appear to be the same chart repeated in different sections of the document (one in the executive summary and one in the detailed findings).\n\nLet me verify if there are any other mudslinging-related charts... No, I only find these two instances, which appear to be the same chart shown in different parts of the document.\n\nSince the question asks how many charts are related to mudslinging, and there are 2 instances of mudslinging charts in the document (even though they show the same data), the answer would be 2.\n\nHowever, if we're counting unique charts, it would be 1 chart that appears twice.\n\nLooking at the context of the question, it seems to be asking for the count of chart occurrences in the document, which would be 2.\n\nAnswer: 2", "citations": [], "input_tokens": 24392, "output_tokens": 372, "cost_micros": 78756, "latency_ms": 10358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts in the document that are related to \"mudslinging.\"\n\nLet me search through the document for references to mudslinging:\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows percentages of voters who say there was more or less mudslinging than in past elections from 1996-2016.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows percentages of voters who say there was more mudslinging than usual and less mudslinging than usual from 1992-2016.\n\nLet me verify there are no other charts about mudslinging by carefully reviewing the document again.\n\nAfter reviewing the document thoroughly, I can confirm there are 2 charts specifically related to mudslinging:\n- One on page 6 showing the record share saying there was more mudslinging\n- One on page 33 showing mud-slinging trends and comparison with issue discussion\n\nAnswer: 2", "citations": [], "input_tokens": 21613, "output_tokens": 237, "cost_micros": 68394, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/tables in the document that are related to \"mudslinging\" or negative campaigning.\n\nLet me search through the document:\n\n1. On page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" - This shows data about mudslinging from 1992-2016.\n\n2. On page 32, there is a chart titled \"More see 'mud-slinging,' less focus on issues\" - This shows data about mudslinging and discussion of issues from 1992-2016.\n\nThese appear to be the only two charts specifically related to mudslinging in the document. The first chart shows the percentage of voters who say there was more mudslinging than in past elections over different election years. The second chart also shows mudslinging data alongside data about discussion of issues.\n\nAnswer: 2", "citations": [], "input_tokens": 24353, "output_tokens": 194, "cost_micros": 75969, "latency_ms": 8342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find charts related to mudslinging.Let me read the full document to search for content related to mudslinging and identify charts:Let me continue reading to see if there are more charts related to mudslinging:Based on my thorough reading of the document, I found the following charts related to mudslinging:\n\n1. **Lines 788-853 (chunk 49482)**: A chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages of voters who said there was more or less mudslinging than in past elections, with data from years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\n2. **Lines 3933-4048 (chunk 49595)**: A chart in the section \"More see 'mud-slinging,' less focus on issues\" showing percentages of voters who say there was \"More mudslinging than usual\" and \"Less mudslinging than usual\" across election years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\nThese appear to be two representations of the same data - one earlier in the document and one later in a more detailed section about campaign evaluations.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62437, "error": null, "extra": {"thread_id": 1926, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bd535bded05431a9423ba96684e000e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 344, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically looking for percentages who feel \"uneasy\" and \"excited.\"\n\nLooking through the document, I can find on page 6 (document page 8) a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows voter reactions.\n\nThe chart shows:\n- 53% say Trump's election makes them feel \"uneasy\"\n- 51% say it makes them feel \"hopeful\"\n- 41% say \"sad\"\n- 41% say \"scared\"\n- 36% say \"proud\"\n- 31% say \"angry\"\n\nI don't see \"excited\" listed as one of the emotional reactions measured. The document shows \"hopeful,\" \"proud,\" \"uneasy,\" \"sad,\" \"scared,\" and \"angry\" but not \"excited.\"\n\nFor \"uneasy,\" the answer is 53%.\n\nSince \"excited\" is not measured in this survey, I cannot provide a percentage for it.\n\nAnswer: 53% uneasy, excited not measured", "citations": [], "input_tokens": 90783, "output_tokens": 245, "cost_micros": 276024, "latency_ms": 34905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for voters' reactions to Trump's victory:\n\nFrom page 7, there is a table titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n- Uneasy: 53%\n\nFrom page 8, there is a word cloud table showing one-word reactions. Under \"Trump voters,\" I can see:\n- Excited: 16 (this is the actual number of respondents who mentioned this word, not a percentage)\n\nHowever, the question asks for percentages. Looking at page 11, there is a more detailed table showing emotional reactions:\n\"% of voters who say the election of Donald Trump makes them feel ...\"\n- Uneasy: 53%\n\nThe document does not provide \"excited\" as a percentage in the main emotional reactions table on pages 7 and 11. The word \"excited\" only appears in the word cloud with an actual count of 16 mentions from Trump voters, but this is not presented as a percentage of all voters.\n\nBased on the data provided in percentage form:\n- Uneasy: 53%\n- Excited: Not provided as a percentage of all voters\n\nAnswer: 53%, Not provided", "citations": [], "input_tokens": 19136, "output_tokens": 269, "cost_micros": 61443, "latency_ms": 9281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not provided", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, on page 6 (and repeated on page 10), there is a section titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo \"uneasy\" is 53%.\n\nHowever, I don't see \"excited\" listed as one of the reaction options in this chart.\n\nLooking at page 7, there is a word cloud section showing individual words mentioned by Trump and Clinton voters. For Trump voters, \"Excited\" appears with 16 mentions, but this is not a percentage - these are actual counts of how many people mentioned that specific word.\n\nThe main emotional reactions chart on page 6 does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nSince \"excited\" is not measured as a percentage in the same way as \"uneasy,\" I can only provide the percentage for \"uneasy\" which is 53%.\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24415, "output_tokens": 331, "cost_micros": 78210, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel \u2026\n\n| Feeling | % of Voters |\n| ------- | ----------- |\n| Uneasy | 53 |\n| Hopeful | 51 |\n| Proud | 36 |\n| Scared | 41 |\n| Sad | 41 |\n| Angry | 31 |\n\nI can see that 53% feel \"uneasy.\"\n\nFor \"excited,\" I need to look at the word cloud section titled \"Trump's victory in a word\" which shows responses from Trump voters and Clinton voters. Under Trump voters, I can see:\n- 16 Excited\n\nHowever, this appears to be a count (16 respondents), not a percentage. The note states \"Figures show actual number of respondents who offered each response; these numbers are not percentages.\"\n\nLooking back at the main emotional reactions table on the previous page, \"excited\" is not listed as one of the specific emotions measured in the survey question about how Trump's election makes them feel.\n\nBased on the available data in the document:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the emotional reactions survey (only appears as a word count of 16 in the open-ended response)\n\nAnswer: 53%, not measured as percentage", "citations": [], "input_tokens": 21636, "output_tokens": 364, "cost_micros": 70368, "latency_ms": 11468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not measured as percentage", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find information about emotional reactions to Trump's election on page 10:\n\n\"Voters express a mix of emotional reactions to the election of Donald Trump. On the positive side, 51% say that Trump's election makes them feel hopeful; somewhat fewer say it makes them feel proud (36%).\n\nOn the negative side, 53% say Trump's election makes them feel uneasy. About four-in-ten say his election makes them feel sad (41%) or scared (41%), and 31% say the election of Trump makes them feel angry.\"\n\nSo 53% felt \"uneasy.\"\n\nLooking at the table on page 6 titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" and the word cloud on page 7, I can see the following reactions listed:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nIn the word cloud on page 7 showing \"Trump's victory in a word,\" I can see \"Excited\" listed among Trump voters with 16 mentions, but this is showing the actual number of respondents, not percentages.\n\nThe main table showing percentage reactions does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nBased on the available data:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the survey\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24376, "output_tokens": 401, "cost_micros": 79143, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the specific percentages for \"uneasy\" and \"excited\" reactions to Trump's victory.Let me read the specific section with the data about voter reactions to see the percentages clearly.Based on the document, I can see the percentages clearly in the figure at lines 890-916:\n\n- **Uneasy: 53%**\n- **Excited: 16** (this appears to be a count in the open-ended word table, not a percentage)\n\nLet me verify if there's a percentage for \"excited\" elsewhere in the document.The document shows a figure with percentages of voter reactions to Trump's victory. The measured reactions with percentages are:\n- Hopeful: 51%\n- Proud: 36%\n- **Uneasy: 53%**\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\n\"Excited\" appears in a different table showing word counts from open-ended responses (16 mentions from Trump voters), not as a percentage measurement of all voters.\n\nBased on the document, the percentage for \"uneasy\" is **53%**. There is no percentage given for \"excited\" as a reaction category - it only appears as a word count (16) in the open-ended responses table.\n\nAnswer: 53%, 16", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44733, "error": null, "extra": {"thread_id": 1927, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5850dcc4518c4b98b48b2eae8c928f95", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 326, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "16, 53", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically the percentage who felt \"angry.\"\n\nLooking through the document, I find a section on page 10 titled \"Emotional reactions to Trump's election\" with a chart showing \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\"\n\nThe chart shows percentages of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nAnswer: 31", "citations": [], "input_tokens": 90769, "output_tokens": 150, "cost_micros": 274557, "latency_ms": 34717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's victory.\n\nLooking through the document for information about emotional reactions to Trump's election:\n\nOn page 7, there is a section titled \"Election reactions: Nearly all Trump supporters feel 'hopeful'\" which includes a chart showing \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\"\n\nThe chart shows the percentage of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states: \"About half of voters (53%) say his election makes them feel 'uneasy,' while nearly as many (51%) say it makes them feel 'hopeful'. Smaller shares say his election triumph makes them feel 'scared', 'sad' (41% each), 'proud' (36%) or 'angry' (31%).\"\n\nAnswer: 31", "citations": [], "input_tokens": 19122, "output_tokens": 243, "cost_micros": 61011, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document for information about voters' reactions to Trump's victory.\n\nOn page 6 (and repeated on page 10), there is a chart titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows the percentage of voters who say the election of Donald Trump makes them feel various emotions.\n\nAccording to this chart:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states that 31% of voters say Trump's election makes them feel \"angry.\"\n\nAnswer: 31", "citations": [], "input_tokens": 24401, "output_tokens": 157, "cost_micros": 75558, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's election.\n\nLooking through the document for information about voters' emotional reactions to Trump's victory...\n\nOn page 12 (in the section \"Voters express a mix of emotional reactions to the election of Donald Trump\"), I find a table showing:\n\n\"% of voters who say the election of Donald Trump makes them feel \u2026\"\n\nThe feelings listed include:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo 31% of voters say the election of Trump makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 21622, "output_tokens": 166, "cost_micros": 67356, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for emotional reactions to Trump's victory...\n\nOn page 6, I find the section \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel ...\n\n| Reaction | % |\n| -------- | -- |\n| Hopeful | 51 |\n| Proud | 36 |\n| Uneasy | 53 |\n| Sad | 41 |\n| Scared | 41 |\n| Angry | 31 |\n\nThe table clearly shows that 31% of voters say Trump's election makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 24362, "output_tokens": 166, "cost_micros": 75576, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about voter reactions to Trump's victory.Let me search for sections that discuss reactions or emotions about Trump's victory.Great! I found a reference to \"angry\" in the context of reactions. Let me read the specific chunk that contains this information.Perfect! I found the information. According to the document, 31% of voters say Trump's election makes them feel \"angry\".\n\nAnswer: 31", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32359, "error": null, "extra": {"thread_id": 1928, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4e0d0df3d4f749b8a5376414cce75543", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 148, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find data from 2015 about how many people believed the government was doing very/fairly well in reducing the threat of terrorism.\n\nLooking at page 2, I can see a chart titled \"For first time since 9/11, government's job rating on terrorism turns negative\" which shows the percentage who say the government is doing \"Very/Fairly well\" in reducing the threat of terrorism.\n\nFrom the chart on page 2, at the end point in 2015 (specifically December 2015), I can see:\n- Total: 46%\n\nLooking at page 10-11, there's a detailed breakdown by party. The chart on page 10 shows \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" with partisan breakdowns.\n\nFrom the chart on page 10, for 2015 (the rightmost data point):\n- Republican: 27%\n- Democrat: 64%\n- Independent: 44%\n\nSo for the question asking about adults (total), Republicans, and Democrats in 2015:\n- Adults (Total): 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 58172, "output_tokens": 269, "cost_micros": 178551, "latency_ms": 22812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentage of adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section \"Views of how the government is handling the terrorist threat\" on page 10-11.\n\nThe document states:\n- Total (all adults): 46% say the government is doing very/fairly well\n- Democrats: 64% say the government is doing very/fairly well \n- Republicans: 27% say the government is doing very/fairly well\n\nThis data is from the December 2015 survey (conducted Dec. 8-13, 2015).\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 13406, "output_tokens": 163, "cost_micros": 42663, "latency_ms": 5468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for the specific data on government performance in reducing the threat of terrorism in 2015:\n\nFrom the chart on page 10 titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" showing percentages saying government is doing very/fairly well in reducing the threat of terrorism:\n\nFor December 2015:\n- Total/All adults: 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 19266, "output_tokens": 113, "cost_micros": 59493, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentages of surveyed adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" on page 10.\n\nThe chart shows \"% saying govt is doing very/fairly well in reducing the threat of terrorism\" for December 2015:\n- Total (all adults): 46%\n- Republican: 27%\n- Democrat: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 14501, "output_tokens": 140, "cost_micros": 45603, "latency_ms": 5233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on how well the government is doing in reducing the threat of terrorism in 2015:\n\nFrom the table on page 10 titled \"How well is govt doing reducing threat of terrorism?\" and the section \"Views of how the government is handling the terrorist threat\" on pages 10-11:\n\nIn December 2015:\n- Total: 46% said very/fairly well\n- Republican: 27% said very/fairly well \n- Democrat: 64% said very/fairly well\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 18633, "output_tokens": 125, "cost_micros": 57774, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the specific data about beliefs regarding government performance in reducing terrorism threats in 2015.Let me search for data about government performance in reducing terrorism threats:I can see a figure showing data about \"How well is govt doing reducing threat of terrorism?\" with percentages for \"Very/Fairly well\" across years. I can see 46 for 2015. Now let me look for the breakdown by party affiliation (Republicans and Democrats):Let me search more specifically for partisan breakdown of government performance on terrorism:I found a reference to partisan data. Let me read that section:The figure I found shows partisan data over time. Let me read more context around this section to understand the 2015 data points:Perfect! I found the data. The text clearly states:\n\n- Overall in 2015: 46% say government is doing very or fairly well\n- Democrats in 2015: 64% say government is doing at least fairly well \n- Republicans in 2015: 27% say government is doing very or fairly well\n\nThis is also confirmed by the figure showing the data points for year '15' with Democrat at 64, Republican at 27.\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42844, "error": null, "extra": {"thread_id": 1929, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cb2b43cade5c41b98919c13d86621c55", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 281, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nLooking at page 15, I can see a chart titled \"ISIS rises higher atop public's threat list\" showing various threats with their percentages in December 2015 and August 2014, plus the change.\n\nThe threats that increased (positive change) from August 2014 to December 2015 are:\n- ISIS: +16 points (67% to 83%)\n- Iran's nuclear program: +3 points (59% to 62%)\n- North Korea's nuclear program: +2 points (57% to 59%)\n- China's emergence as a world power: +1 point (48% to 49%)\n- Global climate change: +1 point (48% to 49%)\n\nNow I need to find the R-D difference for each of these threats. Looking at page 15, there's another chart showing \"Partisan differences in views of top threats to U.S.\" with R-D diff column:\n\n- Iran's nuclear program: R+27\n- ISIS: R+14\n- China's emergence as a world power: R+18\n- North Korea's nuclear program: R+13\n- Global climate change: D+51 (this is actually a Democratic advantage, so -51 for R-D)\n\nAmong the threats that increased, Iran's nuclear program has the biggest R-D difference at R+27.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 58152, "output_tokens": 342, "cost_micros": 179586, "latency_ms": 35328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find which threats had bigger percentages in December 2015 compared to August 2014, and then determine which has the largest Republican-Democrat difference.\n\nLooking at the table \"ISIS rises higher atop public's threat list\" on page 14:\n\nThreats that increased from August 2014 to December 2015:\n1. ISIS: 67% \u2192 83% (+16)\n2. Iran's nuclear program: 59% \u2192 62% (+3)\n3. North Korea's nuclear program: 57% \u2192 59% (+2)\n4. China's emergence as a world power: 48% \u2192 49% (+1)\n5. Global climate change: 48% \u2192 49% (+1)\n\nNow I need to find the R-D differences for these threats from the table \"Partisan differences in views of top threats to U.S.\" on page 15:\n\n1. ISIS: R-D difference = +14 (93% Republican - 79% Democrat)\n2. Iran's nuclear program: R-D difference = +27 (79% Republican - 52% Democrat)\n3. North Korea's nuclear program: R-D difference = +13 (70% Republican - 57% Democrat)\n4. China's emergence as a world power: R-D difference = +18 (62% Republican - 44% Democrat)\n5. Global climate change: R-D difference = -51 (22% Republican - 73% Democrat = -51, but this is negative)\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 percentage points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 13386, "output_tokens": 380, "cost_micros": 45858, "latency_ms": 8984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nFrom page 14, the table \"ISIS rises higher atop public's threat list\" shows changes from August 2014 to December 2015:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n\nFrom page 15, the table \"Partisan differences in views of top threats to U.S.\" shows R-D differences in December 2015:\n- ISIS: 93% (R) - 79% (D) = +14 R-D difference\n- Iran's nuclear program: 79% (R) - 52% (D) = +27 R-D difference\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13 R-D difference\n- China's emergence as a world power: 62% (R) - 44% (D) = +18 R-D difference\n\nAmong threats that increased from August 2014 to December 2015, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 19246, "output_tokens": 314, "cost_micros": 62448, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the threats that increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" (page 14), the threats that increased are:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence: +1 point\n- Global climate change: +1 point\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" (page 15), the R-D differences are:\n- ISIS: 93% (R) - 79% (D) = +14\n- Iran's nuclear program: 79% (R) - 52% (D) = +27\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13\n- China's emergence: 62% (R) - 44% (D) = +18\n\nAmong the threats that increased from August 2014, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 14481, "output_tokens": 271, "cost_micros": 47508, "latency_ms": 7327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find threats where concern increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" on page 14, I can see the changes from August 2014 to December 2015:\n\n- ISIS: +16 points (83% in Dec 2015 vs 67% in Aug 2014)\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n- Israeli-Palestinian conflict: -5 points (decreased)\n- Growing authoritarianism in Russia: -11 points (decreased)\n\nSo the threats that increased are: ISIS, Iran's nuclear program, North Korea's nuclear program, China's emergence, and global climate change.\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" on page 15, the R-D differences in December 2015 are:\n\n- ISIS: +14 (93% Rep - 79% Dem)\n- Iran's nuclear program: +27 (79% Rep - 52% Dem)\n- North Korea's nuclear program: +13 (70% Rep - 57% Dem)\n- China's emergence as a world power: +18 (62% Rep - 44% Dem)\n- Global climate change: -51 (22% Rep - 73% Dem) - Note: This is negative, meaning Democrats see it as more of a threat\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 18613, "output_tokens": 402, "cost_micros": 61869, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that appears most relevant based on the priority list: the ISIS and terrorism release from December 2015.Let me continue reading to find the data about threats and R-D differences:I can see the document mentions ISIS increased from 67% in August 2014 to 83% as a major threat. Let me continue reading to find data about other threats and their R-D differences:Let me search for data about threats to well-being and R-D differences. I need to find a table or chart comparing different threats:Good! I found references to \"R-D diff\" in the document. Let me read the section around line 1364:This table shows the \"most important problem\" but I need to find the table about \"threats to well-being\" comparing 2015 to August 2014. Let me continue searching:Perfect! I found the section about threats. Let me read the full table around line 1974-2076:Great! Now I need to see the partisan breakdown table with R-D differences. Let me continue reading:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1930, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd9ddaac9b56498ab35c8337203470ec", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 315, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document that appears most relevant based on priority list isis and terrorism release from december 2015 let me continue reading to find data about threats and r d differences i can see document mentions isis increased from 67 in august 2014 to 83 as major threat let me continue reading to find data about other threats and their r d differences let me search for data about threats to well being and r d differences i need to find table or chart comparing different threats good i found references to r d diff in document let me read section around line 1364 this table shows most important problem but i need to find table about threats to well being comparing 2015 to august 2014 let me continue searching perfect i found section about threats let me read full table around line 1974 2076 great now i need to see partisan breakdown table with r d differences let me continue reading", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll go through the document systematically to count all bar charts.\n\nLooking through each page:\n\nPage 1: Title page - no charts\n\nPage 2: \n- One line chart showing \"Views of Government's Handling of Terrorism\"\n- No bar charts\n\nPage 3:\n- One line chart showing partisan gaps\n- One pie chart showing scrutiny of Muslims\n- No bar charts\n\nPage 4:\n- One table showing party preferences on various issues\n- No bar charts\n\nPage 5:\n- One line chart showing views on Islam and violence over time\n- One line chart showing partisan divide\n- One table with demographic data\n- No bar charts\n\nPage 6:\n- Multiple horizontal bar charts showing \"Should Muslims be subject to greater scrutiny\" broken down by different groups\n- Count: approximately 3-4 bar charts on this page\n\nPage 7:\n- Multiple horizontal bar charts continuing the scrutiny question by demographics\n- Count: approximately 3-4 bar charts on this page\n\nPage 8:\n- One table showing \"Most important problem facing nation\"\n- No bar charts\n\nPage 9:\n- One table showing partisan differences\n- No bar charts\n\nPage 10:\n- One line chart showing government ratings over time by party\n- No bar charts\n\nPage 11:\n- One table showing demographic views\n- No bar charts\n\nPage 12:\n- One line chart showing concerns about anti-terrorism policies\n- No bar charts\n\nPage 13:\n- One line chart showing partisan views on anti-terrorism policies\n- One table\n- No bar charts\n\nPage 14:\n- Multiple horizontal bar charts showing ISIS as threat\n- Count: approximately 2 bar charts\n\nPage 15:\n- Multiple horizontal bar charts showing partisan differences in threats\n- Count: approximately 2 bar charts\n\nPage 16:\n- Line charts showing views of U.S. campaign\n- No bar charts\n\nPage 17:\n- Horizontal bar charts showing current assessments\n- Count: approximately 2 bar charts\n\nPage 18:\n- Multiple horizontal bar charts about concerns and ground troops\n- Count: approximately 3-4 bar charts\n\nPage 19:\n- Line chart and horizontal bar charts\n- Count: approximately 2 bar charts\n\nPage 20:\n- Line charts showing views on military force\n-", "citations": [], "input_tokens": 58124, "output_tokens": 512, "cost_micros": 182052, "latency_ms": 17578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with bar-like elements showing percentages, but it's formatted as a comparative table.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table.\n\n8. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n9. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n10. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n11. Page 9: \"More Republicans than Democrats cite immigration, terrorism as top problems\" - This is a table.\n\n12. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n13. Page 11: \"Evaluations of the government's job reducing the threat of terrorism\" - This is a table.\n\n14. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n15. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n16. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n17. Page 14: \"ISIS rises higher atop", "citations": [], "input_tokens": 13358, "output_tokens": 512, "cost_micros": 47754, "latency_ms": 11733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart, not a bar chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart, not a bar chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with horizontal bars showing differences.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - Line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - Line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This shows horizontal bar comparisons.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - Horizontal bars.\n\n9. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - Line chart.\n\n10. Page 12: \"More concern with country's protection than civil liberties\" - Line chart.\n\n11. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - Line chart.\n\n12. Page 14: \"ISIS rises higher atop public's threat list\" - Horizontal bars.\n\n13. Page 15: \"Partisan differences in views of top threats to U.S.\" - Horizontal bars.\n\n14. Page 16: \"Most continue to support U.S. military campaign against ISIS\" - Line chart.\n\n15. Page 17: \"Few say current military effort in Iraq and Syria is going well\" - Horizontal bars.\n\n16. Page 17: \"More long-term optimism in ISIS fight\" - Vertical bars.\n\n17. Page 18: \"Bigger concern: Will U.S. go too far or not far enough in Iraq and Syria?\" - Table format with categories.\n\n18. Page ", "citations": [], "input_tokens": 19218, "output_tokens": 512, "cost_micros": 65334, "latency_ms": 13172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully go through the document to count all bar charts.\n\nLet me examine each chart in the document:\n\n1. Page 2: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line graph, not a bar chart.\n\n2. Page 3: \"Most say more scrutiny of Muslims not warranted\" - This is a bar chart showing percentages for Republicans, Independents, and Democrats.\n\n3. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a bar chart comparing Democratic Party vs Republican Party on various issues.\n\n4. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line graph.\n\n5. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a bar chart showing Republican, Independent, and Democrat responses.\n\n6. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table, not a bar chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a bar chart showing responses by different political groups.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - This is a bar chart showing responses by different demographic groups.\n\n9. Page 8: \"Most important problem facing nation\" - This is a table, not a bar chart.\n\n10. Page 9: \"Most important problem facing nation\" (partisan breakdown) - This is a table, not a bar chart.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a bar chart showing percentages by party affiliation.\n\n12. Page 11: \"Older, less educated give more negative ratings\" - This is a bar chart showing responses by age and education.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line graph.\n\n14. Page 13: Bar chart showing partisan views on anti-terrorism policies - This is a line graph.\n\n15. Page 13: \"Young adults more concerned about civil liberties\" - This is a bar chart showing responses by age group.\n\n16. Page 14: \"", "citations": [], "input_tokens": 14453, "output_tokens": 512, "cost_micros": 51039, "latency_ms": 11922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "16", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This appears to be a simple text display with percentages, not a chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a table, not a bar chart.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n9. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n10. Page 9: \"Most important problem facing nation\" - This is a table.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n12. Page 11: \"How well is govt doing reducing threat of terrorism?\" - This is a table.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n14. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n15. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n16. Page 14: \"ISIS rises higher atop public's threat list\" - This is a table.\n\n17. Page 15: \"Partisan differences in views of top threats to U.S.\" - This is a table.", "citations": [], "input_tokens": 18585, "output_tokens": 512, "cost_micros": 63435, "latency_ms": 12656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the bar charts in the report.Let me continue reading to count all the bar charts in the document.Let me search for \"figure\" tags and examine the visual content descriptions more carefully:I found 31 figures in the document. Now I need to examine each one to determine which are bar charts. Let me read through the document systematically to identify the type of each chart. Based on what I've already read, I can see several line charts and some other visualizations. Let me look for more specific patterns that indicate bar charts.\n\nLooking at the content I've already read, I can identify the following charts:\n\n1. Lines 188-253: Line chart showing government's job rating on terrorism over time\n2. Lines 306-354: Line chart showing partisan gap in views of whether Islam encourages violence \n3. Lines 372-413: Pie chart showing views on Muslim scrutiny\n4. Lines 436-505: Horizontal bar chart comparing parties on various issues (terrorist threat, gun control, economy, immigration, climate change, environment)\n5. Lines 563-617: Line chart about Islam and violence over time\n6. Lines 618-657: Line chart showing partisan differences over time\n7. Lines 937-1006: Horizontal bar chart showing Muslim scrutiny views by demographic groups\n8. Lines 1055-1193: Horizontal bar chart showing scrutiny views by various demographics\n9. Lines 1508-1563: Line chart showing government ratings on terrorism by party\n10. Lines 1738-1802: Line chart about civil liberties concerns\n11. Lines 1850-1905: Line chart showing partisan views on anti-terror policies\n12. Lines 2004-2072: Vertical bar chart comparing threat levels (ISIS, Iran, North Korea, etc.)\n13. Lines 2117-2210: Horizontal bar chart showing partisan differences in threat views\n14. Lines 2239-2304: Line chart about military campaign approval\n15. Lines 2353-2425: Two separate charts about military campaign assessment\n\nLet me carefully review the entire document to count specifically the bar charts (not line charts or pie charts).\n\nBased on my thorough reading of the document, I can identify the following **bar charts**:\n\n1. **Page 4** (lines 436-505): Horizontal bar chart - \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - comparing Democratic Party vs Republican Party on 6 issues\n2. **Page 6** (lines 937-1006): Horizontal bar chart - \"Should Muslims be subject to greater scrutiny because of their religion?\" - showing responses by party/ideology\n3. **Page 7** (lines 1055-1193): Horizontal bar chart - \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - showing responses by demographics\n4. **Page 14** (lines 2004-2072): Mixed chart showing ISIS and other threats with numerical comparisons\n5. **Page 15** (lines 2117-2210): Horizontal bar chart - \"Partisan differences in views of top threats to U.S.\"\n\nAfter careful examination, the document contains **5 bar charts**.\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46078, "error": null, "extra": {"thread_id": 1931, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6dc6e43bf0ee4e80af05e0ae2c870d11", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 404, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically page 17 (document page 18), I can find information about public attitudes regarding the ISIS fight.\n\nThe chart titled \"More long-term optimism in ISIS fight\" shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria.\n\nThe text confirms: \"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nAnswer: Two-thirds believe the U.S. and allies will succeed", "citations": [], "input_tokens": 58160, "output_tokens": 159, "cost_micros": 176865, "latency_ms": 22375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Let me search the document for information about people's attitudes regarding the ultimate success of the U.S. and its allies in the fight against ISIS.\n\nI found the relevant section on page 17, which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also notes this represents an increase: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Long-term optimism that U.S. and allies will succeed", "citations": [], "input_tokens": 13394, "output_tokens": 182, "cost_micros": 42912, "latency_ms": 6734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "long term optimism that u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, on page 17, there is a section titled \"More long-term optimism in ISIS fight\" which shows:\n\n\"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail. The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Will definitely or probably succeed", "citations": [], "input_tokens": 19254, "output_tokens": 118, "cost_micros": 59532, "latency_ms": 4703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "will definitely or probably succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically the section titled \"More long-term optimism in ISIS fight\" on page 16, I can find the answer.\n\nThe document shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria, compared to only 27% who said they will \"Definitely/Probably fail.\"\n\nThe text states: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: They will succeed/be successful", "citations": [], "input_tokens": 14489, "output_tokens": 141, "cost_micros": 45582, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4444444444444445, "method": "str_norm", "normalised_pred": "they will succeed be successful", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document for information about public attitudes on the ultimate success of the U.S. and its allies against ISIS in 2015...\n\nFrom page 17, I can see the section \"Current assessments of U.S. military campaign, prospects for success\" which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also shows that \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Two-thirds believe U.S. and allies will succeed", "citations": [], "input_tokens": 18621, "output_tokens": 191, "cost_micros": 58728, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about public attitudes toward the U.S. and its allies' efforts against ISIS in 2015.Let me continue reading to find the specific information about public attitudes:Let me search for information about attitudes toward the fight against ISIS and whether the U.S. and allies will succeed:Perfect! I found the information. Let me read a bit more to confirm the complete data:Based on the document, I found the answer. The document states that although few say the current military effort in Iraq and Syria is going well (only 35% in December 2015 say it's going very/fairly well), when it comes to long-term outlook, two-thirds (66%) believe the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria.\n\nAnswer: Two-thirds (66%) believe the U.S. and its allies will succeed", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43858, "error": null, "extra": {"thread_id": 1932, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a011d8590eae4d86a7883730ba9de008", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.26666666666666666, "method": "str_norm", "normalised_pred": "two thirds 66 believe u s and its allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1 on page 26:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nand\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 59872, "output_tokens": 134, "cost_micros": 181626, "latency_ms": 31250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 14:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 32, the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 19062, "output_tokens": 151, "cost_micros": 59451, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 18:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 31, we can see that the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22767, "output_tokens": 155, "cost_micros": 70626, "latency_ms": 4282, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the output dimension of the linear_class layer in the DETR PyTorch inference code.\n\nLooking at the code in Listing 1:\n\n```python\ndef __init__(self, num_classes, hidden_dim, nheads,\n num_encoder_layers, num_decoder_layers):\n super().__init__()\n # ... other layers ...\n self.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe linear_class layer is defined as `nn.Linear(hidden_dim, num_classes + 1)`, which means it takes `hidden_dim` as input and outputs `num_classes + 1` dimensions.\n\nThen looking at line 33:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe model is instantiated with `num_classes=91`.\n\nTherefore, the output dimension of the linear_class layer is:\nnum_classes + 1 = 91 + 1 = 92\n\nAnswer: 92", "citations": [], "input_tokens": 19551, "output_tokens": 270, "cost_micros": 62703, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 15:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 33:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe `linear_class` layer has an output dimension of `num_classes + 1`, where `num_classes=91`.\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22903, "output_tokens": 155, "cost_micros": 71034, "latency_ms": 4562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the DETR document to find the output dimension of the linear_class layer in the PyTorch inference code.Let me search for \"linear_class\" in the document to find the PyTorch inference code:Let me search for PyTorch code or inference code in the document:Now let me read the section with the PyTorch inference code around line 2864:Perfect! I found the PyTorch inference code. Looking at line 2898, I can see:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 2915, the model is instantiated with:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nSo the output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39343, "error": null, "extra": {"thread_id": 1933, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e081bc008e9a4c03bbbaf39b68e10bd7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 162, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about missed instances when the number of visible instances is 60.\n\nLooking at Figure 12 on page 25, I can see a graph titled \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nThe graph shows three colored lines representing different categories:\n- Dog (blue line)\n- Person (orange line)\n- Apple (green line)\n\nThe x-axis shows \"Number of visible instances\" and the y-axis shows \"% of missed instances.\"\n\nAt the point where the number of visible instances = 60, I need to compare the values for each category:\n\nLooking at the graph at x=60:\n- The dog (blue) line appears to be at approximately 20-25%\n- The person (orange) line appears to be at approximately 15-20%\n- The apple (green) line appears to be at approximately 10-15%\n\nAt 60 visible instances, the dog category shows the highest percentage of missed instances.\n\nAnswer: dog", "citations": [], "input_tokens": 59873, "output_tokens": 231, "cost_micros": 183084, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, I can see a graph plotting \"% of missed instances\" (y-axis) against \"Number of visible instances\" (x-axis) for three categories: dog, person, and apple.\n\nAt 60 visible instances, I need to identify which line/category has the highest percentage of missed instances:\n\n- The dog line (appears to be one color)\n- The person line (appears to be another color) \n- The apple line (appears to be a third color)\n\nFrom the graph, at the 60 visible instances mark, the \"apple\" category appears to have the highest percentage of missed instances, showing approximately 20-25% missed instances, while \"dog\" and \"person\" show lower percentages (around 10-15%).\n\nAnswer: apple", "citations": [], "input_tokens": 19063, "output_tokens": 203, "cost_micros": 60234, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nLooking at the graph in Figure 12, I can see three lines representing different categories: dog, person, and apple.\n\nAt 60 visible instances (x-axis), I need to compare the percentage of missed instances (y-axis) for each category:\n\n- Dog (appears to be around 20-25%)\n- Person (appears to be around 15-20%)\n- Apple (appears to be the highest, around 30-35%)\n\nThe apple category clearly shows the highest percentage of missed instances at 60 visible instances.\n\nAnswer: apple", "citations": [], "input_tokens": 22768, "output_tokens": 160, "cost_micros": 70704, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, it shows a graph with \"Number of visible instances\" on the x-axis (from 0 to 100) and \"% of missed instances\" on the y-axis (from 0 to 70). The graph shows three lines for different categories: dog, apple, and one other category.\n\nAt the point where the number of visible instances = 60, I need to identify which category line is highest (meaning the largest proportion of missed instances).\n\nFrom the graph, at 60 visible instances:\n- The \"dog\" line (appears to be one line)\n- The \"apple\" line (appears to be another line)\n- There appears to be a third line\n\nLooking at the vertical position at x=60, the \"dog\" line appears to be the highest among the three categories shown, indicating the largest proportion of missed instances at that point.\n\nAnswer: dog", "citations": [], "input_tokens": 19552, "output_tokens": 226, "cost_micros": 62046, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 12 (the table on page 25), I need to find the number of missed instances for each category when there are 60 visible instances.\n\nFrom the table:\n- dog: 5 missed instances\n- person: 22 missed instances \n- apple: 15 missed instances\n\nOut of 60 visible instances:\n- dog: 5/60 = 8.3%\n- person: 22/60 = 36.7%\n- apple: 15/60 = 25%\n\nThe person category has the largest proportion of missed instances.\n\nAnswer: person", "citations": [], "input_tokens": 22904, "output_tokens": 139, "cost_micros": 70797, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "person", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about visible instances and missed instances by category.Let me search for content about \"missed instances\" and look for figures or charts.Let me read the section with Figure 12 which discusses missed instances:Perfect! I found Figure 12 which shows the chart. Let me read the data more carefully. The chart shows:\n- Y-axis: % of missed instances (0 to 70)\n- X-axis: Number of visible instances (20 to 100)\n- Three categories plotted: dog, person, and apple\n\nAt 60 visible instances, I need to identify which category has the largest proportion of missed instances. From the text data shown:\n- Lines 2795-2796: dog\n- Lines 2797-2798: person \n- Lines 2801-2802: apple\n\nThe chart shows these three categories, and I need to determine which has the highest value at x=60. Based on the chart layout and the typical pattern where lines are listed in the legend in order of their values, and given that \"dog\" is listed first in the legend (line 2795), followed by \"person\" (line 2797), and then \"apple\" (line 2801), this typically indicates their relative positions on the chart.\n\nLet me look for any additional data that might give exact values:Based on the chart data from Figure 12, I can see:\n- Y-axis values listed: 70, 60, 50, 40, 30, 20, 10, 0\n- X-axis values: 20, 40, 60, 80, 100\n- Three categories in the legend: dog, person, apple\n\nThe legend positioning typically indicates the order of lines on the chart. In this case, at x=60 (60 visible instances), \"dog\" appears to be listed first in the legend (lines 2795-2796), suggesting it has the highest proportion of missed instances at that point, followed by \"person\" and then \"apple\".\n\nGiven the layout where:\n- \"dog\" is listed at the top of the legend\n- \"person\" is in the middle\n- \"apple\" is at the bottom\n\nAnd the typical convention where legend items are ordered by their y-values at key points or overall prominence, \"dog\" would have the largest proportion of missed instances when the number of visible instances equals 60.\n\nAnswer: dog", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47125, "error": null, "extra": {"thread_id": 1934, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39684984e24b48b0a29495ffed5edb23", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 304, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 on page 13, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP50, \u0394, APS, APM, APL\n\nThe rows show different combinations:\n- Row 1: X (class), X (\u21131), blank (no GIoU): AP=35.8, AP50=57.3\n- Row 2: X (class), blank (no \u21131), X (GIoU): AP=39.9, AP50=61.6\n- Row 3: X (class), X (\u21131), X (GIoU): AP=40.6, AP50=61.6\n\nThe question asks for DETR with L1 loss and WITHOUT GIoU loss, which corresponds to the first row where class=X, \u21131=X, and GIoU is blank.\n\nAnswer: 57.3", "citations": [], "input_tokens": 59883, "output_tokens": 274, "cost_micros": 183759, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three different configurations:\n\n1. First row: class \u2713, L1 \u2717, GIoU \u2713 \u2192 AP: 35.8, AP50: 57.3\n2. Second row: class \u2713, L1 \u2713, GIoU \u2717 \u2192 AP: 39.9, AP50: 61.6\n3. Third row (baseline): class \u2713, L1 \u2713, GIoU \u2713 \u2192 AP: 40.6, AP50: 61.6\n\nThe question asks for the configuration with L1 loss and without GIoU loss, which corresponds to the second row.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19073, "output_tokens": 223, "cost_micros": 60564, "latency_ms": 5608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three rows with different loss combinations:\n\n1. Row 1: class \u2612, l1 \u2612, GIoU \u2610 (class and L1, but no GIoU)\n - AP: 35.8\n - AP50: 57.3\n\n2. Row 2: class \u2612, l1 \u2610, GIoU \u2612 (class and GIoU, but no L1)\n - AP: 39.9\n - AP50: 61.6\n\n3. Row 3 (baseline): class \u2612, l1 \u2612, GIoU \u2612 (all three losses)\n - AP: 40.6\n - AP50: 61.6\n\nThe question asks for DETR with L1 loss and without GIoU loss, which corresponds to the first row in Table 4.\n\nAnswer: 57.3", "citations": [], "input_tokens": 22778, "output_tokens": 278, "cost_micros": 72504, "latency_ms": 7140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see the ablation study for different loss combinations:\n\nThe table shows three rows:\n1. class \u2713, L1 \u2717, GIoU \u2713: AP = 35.8, AP50 = 57.3\n2. class \u2713, L1 \u2713, GIoU \u2717: AP = 39.9, AP50 = 61.6\n3. class \u2713, L1 \u2713, GIoU \u2713: AP = 40.6, AP50 = 61.6 (baseline)\n\nThe second row shows the model with L1 loss but without GIoU loss (class \u2713, L1 \u2713, GIoU \u2717).\n\nThe AP50 value for this configuration is 61.6.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19562, "output_tokens": 256, "cost_micros": 62526, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP\u2085\u2080, \u0394, AP\u209b, AP\u2098, AP\u2097\n\nThe rows show different combinations:\n1. Row with \u2713 for class and \u21131, but no GIoU: AP = 35.8, AP\u2085\u2080 = 57.3\n2. Row with \u2713 for class and GIoU, but no \u21131: AP = 39.9, AP\u2085\u2080 = 61.6\n3. Row with \u2713 for all three (class, \u21131, GIoU): AP = 40.6, AP\u2085\u2080 = 61.6\n\nThe first row corresponds to the model with L1 loss but without GIoU loss, which shows AP\u2085\u2080 = 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 22914, "output_tokens": 285, "cost_micros": 73017, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for the AP50 value of DETR with L1 loss and without GIoU loss in the provided document.Let me read the table that shows the effect of loss components:Perfect! I found Table 4 which shows the effect of loss components on AP. Looking at the table:\n\n- The first row shows: class=\u2612, l1=\u2612, GIoU=(empty), which means L1 loss is used but GIoU loss is not used\n- For this configuration, the AP50 value is 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32109, "error": null, "extra": {"thread_id": 1935, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_984a5f35790043fbba33381ecf31e0b6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 on page 22, which shows the \"Architecture of DETR's transformer,\" I can see the Multi-Head Self-Attention layers are shown in the diagram.\n\nThe Multi-Head Self-Attention boxes in the diagram are colored pink/salmon.\n\nAnswer: Pink (or salmon)", "citations": [], "input_tokens": 59883, "output_tokens": 71, "cost_micros": 180714, "latency_ms": 31750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "pink or salmon", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 \"Architecture of DETR's transformer\" in the document, I need to identify the color of the Multi-Head Self-Attention layer.\n\nIn the diagram, the Multi-Head Self-Attention layer appears in both the Encoder and Decoder sections. These layers are shown in a light orange or peach color.\n\nAnswer: Orange (or peach/light orange)", "citations": [], "input_tokens": 19073, "output_tokens": 89, "cost_micros": 58554, "latency_ms": 3953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange or peach light orange", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I can see the architecture diagram of DETR's transformer.\n\nThe Multi-Head Self-Attention layer boxes in the diagram are colored blue.\n\nAnswer: Blue", "citations": [], "input_tokens": 22778, "output_tokens": 48, "cost_micros": 69054, "latency_ms": 4203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I need to identify the color of the Multi-Head Self-Attention layer in the architecture diagram.\n\nIn the figure, the Multi-Head Self-Attention layer appears in the Encoder section of the architecture.\n\nAnswer: Orange", "citations": [], "input_tokens": 19562, "output_tokens": 62, "cost_micros": 59616, "latency_ms": 3406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, which shows the architecture of DETR's transformer:\n\nThe Multi-Head Self-Attention layers are shown in the diagram boxes in the Encoder and Decoder sections.\n\nAnswer: The diagram does not specify colors for the layers in Fig 10", "citations": [], "input_tokens": 22914, "output_tokens": 65, "cost_micros": 69717, "latency_ms": 3734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "diagram does not specify colors for layers in fig 10", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 10 and identify the color of the Multi-Head Self-Attention layer in DETR's transformer architecture.Let me search for \"Fig 10\" or \"Figure 10\" in the document:Let me search for references to transformer architecture figures:Great! I found that Figure 10 shows the \"Architecture of DETR's transformer\" at line 2409. Let me read that section:I can see the text content of Figure 10, but I need to look for color information. The document mentions it's embedded as an image. Let me search for more detailed description of the figure or any color information:The visual description I found was for a different image (zebras). Let me go back and look more carefully at the Figure 10 architecture diagram. Let me search around that area more carefully:I see the text extracted from Figure 10, but the visual description that follows (zebras) is clearly for a different image (Im7.png). Let me search for visual descriptions that might be associated with the transformer architecture diagram. Let me look before Figure 10:Let me search for where there might be an embedded image description for Figure 10. Let me check if there's an image description before line 2400:Let me search for image descriptions or embedded images related to Figure 10. Let me check what comes right before the figure starts:Let me look more carefully. The embedded image Im7.png comes AFTER Figure 10. Let me check if there's an embedded image BEFORE Figure 10 that might contain the actual architecture diagram:The document doesn't seem to have a visual description embedded for Figure 10's architecture diagram itself within the accessible text. However, looking at the structure, it appears Figure 10 is presented as a figure with text labels but the actual image with colors might not have been described in the OCR/visual description section.\n\nLet me search more broadly for any mentions of colors in the architecture or in figures:Let me search for other color terms that might be in Figure 10:These color mentions don't seem to be about Figure 10. Let me try to find if there's an image file reference specifically for the transformer architecture. Let me look for \"Im\" images around Figure 10:Image Im8.png is not Figure 10 either. Let me look between Figure 10 text and Im7.png more carefully. Actually, let me check if there's an embedded image right after the tag for Figure 10. Let me look at the exact area around the figure closing:So Figure 10 ends at line 2485, and then Im7.png starts at line 2489, which is clearly about zebras, not the transformer architecture. The Figure 10 text content doesn't include a visual description with colors. Let me search for if there's any embedded image metadata that might describe colors for transformer diagrams. Let me try searching for \"green\" or \"red\" along with terms near Figure 10 context:Let me try a different approach. Perhaps the PDF was processed and the image containing Figure 10 wasn't given a separate visual description. Let me look for if there's any image file reference or visual annotation BEFORE Figure 10 that might be the diagram itself:It seems the document processing didn't include a detailed visual description with colors for Figure 10's architecture diagram. This could mean the image was processed but only the text labels were extracted, not the visual styling information like colors of the boxes.\n\nLet me try searching the entire document for any reference to colors of \"Self-Attention\" layers or transformer layers:Based on my thorough search through the document, I cannot find any visual description that specifies the color of the Multi-Head Self-Attention layer in Figure 10. The document contains the text content and labels from Figure 10, but the visual styling information (colors of boxes/components) was not captured in the OCR or visual description process.\n\nWithout access to the actual image file or a visual description that includes color information, I cannot determine from this document what color the Multi-Head Self-Attention layer is depicted as in Figure 10.\n\nAnswer: Color information not available in the provided document text.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 122281, "error": null, "extra": {"thread_id": 1936, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_be29632a506941cfa603de6bebd1c2eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 844, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color information not available in provided document text", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "JSONDecodeError: Expecting value: line 169 column 1 (char 924)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the number of multi-head self-attention layers in DETR's default structure.\n\nLooking at the document, I need to identify the default configuration of DETR.\n\nFrom Section 4 (Experiments), specifically the \"Technical details\" subsection:\n\"we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nHowever, I need to understand the architecture better. Looking at Section 3.2 and Figure 10:\n\n1. The **encoder** has 6 layers, and each encoder layer contains one multi-head self-attention module (as stated: \"Each encoder layer has a standard architecture and consists of a multi-head self-attention module and a feed forward network (FFN)\")\n\n2. The **decoder** has 6 layers. Looking at the description and Figure 10, each decoder layer contains:\n - Multi-head self-attention\n - Multi-head attention (encoder-decoder attention)\n\nSo each decoder layer has one multi-head self-attention layer.\n\nFrom the default configuration mentioned in the experiments:\n- 6 encoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n- 6 decoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19056, "output_tokens": 334, "cost_micros": 62178, "latency_ms": 8546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about DETR's default structure regarding multi-head self-attention layers.\n\nFrom Section 4.1 \"Comparison with Faster R-CNN\":\n\"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nFrom Section 4.2 \"Ablations\":\n\"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\nFrom the architecture description in Section 3.2:\n- The transformer encoder has multiple layers, each containing a multi-head self-attention module\n- The transformer decoder has multiple layers, each containing multi-head self-attention and encoder-decoder attention mechanisms\n\nSo the default DETR structure has:\n- 6 encoder layers (each with 1 multi-head self-attention layer) = 6 layers\n- 6 decoder layers (each with 1 multi-head self-attention layer) = 6 layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22761, "output_tokens": 271, "cost_micros": 72348, "latency_ms": 7187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and the number of multi-head self-attention layers.\n\nLooking through the document:\n\n1. In Section 4 \"Experiments\" under \"Technical details\", it states:\n\"We use scale augmentation... we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\n2. In Section A.3 \"Detailed architecture\", the document describes the transformer architecture with both encoder and decoder layers.\n\n3. From the architecture description and Figure 10:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing:\n - Multi-head self-attention\n - Multi-head attention (encoder-decoder attention)\n\n4. From the technical details in Section 4:\n- 6 encoder layers (each with multi-head self-attention)\n- 6 decoder layers (each with multi-head self-attention)\n\nSo the total number of multi-head self-attention layers would be:\n- 6 encoder layers \u00d7 1 self-attention layer each = 6\n- 6 decoder layers \u00d7 1 self-attention layer each = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19545, "output_tokens": 287, "cost_micros": 62940, "latency_ms": 7234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and count the multi-head self-attention layers.\n\nFrom the document, I can find the architecture details:\n\n1. In Section 4.2 (Ablations), it states: \"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\n2. Looking at Figure 10 and Section A.3 (Detailed architecture), the structure shows:\n - Encoder has N layers, each with Multi-Head Self-Attention\n - Decoder has M layers, each with Multi-Head Self-Attention AND Multi-Head Attention (cross-attention)\n\n3. From the baseline model description: \"we choose a model with 6 transformer and 6 decoder layers\"\n\n4. In the encoder: Each of the 6 encoder layers contains 1 multi-head self-attention layer = 6 layers\n\n5. In the decoder: Each of the 6 decoder layers contains 1 multi-head self-attention layer = 6 layers\n (Note: The decoder also has multi-head cross-attention, but that's different from self-attention)\n\nTotal multi-head self-attention layers = 6 (encoder) + 6 (decoder) = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22897, "output_tokens": 303, "cost_micros": 73236, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll search for information about DETR's structure in the provided document.Now I found the key information. Let me search for more details about the architecture to confirm the total number of self-attention layers.Let me check the decoder structure to understand how many self-attention layers are in the decoder:Now let me look at the supplementary material section that describes the architecture in detail:Based on the document, I can see that:\n\n1. The default DETR model has **6 encoder layers** (line 1054, 1073: \"6 transformer and 6 decoder layers\" and \"6 encoder, 6 decoder layers\")\n\n2. Each encoder layer has a **multi-head self-attention module** (line 624)\n\n3. The default DETR model has **6 decoder layers** (line 1054, 1073)\n\n4. Each decoder layer has **multi-head self-attention** (line 2403-2404: \"multiple multi-head self-attention and decoder-encoder attention\")\n\nSo the total number of multi-head self-attention layers in DETR's default structure is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61719, "error": null, "extra": {"thread_id": 1937, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_12c4837a12434eeca194f2ee2772f40e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 320, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this InVue 2021 Product Catalog.\n\nLooking through the document systematically:\n\n1. **Series 2865** (page 9) - For iPad Pro, iPad Air, iPad, iPad mini 4\n - Multi-position security system with sensors\n\n2. **OnePOD** (page 10) - For iPad Pro, iPad Air, iPad, iPad mini 4\n - POD display solution\n\n3. **Series 960** (page 12) - For iPad Pro, iPad Air, iPad, iPad mini 4\n - POD with exposed cable\n\n4. **iPad + Keyboard Solutions** (pages 17-19):\n - Standard Solution for iPad Pro + Keyboards (page 18)\n - High Security Solution for iPad Pro + Keyboards (page 19)\n\n5. **iPad Digital Display** (pages 15-16) - Bracketed pedestal solution\n\nSo counting the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. iPad + Keyboard Standard Solution\n5. iPad + Keyboard High Security Solution\n6. iPad Digital Display\n\nAnswer: 6", "citations": [], "input_tokens": 96851, "output_tokens": 271, "cost_micros": 294618, "latency_ms": 54405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this Apple catalog document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** (pages 3-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 6-9) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (pages 10-12) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 13) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 14) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 15-16) - A bracketed pedestal solution for iPads as digital displays\n\n7. **iPad + Keyboard Solutions** (pages 17-19):\n - iPad Pro + Keyboards Standard Solution (page 18)\n - iPad Pro + Keyboards High Security Solution (page 19)\n\nLet me count the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nAnswer: 8", "citations": [], "input_tokens": 11786, "output_tokens": 335, "cost_micros": 40383, "latency_ms": 6532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays\n\n7. **iPad Pro + Keyboards Standard Solution** (page 11) - For iPad Pro with Magic Keyboard and Smart Keyboard\n\n8. **iPad Pro + Keyboards High Security Solution** (page 12) - For iPad Pro with keyboards\n\nSo there are **8** different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 18388, "output_tokens": 271, "cost_micros": 59229, "latency_ms": 6733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions mentioned in the document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without Keyboard) on page 38\n\n2. **OnePOD** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 40\n\n3. **Series 960** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 41\n\n4. **iPad Digital Display** - mentioned as a \"Bracketed pedestal for utilizing iPads as digital displays on Apple Fixtures\" on pages 11-12\n\n5. **iPad Pro + Keyboards Standard Solution** - mentioned on page 11 for \"iPad Pro, Magic Keyboard, Smart Keyboard and Apple Pencil\"\n\n6. **iPad Pro + Keyboards High Security Solution** - mentioned on page 12\n\nSo I can identify 6 different iPad solutions in the document:\n- Series 2865 (for standard iPads)\n- OnePOD (for standard iPads)\n- Series 960 (for standard iPads)\n- iPad Digital Display\n- iPad Pro + Keyboards Standard Solution\n- iPad Pro + Keyboards High Security Solution\n\nAnswer: 6", "citations": [], "input_tokens": 12620, "output_tokens": 289, "cost_micros": 42195, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in the document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays on Apple Fixtures\n\n7. **iPad + Keyboard Solutions - Standard Solution** (page 11) - For iPad Pro with Magic Keyboard, Smart Keyboard and Apple Pencil\n\n8. **iPad + Keyboard Solutions - High Security Solution** (page 12) - For iPad Pro with Magic and Smart Folio keyboards\n\nSo there are 8 different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 16954, "output_tokens": 288, "cost_micros": 55182, "latency_ms": 7030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for iPad solutions in the provided document.Let me search for \"solution\" to find where iPad solutions are mentioned:Now let me read the table of contents section more carefully to see the iPad solutions listed:I can see there are two main sections: \"iPhone & iPad Solutions\" and \"iPad + Keyboard Solutions\". Let me now read through these sections to count the individual iPad solutions. Let me start with the \"iPhone & iPad Solutions\" section:Let me read the document more systematically. Let me check the pages 1-10 which should cover the \"iPhone & iPad Solutions\" section:Let me search for specific product series names to count the solutions:Let me look more systematically at the document. Let me read pages that should list individual iPad solutions:I can see Series 2865, OnePOD, and Series 960 mentioned. Let me continue reading to find all the different solutions and then look at the iPad + Keyboard Solutions section:Let me search for more product names. Let me look for what comes after Series 960:Good, I found \"One60 Tethered\". Let me continue:Good, I found \"One90QR\". Let me continue searching through the document:Good, I found \"iPad Digital Display\". Now let me check if there are more iPhone & iPad solutions and then look at the iPad + Keyboard Solutions section. Let me look at page 10 where iPad + Keyboard Solutions should start:Good! I found \"iPad Pro + Keyboards Standard Solution\". Let me continue to find the next one:Good! I found \"iPad Pro + Keyboards High Security Solution\". Now let me continue to see if there are more iPad solutions. Let me check what comes on page 12 and beyond, which should start MacBook Solutions (page 13 according to the table of contents):Perfect! Now I can see that page 13 starts MacBook Solutions, which means the iPad + Keyboard Solutions section ends at page 12. Let me now compile all the iPad solutions I found. Let me verify by checking if there are any other iPad product names I might have missed. Let me look at pages 10-11 more carefully:Now let me look more carefully at page 9 to see if there are more solutions listed on that introductory page:Good. Now let me compile the complete list. I need to be systematic. Let me re-examine the section headers carefully. Let me look at what page 9 shows at the bottom to clarify:\n\nActually, looking back at my findings, page 9 seems to be a transition page showing the \"iPad + Keyboard Solutions\" section header with compatibility information. Pages 10-11 then have two specific solutions.\n\nLet me now count all the different iPad solutions I found in the document:\n\n**From \"iPhone & iPad Solutions\" section (pages 1-8):**\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n\n**From \"iPad + Keyboard Solutions\" section (pages 10-11):**\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nThat gives me a total of **8** different iPad solutions.\n\nBut wait, let me verify this more carefully. OnePOD was mentioned on page 2 in the product comparisons - let me verify if it's a distinct solution:Yes, OnePOD is a distinct solution. Let me also verify the others are all distinct iPad solutions and not just iPhones. Let me check the compatibility sections more carefully. Looking back at my readings, all of these products list iPad compatibility:\n\n- Series 2865: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- OnePOD: There's a specific OnePOD section for iPad ordering\n- Series 960: Compatible with iPads\n- One60 Tethered: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- One90QR: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- iPad Digital Display: Specifically for iPads on Apple Fixtures\n- iPad Pro + Keyboards Standard Solution: For iPad Pro with keyboards\n- iPad Pro + Keyboards High Security Solution: For iPad Pro with keyboards\n\nAll 8 are indeed iPad solutions. \n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 126703, "error": null, "extra": {"thread_id": 1938, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_18872fac42c746e097bc8836213e95a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 870, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features are listed but not shown in the illustration.\n\nLooking at page 15 (document page 22), I can see the \"MacBook High Security Solution\" section.\n\nThe \"FEATURES AND BENEFITS\" lists 6 numbered features:\n\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow looking at the illustrations on the same page, I can see:\n- Image showing features 3, 6, 1, 6, and 2 labeled\n- Image showing feature 5 labeled\n\nLet me verify which features are NOT shown in the illustrations:\n- Feature 1: Shown (labeled in bottom left image)\n- Feature 2: Shown (labeled in bottom left image)\n- Feature 3: Shown (labeled in bottom left image)\n- Feature 4: NOT shown in any illustration\n- Feature 5: Shown (labeled in top right image)\n- Feature 6: Shown (labeled in bottom left image)\n\nAnswer: 1", "citations": [], "input_tokens": 96859, "output_tokens": 327, "cost_micros": 295482, "latency_ms": 47640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which feature numbers are mentioned in the text but not shown in the illustration.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section.\n\nThe FEATURES AND BENEFITS list shows:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to look at the illustration to see which numbers are labeled in the image.\n\nFrom the illustration, I can see the following numbers are clearly marked:\n- 1 (pointing to the rear anchor area)\n- 2 (pointing to the cable/tether area)\n- 3 (pointing to bracket/arm area)\n- 5 (pointing to the metal tabs)\n- 6 (appears twice, pointing to the molded feet)\n\nThe numbers NOT shown in the illustration are:\n- 4 (Ability to power/alarm MacBook with USB-C Port)\n\nAnswer: 4", "citations": [], "input_tokens": 11794, "output_tokens": 307, "cost_micros": 39987, "latency_ms": 8657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed in text with what's shown in the illustration.\n\nLooking at pages 14-17, I find the MacBook High Security Solution on page 15.\n\nThe text lists these FEATURES AND BENEFITS:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration to see which features are labeled. The illustration shows numbered callouts (1, 2, 3, 6) pointing to different parts of the MacBook security solution.\n\nFrom the visible callouts in the illustration:\n- Feature 1 is shown\n- Feature 2 is shown\n- Feature 3 is shown\n- Feature 6 is shown\n\nFeatures NOT shown in the illustration:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nTherefore, 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 18396, "output_tokens": 323, "cost_micros": 60033, "latency_ms": 9530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed with what's shown in the illustration.\n\nLooking at page 15, I can see the \"HIGH SECURITY EXCEPTION PROGRAM - MACBOOK\" section titled \"MacBook High Security Solution\".\n\nThe FEATURES AND BENEFITS section lists 6 numbered features:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration on that page to see which features are NOT shown visually. Looking at the image description \"delete alarm\" and \"return\" text visible, this appears to be showing a MacBook with the security solution.\n\nFrom the numbered features:\n- Feature 4 (USB-C Port) - this is an internal capability, not visually shown in an illustration\n- Feature 5 (Metal tabs with rubber pads preventing closing) - this would be visible if shown\n- Feature 6 (Stable, molded feet) - these would be underneath and likely not visible in a standard product photo\n\nWithout being able to see the exact details of the illustration clearly, but based on typical product photography, features that are functional/internal capabilities (like feature 4 - power/alarm ability) or underneath components (like feature 6 - molded feet) are typically not shown in product illustrations.\n\nBased on the context and typical product documentation, features 4 and 6 would most likely not be presented in the illustration, giving us 2 features.\n\nAnswer: 2", "citations": [], "input_tokens": 12628, "output_tokens": 418, "cost_micros": 44154, "latency_ms": 12765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features listed are NOT shown in the illustrations.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section with:\n\n**FEATURES AND BENEFITS** (numbered 1-6):\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe page shows 4 images with numbered callouts:\n- Image 1 (Top Left): Shows callout 5\n- Image 2 (Top Right): Shows callout 5 (close-up)\n- Image 3 (Bottom Left): Shows callouts 1, 2, 3, and 6\n- Image 4 (Bottom Right): Shows callout 2 (close-up)\n\nFeatures shown in illustrations:\n- Feature 1: YES (shown in Image 3)\n- Feature 2: YES (shown in Images 3 and 4)\n- Feature 3: YES (shown in Image 3)\n- Feature 4: NO (not shown in any image)\n- Feature 5: YES (shown in Images 1 and 2)\n- Feature 6: YES (shown in Image 3)\n\nOnly Feature 4 (Ability to power/alarm MacBook with USB-C Port) is NOT presented in the illustrations.\n\nAnswer: 4", "citations": [], "input_tokens": 16962, "output_tokens": 396, "cost_micros": 56826, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about MacBook high security solution and identify which features are not presented in the illustration.Let me search for \"security\" in the document to find the relevant section:Let me search for \"MacBook\" and \"high security\" together to find the specific section:Now let me read the specific section about \"MacBook High Security Solution\":Perfect! I found the MacBook High Security Solution section. Now let me read it more carefully to see the full list of features and the illustration. Let me go back to line 3132:I can see the features listed are numbered 1-6:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe illustration shows numbers 1, 2, 3, and 6 marked on the MacBook keyboard image in the figure. The OCR text shows these numbers visible in the illustration. However, I notice the visual description after the figure is describing a smartwatch, which seems incorrect. Let me check if there's a better description of the MacBook illustration by reading a bit more context around this section.\n\nLooking at the figure content (lines 3159-3225), I can see the illustration has:\n- Number 1 appears twice (lines 3185, 3189, 3223)\n- Number 2 appears twice (lines 3137, 3179, 3191, 3217)\n- Number 3 appears twice (lines 3173, 3215)\n- Number 6 appears three times (lines 3213, 3221)\n\nFrom the text, the 6 features are listed, and from the illustration markers I can identify which numbers are shown:\n- 1 is shown (rigid anchor design)\n- 2 is shown (steel cable)\n- 3 is shown (bracket bar arms)\n- 6 is shown (stable molded feet)\n\nThe features that are NOT shown in the illustration are:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nSo 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46969, "error": null, "extra": {"thread_id": 1939, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_abe777eda57d4187b59395c83dff7b8b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 346, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about \"One40\"...\n\nI cannot find any mention of \"One40\" in this Apple Solutions 2021 Product Catalog from InVue. The document discusses various security solutions for Apple products including:\n\n- Series 2865\n- OnePOD (One55, One60, One65)\n- Series 960\n- W2000\n- WS2\n- HSW100\n- One60 Wearable\n- OnePOD Wearable\n\nBut there is no product called \"One40\" mentioned in this catalog.\n\nAnswer: Cannot determine - product not found in document", "citations": [], "input_tokens": 96873, "output_tokens": 141, "cost_micros": 292734, "latency_ms": 38656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine product not found in document", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and whether it can only be used for Apple Watch.\n\nOn page 21, in the \"OnePOD Wearable / Apple Watch Solutions\" section, under \"FEATURES AND BENEFITS,\" it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is mentioned in the context of Apple Watch solutions. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nSearching through the rest of the document, One40 is only mentioned in this one location in relation to Apple Watch compatibility. There is no indication that One40 is used for any other products in this catalog.\n\nHowever, the question asks if it can \"only\" be used for Apple Watch. The document doesn't explicitly state that One40 cannot be used for other purposes - it simply doesn't mention One40 being used for anything else in this particular catalog.\n\nGiven that this is specifically an Apple catalog and One40 is only mentioned in the Apple Watch section without any other applications shown, and based solely on what is presented in this document:\n\nAnswer: yes", "citations": [], "input_tokens": 11808, "output_tokens": 265, "cost_micros": 39399, "latency_ms": 8577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and what products it can be used with.\n\nOn page 21, under \"OnePOD Wearable\" section for Apple Watch Solutions, it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is compatible with the OnePOD Wearable system for Apple Watch. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nLooking through the rest of the document, I can see that One40 is only mentioned in the context of Apple Watch solutions. There is no mention of One40 being used for iPhones, iPads, or other Apple products in their respective sections.\n\nAnswer: No", "citations": [], "input_tokens": 18410, "output_tokens": 179, "cost_micros": 57915, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I need to search the document for information about \"One40\" and what products it can be used with.\n\nLooking through the document, I can find mentions of \"One40\" on page 21 in the OnePOD Wearable section:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis appears in the context of Apple Watch solutions, but it only states compatibility with these stands. It doesn't state that One40 can ONLY be used for Apple Watch or that it's exclusive to Apple Watch.\n\nThe document doesn't provide enough information to definitively say whether One40 can only be used for Apple Watch or if it has other uses beyond what's mentioned in this catalog.\n\nHowever, since the question asks for a direct yes/no answer based on what's in the document, and the document only shows One40 mentioned in the Apple Watch context without explicitly stating it's exclusive to Apple Watch, I cannot confirm the statement is true.\n\nAnswer: No", "citations": [], "input_tokens": 12642, "output_tokens": 221, "cost_micros": 41241, "latency_ms": 6593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Let me search through the document for information about One40 and its compatibility.\n\nLooking at the Apple Watch Solutions section, I can see mentions of:\n- W2000\n- WS2\n- OnePOD Wearable (compatible with One55, One60, One65 and One40)\n- One60 Wearable (High Security)\n- HSW100\n\nOn page 21, under \"OnePOD Wearable\" features and benefits, it states:\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is a stand that is compatible with the OnePOD Wearable system for Apple Watch. However, the document does not explicitly state that One40 can ONLY be used for Apple Watch. The document doesn't provide comprehensive information about all possible uses of the One40 stand.\n\nBased on the limited information provided in the document, I cannot definitively confirm that One40 is exclusively for Apple Watch only.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 16976, "output_tokens": 239, "cost_micros": 54513, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about One40 and its compatibility.Let me search for \"One40\" in the document.Now let me read the section around line 4032 to get more context about One40.This section is about Apple Watch products. Let me search for more information about One40 to see if it's used with other products.Let me search for other mentions of One40 in the document to see if it's used with other products besides Apple Watch.Let me search for \"One55\" or \"One60\" to understand what these stands are used for, which will help determine if One40 is only for Apple Watch.Good! I can see One55 is used for iPhone and iPad. Now let me check around line 1666 to see what products use these stands.Let me look for what products are associated with One40 specifically. Let me search for \"One40\" more broadly in the document.Let me search the entire document more carefully. I'll look for sections that mention what One40 is used for.I notice the table only shows One55, One65, and One60, but not One40. Let me look at sections that specifically discuss iPhone and iPad product ordering to see if One40 is mentioned there.I can see iPhone ordering uses One55, One60, and One65, but not One40. Let me check iPad ordering and then look specifically for what products use One40.iPad also uses One55, not One40. Now let me search for a broader section around the Apple Watch mention to understand the context better. Let me look for tables or product ordering sections related to Apple Watch and One40.Let me search for Apple Watch product ordering section specifically.Now let me read the Apple Watch product ordering section around line 6290.I can see the Apple Watch product ordering sections, but they don't mention One40 in the stand options. They mention DBD910-W, DBD600-W, DBD650-W, AC271-W, and ZW2000 for Apple Watch. Let me go back to where One40 was mentioned (line 4032) and read more context to understand what section that is part of.Perfect! Now I have clear context. The section starting at line 3973 is titled \"ONEPOD WEARABLE\" and is under \"/ Apple Watch Solutions\" (line 3974). The section at line 4021-4060 describes the OnePOD Wearable product which:\n\n1. Has \"Harness bands secure Apple Watch to sensor\" (line 4028)\n2. Is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\" (line 4032)\n3. Has \"OEM charging compatibility for Apple Watch models\" (line 4035)\n4. Shows compatibility with various Apple Watch Series (lines 4043-4059)\n\nThis clearly indicates that One40 is mentioned specifically in the Apple Watch section. However, the statement says One40 is compatible with the OnePOD Wearable sensor/product along with One55, One60, and One65 stands. Let me verify if One40 stand is used for other products as well by searching more thoroughly.One40 is mentioned only once in the entire document, and it's in the Apple Watch section. However, I notice that it says the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\". This means the Apple Watch sensor can work with multiple stand types including One40, but it doesn't necessarily mean One40 can ONLY be used for Apple Watch.\n\nLet me check if there's a definition or description of what One40 is elsewhere, or if it appears in any product tables.Let me look at what the table at line 1666 shows - it lists One55, One65, and One60 but notably does NOT include One40. This suggests One40 might be a different or less common stand type. Let me see if I can find any section that specifically describes One40 as a product.The table comparison shows One55, One65, and One60, but NOT One40. This table appears on page 3 under \"/ iPhone & iPad Solutions\" and \"ONEPOD\u2122\" headers (lines 1615-1616).\n\nBased on my search:\n1. One40 is mentioned only ONCE in the entire document (line 4032)\n2. It's mentioned in the \"Apple Watch Solutions\" section under \"OnePOD Wearable\" product description\n3. The comparative table of stands (lines 1658-1730) for iPhone/iPad products shows One55, One65, and One60, but NOT One40\n4. iPhone ordering sections show One55, One60, One65 but not One40\n5. iPad ordering sections show One55 but not One40\n6. Apple Watch ordering sections don't explicitly list One40 in their product codes\n\nThe document states that the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands.\" This indicates One40 CAN be used with Apple Watch, but since One55, One60, and One65 are used for multiple products (iPhone, iPad) and are also compatible with Apple Watch, it's possible One40 could also be used for other products.\n\nHowever, the fact that:\n- One40 appears nowhere else in the document\n- It's not in the comparison table\n- It's not in iPhone/iPad ordering sections\n- It only appears in the Apple Watch compatibility statement\n\nThis strongly suggests One40 is specifically or primarily designed for Apple Watch, though I cannot definitively say it can ONLY be used for Apple Watch based solely on this document.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 125969, "error": null, "extra": {"thread_id": 1940, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_274957bdc18c45a58945780a57a038df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 928, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the paper to identify the function names with blue background in the program execution.\n\nLooking at Figure 1 (Overview of PROGRAMFC model) on page 2, I can see the \"Functions\" column which shows the program execution functions. The functions shown with blue background are:\n\n1. Fact Checker (appears twice - for S1 and S3)\n2. QA Model (for S2)\n3. Logical Reasoner (for S4)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 65107, "output_tokens": 141, "cost_micros": 197436, "latency_ms": 20703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names with blue background in the \"Functions\" column of the Program Execution section.\n\nFrom the figure, I can see the following functions are called in the reasoning program:\n- S1: Verify (Fact Checker)\n- S2: Question (QA Model)\n- S3: Verify (Fact Checker)\n- S4: Predict (Logical Reasoner)\n\nThe functions with blue background are:\n- Fact Checker\n- QA Model\n- Logical Reasoner\n\nHowever, the question asks for \"function names\" which appear to be the ACTION names in the program format. Looking at the actual function calls in the program:\n- Verify\n- Question\n- Predict\n\nIn ascending alphabetical order:\n\nAnswer: Predict, Question, Verify", "citations": [], "input_tokens": 26912, "output_tokens": 192, "cost_micros": 83616, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I can see the \"Functions\" column which shows the sub-task functions that are called during program execution.\n\nThe functions shown with blue backgrounds in the figure are:\n- Fact Checker (called in steps S1 and S3 for the Verify function)\n- QA Model (called in step S2 for the Question function)\n- Logical Reasoner (called in step S4 for the Predict function)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 29934, "output_tokens": 135, "cost_micros": 91827, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names that appear with blue background in the \"Functions\" column during program execution.\n\nFrom the figure, I can see the following functions listed in the execution steps:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, examining the actual function names called in the \"Reasoning Program\" column:\n- FACT_1 uses a fact verification function\n- ANSWER_1 uses Question (QA)\n- FACT_2 uses a fact verification function\n- PREDICTED_LABEL uses Predict (logical reasoning)\n\nLooking at Section 3.4 which describes the sub-task functions, the three function names are:\n1. QUESTION\n2. VERIFY\n3. PREDICT\n\nThese correspond to the blue-backgrounded function types shown in Figure 1.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 22321, "output_tokens": 230, "cost_micros": 70413, "latency_ms": 7172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names shown in the \"Functions\" column that correspond to the program execution steps.\n\nFrom the figure, the functions shown are:\n- Fact Checker (for S1 and S3)\n- QA Model (for S2)\n- Logical Reasoner (for S4)\n\nHowever, looking more carefully at the actual function names used in the reasoning program section and throughout the paper, the standardized function names are:\n- VERIFY (for fact checking)\n- QUESTION (for question answering)\n- PREDICT (for logical reasoning)\n\nThese are the three sub-task functions described in Section 3.4.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 29236, "output_tokens": 174, "cost_micros": 90318, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I can see Figure 1 in the document. Let me search for information about the program execution functions with blue backgrounds. I need to look more carefully at the figure description and any additional details about the model architecture.Let me look at the actual figure more carefully. I need to examine the visual description and the figure content in detail to identify functions with blue backgrounds.I can see the figure content, but I need to look for more detailed visual information about the figure. Let me search for image descriptions or additional figure details.Let me look for any embedded images or visual descriptions that might contain details about the colors in Figure 1.Let me search for more embedded images near Figure 1 to see if there's a better visual description.Let me look for the actual embedded image description near Figure 1 more carefully. Let me search around line 408 where Im5.png is mentioned.Let me search for more specific information about the visual elements of Figure 1. Let me look for descriptions that might mention colors or boxes in the figure.Let me look for image descriptions near the figure that might contain more details about the visual layout. Let me read more around the figure area to find additional embedded images or descriptions.Based on Figure 1 in the document, I can see the \"Functions\" column which shows the program execution functions. Looking at the structure, I can see:\n- S1: Verify \u2192 Fact Checker\n- S2: Question \u2192 QA Model \n- S3: Verify \u2192 Fact Checker\n- S4: Predict \u2192 Logical Reasoner\n\nHowever, I need to find which ones have blue backgrounds. Let me search more carefully for visual information about the figure or look for any other embedded images that might be Figure 1 itself.From Figure 1, I can see the functions column showing:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, the text-based XML doesn't explicitly specify which ones have blue backgrounds. Let me search the document for any other references to colors or visual styling in Figure 1, or look for the actual Figure 1 image description more thoroughly. Let me search for \"Fact Checker\", \"QA Model\", \"Logical Reasoner\" to understand the context better.Based on my analysis of Figure 1 from the document, I can see the \"Functions\" column contains:\n- Fact Checker (appears for S1 and S3 - Verify operations)\n- QA Model (appears for S2 - Question operation)\n- Logical Reasoner (appears for S4 - Predict operation)\n\nGiven the typical visualization patterns in academic papers where functions are color-coded, and looking at the structure where the three main function types are displayed, the functions with blue background would likely be the distinct function types shown in the \"Functions\" column. Without explicit color information in the XML, but based on the structure and common visualization practices, the three function types are: \"Fact Checker\", \"Logical Reasoner\", and \"QA Model\".\n\nIn alphabetical order, these would be:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 80155, "error": null, "extra": {"thread_id": 1941, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6bd06724d924284a60194ba98ad4765", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 579, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to find how many prompt examples are shown in the paper's main body (not appendices).\n\nLet me search through the document for prompt examples:\n\n1. **Figure 1** (page 2): Shows a complete example of the PROGRAMFC model with a reasoning program for the claim about James Cameron and Interstellar. This includes the full program structure.\n\n2. **Figure 2** (page 5): Shows \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the .\" This shows one example of a reasoning program for the James Cameron/Interstellar claim.\n\n3. **Figure 3** (page 5): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book).\n\n4. **Section 3.4** (page 4): Shows prompt templates for the QUESTION, VERIFY, and PREDICT functions in text form.\n\nLooking at Figure 2 specifically, it shows one complete example of a prompt with the reasoning program for the James Cameron claim.\n\nIn the main body (before Appendices which start at page 14), the primary prompt example shown is in Figure 2, which displays one reasoning program example. The text also mentions using \"K = 20\" in-context examples (page 4), but these full 20 examples are not shown in the main body - they are referenced to be in Appendix D.\n\nThe main body shows **1** complete prompt example explicitly in Figure 2.\n\nAnswer: 1", "citations": [], "input_tokens": 65062, "output_tokens": 356, "cost_micros": 200526, "latency_ms": 28827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLet me go through the paper systematically:\n\n1. **Figure 2** (page 6985): Shows one prompt template for Codex used to generate reasoning programs. This includes:\n - Task instruction\n - One example showing a claim about James Cameron and Interstellar\n - Mentions \"more in-context examples here\"\n - Input claim placeholder\n This counts as **1 prompt example**.\n\n2. **Figure 3** (page 6985): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book). These show prompt templates for FLAN-T5 with question-answering format. This counts as **1 prompt example** (showing variations for different settings).\n\n3. **Section 3.4** (page 6984): Shows the VERIFY module prompt:\n ```\n EVIDENCE\n Q: Is it true that CLAIM ? True or False? The answer is:\n ```\n This counts as **1 prompt example**.\n\n4. The paper mentions prompts for baselines in Appendix A, but those are not in the main body.\n\n5. The detailed prompts in Appendix D and E are explicitly in appendices, not the main body.\n\nLet me recount more carefully:\n- Figure 2: 1 prompt template\n- Figure 3: 3 variations of the same prompt structure (but essentially 1 prompt design)\n- Section 3.4 VERIFY: 1 prompt\n\nLooking at the question more carefully - it asks for \"prompt examples\" shown in the main body. The main body ends before the appendices.\n\nIn the main body:\n1. Figure 2 shows one complete prompt example\n2. Figure 3 shows prompt variations (3 settings)\n3. Section 3.4 shows the VERIFY prompt\n\nIf counting each distinct prompt shown: Figure 2 (1), Figure 3 (3 variations but let me check if they're separate examples), Section 3.4 (1).\n\nActually, looking at Figure 3 more carefully, it shows one prompt design with three different implementations for three settings. The question", "citations": [], "input_tokens": 26867, "output_tokens": 512, "cost_micros": 88281, "latency_ms": 15515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in the appendices).\n\nLooking through the paper:\n\n1. **Figure 2** (page 6985) shows a prompt template for generating reasoning programs with Codex. This includes:\n - A task instruction\n - One complete example showing the claim about James Cameron and the director of Interstellar\n - A note saying \"(...more in-context examples here...)\"\n - An input claim placeholder\n\nThis appears to be showing **1 complete example** in the prompt template.\n\n2. **Figure 1** (page 6982) shows an overview diagram with the same example claim about James Cameron, but this is showing the system architecture rather than being a separate prompt example.\n\n3. **Section 3.4** describes the prompt formats for different sub-task functions (QUESTION, VERIFY, PREDICT), but these are described in text format rather than being full examples.\n\n4. **Section 4.4** mentions prompts for closed-book fact-checking and states \"The detailed prompting templates are given in Appendix E\" - so the full examples are in the appendix, not the main body.\n\nIn the main body, I can only count **1** complete prompt example that is actually shown (the one in Figure 2 about James Cameron and Interstellar).\n\nAnswer: 1", "citations": [], "input_tokens": 29889, "output_tokens": 308, "cost_micros": 94287, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the document systematically:\n\n1. **Figure 1**: This shows an overview of the model with what appears to be a simplified example of the reasoning process for the claim about James Cameron and Christopher Nolan. This shows one example of how the program works but not a full prompt template.\n\n2. **Figure 2**: This is explicitly labeled as \"The Codex prompt template used to generate reasoning programs\" and shows:\n - A task instruction\n - One complete in-context example (the James Cameron/Interstellar claim)\n - A note that there are \"more in-context examples here\"\n - A prompt template for the input claim\n\n So this shows **1 complete prompt example** in the template.\n\n3. **Figure 3**: This shows the implementation of the question-answering sub-task function with different prompt formats for different settings (Gold Evidence, Open-book, Closed-book). These are partial prompts for sub-tasks, showing **3 prompt format variations**.\n\n4. In the text around Section 3.4, there are descriptions of prompts:\n - For QUESTION (closed-book): \"Q: QUESTION ? The answer is:\" - **1 prompt**\n - For QUESTION (other settings): \"EVIDENCE Q: QUESTION ? The answer is:\" - **1 prompt**\n - For VERIFY: \"EVIDENCE Q: Is it true that CLAIM ? True or False? The answer is:\" - **1 prompt**\n\nHowever, I need to be careful about what counts as \"shown\" vs just described in text.\n\nLooking more carefully at what is explicitly displayed in figures/boxes in the main body:\n- Figure 1: Shows the example execution but not a full prompt\n- Figure 2: Shows 1 complete prompt example\n- Figure 3: Shows 3 prompt format examples (though these are sub-task prompts)\n\nThe question asks for \"prompt examples\" in the main body. The most prominent and complete prompt example shown is in Figure 2, which displays **1** complete example.\n\nAnswer: 1", "citations": [], "input_tokens": 22276, "output_tokens": 485, "cost_micros": 74103, "latency_ms": 14203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the main body of the paper:\n\n1. **Figure 2** (page 6984): Shows the Codex prompt template used to generate reasoning programs. This shows one prompt example with the claim about James Cameron and Interstellar.\n\n2. **Figure 3** (page 6985): Shows implementation of question-answering sub-task function with different prompt formats for three settings (Gold Evidence, Open-book, Closed-book). This shows prompt examples for the QA function.\n\n3. **Section 3.4** discusses the VERIFY and PREDICT functions with their prompt formats shown inline in the text.\n\nLet me count more carefully:\n\nIn **Figure 2**: This shows 1 complete prompt template example.\n\nIn **Section 3.4** (page 6984):\n- QUESTION function shows prompts for closed-book and other settings (2 prompt formats)\n- VERIFY function shows 1 prompt format\n\nSo in the main body, I can identify:\n- Figure 2: 1 prompt example\n- Section 3.4 for QUESTION: Shows prompt formats but these are templates\n- Section 3.4 for VERIFY: Shows 1 prompt template\n\nActually, looking at Figure 2 more carefully, it shows one example claim with its program within a larger prompt template that includes \"(... more in-context examples here ...)\".\n\nThe question asks for \"prompt examples\" - I should count distinct prompt templates or examples shown.\n\nIn the main body:\n1. Figure 2 - shows the program generation prompt template (1)\n2. Figure 3 - shows QA implementation prompts (multiple formats shown)\n3. Section 3.4 text - shows VERIFY prompt format (1)\n\nBut Figure 2 actually shows a full prompt with one complete example visible.\n\nLooking at this more carefully, Figure 2 shows **one** concrete example in the prompt template.\n\nAnswer: 1", "citations": [], "input_tokens": 29191, "output_tokens": 456, "cost_micros": 94413, "latency_ms": 14032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find how many prompt examples are shown in the paper's main body.Let me continue reading to see the document structure and find prompt examples:Let me search for prompt examples in the document:Now let me look at Figure 2 and Figure 3 which show the prompt templates:Let me search for \"Figure\" to understand how many figures/examples are in the main body:Let me check where the appendix starts to understand what's in the main body vs appendix:Let me look for where the paper sections end and appendix begins:Now let me search for where the appendix starts:Good! So \"## A Implementation Details about the Baselines\" starts the appendix. Let me find the line number:So the appendix starts at line 2301. Now let me search for all figures and tables to count prompt examples in the main body (before line 2301):I can see Figure 2 shows a prompt template with one example shown (the James Cameron / Interstellar example), and it says \"( ... more in-context examples here .. . )\" indicating there are more but not shown. Let me look at Figure 6 which shows an error case:Let me look before this to see the actual example in Figure 6:Good! Figure 6 shows one example. Now let me check if there are any other figures or examples shown in the main body. Let me look for examples in sections discussing the prompts:Now let me look at the Limitations section which might have another example:Good! I found another example in the Limitations section - the \"Aristotle couldn't have used a laptop\" example. Let me now carefully count all prompt/program examples shown in the main body (before line 2301 where Appendix starts):\n\n1. **Figure 1** (line 335-402) - Shows the \"James Cameron and Interstellar director\" example with the full reasoning program\n2. **Figure 2** (line 747-770) - Shows the same \"James Cameron and Interstellar director\" example as part of the prompt template \n3. **Figure 6** (line 1441-1464) - Shows the \"Emery ghost town\" error case example\n4. **Limitations section** (line 1699-1706) - Shows the \"Aristotle couldn't have used a laptop\" example\n\nHowever, I need to be careful - Figure 1 and Figure 2 show the SAME example (James Cameron/Interstellar). So let me count unique examples:\n\n1. James Cameron/Interstellar example (shown in both Figure 1 and Figure 2)\n2. Emery ghost town example (Figure 6)\n3. Aristotle laptop example (Limitations section)\n\nThat's **3 unique prompt/program examples** shown in the main body of the paper.\n\nWait, let me re-read the question: \"How many prompts examples are shown in the paper's main body?\" - this could mean distinct prompt templates or distinct claim examples with programs. Let me verify by looking at what constitutes an example.\n\nLooking back at the figures:\n- Figure 2 explicitly shows a prompt template with ONE example shown (the James Cameron one) plus \"( ... more in-context examples here .. . )\"\n- Figure 6 shows ONE error case example\n- Limitations shows ONE example\n\nSo there are **3 prompt examples** shown in the main body.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 87217, "error": null, "extra": {"thread_id": 1942, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fcb323f039bb45c4bc9d5aa08b03f871", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 646, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at page 6985 (which is page 5 of the document), I can see Figure 2 which shows a prompt template for Codex. The figure contains in-context examples demonstrating the program generation format.\n\nIn the figure, I can see:\n1. One complete example starting with \"# The claim is that Both James Cameron and the director of the film Interstellar were born in Canada.\" with a full program definition.\n2. After that, there is text \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples.\n3. Then \"# The claim is that \" followed by \"def program():\" which is the prompt for the input.\n\nSo there is 1 complete in-context example explicitly shown in full in this figure, with an indication that more examples would be included in the actual prompt.\n\nAnswer: 1", "citations": [], "input_tokens": 65078, "output_tokens": 222, "cost_micros": 198564, "latency_ms": 13453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at Figure 2 on page 6985, which shows \"The Codex prompt template used to generate reasoning programs\", I can see:\n\n1. One complete example about James Cameron and the director of Interstellar (showing the full program with fact_1, Answer_1, fact_2, and label)\n\n2. After that, there's a notation \"( ... more in-context examples here . . . )\" indicating additional examples are included but not shown\n\n3. Then there's the prompt for the input claim\n\nSo the figure explicitly shows **1** complete in-context example in full detail, though it indicates more examples are present in the actual implementation (they're just not displayed in the figure).\n\nAnswer: 1", "citations": [], "input_tokens": 26883, "output_tokens": 187, "cost_micros": 83454, "latency_ms": 7171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at page 6985, I can see Figure 2, which shows \"The Codex prompt template used to generate reasoning programs.\"\n\nThe figure contains:\n1. A task instruction at the top\n2. One complete example showing:\n - A claim: \"Both James Cameron and the director of the film Interstellar were born in Canada.\"\n - A program with:\n - fact_1 = Verify(\"James Cameron was born in Canada.\")\n - Answer_1 = Question(\"Who is the director of the film Interstellar?\")\n - fact_2 = Verify(\"{Answer_1} was born in Canada.\")\n - label = Predict(fact_1 and fact_2)\n3. A note saying \"( ... more in-context examples here .. . )\"\n4. A prompt for the input claim\n\nSo there is **1** complete in-context example fully shown in this figure.\n\nAnswer: 1", "citations": [], "input_tokens": 29905, "output_tokens": 242, "cost_micros": 93345, "latency_ms": 7921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at the document, page 6985 corresponds to the section showing Figure 2, which displays the Codex prompt template used to generate reasoning programs.\n\nIn Figure 2, I can see:\n1. A task instruction at the top explaining the three functions that can be called\n2. One complete example showing:\n - The claim about \"Both James Cameron and the director of the film Interstellar were born in Canada\"\n - The complete program with multiple steps (fact_1, Answer_1, fact_2, label)\n3. A note saying \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples are present but not shown\n4. The input prompt template at the bottom\n\nThe figure shows 1 complete in-context example explicitly displayed in full, with an indication that more examples exist but are not shown in the figure.\n\nAnswer: 1", "citations": [], "input_tokens": 22292, "output_tokens": 222, "cost_micros": 70206, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the figure on page 6985 (Figure 2: The Codex prompt template), I can see the prompt template structure.\n\nThe template shows:\n1. A task instruction at the top\n2. One complete example showing a claim about James Cameron and the Interstellar director, with its full program implementation\n3. A note \"(... more in-context examples here ...)\" indicating additional examples would go here\n4. The input claim placeholder at the bottom\n\nSo there is **1** complete in-context example explicitly shown in the figure, though the text indicates more examples would be included in the actual prompt (the paper mentions using K=20 examples total).\n\nAnswer: 1", "citations": [], "input_tokens": 29207, "output_tokens": 151, "cost_micros": 89886, "latency_ms": 7562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 6985 and count the complete in-context examples shown.Let me search for page 6985 in the document:Let me read through the document to find the page number references:I can see the document is from ACL conference pages 6981-7004. Let me search for figures and read the content more systematically:Now let me look at Figure 2 which seems to be the prompt template. Let me read around line 769 where Figure 2 is mentioned:I can see Figure 2 template ends around line 770. Now I need to find what page this corresponds to. Let me check if there are any page markers. Let me look for the beginning of the document to understand the structure:Good! I can see page numbers now. Line 327 shows \"\". Now let me search for page 6985:Great! Page 6985 starts at line 902. Let me read the content around that line to find the figure at the top of the page:I can see that at the top of page 6985 (starting at line 902), there's a table (Table 1), not a figure. Let me continue reading to see if there's a figure further down on this page:I see Figure 4 is on page 6986 (line 1108). Let me look back more carefully. The question asks about the figure at the TOP of page 6985. Let me check if there's a figure that spans from page 6984 to 6985, or if I need to look more carefully at what's immediately after the page break at line 902:So at the top of page 6985 (line 902-906), there's a Table 1, not a figure. Let me check what's on page 6984 to see if there's a figure that might be spanning or near the top of 6985:Let me read around line 744 to see what's on page 6984:I can see that Figure 2 is on page 6984 (lines 747-769), and it shows the Codex prompt template. Looking at the content of Figure 2, I can see:\n\n- Line 747-750: Task instruction\n- Lines 752-762: One complete in-context example showing the claim about James Cameron and the director of Interstellar\n- Line 764: \"( ... more in-context examples here .. . )\" - indicating there are more examples\n- Lines 766-767: The prompt for the input claim\n\nThe figure caption at line 769 says \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the .\"\n\nHowever, I see only ONE complete example shown explicitly (lines 752-762), and then it says \"more in-context examples here\" without showing them.\n\nWait, let me re-read the question. It asks about \"the figure at the top of page 6985\". Let me check if Figure 2 actually spans to page 6985 or if there's another figure. Looking at the structure:\n- Page 6984 starts at line 744\n- Figure 2 content is at lines 747-769\n- Page 6985 starts at line 902\n\nSo Figure 2 is entirely on page 6984, not 6985. At the top of page 6985 (line 902-906) there's Table 1, not a figure. Let me search more carefully for any figures that might be between these pages or check if I'm missing something:Wait, I think I need to reconsider. Let me look at the actual visual structure. Lines 747-769 show Figure 2 which is on page 6984. Let me check if the complete Figure 2 example I see is showing all the in-context examples or if there's more detail I should look at. Let me read the full Figure 2 content more carefully:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 91078, "error": null, "extra": {"thread_id": 1943, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_479f734c9e744aafac61b60b60655bab", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 680, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Reasoning program evaluation for incorrectly-predicted examples from each hop length in HOVER\":\n\nFor semantic errors:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 65070, "output_tokens": 105, "cost_micros": 196785, "latency_ms": 20235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs across HOVER 2-hop, 3-hop, and 4-hop claims:\n\nFor semantic errors, the three types and their totals are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 26875, "output_tokens": 120, "cost_micros": 82425, "latency_ms": 4672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, which shows the error analysis for reasoning programs on the HOVER dataset broken down by hop length (2-hop, 3-hop, 4-hop):\n\nThe three types of semantic errors are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29897, "output_tokens": 116, "cost_micros": 91431, "latency_ms": 5218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs on the HOVER dataset, I need to examine the semantic error breakdown across the three hop types (2-hop, 3-hop, and 4-hop).\n\nThe semantic errors are broken down into three categories:\n- Token: 8% (2-hop) + 20% (3-hop) + 18% (4-hop) = 46% total\n- Structure: 19% (2-hop) + 13% (3-hop) + 57% (4-hop) = 89% total\n- Subtask: 2% (2-hop) + 5% (3-hop) + 2% (4-hop) = 9% total\n\nAnswer: Subtask", "citations": [], "input_tokens": 22284, "output_tokens": 180, "cost_micros": 69552, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 which shows the error analysis for the HOVER dataset across 2-hop, 3-hop, and 4-hop claims:\n\n**Subtask errors:**\n- 2-hop: 2%\n- 3-hop: 5%\n- 4-hop: 2%\n- Total: 9%\n\n**Token errors:**\n- 2-hop: 8%\n- 3-hop: 20%\n- 4-hop: 18%\n- Total: 46%\n\n**Structure errors:**\n- 2-hop: 19%\n- 3-hop: 13%\n- 4-hop: 57%\n- Total: 89%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29199, "output_tokens": 171, "cost_micros": 90162, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about semantic errors in the HOVER dataset.Let me search for information about semantic errors and HOVER dataset in the document:Let me search for tables or figures that show error analysis:Now let me read the section with Table 2 that shows the error analysis:Perfect! I found Table 2 which shows the error analysis for the HOVER dataset. Let me examine the semantic error types and their percentages:\n\nFrom the table, the semantic errors are broken down into three types:\n- Token: 8% (2-hop), 20% (3-hop), 18% (4-hop) = Total: 46%\n- Structure: 19% (2-hop), 13% (3-hop), 57% (4-hop) = Total: 89%\n- Subtask: 2% (2-hop), 5% (3-hop), 2% (4-hop) = Total: 9%\n\nThe semantic error type with the lowest total percentages across all HOVER dataset hop lengths is \"Subtask\" with 2% + 5% + 2% = 9%.\n\nAnswer: Subtask", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41280, "error": null, "extra": {"thread_id": 1944, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_964d1076448248edb0a0c213d4b3aab0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 on page 7, I can see the retrieval recall@10 comparison:\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nFEVEROUS-S has the highest ProgramFC retrieval recall@10 at 85.65.\n\nNow I need to find the InstructGPT Self-Ask performance on FEVEROUS-S in the closed-book setting.\n\nLooking at Table 3 on page 8, which shows closed-book setting results:\n- InstructGPT - Self-Ask on FEVEROUS: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 65104, "output_tokens": 223, "cost_micros": 198657, "latency_ms": 22157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. What is the InstructGPT Self-Ask performance on that dataset in the closed-book setting\n\nFirst, let me look at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ProgramFC = 77.13\n- HOVER (3-hop): ProgramFC = 73.18\n- HOVER (4-hop): ProgramFC = 85.65\n- FEVEROUS-S: ProgramFC = 76.25\n\nThe highest ProgramFC retrieval recall@10 is 85.65 for HOVER (4-hop).\n\nNow, I need to find the InstructGPT Self-Ask performance on HOVER 4-hop in the closed-book setting from Table 3:\n\nLooking at Table 3 (Closed-book setting):\n- InstructGPT - Self-Ask on HOVER 4-hop: 52.45\n\nHowever, I notice the column headers are \"2-hop\", \"HOVER 3-hop\", \"4-hop\", and \"FEVEROUS\". The 4-hop column shows 52.45 for Self-Ask.\n\nAnswer: 52.5", "citations": [], "input_tokens": 26909, "output_tokens": 292, "cost_micros": 85107, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ~73.18\n- HOVER (3-hop): ~77.13\n- HOVER (4-hop): ~85.65\n- FEVEROUS-S: ~76.25\n\nThe highest ProgramFC retrieval recall@10 is for HOVER (4-hop) at approximately 85.65.\n\nNow looking at Table 3 (Closed-book setting) for InstructGPT with Self-Ask on HOVER (4-hop):\nThe Self-Ask row shows: 52.45 for HOVER (4-hop)\n\nAnswer: 52.5", "citations": [], "input_tokens": 29931, "output_tokens": 198, "cost_micros": 92763, "latency_ms": 9953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall at 10\n2. The InstructGPT model with Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 (mentioned in the text on page 7), which discusses retrieval performance, I need to find the retrieval recall@10 values. However, the actual figure is not fully detailed in the text.\n\nFrom Table 3 on page 7, I can see closed-book fact-checking results. The table shows performance across HOVER (2-hop, 3-hop, 4-hop) and FEVEROUS datasets.\n\nLooking at Table 3:\n- The models listed include: InstructGPT (with variants Direct, ZS-CoT, CoT, Self-Ask), Codex, FLAN-T5, and ProgramFC\n- Self-Ask row shows: 51.54, 51.47, 52.45, 56.82\n\nThe text mentions that Figure 5 compares retrieval performance, and discusses that \"iterative retrieval guided by the reasoning program yields better results.\" However, without seeing the actual Figure 5 data clearly showing which dataset has the highest recall@10 for ProgramFC, I need to infer from the context.\n\nBased on the text discussing HOVER and FEVEROUS-S datasets, and looking at Table 3's structure with columns for \"2-hop\", \"3-hop\", \"4-hop\", and what appears to be FEVEROUS, the Self-Ask scores are: 51.54, 51.47, 52.45, 56.82.\n\nThe highest value for Self-Ask appears to be 56.82, which corresponds to the FEVEROUS column.\n\nAnswer: 56.8", "citations": [], "input_tokens": 22318, "output_tokens": 404, "cost_micros": 73014, "latency_ms": 14375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 (Retrieval recall@10):\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is FEVEROUS-S at 85.65.\n\nFrom Table 3 (Closed-book setting macro-F1 scores), looking at InstructGPT with Self-Ask on FEVEROUS:\n- Self-Ask: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 29233, "output_tokens": 186, "cost_micros": 90489, "latency_ms": 6452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I'll help you find this information from the document. Let me start by reading the relevant document that was identified as most relevant.Let me continue reading to find information about ProgramFC retrieval recall and InstructGPT performance with Self-Ask.Let me search for \"recall\" to find tables with retrieval metrics:Good! I found information about retrieval recall@10. Let me read the section with Figure 5 and the surrounding content:Perfect! I can see from Figure 5 that the ProgramFC retrieval recall@10 values for different datasets are:\n- HOVER (2-hop): 73.18\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 51.33\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is **85.65** for **FEVEROUS-S**.\n\nNow I need to find the performance of InstructGPT with Self-Ask in the closed-book setting on FEVEROUS-S. Let me search for tables with these results:Let me read Table 1 which shows performance on FEVEROUS-S:This table shows results but doesn't specifically mention InstructGPT with Self-Ask in a closed-book setting. Let me search for \"closed-book\" or \"closed book\":Let me read the section about closed-book setting evaluation:Perfect! I found Table 3 which shows the closed-book setting results. Looking at the table:\n\nFor **FEVEROUS** (which corresponds to FEVEROUS-S in this context):\n- InstructGPT with Self-Ask: **56.82**\n\nLet me verify this is indeed FEVEROUS-S by checking if they use the same notation:The table says \"FEVEROUS\" but earlier in the document they established that they use FEVEROUS-S (the sentence-only subset). Let me verify by checking the dataset description section again:Yes, they use FEVEROUS-S throughout the paper. In Table 3, \"FEVEROUS\" refers to FEVEROUS-S. \n\nNow let me confirm the answer:\n1. From Figure 5, the highest ProgramFC retrieval recall@10 is **85.65** for FEVEROUS-S\n2. From Table 3, InstructGPT with Self-Ask in the closed-book setting on FEVEROUS achieves **56.82**\n\nBut let me double-check this value is actually 56.82 by reading that specific cell again:Perfect! Confirmed. The row for \"Self-Ask\" shows:\n- HOVER 2-hop: 51.54\n- HOVER 3-hop: 51.47\n- HOVER 4-hop: 52.45\n- FEVEROUS: 56.82\n\nThe answer is **56.82**, which in 1 decimal format is **56.8**.\n\nWait, let me reconsider. The question asks for \"1 decimal\" which means I should provide it with 1 decimal place. 56.82 rounded to 1 decimal place would be 56.8.\n\nHowever, looking at the exact value in the table, it shows 56.82. Since they're asking for float format with 1 decimal, I should present it as 56.8.\n\nAnswer: 56.8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84281, "error": null, "extra": {"thread_id": 1945, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f2cf8d23b121431e8f0ffe2a73635d4e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 567, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to FLAN-T5-small.\n\nLooking at Figure 4 on page 7, which shows \"F1 score for fact-checking with gold evidence using FLAN-T5 (blue line) and PROGRAMFC (green line) for language models of increasing sizes.\"\n\nThe figure shows three graphs for:\n1. HOVER (2-hop)\n2. HOVER (3-hop)\n3. HOVER (4-hop)\n\nFor FLAN-T5-small (80M), I can read the values:\n- HOVER (2-hop): FLAN-T5 is around 47.75, ProgramFC is around 64.35\n- HOVER (3-hop): FLAN-T5 is around 49.29, ProgramFC is around 62.23\n- HOVER (4-hop): FLAN-T5 is around 48.59, ProgramFC is around 62.46\n\nThe differences (ProgramFC - FLAN-T5) are:\n- HOVER (2-hop): 64.35 - 47.75 = 16.6\n- HOVER (4-hop): 62.46 - 48.59 = 13.9\n- HOVER (3-hop): 62.23 - 49.29 = 12.9\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 65112, "output_tokens": 350, "cost_micros": 200586, "latency_ms": 12921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4, which shows F1 scores for fact-checking with gold evidence using FLAN-T5 and PROGRAMFC for different model sizes including FLAN-T5-small (80M):\n\nFor HOVER 2-hop:\n- FLAN-T5-small: approximately 47.75\n- ProgramFC with FLAN-T5-small: approximately 64.35\n- Difference: 64.35 - 47.75 = 16.6\n\nFor HOVER 3-hop:\n- FLAN-T5-small: approximately 48.59\n- ProgramFC with FLAN-T5-small: approximately 61.36\n- Difference: 61.36 - 48.59 = 12.8\n\nFor HOVER 4-hop:\n- FLAN-T5-small: approximately 49.29\n- ProgramFC with FLAN-T5-small: approximately 63.05\n- Difference: 63.05 - 49.29 = 13.8\n\nSorting in descending order: 16.6, 13.8, 12.8\n\nAnswer: 16.6, 13.8, 12.8", "citations": [], "input_tokens": 26917, "output_tokens": 288, "cost_micros": 85071, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 13 8, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to find the F1 score differences between ProgramFC and FLAN-T5 using FLAN-T5-small (80M) for gold evidence setting across the three HOVER datasets.\n\nFrom Figure 4:\n- HOVER (2-hop): ProgramFC \u2248 62.23, FLAN-T5 \u2248 47.75, difference = 14.5\n- HOVER (3-hop): ProgramFC \u2248 61.36, FLAN-T5 \u2248 48.59, difference = 12.8\n- HOVER (4-hop): ProgramFC \u2248 63.39, FLAN-T5 \u2248 49.29, difference = 14.1\n\nSorting in descending order:\n\nAnswer: 14.5, 14.1, 12.8", "citations": [], "input_tokens": 29939, "output_tokens": 219, "cost_micros": 93102, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 14 1, 14 5", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence...\n\nIn Figure 4 (Section 4.2), the document discusses comparing performance across different model sizes, including FLAN-T5-small. The text mentions:\n\n\"Our results show that the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nHowever, Figure 4 is referenced but the actual figure with specific F1 scores is not fully visible in the provided text. The document shows results for different hop counts (2-hop, 3-hop, 4-hop) on HOVER dataset in the gold evidence setting.\n\nFrom Table 1, I can see overall results but these are for FLAN-T5-XXL, not FLAN-T5-small.\n\nThe text in Section 4.2 discusses Figure 4 which would contain the specific comparisons with FLAN-T5-small, but the exact numerical values for the F1 score differences are not explicitly stated in the text portions provided.\n\nWithout access to the actual Figure 4 with the specific numerical values, I cannot provide the exact F1 scores requested.\n\nAnswer: Unable to determine from provided text", "citations": [], "input_tokens": 22326, "output_tokens": 321, "cost_micros": 71793, "latency_ms": 9483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided text", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence.\n\nFrom Figure 4, I can extract the following F1 scores for ProgramFC (green line) with FLAN-T5-small (80M):\n- HOVER 2-hop: 64.35\n- HOVER 3-hop: 62.23\n- HOVER 4-hop: 62.46\n\nAnd for FLAN-T5-small (80M):\n- HOVER 2-hop: 47.75\n- HOVER 3-hop: 49.29\n- HOVER 4-hop: 48.59\n\nThe differences (ProgramFC - FLAN-T5-small):\n- HOVER 2-hop: 64.35 - 47.75 = 16.6\n- HOVER 4-hop: 62.46 - 48.59 = 13.9\n- HOVER 3-hop: 62.23 - 49.29 = 12.9\n\nSorting in descending order with one decimal place:\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 29241, "output_tokens": 279, "cost_micros": 91908, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to Flan-T5-small.Let me search for tables containing F1 scores for ProgramFC with gold evidence and Flan-T5-small:Let me read the section with Figure 4 and look for tables with F1 scores:Let me search for tables with gold evidence:Let me read Table 1 which contains F1 scores with gold evidence:Now I have the table data. I can see:\n- FLAN-T5 Gold scores: 73.69 (HOVER 2-hop), 65.66 (HOVER 3-hop), 58.08 (HOVER 4-hop), 90.81 (FEVEROUS-S)\n- ProgramFC (N=5) Gold scores: 75.65 (HOVER 2-hop), 68.48 (HOVER 3-hop), 66.75 (HOVER 4-hop), 92.69 (FEVEROUS-S)\n\nHowever, I need to check if these are specifically for Flan-T5-small. Let me look at Figure 4 data more carefully, as it mentions different sizes of Flan-T5:Looking at the data structure in Figure 4, I need to identify which values correspond to Flan-T5-small (80M) for ProgramFC. The chart shows alternating values. Let me read more of this section:Based on Figure 4 data, I can see there are three charts (HOVER 2-hop, 3-hop, 4-hop) with values at different model sizes (80M, 250M, 780M, 3B, 11B). \n\nLooking at the structure:\n- The first chart (HOVER 2-hop) has values from lines 1130-1148\n- The second chart (HOVER 3-hop) has values from lines 1152-1188 \n- The third chart (HOVER 4-hop) has values from lines 1192-1210\n\nFor 80M (FLAN-T5-small), looking at the ProgramFC values (green line):\n- HOVER 2-hop: 75.65 (line 1132)\n- HOVER 3-hop: 68.48 (line 1156)\n- HOVER 4-hop: 58.08 (line 1198)\n\nWait, let me verify this more carefully by looking at the pattern. The chart shows alternating FLAN-T5 and ProgramFC values. Let me trace through more carefully:\n\nFor HOVER 2-hop at 80M:\n- Lines around 1130-1135 show values near 76-77\n- This corresponds to 80M (first position)\n\nFor HOVER 3-hop at 80M:\n- Starting around line 1152-1158\n- Values around 67-69\n\nFor HOVER 4-hop at 80M:\n- Starting around line 1192-1198\n- Values around 56-58\n\nLooking at the caption again: \"FLAN-T5 (blue line) and PROGRAMFC (green line)\"\n\nFrom the table structure and comparing with Table 1 values (FLAN-T5 has 73.69 for 2-hop, ProgramFC N=5 has 75.65), I can deduce:\n\nFor 80M (FLAN-T5-small) with gold evidence, ProgramFC scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48 \n- HOVER 4-hop: 58.08\n\nIn descending order: 75.65, 68.48, 58.08\n\nHowever, I should check if there are more datasets. Let me verify if FEVEROUS-S is included in this figure:Figure 4 only shows HOVER 2-hop, 3-hop, and 4-hop. Let me check if there's a figure or table specifically showing FEVEROUS-S scores broken down by model size. But based on Table 1, the FEVEROUS-S score for ProgramFC (N=5) with gold evidence is 92.69, and this appears to be using FLAN-T5-XXL based on the context.\n\nLet me search for more information about which model size was used for the main results:Now I need to carefully parse Figure 4 data. The figure shows three charts for HOVER 2-hop, 3-hop, and 4-hop. Looking at the data structure, I see values followed by model sizes (80M, 250M, 780M, 3B, 11B) repeated three times.\n\nLet me trace through the data more systematically. From line 1130 onwards, reading in groups:\n\nHOVER 2-hop chart appears to show pairs of values for each model size (FLAN-T5 then ProgramFC based on the caption mentioning blue and green lines):\n- At 80M: 76.11, 75.65\n- Then 77.62\n\nLet me look for a clearer pattern by examining all the values more carefully:Good! I found key information at lines 1385-1388: \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nThis confirms that Figure 4 shows results using different-sized FLAN-T5 models. Now let me parse the Figure 4 data correctly. Based on the structure and comparing with Table 1:\n\nFrom Table 1, I know:\n- FLAN-T5 gold evidence scores: 73.69 (2-hop), 65.66 (3-hop), 58.08 (4-hop)\n- ProgramFC (N=5) gold evidence scores: 75.65 (2-hop), 68.48 (3-hop), 66.75 (4-hop)\n\nThese match values in Figure 4 data. Now looking at the chart data structure more carefully, the values appear to be organized by model size. Let me trace the 80M (FLAN-T5-small) values for ProgramFC:\n\nLooking at lines 1130-1246, the pattern seems to show data points for both lines (FLAN-T5 and ProgramFC) across multiple model sizes for three different charts.\n\nFrom the visible data and comparing with Table 1 values at line 1148 (73.69) which is FLAN-T5 for HOVER 2-hop, and line 1132 (75.65) which matches ProgramFC (N=5) for HOVER 2-hop:\n\nFor FLAN-T5-small (80M) ProgramFC with gold evidence:\n- HOVER 2-hop: 72.56 (line 1140)\n- HOVER 3-hop: 68.55 (line 1154) \n- HOVER 4-hop: 63.39 (line 1194)\n\nWait, let me reconsider. Looking at the x-axis labels (80M, 250M, 780M, 3B, 11B) appearing three times (once per chart), and the fact that there are two lines per chart (FLAN-T5 and ProgramFC), I need to identify which values correspond to which.\n\nLet me look for clearer structure or additional tables:Figure 4 only shows HOVER datasets (2-hop, 3-hop, 4-hop), not FEVEROUS-S. Let me carefully re-parse the figure data. \n\nLooking at the chart data structure: there are 3 separate line charts, each showing F1 scores for 5 different model sizes. Each chart has 2 lines (FLAN-T5 and ProgramFC).\n\nThe data flows:\n- Lines 1130-1148: HOVER 2-hop chart data\n- Lines 1152-1188: HOVER 3-hop chart data \n- Lines 1192-1210: HOVER 4-hop chart data\n\nFor each chart, there should be approximately 10 data points (2 lines \u00d7 5 model sizes).\n\nLet me map this out for HOVER 2-hop (starting line 1130):\nLooking at the pattern and knowing from Table 1 that ProgramFC (N=5) gets 75.65 for 2-hop (line 1132), this should be for the largest model (11B based on Table 1 context).\n\nActually, let me reconsider the entire approach. The question asks for F1 scores for \"ProgramFC on different datasets with gold evidence in relation to Flan-T5-small\". \n\nThis might mean: the F1 scores when ProgramFC uses Flan-T5-small as its sub-task solver, across different datasets.\n\nFrom Figure 4 caption and the data structure, for 80M (FLAN-T5-small) as the sub-task solver in ProgramFC:\n\nLooking at line 1140 (72.56), 1154 (68.55), 1194 (63.39) - these appear to be the first ProgramFC values in each chart.\n\nBut I need to verify this interpretation. Let me check if there are explicit values stated in the text:From the text at lines 1368-1388, it discusses Figure 4 and mentions that \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers\" achieves certain performance. This confirms Figure 4 shows ProgramFC results when using different sizes of FLAN-T5 as sub-task solvers.\n\nNow I need to carefully extract the FLAN-T5-small (80M) ProgramFC values from Figure 4 data. Let me re-examine the data structure.\n\nBased on the chart showing 5 model sizes (80M, 250M, 780M, 3B, 11B) and 2 lines (FLAN-T5 and ProgramFC), there should be interleaved or grouped values.\n\nLooking at lines 1128-1248, I see numeric values followed by model size labels. The pattern suggests the chart data is organized with Y-axis values first, then X-axis labels.\n\nFor a line chart with 5 points on each of 2 lines, I'd expect 10 Y values per chart. Let me count the values before the first \"80M\" marker at line 1218:\n\nFrom 1130-1216, there appear to be values organized in groups. \n\nLet me try a different approach - look at the actual layout. The values from 1130-1148 before seeing the next section, and knowing Table 1 shows ProgramFC (N=5) has 75.65 for HOVER 2-hop with presumably the larger model:\n\nFirst chart (HOVER 2-hop) - 5 model sizes, 2 lines = 10 points\nLet me identify based on comparing with known Table 1 values. Line 1148 shows 73.69, which exactly matches FLAN-T5 HOVER 2-hop gold from Table 1. This suggests this is for the larger model (XXL/11B).\n\nLooking backward from there and forward, the organization seems to show alternating or separate line data. Since line charts typically show both lines across all X values, let me map:\n\nValues around lines 1130-1148 for HOVER 2-hop\nValues around lines 1152-1188 for HOVER 3-hop\nValues around lines 1192-1210 for HOVER 4-hop\n\nFor 80M (first model size), I need the first pair of values (FLAN-T5 and ProgramFC) for each chart.\n\nGiven the complexity and my uncertainty about the exact data structure, let me look for any other tables or explicit statements:Looking at this continuous stream helps! Let me parse it:\n\n\"80 76.11 75.65 77.62 80 80 72.56 77.07 70 71.69 73.69 70 67.88 68.55 68.48 69.56 70 68.37 68.56 66.75 68.18 64.35 68.24 62.23 66.89 62.46 60 60 63.05 65.07 65.66 60 61.36 63.39 56.58 58.08 50 50 50 47.75 49.29 48.59 40 40 40 80M 250M 780M 3...\"\n\nThe \"80\" values appear to be Y-axis scale markers. Let me focus on the actual data values and model sizes.\n\nParsing more carefully:\n- After the initial scale markers, values like 76.11, 75.65, 77.62 appear\n- Then 72.56, 77.07\n- Then 71.69, 73.69\n- Etc.\n\nKnowing from Table 1 that:\n- FLAN-T5 HOVER 2-hop gold = 73.69 \n- ProgramFC (N=5) HOVER 2-hop gold = 75.65\n\nI can see 73.69 and 75.65 in the data stream. These are likely for the XXL model since Table 1 results use the larger model.\n\nIf the data is organized as alternating FLAN-T5/ProgramFC for each model size from 80M to 11B, then:\n- First pair (80M): 76.11 (FLAN-T5), 75.65 (ProgramFC)? But 75.65 matches Table 1 ProgramFC which should be for the larger model...\n\nThis is confusing. Let me try another interpretation: perhaps all FLAN-T5 values come first, then all ProgramFC values for each chart.\n\nOr perhaps the values are arranged as: HOVER 2-hop FLAN-T5 (all 5 sizes), HOVER 2-hop ProgramFC (all 5 sizes), then HOVER 3-hop data, etc.\n\nWithout seeing the actual visual chart, this is challenging. Let me look for any other references or tables that might clarify:From the stream, 68.48 from Table 1 (ProgramFC N=5 HOVER 3-hop gold) appears right after 68.55.\n\nLet me hypothesize: if the chart shows both lines across all model sizes, and the data is organized left-to-right, then for each X position (model size) moving left to right, we have interleaved FLAN-T5 and ProgramFC Y values.\n\nFor HOVER 2-hop at 80M (first model size):\n- Looking at the early values: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69...\n\nOne pattern could be: pairs of (FLAN-T5, ProgramFC) for 80M, then for 250M, etc.\n- 80M: (76.11, 75.65)? But this doesn't align with Table 1 where 75.65 is the high-model result...\n\nOr: all 5 FLAN-T5 points, then all 5 ProgramFC points.\n\nLet me try: \nHOVER 2-hop FLAN-T5: 76.11, 77.62, 72.56, 71.69, 73.69 (for 80M, 250M, 780M, 3B, 11B)\nHOVER 2-hop ProgramFC: 75.65, 77.07, ... (for 80M, 250M, 780M, 3B, 11B)\n\nBut wait, 73.69 matches Table 1 FLAN-T5 which should be for 11B based on Table 1 being the best model. So if 73.69 is the 5th FLAN-T5 value (11B), then:\nHOVER 2-hop FLAN-T5: 76.11 (80M), 77.62 (250M), 72.56 (780M), 71.69 (3B), 73.69 (11B)\n\nThat doesn't make sense as performance should generally increase with model size, but we see 76.11 \u2192 77.62 \u2192 72.56 which decreases.\n\nLet me reconsider: perhaps the visual layout in the PDF is organized differently than the XML text extraction shows. The values might not be in simple left-to-right order.\n\nGiven the time constraints and complexity, let me make my best educated guess based on patterns:\n\nLooking at Table 1 reference values and Figure 4 caption mentioning these are results across different model sizes, and considering that:\n1. Figure 4 shows HOVER 2-hop, 3-hop, 4-hop only (not FEVEROUS-S)\n2. The question asks for ProgramFC scores with gold evidence for Flan-T5-small across different datasets\n\nThe likely answer based on extracting what appear to be the first ProgramFC values in each section that would correspond to the smallest model (80M):\n- HOVER 2-hop: 72.56\n- HOVER 3-hop: 68.55\n- HOVER 4-hop: 63.39\n\nIn descending order: 72.56 \u2192 68.55 \u2192 63.39\n\nBut let me verify one more time by looking at the complete pattern:Now I see the pattern more clearly! The model size labels (80M, 250M, 780M, 3B, 11B) appear THREE times (lines 1218-1246), once for each of the three charts (HOVER 2-hop, 3-hop, 4-hop).\n\nSo the data structure is:\n- Lines 1130-1148: HOVER 2-hop chart Y-axis values\n- Lines 1152-1188: HOVER 3-hop chart Y-axis values\n- Lines 1192-1210: HOVER 4-hop chart Y-axis values\n- Lines 1218-1246: X-axis labels (model sizes) for all three charts\n\nFor each chart with 2 lines and 5 X-values, I'd expect about 10 Y-values.\n\nHOVER 2-hop (lines 1130-1148): 76.11, 75.65, 77.62, (80, 80 are axis markers), 72.56, 77.07, (70), 71.69, 73.69, (70)\n\nRemoving axis markers: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69\n\nThat's 7 values. For 2 lines with 5 points each = 10 values, so I might be missing some or there's overlap.\n\nLooking at known reference: 73.69 (Table 1 FLAN-T5 2-hop) and 75.65 (Table 1 ProgramFC 2-hop) both appear in this section.\n\nIf arranged as: FLAN-T5 first 5 values, then ProgramFC 5 values:\nFLAN-T5: 76.11 (80M), 75.65 (250M), 77.62 (780M), 72.56 (3B), 77.07 (11B)? \nBut 73.69 should be FLAN-T5 and it's not in this sequence...\n\nAlternative: interleaved pairs:\n- (76.11, 75.65) = (FLAN-T5, ProgramFC) at 80M\n- (77.62, 72.56) = (FLAN-T5, ProgramFC) at 250M \n- (77.07, 71.69) = (FLAN-T5, ProgramFC) at 780M\n- (73.69, ???) = (FLAN-T5, ProgramFC) at some size\n\nBut this only gives 4 pairs...\n\nLet me try yet another approach. Reading the values without the axis scale markers (70, 80):\n76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69 for HOVER 2-hop\n\nIf this represents 2 lines of 5 points each minus 3 values (perhaps some overlap at endpoints or data points), let me see if the pattern makes sense as:\n\nLine 1 (FLAN-T5): 71.69, 73.69, 72.56, 77.07, 76.11 (in size order 80M to 11B)? Reverse order?\nLine 2 (ProgramFC): 75.65, 77.62, ..., ..., ... \n\nThis is getting too speculative. Let me use logical reasoning instead:\n\n1. Table 1 shows ProgramFC (N=5) achieves 75.65, 68.48, 66.75 for HOVER 2/3/4-hop with gold evidence\n2. These are the best results, so likely using the largest model (11B/XXL)\n3. Figure 4 shows how performance varies with model size for both FLAN-T5 and ProgramFC\n4. The text states that FLAN-T5-small (80M) ProgramFC can achieve comparable performance to the large model for 4-hop claims\n\nLooking at 4-hop values (lines 1192-1210): 61.36, 63.39, 56.58, 58.08, (50s), 47.75, 49.29, 48.59, (40s)\n\n58.08 appears here and also matches Table 1 FLAN-T5 4-hop gold.\n66.75 (ProgramFC 4-hop from Table 1) does NOT appear in this 4-hop section, which is strange...\n\nUnless Table 1 uses N=5 program aggregation while Figure 4 uses N=1? Let me check:\n\nLooking back at Table 1, there's both \"ProgramFC (N=1)\" and \"ProgramFC (N=5)\". \n\nProgramFC (N=1) gold scores from Table 1:\n- HOVER 2-hop: 74.10\n- HOVER 3-hop: 66.13\n- HOVER 4-hop: 65.69\n\nProgramFC (N=5) gold scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48\n- HOVER 4-hop: 66.75\n\nLet me check if 74.10, 66.13, or 65.69 appear in Figure 4 data... No, they don't appear in the extracted values.\n\nGiven this complexity and the fact that the visual chart layout isn't perfectly captured in the XML extraction, let me make my best inference:\n\nFor FLAN-T5-small (80M) as sub-task solvers in ProgramFC with gold evidence:\n- The first ProgramFC value in each chart section should correspond to 80M\n- Based on positioning and eliminating known FLAN-T5 values from Table 1:\n\nHOVER 2-hop: 72.56\nHOVER 3-hop: 68.55\nHOVER 4-hop: 63.39\n\nIn descending order: 72.6, 68.6, 63.4 (to one decimal place)\n\nAnswer: 72.6, 68.6, 63.4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 174562, "error": null, "extra": {"thread_id": 1946, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b81fd9ccbe6f4839823c9313fb619eb2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2087, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "63 4, 68 6, 72 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 on page 5, I can see the symbolic solvers used for different datasets:\n- Pyke: ProntoQA, ProofWriter\n- Prover9: FOLIO\n- python-constraint: LogicalDeduction\n- Z3: AR-LSAT\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section on page 6:\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAlso, Table 5 in Appendix B on page 14 confirms:\n- FOLIO has a Test Size of 204\n\nAnswer: 204", "citations": [], "input_tokens": 53261, "output_tokens": 253, "cost_micros": 163578, "latency_ms": 13937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProntoQA and ProofWriter use Pyke\n- FOLIO uses Prover9\n- LogicalDeduction uses python-constraint\n- AR-LSAT uses Z3\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 22914, "output_tokens": 223, "cost_micros": 72087, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nFrom Table 1 in the document, I can see that Prover9 is used as the solver for the FOLIO dataset, which is categorized under \"First-Order Logic\" reasoning.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at Table 5 \"Statistics of the logical reasoning datasets\" in Appendix B:\n\nFOLIO:\n- Reasoning: FOL\n- Test Size: 204\n- #Opts: 3\n\nThe document also confirms in the main text (Section 4, Datasets): \"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 26784, "output_tokens": 232, "cost_micros": 83832, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProofWriter uses \"ProntoQA, Pyke\"\n- FOLIO uses \"Prover9\"\n- LogicalDeduction uses \"phn-constraint\"\n- AR-LSAT uses \"Z3\"\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the solutions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 20095, "output_tokens": 232, "cost_micros": 63765, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find which dataset uses Prover9 as the symbolic solver and how many examples are in its test set.\n\nFrom Table 1 (page 3810), I can see that FOLIO uses Prover9 as its symbolic solver under the \"FOL\" (First-Order Logic) problem type.\n\nFrom Table 5 in Appendix B (page 3819), which shows dataset statistics, I can see:\n- FOLIO has a Test Size of 204\n\nThis is also confirmed in the text on page 3811 where it states: \"We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 25913, "output_tokens": 159, "cost_micros": 80124, "latency_ms": 5094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about a dataset that uses Prover9 as the symbolic solver and determine how many examples are used for evaluation.Let me search for \"Prover9\" in the document to find the relevant information:Now let me search for sections that discuss datasets and evaluation:Let me read the section about datasets more carefully. Let me look for \"ProofWriter\" which seems to be mentioned and might use Prover9:Let me search more specifically for information about which dataset uses Prover9:I see \"First-order Logic Prover\" mentioned. Let me search for more specific information about datasets and their properties:Now let me look at the appendix section that might have more details about data statistics and which solver is used for which dataset:Let me search for tables or sections that describe which solver is used for each dataset:Great! I found a reference to Table 1 which summarizes the symbolic solvers used for each dataset. Let me read that table:Perfect! I found Table 1 which shows that **Prover9** is used as the symbolic solver for the **FOLIO** dataset. Now I need to find how many examples from FOLIO are used for evaluation. Let me go back to the section where FOLIO is described:Perfect! I found the answer. According to the document:\n\n1. From Table 1 (lines 846-849): **Prover9** is the symbolic solver used for the **FOLIO** dataset.\n2. From the dataset description (lines 1024-1026): \"We use the entire FOLIO test set for evaluation, consisting of **204 examples**.\"\n\nAnswer: 204", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64250, "error": null, "extra": {"thread_id": 1947, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e89f611945d040b28ca2dcfe99b6b2c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 410, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5 (Page 9):**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\" Netflix shows.\n- The predicted answer is shown as: B\n- No indication of whether this is correct or incorrect is given in the figure.\n\n**Figure 6 (Page 19):**\nThis shows 4 examples with predicted answers:\n\n1. **Example 1** (Wild turkeys): \n - Predicted answer: C \u2192 A\n - The arrow indicates the prediction changed from C to A\n\n2. **Example 2** (Legend of Zelda):\n - Predicted answer: A\n - No indication of correctness shown\n\n3. **Example 3** (Golf tournament):\n - Predicted answer: E \u2192 B\n - The arrow indicates the prediction changed from E to B\n\n4. **Example 4** (Restaurant/Subway):\n - Predicted answer: C \u2192 A\n - The arrow indicates the prediction changed from C to A\n\nLooking at the figure caption: \"Examples of generated symbolic representations and predicted answers. The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThe red and green markings indicate errors in the symbolic formulation, not whether the final answer was correct. The figure shows the symbolic representations had errors that were corrected, but doesn't explicitly state which final answers were correct.\n\nHowever, based on the examples shown, I cannot definitively determine from the information provided how many predictions were actually correct, as the figure focuses on showing the symbolic formulation errors rather than answer correctness.\n\nSince I cannot determine this with certainty from the document, I need to reconsider. The document shows 5 total examples (1 in Figure 5, 4 in Figure 6) but doesn't explicitly mark which final predictions were correct.\n\nAnswer: 0", "citations": [], "input_tokens": 53263, "output_tokens": 443, "cost_micros": 166434, "latency_ms": 15125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "answer_line", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThere is 1 example shown with:\n- Problem about \"Stranger Things\" and \"Black Mirror\"\n- Predicted answer: B\n- No indication that this is incorrect\n\n**Figure 6:**\nThere are 4 examples shown:\n\n**Example 1:** Wild turkeys problem\n- Predicted answer: \u20ac > A (this appears to be garbled text)\n- The arrow pointing to \"A\" suggests the correct answer should be A\n- The red marking indicates this is incorrect\n\n**Example 2:** Legend of Zelda problem\n- Predicted answer: A\n- No red markings indicating error\n- This appears correct\n\n**Example 3:** Golf tournament problem\n- Predicted answer: + + B (garbled)\n- The arrow pointing to \"B\" and red marking indicates this is incorrect\n\n**Example 4:** Restaurant/Subway problem\n- Predicted answer: ( + A (garbled)\n- The arrow pointing to \"A\" and red marking indicates this is incorrect\n\nFrom the caption of Figure 6: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis indicates that Figure 6 shows ERROR cases where Logic-LM made mistakes.\n\nFigure 5 shows a successful case (no red markings).\n\nSo:\n- Figure 5: 1 correct\n- Figure 6: 0 correct (all 4 are error examples)\n\nTotal correct: 1\n\nAnswer: 1", "citations": [], "input_tokens": 22916, "output_tokens": 365, "cost_micros": 74223, "latency_ms": 9516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to check each example in Figure 5 and Figure 6 to see if Logic-LM predicted the correct answer.\n\n**Figure 5:**\n- Problem about wild turkeys and Tom\n- Question: \"Is the following statement true, false, or uncertain? 'Black Mirror' is popular.\"\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: B\n- Looking at the logic: The problem states \"Karen does not download 'Black Mirror'\" and \"If and only if Karen binge-watches a Netflix show, she will download it.\" This means Karen doesn't binge-watch Black Mirror. Since \"If a Netflix show is popular, Karen will binge-watch it,\" this means Black Mirror is NOT popular.\n- So B (False) is correct. \u2713\n\n**Figure 6:**\n\n**Example 1:**\n- About Tom the wild turkey\n- Question: Is Tom an Ocellated wild turkey?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A (marked with ++)\n- Logic: Tom is a wild turkey but NOT Eastern, Osceola, Gould's, Merriam's, or Rio Grande. Since there are only 6 types and Tom is eliminated from 5, he must be Ocellated.\n- A (True) is correct. \u2713\n\n**Example 2:**\n- About Legend of Zelda\n- Question: Is it in the Top 10 list?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A\n- Logic: Legend of Zelda sold more than 1 million copies \u2192 selected into Top 10 list.\n- A (True) is correct. \u2713\n\n**Example 3:**\n- Golf tournament with 5 golfers\n- Question: Which finished third?\n- Options: (A) Rob, (B) Eve, (C) Eli, (D) Amy, (E) Dan\n- Predicted answer: B (marked with =>)\n- But there's an error in the constraints shown: \"Dan > Eve > Dan < Eve\" which is contradictory\n- The problem states: Dan finished 2nd, Dan", "citations": [], "input_tokens": 26786, "output_tokens": 512, "cost_micros": 88038, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\". The predicted answer is B, and there's no indication this is wrong, so I'll assume it's correct: **1 correct**\n\n**Figure 6 (labeled as \"Example Generations of LOGIC-LM\"):**\nThis contains 4 examples:\n\n1. **Example 1** (Wild turkey problem): \n - Predicted answer: C \u2192 A\n - The arrow notation indicates the prediction was C (Uncertain) but should have been A (True)\n - **Incorrect**\n\n2. **Example 2** (Legend of Zelda problem):\n - Predicted answer: A\n - No arrow or correction shown, indicating this is correct\n - **Correct**\n\n3. **Example 3** (Golf tournament problem):\n - Predicted answer: E \u2192 B\n - The arrow indicates the prediction was E (Dan finished third) but should have been B (Eve finished third)\n - **Incorrect**\n\n4. **Example 4** (Restaurant/Subway problem):\n - Predicted answer: C \u2192 A\n - The arrow indicates the prediction was C (Uncertain) but should have been A (True)\n - **Incorrect**\n\n**Total count:**\n- Figure 5: 1 correct\n- Figure 6: 1 correct (Example 2 only)\n\n**Total: 2 correct predictions**\n\nAnswer: 2", "citations": [], "input_tokens": 20097, "output_tokens": 361, "cost_micros": 65706, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many examples in Figure 5 and Figure 6 have Logic-LM predicting the correct answer.\n\n**Figure 5:**\n- Example 1: Predicted answer is B (shown as correct based on the context)\n- Count: 1 correct\n\n**Figure 6:**\n- Example 1: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\n- Example 2: Predicted answer is A (shown as correct, no corrections indicated)\n- Count: 1 correct\n\n- Example 3: Predicted answer changed from E to B. The arrows indicate E was wrong and B is the correction, meaning the final prediction is B (correct).\n- Count: 1 correct\n\n- Example 4: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\nTotal examples with correct predictions: 1 (Figure 5) + 4 (Figure 6) = 5\n\nAnswer: 5", "citations": [], "input_tokens": 25915, "output_tokens": 263, "cost_micros": 81690, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I'll read the document to find Figures 5 and 6 and count how many examples Logic-LM predicted correctly.Let me search for Figure 5 and Figure 6 in the document:Let me search for the actual content around these figures:Now let me look for Figure 6 in the appendix:I need to find the actual content of Figure 6 with the examples. Let me search more carefully:Now let me look back at Figure 5 to get the complete example:Perfect! Now I have both figures. Let me analyze them:\n\n**Figure 5** (lines 1612-1645): Contains 1 example about \"Black Mirror\" and \"Stranger Things\"\n- Predicted answer: B\n- I need to check if this is correct by looking at the problem logic\n\n**Figure 6** (lines 2993-3146): Contains 4 examples:\n1. Example 1 (lines 2994-3018): Wild turkey problem - Predicted answer: A (with ++ A notation, but marked with red X's indicating errors)\n2. Example 2 (lines 3021-3044): Legend of Zelda problem - Predicted answer: A\n3. Example 3 (lines 3047-3109): Golf tournament problem - Predicted answer: B (with => B notation, and red X's indicating errors)\n4. Example 4 (lines 3115-3146): Subway restaurant problem - Predicted answer: A (with <> A notation, and red X's with checkmarks indicating errors)\n\nAccording to the caption at line 3148-3149: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis means Figure 6 shows ERROR CASES where Logic-LM made mistakes. The red X's (\u2612) indicate incorrect segments.\n\nFor Figure 5, I need to verify if the answer is correct. Looking at the logic:\n- \"Black Mirror\" is a Netflix show\n- Karen does not download \"Black Mirror\"\n- If and only if Karen binge-watches a show, she will download it\n- Therefore, Karen does not binge-watch \"Black Mirror\"\n- If a Netflix show is popular, Karen will binge-watch it\n- Since Karen doesn't binge-watch \"Black Mirror\", it is NOT popular\n- Answer B (False) appears to be CORRECT\n\nSo the count is:\n- Figure 5: 1 example, 1 correct (Example about Black Mirror)\n- Figure 6: 4 examples, 1 correct (Example 2 - Legend of Zelda), 3 incorrect (Examples 1, 3, and 4 have red X marks)\n\nTotal correct: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54812, "error": null, "extra": {"thread_id": 1948, "search_space_id": 55, "answer_letter_strategy": "answer_line", "user_message_id": "msg_4ece07b065344ab588935735051340b9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 440, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\" starting on page 14:\n\nThe appendix states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThe sections are:\n- C.1 PrOntoQA Prompts (3 types: Standard, CoT, Logic-LM)\n- C.2 ProofWriter Prompts (3 types: Standard, CoT, Logic-LM)\n- C.3 FOLIO Prompts (3 types: Standard, CoT, Logic-LM)\n- C.4 LogicalDeduction Prompts (3 types: Standard, CoT, Logic-LM)\n- C.5 AR-LSAT Prompts (3 types: Standard, CoT, Logic-LM)\n\nEach subsection (C.1 through C.5) contains 3 prompt types (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM).\n\nSo there are 5 datasets \u00d7 3 prompt types = 15 prompt examples total.\n\nAnswer: 15", "citations": [], "input_tokens": 53247, "output_tokens": 276, "cost_micros": 163881, "latency_ms": 13389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\", I can see it covers prompts for different datasets:\n\n1. **C.1 PrOntoQA Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n2. **C.2 ProofWriter Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n3. **C.3 FOLIO Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n4. **C.4 LogicalDeduction Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n5. **C.5 AR-LSAT Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 22900, "output_tokens": 398, "cost_micros": 74670, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples given in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\":\n\n**C.1 PrOntoQA Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 LogicalDeduction Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompting methods = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 26770, "output_tokens": 323, "cost_micros": 85155, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in the Appendix section of the document.\n\nLooking at the Appendix sections:\n\n**Section C: Prompt Examples**\n\nThe document states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThen it provides:\n\n**C.1 PrOntoQA Prompts:**\n- Standard In-Context Learning (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 Logical Deduction Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count:\n- C.1: 2 examples\n- C.2: 3 examples\n- C.3: 3 examples\n- C.4: 3 examples\n- C.5: 3 examples\n\nTotal: 2 + 3 + 3 + 3 + 3 = 14 examples\n\nAnswer: 14", "citations": [], "input_tokens": 20081, "output_tokens": 407, "cost_micros": 66348, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C (Prompt Examples), I can see it's organized by dataset:\n\n**C.1 PrOntoQA Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.2 ProofWriter Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.3 FOLIO Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.4 LogicalDeduction Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.5 AR-LSAT Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 25899, "output_tokens": 346, "cost_micros": 82887, "latency_ms": 6280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about prompt examples in the Appendix.Let me search for the Appendix section in the document to find information about prompt examples.Now let me read the section with prompt examples to count them. Let me find where this content is located.Let me search for the Appendix C section with prompt examples more directly:Let me continue to search for all the sections to count the prompt examples. Based on what I've seen so far, I need to count the subsections in Appendix C.Perfect! I can see there are 5 subsections in Appendix C:\n- C.1 PrOntoQA Prompts\n- C.2 ProofWriter Prompts \n- C.3 FOLIO Prompts\n- C.4 LogicalDeduction Prompts\n- C.5 AR-LSAT Prompts\n\nEach subsection shows prompts for 3 different methods (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM), and according to the introduction text, each shows \"only a single demonstration for each setting.\"\n\nSo the count is: 5 datasets \u00d7 3 methods = 15 prompt examples in Appendix C.\n\nAnswer: 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1949, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_28321722a0164737934f82e2d1d378ae", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 318, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the performance comparison between Logic-LM and the two baseline models (Standard and CoT) when using GPT-4 as the base language model.\n\nLooking at Table 2, which shows the accuracy results for different models across five datasets, I can see the GPT-4 (gpt-4) columns:\n\n1. **PrOntoQA**: \n - Standard: 77.40\n - CoT: 98.79\n - Logic-LM: 83.20\n - Logic-LM does NOT outperform CoT (98.79 > 83.20), but does outperform Standard\n\n2. **ProofWriter**:\n - Standard: 52.67\n - CoT: 68.11\n - Logic-LM: 79.66\n - Logic-LM outperforms both baselines \u2713\n\n3. **FOLIO**:\n - Standard: 69.11\n - CoT: 70.58\n - Logic-LM: 78.92\n - Logic-LM outperforms both baselines \u2713\n\n4. **LogicalDeduction**:\n - Standard: 71.33\n - CoT: 75.25\n - Logic-LM: 87.63\n - Logic-LM outperforms both baselines \u2713\n\n5. **AR-LSAT**:\n - Standard: 33.33\n - CoT: 35.06\n - Logic-LM: 43.04\n - Logic-LM outperforms both baselines \u2713\n\nLogic-LM outperforms BOTH baseline models in 4 out of 5 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 53272, "output_tokens": 424, "cost_micros": 166176, "latency_ms": 23500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, I need to compare Logic-LM's performance against both Standard and CoT baselines for GPT-4 across all 5 datasets.\n\nFor GPT-4:\n\n1. **PrOntoQA**: \n - Logic-LM: 83.20\n - Standard: 77.40 \u2713\n - CoT: 98.79 \u2717\n - Logic-LM outperforms Standard only\n\n2. **ProofWriter**:\n - Logic-LM: 79.66\n - Standard: 52.67 \u2713\n - CoT: 68.11 \u2713\n - Logic-LM outperforms both\n\n3. **FOLIO**:\n - Logic-LM: 78.92\n - Standard: 69.11 \u2713\n - CoT: 70.58 \u2713\n - Logic-LM outperforms both\n\n4. **LogicalDeduction**:\n - Logic-LM: 87.63\n - Standard: 71.33 \u2713\n - CoT: 75.25 \u2713\n - Logic-LM outperforms both\n\n5. **AR-LSAT**:\n - Logic-LM: 43.04\n - Standard: 33.33 \u2713\n - CoT: 35.06 \u2713\n - Logic-LM outperforms both\n\nLogic-LM outperforms both baseline models in 4 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 22925, "output_tokens": 391, "cost_micros": 74640, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Table 2 to compare Logic-LM's performance against the two baselines (Standard and CoT) when using GPT-4.\n\nFrom Table 2, the GPT-4 results are:\n\n**PrOntoQA:**\n- Standard: 77.40\n- CoT: 98.79\n- Logic-LM: 83.20\n- Logic-LM does NOT outperform both (CoT is better)\n\n**ProofWriter:**\n- Standard: 52.67\n- CoT: 68.11\n- Logic-LM: 79.66\n- Logic-LM outperforms both \u2713\n\n**FOLIO:**\n- Standard: 69.11\n- CoT: 70.58\n- Logic-LM: 78.92\n- Logic-LM outperforms both \u2713\n\n**LogicalDeduction:**\n- Standard: 71.33\n- CoT: 75.25\n- Logic-LM: 87.63\n- Logic-LM outperforms both \u2713\n\n**AR-LSAT:**\n- Standard: 33.33\n- CoT: 35.06\n- Logic-LM: 43.04\n- Logic-LM outperforms both \u2713\n\nLogic-LM outperforms both baseline models on 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 26795, "output_tokens": 332, "cost_micros": 85365, "latency_ms": 7342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2 in the document, I need to compare Logic-LM's performance against Standard LLMs and Chain-of-Thought (CoT) for GPT-4 across all datasets.\n\nFrom Table 2, the GPT-4 results show:\n\n1. **PrOntoQA**: \n - Standard: 99.4%\n - CoT: 84.9%\n - Logic-LM: 100.0%\n - Logic-LM outperforms both \u2713\n\n2. **ProofWriter**:\n - Standard: 87.3%\n - CoT: 73.6%\n - Logic-LM: 99.0%\n - Logic-LM outperforms both \u2713\n\n3. **FOLIO**:\n - Standard: 66.7%\n - CoT: 61.8%\n - Logic-LM: 79.9%\n - Logic-LM outperforms both \u2713\n\n4. **LogicalDeduction**:\n - Standard: 11.3%\n - CoT: 57.7%\n - Logic-LM: 60.0%\n - Logic-LM outperforms both \u2713\n\n5. **AR-LSAT**:\n - Standard: 32.6%\n - CoT: 60.0%\n - Logic-LM: 39.8%\n - Logic-LM does NOT outperform CoT (39.8% < 60.0%) \u2717\n\nLogic-LM outperforms both baseline models in 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 20106, "output_tokens": 440, "cost_micros": 66918, "latency_ms": 8265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2, for GPT-4 (gpt-4):\n\n1. **PrOntoQA**: \n - Standard: 77.40\n - CoT: 98.79\n - Logic-LM: 83.20\n - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**:\n - Standard: 52.67\n - CoT: 68.11\n - Logic-LM: 79.66\n - Logic-LM outperforms both (79.66 > 68.11 > 52.67) \u2713\n\n3. **FOLIO**:\n - Standard: 69.11\n - CoT: 70.58\n - Logic-LM: 78.92\n - Logic-LM outperforms both (78.92 > 70.58 > 69.11) \u2713\n\n4. **LogicalDeduction**:\n - Standard: 71.33\n - CoT: 75.25\n - Logic-LM: 87.63\n - Logic-LM outperforms both (87.63 > 75.25 > 71.33) \u2713\n\n5. **AR-LSAT**:\n - Standard: 33.33\n - CoT: 35.06\n - Logic-LM: 43.04\n - Logic-LM outperforms both (43.04 > 35.06 > 33.33) \u2713\n\nLogic-LM outperforms both baselines on 4 datasets: ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT.\n\nAnswer: 4", "citations": [], "input_tokens": 25924, "output_tokens": 456, "cost_micros": 84612, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about Logic-LM's performance compared to baseline models when using GPT-4.Let me continue reading to find information about GPT-4 performance comparisons.Let me search for tables or sections that compare GPT-4 performance across datasets.Now let me read the table with GPT-4 results to count the datasets where Logic-LM outperforms baselines.Let me look at the table header to understand the structure better.Perfect! Now I have the complete table. Let me analyze the GPT-4 results:\n\nFor GPT-4 (the last 3 columns):\n- Standard, CoT, Logic-LM\n\nThe two baseline models are:\n1. Standard prompting\n2. Chain-of-thought (CoT) prompting\n\nI need to count how many datasets where Logic-LM (without self-refinement) outperforms BOTH baselines when using GPT-4.\n\nLet me check each dataset:\n\n1. **PrOntoQA**: Logic-LM = 83.20, Standard = 77.40, CoT = 98.79\n - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**: Logic-LM = 79.66, Standard = 52.67, CoT = 68.11\n - Logic-LM > Standard (79.66 > 52.67) \u2713\n - Logic-LM > CoT (79.66 > 68.11) \u2713\n - **Outperforms both** \u2713\n\n3. **FOLIO**: Logic-LM = 78.92, Standard = 69.11, CoT = 70.58\n - Logic-LM > Standard (78.92 > 69.11) \u2713\n - Logic-LM > CoT (78.92 > 70.58) \u2713\n - **Outperforms both** \u2713\n\n4. **LogicalDeduction**: Logic-LM = 87.63, Standard = 71.33, CoT = 75.25\n - Logic-LM > Standard (87.63 > 71.33) \u2713\n - Logic-LM > CoT (87.63 > 75.25) \u2713\n - **Outperforms both** \u2713\n\n5. **AR-LSAT**: Logic-LM = 43.04, Standard = 33.33, CoT = 35.06\n - Logic-LM > Standard (43.04 > 33.33) \u2713\n - Logic-LM > CoT (43.04 > 35.06) \u2713\n - **Outperforms both** \u2713\n\nLogic-LM (without self-refinement) outperforms both baseline models on 4 datasets when using GPT-4 as the base language model.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44437, "error": null, "extra": {"thread_id": 1950, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cfca2042672b455980d6e47d98e5f851", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nFrom Table 1 on page 5, I can see that for \"Deductive Reasoning\" problems (which corresponds to the LP - Logic Programming formulation), the solver used is **Pyke**.\n\nThis is further confirmed in Section 3.2 on page 5, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 53262, "output_tokens": 128, "cost_micros": 161706, "latency_ms": 10532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver implements the logic programming module.\n\nIn Table 1 on page 3810, the paper summarizes the symbolic solvers used for different problem types. For \"Deductive Reasoning\" which uses \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 22915, "output_tokens": 137, "cost_micros": 70800, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nIn Table 1 on page 3810, the paper shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems that use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 26785, "output_tokens": 150, "cost_micros": 82605, "latency_ms": 6202, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about the symbolic solver used for logic programming...\n\nIn Table 1 on page 3805, the document shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems (which correspond to the logic programming formulation mentioned in Figure 1), the solver listed is \"ProntoQA, Pyke\".\n\nAdditionally, in Section 3.2 and footnotes, the document references Pyke as the logic programming engine. In Section D (page 3823), it explicitly states: \"For PrOntoQA and ProofWriter, the Pyke logic programming engine returns the inferred value...\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 20096, "output_tokens": 158, "cost_micros": 62658, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver is used for the logic programming module.\n\nFrom Table 1 on page 3810, I can see that for \"Deductive Reasoning\" problems which use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states:\n\n\"**LP System.** For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 25914, "output_tokens": 143, "cost_micros": 79887, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the symbolic solver used for the logic programming module in Figure 1.Let me search for information about Figure 1 and the symbolic solver:Let me read the document content to find references to Figure 1 and the solver:Good! I can see Figure 1 is mentioned. Now let me search for specific mentions of the solver implementation:I can see references to solvers. Let me look at line 711 where it mentions \"logic programming engine\" and around line 823 where Table 1 is mentioned:Perfect! I found Table 1. Let me continue reading to see all the solvers listed:Perfect! I found the information. Looking at Table 1 and the description in section 3.2, I can see that:\n\n- For **Deductive Reasoning** (Logic Programming / LP), the solver used is **Pyke**\n- For **First-Order Logic** (FOL), the solver used is **Prover9**\n- For **Constraint Satisfaction** (CSP), the solver used is **python-constraint**\n- For **Analytical Reasoning** (SAT), the solver used is **Z3**\n\nNow, let me verify which one corresponds to the \"logic programming module\" mentioned in Figure 1. Based on the document:\n\n1. Figure 1 shows the overall LOGIC-LM framework with a \"Symbolic Reasoner\" component\n2. Section 3.2 describes the symbolic reasoner and mentions that for deductive reasoning using the Logic Programming (LP) language, they use **Pyke**\n3. Section 3.2.1 specifically states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nThe logic programming module shown in Figure 1 is implemented using **Pyke**.\n\nAnswer: Pyke", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58280, "error": null, "extra": {"thread_id": 1951, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_708b2982536441eabb41dbb732343ffa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the datasets table (Table 5 on page 14), I can see the following datasets with their number of options:\n\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options\n- AR-LSAT: 5 options\n\nAR-LSAT has 5 options for all questions. Looking at the references and dataset descriptions, AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions\" and the test set has \"231 multiple-choice questions.\"\n\nFrom the paper text: \"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 53272, "output_tokens": 258, "cost_micros": 163686, "latency_ms": 21859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 1 and the dataset descriptions in Section 4:\n\n- **PrOntoQA (2023)**: 2 options (True/False)\n- **ProofWriter (2021)**: 3 options\n- **FOLIO (2022)**: 3 options\n- **LogicalDeduction (2022)**: Variable options (3, 5, or 7 according to Table 5)\n- **AR-LSAT (2022)**: 5 options\n\nFrom the paper text (Section 4):\n\"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnd from Table 5, AR-LSAT has \"#Opts: 5\".\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 22925, "output_tokens": 253, "cost_micros": 72570, "latency_ms": 7828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for datasets proposed in 2022 with 5-option multiple-choice questions:\n\nFrom Table 5 (Dataset Statistics):\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (varies)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions.\"\n- Table 5 confirms AR-LSAT has 5 options (#Opts column)\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 26795, "output_tokens": 198, "cost_micros": 83355, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find datasets that:\n1. Were proposed in 2022\n2. Have all problems as multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the number of options (#Opts) for each dataset:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (variable)\n- AR-LSAT: 5 options\n\nFrom the Datasets section (Section 4), I can find when each dataset was created:\n- PrOntoQA: 2023 (Saparov and He, 2023)\n- ProofWriter: 2021 (Tafjord et al., 2021)\n- FOLIO: 2022 (Han et al., 2022)\n- LogicalDeduction: 2022 (from BigBench - Srivastava et al., 2022)\n- AR-LSAT: 2022 (Zhong et al., 2022)\n\nAmong the 2022 datasets:\n- FOLIO has 3 options\n- LogicalDeduction has variable options (3, 5, or 7)\n- AR-LSAT has 5 options\n\nOnly AR-LSAT consistently has 5 options for all questions.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 20106, "output_tokens": 343, "cost_micros": 65463, "latency_ms": 7766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the \"#Opts\" column which shows the number of options:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3,5,7 options (varying)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT is described as collecting \"all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\"\n- The citation is \"Zhong et al. 2022\"\n\nLooking at the references, AR-LSAT is cited as:\n\"Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, and Nan Duan. 2022. Analytical reasoning of text.\"\n\nAR-LSAT is the only dataset with consistently 5 options and was published in 2022.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 25924, "output_tokens": 307, "cost_micros": 82377, "latency_ms": 8733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about datasets used in the paper, focusing on those proposed in 2022 with multiple-choice questions having 5 options.Let me search for information about the datasets used in this paper, particularly focusing on those from 2022 and their characteristics.I can see references to datasets from 2022. Let me search for information about AR-LSAT and other datasets, particularly focusing on their characteristics regarding multiple-choice questions with 5 options.Let me search for more specific information about the datasets, particularly looking for details about multiple-choice options.Let me read more about each dataset to find their characteristics. Let me look at the section describing LogicalDeduction.Let me search for information about LogicalDeduction specifically and check the appendix for detailed data statistics.Let me search for \"Appendix B\" which was mentioned as containing detailed data statistics.Perfect! I found the information I need in Table 5. Let me verify the details about AR-LSAT:\n\nFrom the table:\n- AR-LSAT has 5 options (#Opts: 5)\n- AR-LSAT was proposed by Zhong et al., 2022\n\nLet me verify that AR-LSAT is specifically about logical reasoning and all problems are multiple-choice.Based on the document:\n\n1. AR-LSAT was proposed by Zhong et al., 2022\n2. It collects all **analytical logic reasoning questions** from the Law School Admission Test\n3. The test set has 231 **multiple-choice questions**\n4. From Table 5 (Appendix B), AR-LSAT has **5 options** (#Opts: 5)\n\nThis matches all the criteria in the question: proposed in 2022, logical reasoning problems, all multiple-choice questions with 5 options.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69453, "error": null, "extra": {"thread_id": 1952, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4c068961fe9145d382e57492c359de62", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 452, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about application periods for different groups of applicants in this undergraduate prospectus.\n\nLooking at page 25 (document page 47), I can see a table titled \"APPLICATION PERIOD AND FEE\" that shows different application groups and their respective application periods.\n\nFrom this table, I can see the following end dates for different application groups:\n\n1. Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End: 21 Feb 2024\n3. NUS High School Diploma: End: 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n5. Part-Time B.Eng: End: 15 Jan 2024\n6. Part-Time B.Tech in Computing: End: 15 Jan 2024\n\nComparing these dates, the latest end date is 19 Mar 2024, which applies to two groups:\n- Singapore-Cambridge GCE 'A' Level applicants\n- International Baccalaureate (IB) Diploma applicants\n\nAnswer: Singapore-Cambridge GCE 'A' Level applicants, International Baccalaureate (IB) Diploma applicants", "citations": [], "input_tokens": 60375, "output_tokens": 307, "cost_micros": 185730, "latency_ms": 53796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma applicants, singapore cambridge gce level applicants", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the APPLICATION PERIOD AND FEE table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n\n1. **Singapore-Cambridge GCE 'A' Level**: End: 19 Mar 2024\n2. **Diploma Awarded by a Polytechnic or equivalent**: End: 20 Jan 2024\n3. **NUS High School Diploma**: End: 19 Mar 2024\n4. **International Baccalaureate (IB) Diploma**: End: 19 Mar 2024\n5. **Part-Time B.Eng**: End date not fully specified (appears cut off)\n6. **Part-Time B.Tech in Computing**: End: 21 Feb 2024\n\nFrom the complete dates shown, the latest end date is **19 Mar 2024**, which applies to three groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, NUS High School Diploma, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 15947, "output_tokens": 244, "cost_micros": 51501, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, nus high school diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on pages 46-47:\n\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng and Part-Time B.Tech: End 15 Jan 2024\n\nThe latest end date is 19 March 2024, which applies to two groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 19386, "output_tokens": 173, "cost_micros": 60753, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on the document, I need to identify which application groups have the latest end date.\n\nFrom the \"APPLICATION PERIOD AND FEE\" table:\n\n1. Singapore-Cambridge GCE 'A' Level: End date is **19 Mar 2024**\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End date is 21 Feb 2024\n3. NUS High School Diploma: End date is 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End date is **19 Mar 2024**\n\nThe latest end date is 19 Mar 2024, which is shared by two groups:\n- Singapore-Cambridge GCE 'A' Level\n- International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 14659, "output_tokens": 220, "cost_micros": 47277, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application period table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma from Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng: End 15 Jan 2024\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 18593, "output_tokens": 183, "cost_micros": 58524, "latency_ms": 5078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about application periods for different groups of applicants.Let me search more specifically for dates and application-related content:Let me look at pages 46-47 which should contain the table with deadlines:Let me search for typical date formats and applicant categories:Great! I found the table. Let me read the specific section with the application periods:Perfect! I found the application period table. Now I can see the end dates for each group. Let me compare them:\n\n- Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End: 21 Feb 2024\n- NUS High School Diploma: End: 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n- Part-Time B.Eng: End: 15 Jan 2024\n- Part-Time B.Tech in Computing: 15 Jan 2024 (this appears to be both start and end date based on the context)\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n1. Singapore-Cambridge GCE 'A' Level\n2. International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44218, "error": null, "extra": {"thread_id": 1953, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4551739abb3d476cb8bf4c0348ee5447", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 278, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at page 3 of the document, which shows \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\", I need to examine each numbered reason and its corresponding image to find which one does not include any person.\n\nGoing through the reasons with their images:\n\n1. Interdisciplinary Learning - shows a group of students\n2. Space to Innovate - shows people in a collaborative setting\n3. Industry-ready - shows a person at a desk\n4. Immersion in Research - shows a person in a lab setting (Rolls-Royce)\n5. Internship Opportunities - shows the \"AMD\" text/logo area with people\n6. Global Exposure - shows a person in a winter setting\n7. Be Among the Top Ranked - shows students walking\n8. Prestigious Scholarships and Comprehensive Financial Aid - shows students\n9. Most Beautiful Campus - shows an aerial view of the campus with greenery and pathways\n10. Vibrant Campus Life - shows students/people\n\nReason #9 \"Most Beautiful Campus\" shows an aerial photograph of the campus featuring buildings, green spaces, and pathways, but does not show any people in the image.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 60381, "output_tokens": 271, "cost_micros": 185208, "latency_ms": 35592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to identify which reason's corresponding figure/photo does not include any person.\n\nGoing through each reason with its associated image:\n\n1. Interdisciplinary Learning - has people\n2. Space to Innovate - has people \n3. Industry-ready - has a photo credited to Rolls-Royce showing industrial equipment/machinery without visible people\n4. Immersion in Research - has people\n5. Prestigious Scholarships - not clearly shown with people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - appears to be text/rankings\n8. Most Beautiful Campus - shows campus architecture/scenery\n9. Internship Opportunities - has people\n10. Vibrant Campus Life - has people\n\nThe reason labeled \"3 Industry-ready\" with the photo credit to Rolls-Royce shows industrial/laboratory equipment without any visible people in the image.\n\nAnswer: Industry-ready", "citations": [], "input_tokens": 15953, "output_tokens": 236, "cost_micros": 51399, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "industry ready", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section, I need to examine each reason and its corresponding figure to identify which one does not include any person.\n\nGoing through the reasons:\n1. Interdisciplinary Learning - has figures/images with people\n2. Space to Innovate - has people in the image\n3. Industry-ready - has people\n4. Immersion in Research - has people (photo credit: Rolls-Royce)\n5. AMD Internship Opportunities - has people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - this appears to be primarily text/rankings focused\n8. Prestigious Scholarships and Comprehensive Financial Aid - appears to be icons/symbols\n9. Most Beautiful Campus - this is described as about the campus itself\n10. Vibrant Campus Life - has people\n\nLooking at reason #9 \"Most Beautiful Campus\" - the description states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world.\" This reason is about the campus architecture and grounds themselves, and the corresponding figure would logically show the campus landscape/buildings without people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 19392, "output_tokens": 273, "cost_micros": 62271, "latency_ms": 8437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section and examine the figures/images associated with each reason to identify which one does not include any person.\n\nFrom the document, the top 10 reasons are listed on pages 2-3:\n\n1. Be Among the Top Ranked\n2. Space to Innovate\n3. Industry-ready\n4. Prestigious Scholarships and Comprehensive Financial Aid\n5. Most Beautiful Campus\n6. Vibrant Campus Life\n7. Internship Opportunities\n8. Immersion in Research\n9. (appears to have a photo credit: Rolls-Royce)\n10. Vibrant Campus Life (repeated)\n\nThe document mentions \"Photo credit: Rolls-Royce\" which suggests there's an image for reason #9 \"Immersion in Research.\"\n\nLooking at the layout, reason #5 is \"Most Beautiful Campus\" - this reason would typically show campus buildings and architecture rather than people, as it's about the physical beauty of the campus itself.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 14665, "output_tokens": 246, "cost_micros": 47685, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to examine the images associated with each reason.\n\nThe 10 reasons are:\n1. Interdisciplinary Learning\n2. Space to Innovate\n3. Industry-ready\n4. Immersion in Research\n5. Internship Opportunities\n6. Global Exposure\n7. Be Among the Top Ranked\n8. Prestigious Scholarships and Comprehensive Financial Aid\n9. Most Beautiful Campus\n10. Vibrant Campus Life\n\nLooking at the corresponding figures/images described:\n- Reason #4 \"Immersion in Research\" includes a photo credit to Rolls-Royce, and the image appears to show industrial/research equipment rather than people.\n- Reason #9 \"Most Beautiful Campus\" states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world\" and the cover page shows \"a large, modern building with a curved facade featuring prominent vertical wooden louvers or fins\" with \"green landscaping visible at the base\" - this is an architectural shot without people visible.\n\nThe most likely answer based on the description is reason #9, as it explicitly describes a campus building without mentioning any people in the scene.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 18599, "output_tokens": 300, "cost_micros": 60297, "latency_ms": 9328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the top 10 reasons why NTU smart campus is for you and identify which reason has a corresponding figure without any person.Let me search for \"top 10 reasons\" or \"smart campus\" in the document:Let me read the section around line 448-488 where the \"TOP 10\" appears:Good! I can see the beginning of the top 10 reasons. Let me continue reading to see all 10 reasons and their corresponding figures:Let me continue reading to see all the images associated with each reason:Now let me go back and carefully read the section with the 10 reasons to identify each reason and its associated image description. Let me re-read more carefully:Based on my reading of the document, I can now identify the 10 reasons and their corresponding figures. Let me analyze the images:\n\n1. **Interdisciplinary Learning** (Reason 1) - Image shows a female researcher/scientist in a laboratory (person present)\n\n2. **Space to Innovate** (Reason 2) - Image shows a person positioned on left side operating equipment in a laboratory (person present)\n\n3. **Industry-ready** (Reason 3) - Image shows six young adults standing in a horizontal line (people present)\n\n4. **Immersion in Research** (Reason 4) - Image shows a single person standing next to AMD logo (person present)\n\n5. **Internship Opportunities** (Reason 5) - The image associated with reasons 5-10 appears to be Im5.jpg\n\n6. **Global Exposure** (Reason 6) - Image (Im5.jpg) shows a single person standing on snowy terrace/viewing area with ch\u00e2teau backdrop (person present)\n\n7. **Be Among the Top Ranked** (Reason 7) - This appears to share imagery with the ranking pages\n\n8. **Prestigious Scholarships and Comprehensive Financial Aid** (Reason 8) - Part of the same figure group\n\n9. **Most Beautiful Campus** (Reason 9) - The next distinct image is Im7.jpg which shows an aerial view of a modern curved/spiral building with NO PEOPLE mentioned in the description - only architecture and landscape features\n\n10. **Vibrant Campus Life** (Reason 10) - Part of the combined figure\n\nThe reason that does not include any person in the corresponding figure is **Reason 9: Most Beautiful Campus** - the image shows only an aerial view of the building architecture and landscape with no people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48717, "error": null, "extra": {"thread_id": 1954, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_939ae241eba1446aa240613de9b11e4b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 370, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for images of students playing badminton and identify their major.\n\nLooking through the pages, I can see several student testimonials with photos and their majors listed.\n\nOn page 22 (document page 41), there are two images at the bottom of the page showing students in athletic settings. The text mentions:\n\n\"\u2014 JADE LIM SI MIN\nPhilosophy, Class of 2023\n\nNTU has been a springboard for my growth as a student leader. Through the Student Leadership Development Programme, Varsity Sports Association and FISU World Forum, NTU has equipped me with the leadership skills necessary to succeed both in my sport and beyond.\"\n\nAnd:\n\n\"\u2014 GRACE CHUA HUI ZHEN\nMedicine, Class of 2023\n\nNTU has allowed me to pursue my two passions: academics and athletics. From letting me participate in competitions to offering generous subsidies to giving encouragement, I really feel grateful for the school's the tremendous support.\"\n\nThe image on the right shows what appears to be a badminton player in action. Based on the layout and the caption, this appears to be Grace Chua Hui Zhen from Medicine.\n\nAnswer: Medicine", "citations": [], "input_tokens": 60367, "output_tokens": 265, "cost_micros": 185076, "latency_ms": 45125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images or mentions of students playing badminton.\n\nLooking through the document, I can find several student testimonials with their names, majors, and years listed. Let me check for any mention of badminton or sports activities.\n\nOn page 41, there is a section about \"SPORTS @ NTU\" with several images. I can see mentions of:\n- JADE LIM SI MIN, Philosophy, Class of 2023 - with text about varsity sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - with text about academics and athletics\n\nHowever, I need to look more carefully at the actual images in the document. The document mentions \"Griffin at Lee Wee Nam Library\" as an image caption on page 42.\n\nLooking more carefully at the sports section and student profiles, I can see references to students involved in sports, but I need to identify specifically which student's picture shows them playing badminton.\n\nUpon careful review of the document, I cannot definitively identify from the text alone which specific student's picture shows them playing badminton, as the document doesn't explicitly describe the content of each photograph beyond the captions with names and majors.\n\nHowever, based on the context in the sports section where student testimonials are shown, and the typical layout of such brochures, if there is a badminton-related image, it would likely be associated with one of the students featured in the sports section.\n\nAnswer: I cannot determine this from the text provided in the document.", "citations": [], "input_tokens": 15939, "output_tokens": 339, "cost_micros": 52902, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i cannot determine this from text provided in document", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images of someone playing badminton and identify which major that student belongs to.\n\nLooking through the document, I can see several student photos with their names and majors mentioned:\n\n1. Nick Erwin Chia Zhiyang - Renaissance Engineering Programme, Year 4\n2. Gladys Phua Min - Communication Studies, Year 2\n3. Elijah Adam Bin Abdul Haady Aldrin - Chemistry & Biological Chemistry, Year 1\n4. Joey Tay - NTU-NIE TSP, Year 2\n5. Stella Chua Zhi Yun - Mechanical Engineering, Year 3\n6. Seth Low - Communication Studies, Year 4\n7. Balaji Srinivasan - Biological Sciences and Psychology, Year 4\n8. Muhamed Shamir Jaffar Hussein - MBBS, Year 4\n9. Wang Shi Lin Nicole - Business, Year 3\n10. Ruth Koh - Sport Science & Management, Year 3\n\nOn page 41, there is a section about sports and I can see images related to sports activities. Looking at the bottom of page 41, there is a photo with Grace Chua Hui Zhen from Medicine, Class of 2023, and the image appears to show her playing badminton (based on the context of sports activities and the layout).\n\nAnswer: Medicine", "citations": [], "input_tokens": 19378, "output_tokens": 320, "cost_micros": 62934, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for any images or references to badminton playing.\n\nLooking through the document, I can see several student profiles with quotes and their majors listed, but I need to find one specifically associated with a badminton image.\n\nOn page 41, I can see there are sports-related images and student profiles. The document shows:\n\n- JADE LIM SI MIN, Philosophy, Class of 2023 - associated with FISU WORLD FORUM and sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - mentioned in relation to academics and athletics\n\nHowever, I cannot definitively identify which specific image shows badminton or which student's picture is specifically playing badminton from the text content provided. The document mentions \"SPORTS @ NTU\" section and various athletic activities, but the actual image descriptions are not clear enough in the text to determine which major the badminton player belongs to.\n\nBased on the limited visual descriptions available in the text, I cannot provide a definitive answer about which student major is shown playing badminton.\n\nAnswer: Cannot be determined from the provided document text", "citations": [], "input_tokens": 14651, "output_tokens": 250, "cost_micros": 47703, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided document text", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the badminton image...\n\nOn page 41, there is a caption under an image that states:\n\n> GRACE CHUA HUI ZHEN\n> Medicine, Class of 2023\n\nThe caption appears below text mentioning \"a student playing badminton in a competition.\"\n\nAnswer: Medicine", "citations": [], "input_tokens": 18585, "output_tokens": 74, "cost_micros": 56865, "latency_ms": 5875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about a student playing badminton in the brochure document.Let me search for \"badminton\" in the document:Now let me read the context around the badminton image to find the student's major:Let me look earlier in the document to see the context. The badminton image appears on page about \"Communication-Focused\". Let me search for more context around this section:Let me look at what comes before this section to identify which school/major this is part of:I can see this is about CoHASS (College of Humanities, Arts, and Social Sciences). Let me search for where specific majors or student testimonials are mentioned near the badminton image:I see Seth Low is from Communication Studies. Let me check if the badminton image is associated with him or if there's another student profile. Let me look at the pages more carefully:Let me look at the earlier mention at line 2200 to see if there's another student:That's not the badminton image. Let me look more systematically. The badminton image appears around line 3343. Let me check what's before and after to see which student profile or section it's associated with:The badminton image appears on page 20 (based on the page numbers), and the text after it (page 21) shows Seth Low from Communication Studies. But let me look more carefully at the layout. Let me check the section just before the badminton image to see which school section it belongs to:I see on page 19 there's a quote from Seth Low, Communication Studies. Now let me look at page 20 where the badminton image is:Let me look at the structure better. The Im1.jpg is on page 19 (Seth Low's image), then page 20 starts with CoHASS section. The badminton image Im0.jpg should be on page 20. Let me check the page markers more carefully:So page 19 ends at line 3127, then the next page marker is page 21 at line 3387. So the badminton image (around line 3343) is on page 20 which doesn't have an explicit marker. Let me look at the entire section more carefully to see which school this belongs to:Now I see WKWSCI (Wee Kim Wee School of Communication and Information) section starts around line 3548, but the badminton image is earlier around line 3343. Let me trace back to see which section the badminton image actually belongs to. Let me read backwards from line 3320:Perfect! Now I can see the structure. The badminton image (Im0.jpg) appears under the \"Communication-Focused\" section which is part of the CoHASS (College of Humanities, Arts and Social Sciences) overview on page 20. This section appears before the individual school pages. Then on page 21, we see Seth Low from Communication Studies.\n\nSo the badminton image is associated with Communication Studies, which is evident from:\n1. It's on the CoHASS overview page (page 20)\n2. The \"Communication-Focused\" heading right before the badminton image\n3. Seth Low, Communication Studies student, appears on the next page (21)\n4. WKWSCI (Wee Kim Wee School of Communication and Information) is listed as one of the schools under CoHASS\n\nAnswer: Communication Studies", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 83500, "error": null, "extra": {"thread_id": 1955, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2258a5b949624b49af1a1b98a8b2e791", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 658, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "communication studies", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document to identify which colleges/schools/institutes feature Year 3 students on their cover pages or promotional sections.\n\nLooking through the document:\n\n1. Page 10 (College of Engineering section) - Features **Stella Chua Zhi Yun, Mechanical Engineering, Year 3**\n\n2. Page 12 (College of Humanities, Arts and Social Sciences) - Features **Seth Low, Communication Studies, Year 4** (not Year 3)\n\n3. Page 14 (College of Science) - Features **Balaji Srinivasan, Biological Sciences and Psychology, Year 4** (not Year 3)\n\n4. Page 16 (Lee Kong Chian School of Medicine) - Features **Muhamed Shamir Jaffar Hussein, MBBS, Year 4** (not Year 3)\n\n5. Page 17 (Nanyang Business School) - Features **Wang Shi Lin Nicole, Business, Year 3**\n\n6. Page 18 (National Institute of Education) - Features **Ruth Koh, Sport Science & Management, Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 60377, "output_tokens": 272, "cost_micros": 185211, "latency_ms": 42937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to look through the document to identify which colleges/schools/institutes feature Year 3 students on their cover/featured pages.\n\nLet me examine each section:\n\n1. **College of Engineering** (page 16-17): Features STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21): Features SETH LOW, Communication Studies, Year 4 (not Year 3)\n\n3. **College of Science** (page 24-25): Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not Year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28-29): Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not Year 3)\n\n5. **Nanyang Business School** (page 30-31): Features WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33): Features RUTH KOH, Sport Science & Management, Year 3\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 15949, "output_tokens": 282, "cost_micros": 52077, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the document to identify which colleges/schools/institutes feature year 3 students on their cover pages.\n\nLooking through the document:\n\n1. **College of Engineering** (page 16) - Features STELLA CHUA ZHI YUN, Mechanical Engineering, **Year 3**\n\n2. **College of Humanities, Arts and Social Sciences** (page 20) - Features SETH LOW, Communication Studies, Year 4 (not year 3)\n\n3. **College of Science** (page 24) - Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28) - Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not year 3)\n\n5. **Nanyang Business School** (page 30) - Features WANG SHI LIN NICOLE, Business, **Year 3**\n\n6. **National Institute of Education** (page 32) - Features RUTH KOH, Sport Science & Management, **Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 19388, "output_tokens": 278, "cost_micros": 62334, "latency_ms": 6905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document for references to Year 3 students who appear as student models on cover pages or in promotional materials.\n\nLooking through the document, I can find the following Year 3 students quoted/featured:\n\n1. Page 16 - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3 - This is from the **College of Engineering** section\n\n2. Page 30 - WANG SHI LIN NICOLE, Business, Year 3 - This is from the **Nanyang Business School** section (she appears twice on this page)\n\n3. Page 33 - RUTH KOH, Sport Science & Management, Year 3 - This is from the **National Institute of Education** section (she appears twice on this page)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 14661, "output_tokens": 193, "cost_micros": 46878, "latency_ms": 6233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify which colleges/schools/institutes feature Year 3 students on their cover/promotional pages in this prospectus.\n\nLet me search through the document for mentions of students with their year level and their associated colleges/schools:\n\n1. **College of Engineering** (page 16-17):\n - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21):\n - SETH LOW, Communication Studies, Year 4\n\n3. **College of Science** (page 24-25):\n - BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4\n\n4. **Lee Kong Chian School of Medicine** (page 28-29):\n - MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4\n\n5. **Nanyang Business School** (page 30-31):\n - WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33):\n - RUTH KOH, Sport Science & Management, Year 3\n\nFrom this analysis, the colleges/schools/institutes that use Year 3 students as their cover page student models are:\n- College of Engineering (Stella Chua)\n- Nanyang Business School (Wang Shi Lin Nicole)\n- National Institute of Education (Ruth Koh)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 18595, "output_tokens": 352, "cost_micros": 61065, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find which colleges/schools/institutes from NTU use year 3 students as cover page student models.Let me search for information about cover page student models and year 3 students.Let me search for \"Year 3\" with capital letters and look at the beginning of the document which might contain information about cover pages or student models.Now let me search for information about cover pages and models. Let me read the beginning of the document more carefully:Let me search more specifically for information about models or photos. Let me look for text that mentions students being models or featured on covers:Let me look at the end of the document, which often contains credits or acknowledgments about models or photos:Let me search for the total lines in this document and read from the end:Let me read closer to the end of the document where credits or acknowledgments are typically found:Let me continue reading towards the very end where photo credits or acknowledgments might be:Let me search near the very end of the document:The document ended without showing photo credits. Let me search more specifically for mentions of colleges and Year 3 together. Let me look at those specific line numbers from the grep results:Good! I found one: Stella Chua Zhi Yun is from Mechanical Engineering, Year 3. Now let me check the other instances:Good! Wang Shi Lin Nicole is from Business (Nanyang Business School), Year 3. Now let me check the other instances:Good! Ruth Koh is from Sport Science & Management (National Institute of Education), Year 3. Now let me check the next instance:This is another mention of Ruth Koh. Now let me look more carefully at the context. I need to understand which of these students are featured on \"cover pages\" of their respective schools/colleges. Let me search for any section that lists or describes cover page models:None of these matches refer to \"cover page\" in the context I'm looking for. Let me look at the actual pages where these students appear to see if they are featured as cover page models. Let me check around the Mechanical Engineering section:Now let me look before this section to see if there's a cover page for College of Engineering:Perfect! I can see that Stella Chua Zhi Yun (Mechanical Engineering, Year 3) is featured on the cover page for the College of Engineering section. Now let me check the other sections:Great! I can see Wang Shi Lin Nicole (Business, Year 3) is featured on the cover page for Nanyang Business School. Now let me check the NIE section:Perfect! I can see Ruth Koh (Sport Science & Management, Year 3) is featured on the cover page for the National Institute of Education (NIE) section.\n\nNow let me check if there are any other colleges/schools that I might have missed. Let me look at the table of contents and see what major sections exist:Let me check if there's a cover page model for College of Humanities, Arts and Social Sciences (CoHASS):I can see Seth Low (Communication Studies, Year 4) is mentioned but not Year 3. Let me check the College of Science:I can see Balaji Srinivasan (Biological Sciences and Psychology, Year 4) is mentioned for College of Science, but not Year 3. Let me check if there's a cover page before the College of Science section:So for College of Science, the cover page model is Balaji Srinivasan (Year 4), not Year 3. Let me also check Lee Kong Chian School of Medicine:Let me check the cover page before Lee Kong Chian School of Medicine:So for Lee Kong Chian School of Medicine, the cover page model is Muhamed Shamir Jaffar Hussein (MBBS, Year 4), not Year 3.\n\nNow let me systematically check all the major schools/colleges. Based on my review:\n\n**Year 3 students as cover page models:**\n1. **College of Engineering** - Stella Chua Zhi Yun (Mechanical Engineering, Year 3)\n2. **Nanyang Business School** - Wang Shi Lin Nicole (Business, Year 3)\n3. **National Institute of Education** - Ruth Koh (Sport Science & Management, Year 3)\n\nLet me verify there are no other colleges I missed by checking the table of contents more carefully:Based on the table of contents, the main colleges/schools are:\n1. College of Engineering (has Year 3 student - Stella Chua)\n2. Colleges of Humanities, Arts and Social Sciences (has Year 4 student - Seth Low)\n3. Colleges of Science (has Year 4 student - Balaji Srinivasan)\n4. Lee Kong Chian School of Medicine (has Year 4 student - Muhamed Shamir)\n5. Nanyang Business School (has Year 3 student - Wang Shi Lin Nicole)\n6. National Institute of Education (has Year 3 student - Ruth Koh)\n\nSo the colleges/schools/institutes that use Year 3 students as cover page models are:\n1. College of Engineering\n2. Nanyang Business School\n3. National Institute of Education\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 154578, "error": null, "extra": {"thread_id": 1956, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_071293dfad4941b99ec3b52bc671c7da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1092, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the College of Humanities, Arts, and Social Sciences (CoHASS) and count how many schools are listed but not individually introduced in detail.\n\nFrom page 12, I can see the College of Humanities, Arts and Social Sciences section lists \"OUR SCHOOLS\":\n- School of Art, Design and Media\n- School of Humanities\n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 7 entities listed under \"OUR SCHOOLS\".\n\nLooking at pages 13-14, I can see detailed introductions for:\n1. School of Art, Design and Media (ADM) - page 13\n2. School of Humanities (SoH) - page 13\n3. School of Social Sciences (SSS@NTU) - page 13\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - page 13\n\nThese 4 schools have detailed \"ABOUT\" sections with descriptions of their programs and advantages on pages 22-23.\n\nThe entities that are listed but NOT individually introduced in detail are:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 3 entities.\n\nAnswer: 3", "citations": [], "input_tokens": 60364, "output_tokens": 324, "cost_micros": 185952, "latency_ms": 38015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools in the College of Humanities, Arts, and Social Sciences (CoHASS) and determine which ones are NOT introduced in detail.\n\nFrom page 20-21, the CoHASS section lists \"OUR SCHOOLS\":\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n\nIt also mentions:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow, let me check which schools get detailed individual introductions on pages 22-23:\n\n1. **School of Art, Design and Media (ADM)** - YES, has detailed section on page 22\n2. **School of Humanities (SoH)** - YES, has detailed section on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, has detailed section on page 23\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, has detailed section on page 23\n\nThe three centers/institutes mentioned (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences) are listed under \"OUR SCHOOLS\" but do NOT have individual detailed introductions.\n\nTherefore, 3 entities listed under CoHASS schools do not have individual detailed introductions.\n\nAnswer: 3", "citations": [], "input_tokens": 15936, "output_tokens": 340, "cost_micros": 52908, "latency_ms": 8296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools that belong to the College of Humanities, Arts and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFrom page 20, the document lists the schools under CoHASS:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow I need to check which schools have individual detailed introductions on pages 22-23 (the \"LEARN MORE ABOUT OUR SCHOOLS\" section):\n\nPage 22:\n- School of Art, Design and Media (ADM) - YES, has detailed introduction\n- School of Humanities (SoH) - YES, has detailed introduction\n\nPage 23:\n- School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n- Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nSchools that are NOT individually introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 19375, "output_tokens": 302, "cost_micros": 62655, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools within the College of Humanities, Arts, and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFirst, let me find the list of schools under CoHASS from page 20:\n\n**OUR SCHOOLS:**\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nThat's 7 schools total listed under CoHASS.\n\nNow let me check which schools have detailed individual introductions in the document (pages 21-23):\n\n1. **School of Art, Design and Media (ADM)** - YES, detailed introduction on page 22\n2. **School of Humanities (SoH)** - YES, detailed introduction on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, detailed introduction on page 22\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, detailed introduction on page 23\n\nThe following schools are listed but do NOT have individual detailed introductions:\n5. Nanyang Centre for Public Administration - NO detailed introduction\n6. NTU Centre for Contemporary Art Singapore - NO detailed introduction\n7. Centre for Liberal Arts and Social Sciences - NO detailed introduction\n\nTherefore, 3 schools are not individually introduced in detail.\n\nAnswer: 3", "citations": [], "input_tokens": 14648, "output_tokens": 358, "cost_micros": 49314, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify which schools from the College of Humanities, Arts, and Social Sciences (CoHASS) are listed, and then determine which ones are NOT introduced in detail.\n\nFrom the document, the CoHASS schools listed on page 20 are:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow, looking at the detailed introductions on pages 22-23 under \"LEARN MORE ABOUT OUR SCHOOLS\":\n1. School of Art, Design and Media (ADM) - YES, has detailed introduction\n2. School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n3. School of Humanities (SoH) - YES, has detailed introduction\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nThe schools that are NOT introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 18582, "output_tokens": 292, "cost_micros": 60126, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the College of Humanities, Arts, and Social Sciences and count which schools are not individually introduced in detail.Let me search for \"College of Humanities, Arts, and Social Sciences\" in the document:Let me try searching for variations:Let me read the section about the College of Humanities:Let me search for more information about the college structure. Let me look for \"COLLEGE OF HUMANITIES\" in caps:Found it! Let me read that section:Good! I can see the College of Humanities, Arts and Social Sciences has 4 schools listed:\n1. School of Art, Design and Media (page 22)\n2. School of Humanities (page 23)\n3. School of Social Sciences (page 23)\n4. Wee Kim Wee School of Communication and Information (page 24)\n\nNow let me check which ones are introduced in detail. Let me read pages 22-24:Let me search for where each school is detailed. Let me look at the actual page content around lines 3200-3300 where I saw \"School of Humanities\" mentioned:Good! I can see the CoHASS section lists their schools. Now let me find where individual schools are detailed. Let me search for detailed sections of each school:Let me read the ADM section:Great! Now let me check for Wee Kim Wee School:Perfect! Now let me also check if there are any other schools in CoHASS that I might have missed. Let me go back to the list to verify:I see that the College of Humanities, Arts and Social Sciences has the following units listed:\n- School of Art, Design and Media\n- School of Humanities \n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow let me check if the last three (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) have detailed introductions. Let me search:So the three centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) are only listed in the OUR SCHOOLS section but don't have detailed individual introductions like the four main schools do.\n\nNow let me verify the total count by checking what the document considers as \"schools\" in CoHASS. The question asks specifically about \"schools\" - let me see if these centres are considered schools:\n\nLooking at the structure:\n- In the table of contents (lines 398-413), only 4 schools are listed under \"COLLEGES OF HUMANITIES, ARTS AND SOCIAL SCIENCES\":\n 1. School of Art, Design and Media (page 22)\n 2. School of Humanities (page 23)\n 3. School of Social Sciences (page 23)\n 4. Wee Kim Wee School of Communication and Information (page 24)\n\nAll 4 of these have detailed individual introductions as I found above.\n\nBut in the \"OUR SCHOOLS\" section (lines 3216-3236), it lists 7 entities:\n- 4 Schools (the ones mentioned above)\n- 3 Centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences)\n\nThe question asks about \"schools\" specifically. The centres are NOT called \"schools\" - they are called \"centres\". So the question is asking only about the 4 schools.\n\nSince all 4 schools (School of Art, Design and Media, School of Humanities, School of Social Sciences, and Wee Kim Wee School of Communication and Information) ARE individually introduced in detail, the answer is:\n\nAnswer: 0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97500, "error": null, "extra": {"thread_id": 1957, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_84cc170418784e7aaee4655dd9c795d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 722, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to analyze Figure 1 and find other figures that convey similar messages.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" across four datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10). The key message is that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries, especially in the low budget regime.\n\nNow let me look through the document for figures with similar messages:\n\n**Figure 6** (page 16): Shows \"Label diversity yields more performant and robust active querying strategies\" on PathMNIST, OrganAMNIST, and BloodMNIST. This compares strategies with and without label diversity, not the cold start problem specifically.\n\n**Figure 9** (page 20): Shows \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, focuses on label diversity rather than cold start.\n\n**Figure 4** (page 7): Shows \"Quantitative comparison of map-based querying strategies\" comparing easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast with random selection as baseline. This shows performance at low budgets and demonstrates the cold start problem.\n\n**Figure 5** (page 8): Shows \"On the importance of selecting a superior initial query\" with performance across active learning cycles starting from initial queries. This demonstrates how different initial query strategies perform, related to the cold start problem.\n\n**Figure 13** (page 24): Shows performance of active learning strategies with different initial query strategies on BloodMNIST, demonstrating the importance of initial query selection.\n\n**Figure 14** (page 24): Same as Figure 13 but for PathMNIST.\n\nFigures that convey the same core message as Figure 1 (that active learning fails at initial selection/cold start):\n- Figure 4: Shows the cold start problem through comparison with random\n- Figure 5: Shows importance of initial query selection\n- Figure 13: Shows initial query performance across cycles\n- Figure 14:", "citations": [], "input_tokens": 58482, "output_tokens": 512, "cost_micros": 183126, "latency_ms": 44218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" where most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. The figure has 4 subplots (a-d) for different datasets showing AUC performance vs. number of images.\n\nLet me search for other figures with similar messages:\n\n**Figure 6**: \"Label diversity yields more performant and robust active querying strategies\" - Shows 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) with 6 subplots each comparing strategies with/without diversity. This is about diversity improvement, not the cold start problem itself.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10 and CIFAR-10-LT. Also about diversity improvement rather than demonstrating the cold start problem.\n\n**Figure 4**: \"Quantitative comparison of map-based querying strategies\" - Shows random selection as a baseline and compares different strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across 4 datasets. This demonstrates that random selection is competitive and that hard-to-contrast performs best, which is related to the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST\" - This is about subsequent cycles after initial query.\n\n**Figure 14**: Similar to Figure 13 but for PathMNIST - Also about subsequent cycles.\n\n**Figure 5**: \"On the importance of selecting a superior initial query\" on OrganAMNIST - Shows active learning cycles but focuses on the importance of initial query selection.\n\nThe figures that most clearly convey the same core message as Figure 1 (that existing active querying strategies fail to outperform random selection at the start) are:\n\n- **Figure 4**: Directly compares strategies at low budgets and shows random selection as competitive baseline\n- Possibly Figure 6 and 9, but these focus more on the solution (diversity) rather than the", "citations": [], "input_tokens": 21463, "output_tokens": 512, "cost_micros": 72069, "latency_ms": 12625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1's main message is about the \"Cold start problem in vision active learning\" - showing that most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. It displays AUC performance curves across different numbers of images for multiple datasets.\n\nLet me go through the other figures to find similar messages:\n\n**Figure 2**: Shows label diversity of querying criteria - different message (about class distribution bias)\n\n**Figure 3**: Shows Dataset Maps and easy/hard-to-learn vs easy/hard-to-contrast data - different message (about data selection strategies)\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies, comparing hard-to-contrast with other strategies against random selection - **similar message** (comparing active learning strategies with random selection)\n\n**Figure 5**: Shows importance of selecting superior initial query on OrganAMNIST - **similar message** (about initial query performance across active learning cycles)\n\n**Figure 6**: Extended from Table 1, shows label diversity yields more performant strategies - different message (about adding label diversity)\n\n**Figure 7**: Continued from Figure 2, about label diversity - different message\n\n**Figure 8**: Visualization of K-means clustering - different message\n\n**Figure 9**: Similar to Figure 6, about diversity and performance - different message (about label diversity effect)\n\n**Figure 10**: Similar to Figure 2, about label diversity - different message\n\n**Figure 11**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 12**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 13**: Performance across active learning cycles on BloodMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\n**Figure 14**: Performance across active learning cycles on PathMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\nFigures that convey similar messages to Figure 1 (comparing active learning strategies, showing cold start problem):\n- Figure 4\n- Figure 5\n- Figure 13\n- Figure 14\n\nAnswer: 4", "citations": [], "input_tokens": 26468, "output_tokens": 506, "cost_micros": 86994, "latency_ms": 11765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey similar messages to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" - it demonstrates that most existing active querying strategies (like BALD, Consistency, etc.) are outperformed by random selection when selecting initial queries, particularly in low budget regimes.\n\nLet me go through the document to find figures with similar messages:\n\n**Figure 4**: Shows comparison of different querying strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across different datasets. This shows performance comparisons but focuses on the proposed method rather than demonstrating the cold start problem itself.\n\n**Figure 6**: Explicitly states it is \"Extended from Table 1\" and shows \"Label diversity yields more performant and robust active querying strategies.\" It compares active querying strategies with and without label diversity on PathMNIST, OrganAMNIST, and BloodMNIST. This is about the solution (label diversity) rather than demonstrating the cold start problem.\n\n**Figure 9**: States \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, this focuses on the solution rather than demonstrating the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST.\" This compares different strategies across active learning cycles but focuses on the full active learning procedure rather than specifically demonstrating the cold start problem.\n\n**Figure 14**: Similar to Figure 13, shows \"Performance of each active learning querying strategies with different initial query strategies on PathMNIST.\"\n\nLooking back at the document more carefully, none of the other figures directly replicate Figure 1's specific message about the cold start problem where active learning strategies fail to outperform random selection at the first choice. The other figures either:\n- Show solutions to the problem (Figures 6, 9)\n- Show active learning trajectories (Figures 13, 14)\n- Show other aspects like dataset maps or visualizations (Figures 3, 7, 8, 10, 11, 12)\n\nAnswer: 0", "citations": [], "input_tokens": 23168, "output_tokens": 504, "cost_micros": 77064, "latency_ms": 11578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1, which shows the \"Cold start problem in vision active learning\" - demonstrating that most existing active querying strategies are outperformed by random selection when selecting initial queries.\n\nLet me examine each figure:\n\n**Figure 1**: Shows cold start problem - active learning strategies perform worse than random selection at initial queries across multiple datasets.\n\n**Figure 2**: Shows label diversity of querying criteria - demonstrates class distribution bias in active querying strategies.\n\n**Figure 3**: Shows active querying based on Dataset Maps - compares easy/hard-to-learn vs easy/hard-to-contrast data.\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies - compares different strategies including hard-to-contrast vs random selection.\n\n**Figure 5**: Shows importance of selecting superior initial query - correlation analysis showing hard-to-contrast outperforms others across active learning cycles.\n\n**Figure 6**: Extended from Table 1 - shows label diversity yields more performant strategies, comparing with/without label diversity across budgets.\n\n**Figure 7**: Continued from Figure 2 - shows label diversity for OrganAMNIST and BloodMNIST.\n\n**Figure 8**: Visualization of K-means clustering and active selection using UMAP.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10-LT - shows diversity yields more performant strategies.\n\n**Figure 10**: Similar to Figure 2 but for CIFAR-10 and CIFAR-10-LT - shows label diversity.\n\n**Figure 11**: Similar to Figure 3 - Active querying based on Dataset Maps for PathMNIST and OrganAMNIST.\n\n**Figure 12**: Similar to Figure 3 - Active querying based on Dataset Maps for CIFAR-10-LT.\n\n**Figure 13**: Performance of active learning strategies with different initial queries on BloodMNIST - shows correlation across cycles.\n\n**Figure 14**: Performance of active learning strategies with different initial queries on PathMNIST - shows correlation across cycles.\n\nFigures conveying a similar message to Figure 1 (showing the cold start problem where active learning strategies underperform random selection at the start):\n- **Figure ", "citations": [], "input_tokens": 37261, "output_tokens": 512, "cost_micros": 119463, "latency_ms": 12672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to understand the first figure and then identify how many other figures convey similar messages.Let me continue reading to find all figures in the document and understand what the first figure conveys.Now let me read more sections to understand all the figures and their messages better.Let me continue reading to see more figures.Let me continue reading to identify all figures.Let me search for more figures to get a complete count.Now let me understand what message Figure 1 conveys and identify similar figures. Based on my reading, let me analyze:\n\n**Figure 1** (lines 490-584): Shows four subplots (a-d) of line graphs plotting AUC vs Number of images, demonstrating the \"cold start problem\" where active querying strategies are outperformed by random selection in selecting initial queries. The message is: **existing active learning strategies perform worse than random selection at the beginning (cold start problem)**.\n\nNow let me identify which other figures convey similar messages:\n\n1. **Figure 4** (lines 1346-1455): Shows four subplots comparing map-based querying strategies with random selection as baseline, showing that random selection is \"a highly competitive baseline in cold start because it outperforms six popular active querying strategies as shown in Figure 1.\" This conveys the **same message about cold start problem**.\n\n2. **Figure 6** (lines 2545-2805): Shows plots about label diversity yielding more performant strategies - this is about **solutions**, not about demonstrating the cold start problem itself.\n\n3. **Figure 7** (lines 2840-2920): Shows bar charts about label diversity - this is about **class distribution bias**, related but different message.\n\n4. **Figure 9** (lines 3454-3640): Similar to Figure 6, about diversity yielding better strategies - this is about **solutions**.\n\n5. **Figure 10** (lines 3689-3800): Bar charts about label diversity - about **class distribution bias**.\n\nLet me verify by reading more carefully about what each figure demonstrates:Based on my thorough analysis of the document, Figure 1's main message is: **Active learning strategies are outperformed by random selection at the cold start (initial query selection) due to the cold start problem.**\n\nFigures that convey this same or very similar message:\n- **Figure 4**: Explicitly shows random selection as a competitive baseline that outperforms popular active querying strategies in the cold start problem, directly referencing Figure 1.\n\nAll other figures (2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14) convey different messages:\n- Figures 2, 7, 10: Show class distribution bias\n- Figures 3, 11, 12: Show dataset maps for selecting data\n- Figure 5, 13, 14: Show importance of superior initial query across learning cycles\n- Figures 6, 9: Show that diversity improves querying strategies\n- Figure 8: Shows visualization of clustering\n\nTherefore, **1 figure** (Figure 4) conveys a similar message as Figure 1.\n\nAnswer: 1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78500, "error": null, "extra": {"thread_id": 1958, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cace0f3b35134ef58541b082bc26d8e7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 533, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1, which shows the \"Cold start problem in vision active learning,\" I can see performance comparisons on different datasets including CIFAR-10 (subplot d). The figure shows that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries.\n\nLooking at Table 1, which compares different querying strategies on CIFAR-10-LT at 10% and 20% budgets:\n- At 10% budget: Random achieves 91.6\u00b11.1\n- At 20% budget: Random achieves 93.1\u00b10.6\n\nComparing with other strategies at these budgets:\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%, strategies that outperform random (91.6): VAAL (92.6), Entropy (92.0), Margin (91.7), BALD (92.8) = 4 strategies\n\nAt 20%, strategies that outperform random (93.1): Consistency (93.4), VAAL (93.7), Margin (93.2), Coreset (93.6) = 4 strategies\n\nHowever, the question asks generally how many strategies outperform random selection. Looking at the paper's proposed \"Ours\" strategy (hard-to-contrast), the results show it significantly outperforms random", "citations": [], "input_tokens": 58490, "output_tokens": 512, "cost_micros": 183150, "latency_ms": 41641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10, I can see performance curves for different strategies. The document mentions CIFAR-10-LT specifically in several places.\n\nFrom Figure 4(d), I can see a comparison of different strategies on CIFAR-10-LT including:\n- Random (baseline, shown as dotted line)\n- Easy-to-learn\n- Hard-to-learn\n- Easy-to-contrast\n- Hard-to-contrast\n\nLooking at the quantitative results in Figure 4(d) for CIFAR-10-LT at different budget levels (20.0% and 30.0% with 2481 and 3721 images), I can observe which strategies perform above the random baseline.\n\nFrom the text in section 3.2: \"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nLooking at Figure 4(d) more carefully, I can see that at the budget levels shown:\n1. Hard-to-contrast (performs best, clearly above random)\n2. Easy-to-learn (appears to perform above random)\n3. Hard-to-learn (appears close to or slightly above random)\n4. Easy-to-contrast (appears below random)\n\nFrom the visual inspection of Figure 4(d), it appears that 2-3 strategies outperform random selection, with hard-to-contrast being the clear winner, and easy-to-learn also performing above the baseline.\n\nBased on the figure, I count: Hard-to-contrast and Easy-to-learn as clearly outperforming random selection.\n\nAnswer: 2", "citations": [], "input_tokens": 21471, "output_tokens": 478, "cost_micros": 71583, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows the cold start problem on CIFAR-10, I can see performance curves for different strategies compared to random selection. However, the question asks specifically about CIFAR-10-LT.\n\nLet me check the text more carefully. In Section 3.2, it states:\n\n\"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nThis indicates that the \"hard-to-contrast\" strategy (which is the authors' proposed method) outperforms random selection.\n\nLooking at Figure 1(d) which shows CIFAR-10 (not CIFAR-10-LT, but related), most of the active learning strategies shown (BALD, Consistency, Coreset, Margin, VAAL, Entropy) appear to perform worse than or similar to random selection at the initial stages (the \"cold start\" problem that the paper discusses).\n\nThe main point of the paper is that existing active learning strategies fail to outperform random selection at the cold start (initial query selection), which is why they propose their hard-to-contrast method.\n\nLooking at Table 1 and Table 7, which compare different strategies on CIFAR-10-LT with different budget levels, at low budgets (10%, 20%), the proposed method with diversity enforcement shows the best performance. However, most existing strategies WITHOUT diversity enforcement perform poorly or fail to select all classes (marked as \"-\").\n\nBased on the paper's main finding that existing active querying strategies are \"outperformed by random selection\" at the cold start (as stated in Figure 1 caption and throughout the introduction), and that only their proposed \"hard-to-contrast\" method significantly outperforms random selection on CIFAR-10-LT, the answer appears to be:\n\nAnswer: 1", "citations": [], "input_tokens": 26476, "output_tokens": 501, "cost_micros": 86943, "latency_ms": 12703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many active learning strategies outperform random selection on CIFAR-10-LT in the cold start problem (initial query selection).\n\nLooking at Figure 1 in the document, which illustrates the \"Cold start problem in vision active learning,\" I can see panel (d) shows results for CIFAR-10-LT.\n\nThe document states in the caption of Figure 1: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries... However, some classes are not selected by active querying strategies due to selection bias, so their results are not presented in the low budget regime.\"\n\nThe document consistently emphasizes that existing active learning strategies fail to outperform random selection at the cold start (initial query stage). The main contribution of the paper is proposing a new strategy (hard-to-contrast) that does outperform random selection.\n\nLooking at Table 1, for CIFAR-10-LT at 10% and 20% budgets:\n- Random: 91.6\u00b11.1 (10%), 93.1\u00b10.6 (20%)\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%: VAAL (92.6), Entropy (92.0), BALD (92.8), and Margin (91.7) appear close or slightly better than Random (91.6)\nAt 20%: VAAL (93.7), Consistency (93.4), Coreset (93.", "citations": [], "input_tokens": 23176, "output_tokens": 512, "cost_micros": 77208, "latency_ms": 12390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10-LT:\n\nThe figure shows performance curves for different strategies:\n- Random (baseline)\n- BALD (Kirsch et al., 2019)\n- Consistency (Gao et al., 2020)\n- Coreset (Sener et al., 2017)\n- Margin (Balcan et al., 2007)\n- VAAL (Sinha et al., 2019)\n- Entropy (Wang et al., 2014)\n\nThe text states: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries, since random selection is i.i.d. to the entire dataset.\"\n\nFrom Figure 1(d), I can see that at the initial query stage (low budget regime), the random selection baseline appears to outperform most other strategies. The figure caption states \"Active learning fails to select data as effectively as random selection at the first choice.\"\n\nHowever, looking more carefully at the results, I need to determine which strategies actually outperform random at the cold start (initial query selection).\n\nAccording to the paper's main findings and Figure 4, the proposed \"hard-to-contrast\" strategy significantly outperforms random selection on CIFAR-10-LT. The paper states this is their contribution - addressing the cold start problem where existing strategies fail to beat random.\n\nFrom the various results shown, only the authors' proposed strategy (hard-to-contrast) consistently outperforms random selection at the initial query stage on CIFAR-10-LT.\n\nAnswer: 1", "citations": [], "input_tokens": 37269, "output_tokens": 407, "cost_micros": 117912, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about strategies that outperform the random selection baseline on CIFAR-10-LT.Let me search for content related to CIFAR-10-LT and performance comparisons:Let me read through the document more systematically to find tables or figures with results:Let me search for tables that compare strategies on CIFAR-10-LT:Perfect! I found Table 1. Let me continue reading to see the complete table for CIFAR-10-LT results:Now I can see Table 1 clearly. Let me analyze the CIFAR-10-LT columns (10% and 20% budget). The table shows performance with and without enforcing label diversity. Looking at the CIFAR-10-LT columns:\n\nFor the 10% budget (column 9), I need to compare each strategy's score with Random baseline (91.6\u00b11.1):\n- Random (baseline): 91.6\u00b11.1\n- Consistency with diversity (\u2713): 91.4\u00b11.1 - DOES NOT outperform\n- Consistency without diversity (\u2717): 67.1\u00b117.1 - DOES NOT outperform\n- VAAL with diversity (\u2713): 92.6\u00b10.2 - OUTPERFORMS\n- VAAL without diversity (\u2717): - (missing)\n- Margin with diversity (\u2713): 91.7\u00b10.9 - OUTPERFORMS (slightly)\n- Margin without diversity (\u2717): 81.9\u00b10.8 - DOES NOT outperform\n- Entropy with diversity (\u2713): 92.0\u00b11.2 - OUTPERFORMS (slightly)\n- Entropy without diversity (\u2717): 65.6\u00b115.6 - DOES NOT outperform\n- Coreset with diversity (\u2713): 91.5\u00b10.4 - DOES NOT outperform\n- Coreset without diversity (\u2717): 65.9\u00b115.9 - DOES NOT outperform\n- BALD with diversity (\u2713): 92.8\u00b10.1 - OUTPERFORMS\n- BALD without diversity (\u2717): 64.9\u00b114.9 - DOES NOT outperform\n\nLooking at 10% budget, strategies that outperform random (91.6):\n1. VAAL with diversity: 92.6\n2. Margin with diversity: 91.7\n3. Entropy with diversity: 92.0\n4. BALD with diversity: 92.8\n\nThat's 4 strategies at 10% budget.\n\nFor the 20% budget (column 10), comparing with Random baseline (93.1\u00b10.6):\n- Random: 93.1\u00b10.6\n- Consistency with diversity: 93.4\u00b10.2 - OUTPERFORMS\n- Consistency without diversity: 88.6\u00b10.3 - DOES NOT outperform\n- VAAL with diversity: 93.7\u00b10.4 - OUTPERFORMS\n- VAAL without diversity: - (missing)\n- Margin with diversity: 93.2\u00b10.2 - OUTPERFORMS (slightly)\n- Margin without diversity: 86.3\u00b10.3 - DOES NOT outperform\n- Entropy with diversity: 91.9\u00b11.3 - DOES NOT outperform\n- Entropy without diversity: 86.4\u00b10.2 - DOES NOT outperform\n- Coreset with diversity: 93.6\u00b10.2 - OUTPERFORMS\n- Coreset without diversity: 86.9\u00b10.1 - DOES NOT outperform\n- BALD with diversity: 90.8\u00b12.4 - DOES NOT outperform\n- BALD without diversity: 84.7\u00b10.6 - DOES NOT outperform\n\nAt 20% budget, strategies that outperform random (93.1):\n1. Consistency with diversity: 93.4\n2. VAAL with diversity: 93.7\n3. Margin with diversity: 93.2\n4. Coreset with diversity: 93.6\n\nThat's 4 strategies at 20% budget.\n\nBut the question asks about \"CIFAR-10-LT\" in general. Let me count unique strategies (considering both budgets):\n\nStrategies that outperform at 10%: VAAL, Margin, Entropy, BALD (all with diversity)\nStrategies that outperform at 20%: Consistency, VAAL, Margin, Coreset (all with diversity)\n\nCombined unique strategies: VAAL, Margin, Entropy, BALD, Consistency, Coreset = 6 strategies\n\nBut I should check if the question means at ANY budget level or at BOTH budget levels. Looking at Figure 1d in the document might also help clarify. Let me check that figure.I notice Figure 1(d) is labeled \"CIFAR-10\" not \"CIFAR-10-LT\". But the question specifically asks about CIFAR-10-LT. Let me look more carefully at Table 1. The table clearly has a column header \"CIFAR-10-LT\" with two sub-columns (10% and 20%).\n\nBased on Table 1, I need to count how many strategies (considering different variations - with/without diversity) outperform the random baseline on CIFAR-10-LT. Let me be more systematic:\n\nAt 10% budget, Random = 91.6\u00b11.1:\n1. Consistency (\u2713): 91.4 - NO\n2. Consistency (\u2717): 67.1 - NO \n3. VAAL (\u2713): 92.6 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 91.7 - YES\n6. Margin (\u2717): 81.9 - NO\n7. Entropy (\u2713): 92.0 - YES\n8. Entropy (\u2717): 65.6 - NO\n9. Coreset (\u2713): 91.5 - NO\n10. Coreset (\u2717): 65.9 - NO\n11. BALD (\u2713): 92.8 - YES\n12. BALD (\u2717): 64.9 - NO\n\nAt 10%: 4 strategies outperform random\n\nAt 20% budget, Random = 93.1\u00b10.6:\n1. Consistency (\u2713): 93.4 - YES\n2. Consistency (\u2717): 88.6 - NO\n3. VAAL (\u2713): 93.7 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 93.2 - YES\n6. Margin (\u2717): 86.3 - NO\n7. Entropy (\u2713): 91.9 - NO\n8. Entropy (\u2717): 86.4 - NO\n9. Coreset (\u2713): 93.6 - YES\n10. Coreset (\u2717): 86.9 - NO\n11. BALD (\u2713): 90.8 - NO\n12. BALD (\u2717): 84.7 - NO\n\nAt 20%: 4 strategies outperform random\n\nThe question asks \"How many strategies outperform the random selection baseline on CIFAR-10-LT?\" \n\nLooking at both budget levels (10% and 20%), and counting each unique strategy configuration:\n- At 10%: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713) = 4 \n- At 20%: Consistency(\u2713), VAAL(\u2713), Margin(\u2713), Coreset(\u2713) = 4\n\nIf we count unique strategy configurations across both budgets: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713), Consistency(\u2713), Coreset(\u2713) = 6 total\n\nBut the most straightforward interpretation is: at a given budget level. Since the table shows two budget levels and the question doesn't specify which, I should look at what makes most sense. Given that 4 strategies outperform at each budget level separately, and 6 unique strategy variations outperform across at least one budget level, the answer is likely **4** (at each specific budget) or **6** (total unique strategies that ever outperform).\n\nHowever, re-reading the question more carefully - it asks how many strategies outperform, not how many strategy-variants. If we count base strategies (ignoring the diversity modifier):\n- VAAL outperforms (with diversity at both budgets)\n- Margin outperforms (with diversity at both budgets) \n- Entropy outperforms (with diversity at 10%)\n- BALD outperforms (with diversity at 10%)\n- Consistency outperforms (with diversity at 20%)\n- Coreset outperforms (with diversity at 20%)\n\nThat would be 6 unique base strategies.\n\nBut looking at the data more carefully at the initial query (lowest budget), which is what the paper focuses on for \"cold start\", the answer is most likely **4** for the 10% budget.\n\nActually, let me reconsider - in Table 1, the \"Unif.\" column indicates whether uniform/diversity enforcement is applied. Each strategy has 2 rows (with and without diversity). So we have 7 base strategies tested: Random, Consistency, VAAL, Margin, Entropy, Coreset, BALD.\n\nIf I count how many of these 7 base strategies (in any configuration) outperform random at either budget:\n- Consistency: NO at 10%, YES at 20% (with diversity)\n- VAAL: YES at 10%, YES at 20% (with diversity)\n- Margin: YES at 10%, YES at 20% (with diversity)\n- Entropy: YES at 10%, NO at 20%\n- Coreset: NO at 10%, YES at 20% (with diversity)\n- BALD: YES at 10%, NO at 20%\n\nCount = 6 base strategies outperform random in at least one configuration at at least one budget level.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 81906, "error": null, "extra": {"thread_id": 1959, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7833996f4234f1db0fa9af29b8ebd6c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 805, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7, which show label diversity comparisons across different datasets (PathMNIST, OrganAMNIST, BloodMNIST), I can see the class distributions for different querying strategies.\n\nFrom these figures, VAAL consistently shows the poorest label diversity across datasets:\n- In PathMNIST (Figure 2), VAAL heavily selects from the \"muscle\" class while largely ignoring \"mucus\" and \"mucosa\" classes\n- In OrganAMNIST (Figure 7), VAAL prefers \"femur-right\" and \"platelet\" classes while largely ignoring lung, liver and monocyte classes\n- In BloodMNIST (Figure 7), VAAL shows similarly poor label diversity with heavy bias toward specific classes\n\nThis is further supported by Table 1, where VAAL frequently has missing results (marked as \"-\") because it fails to sample at least one data point for each class.\n\nAnswer: VAAL", "citations": [], "input_tokens": 58491, "output_tokens": 229, "cost_micros": 178908, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7 in the document, which show label diversity comparisons across different datasets, I can observe the class distribution patterns for various active querying strategies.\n\nFrom the figures, VAAL consistently shows highly imbalanced distributions with strong bias towards specific classes:\n- In PathMNIST (Figure 2): VAAL heavily favors muscle class while largely ignoring mucus and mucosa\n- In OrganAMNIST (Figure 7a): VAAL prefers femur-right while ignoring lung and liver\n- In BloodMNIST (Figure 7b): VAAL favors platelet while ignoring other classes\n\nThe document also states: \"For example, VAAL prefers selecting data in the muscle class, but largely ignores data in the mucus and mucosa classes.\"\n\nTable 2 further confirms this, showing VAAL has very poor class coverage (0.11-0.30) compared to other methods at low budgets.\n\nAnswer: VAAL", "citations": [], "input_tokens": 21472, "output_tokens": 230, "cost_micros": 67866, "latency_ms": 25187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity figures and tables in the document:\n\nIn Figure 2 (PathMNIST), VAAL shows extremely poor label diversity with an entropy of only 2.800, heavily biased toward the muscle class while largely ignoring mucus and mucosa classes.\n\nIn Figure 7 (OrganAMNIST and BloodMNIST), VAAL again shows the poorest label diversity, preferring specific classes like femur-right and platelet while ignoring others.\n\nIn Table 2 (class coverage), VAAL consistently has the lowest class coverage across datasets:\n- PathMNIST: 0.11 (lowest)\n- OrganAMNIST: 0.18 (lowest)\n- BloodMNIST: 0.13 (lowest)\n- CIFAR-10-LT: 0.30 (tied for lowest)\n\nThe document also notes that VAAL results are often missing (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 26477, "output_tokens": 247, "cost_micros": 83136, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Class coverage of selected data\" across different datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10-LT), I can see the class coverage values for different querying strategies.\n\nVAAL consistently shows the lowest class coverage values across the datasets:\n- PathMNIST: 0.11 at 0.00015%, 0.11 at 0.00030%, 0.18 at 0.001%, 0.18 at 0.002%\n- OrganAMNIST: 0.13 at 0.001%, 0.13 at 0.002%\n- BloodMNIST: values not explicitly shown but indicated as poor\n- CIFAR-10-LT: 0.30 at 0.2%, 0.30 at 0.3%\n\nAdditionally, Table 1 shows that VAAL has many missing results (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 23177, "output_tokens": 263, "cost_micros": 73476, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity analysis in the document, I need to examine Figure 2, Table 2, Figure 7, and Figure 10 which show class distributions and coverage across different querying strategies.\n\nFrom Figure 2 (PathMNIST), VAAL shows extremely poor label diversity - it selects 25% from muscle class but only 1% and 2% from mucosa and mucus classes respectively, with an entropy of 2.800 (lowest among all strategies).\n\nFrom Table 2 (class coverage):\n- VAAL consistently shows the poorest class coverage across datasets\n- PathMNIST (0.00015%): VAAL covers only 0.11 (11% of classes)\n- OrganAMNIST (0.001%): VAAL covers only 0.18 (18% of classes) \n- BloodMNIST (0.001%): VAAL covers only 0.13 (13% of classes)\n- CIFAR-10-LT (0.2%): VAAL covers only 0.30 (30% of classes)\n\nFigure 7 confirms VAAL selects 100% from femur-right and platelet while selecting 0% from most other classes.\n\nFigure 10 shows VAAL fails to select any samples from 9 out of 10 classes in CIFAR-10-LT.\n\nAnswer: VAAL", "citations": [], "input_tokens": 37270, "output_tokens": 330, "cost_micros": 116760, "latency_ms": 9437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find information about querying criteria and label diversity across datasets.Let me continue reading the document to find information about querying criteria and label diversity:Let me search for information about label diversity across different querying criteria in the document:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28125, "error": null, "extra": {"thread_id": 1960, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9f3482197c3247b0aae1aed4062d9a6a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 96, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read priority document to find information about querying criteria and label diversity across datasets let me continue reading document to find information about querying criteria and label diversity let me search for information about label diversity across different querying criteria in document", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each page systematically.\n\nPage 1 (Figure 1): Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. This appears to be 4 figures with multiple lines each.\n\nPage 2 (Figure 1 continued): Same figure from page 1.\n\nPage 4 (Figure 2): Shows a bar chart comparing class distributions, not line plots.\n\nPage 5 (Figure 3): Shows three panels with scatter plots (Dataset Maps), not line plots.\n\nPage 7 (Figure 4): Shows 4 subplots (a-d), each containing line plots with error bars comparing different strategies. This is 4 figures with multiple lines each.\n\nPage 8 (Figure 5): Shows 10 subplots arranged in 2 rows, each containing multiple line plots. This is 10 figures with multiple lines each.\n\nPage 16 (Figure 6): Shows 18 subplots (6 strategies \u00d7 3 datasets), each containing line plots. This is 18 figures with multiple lines each.\n\nPage 17 (Figure 7): Shows bar charts, not line plots.\n\nPage 18 (Figure 8): Shows UMAP visualizations (scatter plots), not line plots.\n\nPage 20 (Figure 9): Shows 12 subplots (6 strategies \u00d7 2 datasets), each containing line plots. This is 12 figures with multiple lines each.\n\nPage 21 (Figure 10): Shows bar charts, not line plots.\n\nPage 22 (Figure 11): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 23 (Figure 12): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 24 (Figure 13 and 14): Each figure shows 10 subplots with line plots. This is 20 figures with multiple lines each.\n\nCounting the figures that contain line plots:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal: 4", "citations": [], "input_tokens": 58479, "output_tokens": 512, "cost_micros": 183117, "latency_ms": 36375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure systematically.\n\n**Figure 1** (page 2): Shows 4 subplots (a-d), each containing multiple line plots comparing different methods. Each subplot has multiple lines.\n\n**Figure 2** (page 4): This is a bar chart showing class distributions, not line plots.\n\n**Figure 3** (page 5): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 4** (page 8): Shows 4 subplots (a-d), each containing multiple line plots (4 different methods plotted).\n\n**Figure 5** (page 9): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 6** (page 16): Shows 3 rows \u00d7 6 columns = 18 subplots, each containing line plots (with and without diversity).\n\n**Figure 7** (page 17): These are bar charts showing class distributions, not line plots.\n\n**Figure 8** (page 18): These are scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9** (page 20): Shows 2 rows \u00d7 6 columns = 12 subplots, each containing line plots.\n\n**Figure 10** (page 21): These are bar charts showing class distributions, not line plots.\n\n**Figure 11** (page 22): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 12** (page 23): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 13** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 14** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\nNow let me count the total number of individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 4 panels \n- Figure 5: 10 panels\n- Figure 6: 18 panels\n- Figure 9: 12 panels\n- Figure 13: 10 panels", "citations": [], "input_tokens": 21460, "output_tokens": 512, "cost_micros": 72060, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document page by page:\n\n**Figure 1 (page 2)**: Contains 4 line plots (a, b, c, d) showing cold start problem comparisons.\n\n**Figure 2 (page 3)**: This is a bar chart showing label diversity, not line plots.\n\n**Figure 3 (page 5)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 4 (page 7)**: Contains 4 line plots (a, b, c, d) comparing map-based querying strategies.\n\n**Figure 5 (page 8)**: Contains 10 line plots (2 rows \u00d7 5 columns) showing training from scratch and fine-tuning results.\n\n**Figure 6 (page 16)**: Contains 18 line plots (3 rows \u00d7 6 columns) showing extended results from Table 1.\n\n**Figure 7 (page 17)**: Contains bar charts, not line plots.\n\n**Figure 8 (page 18)**: Contains scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9 (page 20)**: Contains 12 line plots (2 rows \u00d7 6 columns) showing results on CIFAR-10 and CIFAR-10-LT.\n\n**Figure 10 (page 21)**: Contains bar charts, not line plots.\n\n**Figure 11 (page 22)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 12 (page 23)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 13 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for BloodMNIST results.\n\n**Figure 14 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for PathMNIST results.\n\nTotal count:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal =", "citations": [], "input_tokens": 26465, "output_tokens": 512, "cost_micros": 87075, "latency_ms": 8219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document:\n\n**Figure 1**: Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. Each subplot has multiple lines representing different strategies.\n\n**Figure 4**: Shows 4 subplots for different datasets (PathMNIST, OrganAMNIST, BloodMNIST, CIFAR-10-LT), each with multiple line plots.\n\n**Figure 6**: Shows 6 subplots (one for each strategy: BALD, Consistency, Coreset, Margin, VAAL, Entropy) across 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) = 18 subplots total, each containing 2 line plots (with and without diversity).\n\n**Figure 9**: Shows 6 subplots (one for each strategy) for 2 datasets (CIFAR-10 and CIFAR-10-LT) = 12 subplots total, each containing 2 line plots.\n\n**Figure 13**: Shows performance plots for BloodMNIST with two parts (a) and (b). Each part shows 6 subplots (Random, Entropy, Margin, BALD, Coreset, and appears to show multiple strategies). Each subplot contains multiple line plots showing different initial query strategies.\n\n**Figure 14**: Shows performance plots for PathMNIST with two parts (a) and (b). Similar structure to Figure 13, with 6 subplots in each part, each containing multiple line plots.\n\nLet me count more carefully by looking at each figure:\n\n- Figure 1: 4 subplots \u00d7 ~7 lines each = 28 line plots\n- Figure 4: 4 subplots \u00d7 4 lines each = 16 line plots\n- Figure 6: 18 subplots \u00d7 2 lines each = 36 line plots\n- Figure 9: 12 subplots \u00d7 2 lines each = 24 line plots\n- Figure 13: 2 parts \u00d7 6 subplots \u00d7 ~4 lines = ", "citations": [], "input_tokens": 23165, "output_tokens": 512, "cost_micros": 77175, "latency_ms": 10296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure.\n\n**Figure 1:** Shows 4 subplots (a-d) for PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10. Each subplot contains multiple line plots comparing different strategies. This appears to be 4 plots with multiple lines each.\n\n**Figure 2:** This is a table showing label diversity, not line plots.\n\n**Figure 3:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 4:** Shows 4 sets of comparisons (a-d) with 2 budget levels each. Each comparison shows line plots. This gives us 8 line plot panels.\n\n**Figure 5:** Shows 2 subplots (a-b) for \"Training from scratch\" and \"Fine-tuning from self-supervised pre-training\". Each contains multiple line plots.\n\n**Figure 6:** Shows a 3\u00d76 grid (3 datasets \u00d7 6 methods) = 18 line plot panels.\n\n**Figure 7:** This is a table, not line plots.\n\n**Figure 8:** Shows UMAP visualizations (scatter plots), not line plots.\n\n**Figure 9:** Shows 2 rows \u00d7 6 columns = 12 line plot panels.\n\n**Figure 10:** This is a table, not line plots.\n\n**Figure 11:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 12:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 13:** Shows 2 subplots (a-b) with multiple line plots each.\n\n**Figure 14:** Shows 2 subplots (a-b) with multiple line plots each.\n\nNow let me count more carefully by counting individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 8 panels (4 datasets \u00d7 2 budgets)\n- Figure 5: 2 panels\n- Figure 6: 18 panels (3 datasets \u00d7 6 methods)\n- Figure 9: 12 panels (2 datasets \u00d7 6 methods)\n- Figure 13: 2 panels\n- Figure 14: 2 panels", "citations": [], "input_tokens": 37258, "output_tokens": 512, "cost_micros": 119454, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the paper.Let me continue reading to see the full document and search for figures/plots:Now let me search for mentions of plots, charts, or examine the figures more carefully:I can see Figure 1 has 4 subfigures (1a, 1b, 1c, 1d) and they appear to be line plots. Let me continue examining other figures:Figure 2 appears to be a bar chart/heatmap showing class distributions. Let me check Figure 3:Figure 3 appears to be scatter", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46530, "error": null, "extra": {"thread_id": 1961, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5cc38c9bf2aa4c2ba5f55758d03cf5bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 228, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the optimizers used in this research by searching through the document.\n\nLooking at the implementation configurations section (Appendix A), specifically:\n\n1. In Table 3a (MedMNIST pre-training), the optimizer is listed as \"SGD\"\n2. In Table 3b (CIFAR-10-LT pre-training), the optimizer is listed as \"SGD\"\n3. In Table 6 (Benchmark settings), the optimizer is listed as \"SGD\"\n\nThroughout all the experimental settings described in the document, SGD (Stochastic Gradient Descent) is consistently used as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 58486, "output_tokens": 152, "cost_micros": 177738, "latency_ms": 32796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the optimizers used in the research.\n\nIn Table 3 (page 14), which shows \"Contrastive learning settings on MedMNIST and CIFAR-10-LT,\" I can see:\n\nFor both MedMNIST pre-training (Table 3a) and CIFAR-10-LT pre-training (Table 3b):\n- optimizer: SGD\n\nIn Table 6 (page 15), which shows \"Benchmark settings,\" the optimizer listed is also:\n- optimizer: SGD\n\nThroughout the document, only SGD (Stochastic Gradient Descent) is mentioned as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 21467, "output_tokens": 164, "cost_micros": 66861, "latency_ms": 6155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (pages 13-14), which shows contrastive learning settings:\n- For MedMNIST pre-training: optimizer = SGD\n- For CIFAR-10-LT pre-training: optimizer = SGD\n\nIn Table 6 (page 15), which shows benchmark settings:\n- For training MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 26472, "output_tokens": 120, "cost_micros": 81216, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (page 12), the contrastive learning settings show:\n- optimizer: SGD\n- optimizer momentum: 0.9\n\nIn Table 6 (page 13), the benchmark settings show:\n- optimizer: SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 23172, "output_tokens": 72, "cost_micros": 70596, "latency_ms": 3764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (Contrastive learning settings), the optimizer is listed as **SGD** for both MedMNIST and CIFAR-10-LT pre-training.\n\nIn Table 6 (Benchmark settings), the optimizer is also listed as **SGD** for training MedMNIST, CIFAR-10, and CIFAR-10-LT.\n\nAnswer: SGD", "citations": [], "input_tokens": 37265, "output_tokens": 106, "cost_micros": 113385, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the optimizers used in the research.Let me search for more specific terms related to optimizers:Good! I found SGD. Let me read the sections that mention optimizer settings to get a complete picture:Let me check the benchmark settings table as well:Perfect! I found the optimizer information. Based on the document, I can see that SGD (Stochastic Gradient Descent) is consistently used as the optimizer across different experimental settings:\n\n1. Table 3(a) - MedMNIST pre-training: optimizer = SGD\n2. Table 3(b) - CIFAR-10-LT pre-training: optimizer = SGD \n3. Table 6 - Benchmark settings for MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD (Stochastic Gradient Descent)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42718, "error": null, "extra": {"thread_id": 1962, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eae58ea384884f52967fd1489c25db07", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 204, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "sgd stochastic gradient descent", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 which shows the Dataset Maps for PathMNIST:\n\nThe Data Map by ground truth (panel b) shows data points that are clearly stratified by classes, with distinct vertical bands representing different classes across the confidence axis.\n\nThe Data Map by pseudo-labels (panel c) shows data points that are more continuously distributed across the confidence range, without the same degree of class stratification.\n\nFrom the visual representation, the Data Map by ground truth appears to have data points spread more widely across different confidence levels (from approximately 0.0 to 1.0), while the pseudo-labels map shows a somewhat more compressed distribution in terms of confidence values.\n\nAnswer: Data Map by ground truth", "citations": [], "input_tokens": 58503, "output_tokens": 153, "cost_micros": 177804, "latency_ms": 44546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "data map by ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3b shows the Data Map by ground truth with confidence values ranging from approximately 0.2 to 1.0\n- Figure 3c shows the Data Map by pseudo-labels with confidence values ranging from approximately 0.0 to 0.6\n\nThe map by ground truth shows a wider spread of confidence values across a larger range (approximately 0.8 units) compared to the pseudo-labels map (approximately 0.6 units), and the data points in the ground truth map appear more dispersed across different confidence levels.\n\nAnswer: ground truth", "citations": [], "input_tokens": 21484, "output_tokens": 149, "cost_micros": 66687, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 11(b) shows the Data Map by ground truth\n- Figure 11(c) shows the Data Map by pseudo-labels\n\nExamining the confidence (y-axis) distribution in both maps:\n\nIn the ground truth map (11b), the data points are stratified by classes and spread across different confidence levels, with points distributed from approximately 0.2 to 1.0.\n\nIn the pseudo-labels map (11c), the data points are more clustered, with most points concentrated in a narrower confidence range, roughly between 0.2 and 0.9.\n\nThe ground truth map shows a wider spread of confidence values across the vertical axis compared to the pseudo-labels map.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 26489, "output_tokens": 184, "cost_micros": 82227, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nExamining the y-axis (confidence) range in both maps:\n\nIn Figure 3(b) (ground truth), the confidence values span approximately from 0.2 to 1.0, showing data points distributed across a wide range with clear stratification by classes.\n\nIn Figure 3(c) (pseudo-labels), the confidence values span approximately from 0.0 to 0.6, showing a more compressed distribution.\n\nThe map based on ground truths displays a larger spread in confidence values from low to high, indicating greater variance in confidence compared to the pseudo-labels map which shows a more compressed range.\n\nAnswer: ground truth", "citations": [], "input_tokens": 23189, "output_tokens": 198, "cost_micros": 72537, "latency_ms": 6797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine the data maps for PathMNIST to compare the confidence variance between the map by ground truth and the map by pseudo-labels.\n\nLooking at Figure 3 in the document, which shows \"Active querying based on Dataset Maps\":\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nFrom the visual comparison of these two maps:\n- The map by ground truth (3b) shows confidence values ranging roughly from 0.0 to 1.0\n- The map by pseudo-labels (3c) shows confidence values also ranging roughly from 0.0 to 1.0\n\nLooking more carefully at the spread of points:\n- In the ground truth map (3b), the confidence values appear to span a wider range vertically (approximately 0.0 to 1.0)\n- In the pseudo-labels map (3c), the confidence values appear more compressed, with most points concentrated between roughly 0.1 and 0.6\n\nThis indicates that the map by ground truth has a larger variance in confidence values compared to the map by pseudo-labels.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 37282, "output_tokens": 267, "cost_micros": 115851, "latency_ms": 7093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about PathMNIST dataset maps and confidence variance.Let me search for \"PathMNIST\" in the document to find the relevant section.Let me search for \"dataset map\" or look at figures related to PathMNIST.Good! I found references to Dataset Maps. Let me read the section around line 680-681 and look at Figure 3.Let me continue reading to see the full figure and find PathMNIST specifically.This figure appears to be about BloodMNIST based on the cell types mentioned. Let me search for figures with PathMNIST.Perfect! I found Figure 11 which includes PathMNIST dataset maps. Let me read that section.Perfect! I can see the PathMNIST dataset maps in Figure 11. Now I need to look at the actual visual representation or description to understand which map has larger confidence variance. Let me continue reading to see if there's more detail about the variance.I can see the figure caption and layout, but I need to look at the actual scatter plots more carefully. Looking at the text I've read:\n\nFor PathMNIST (lines 3829-3883):\n- (b) Data Map by ground truth - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n- (c) Data Map by pseudo-labels - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n\nThe figure shows the confidence ranges from 0.0 to 1.0 on the y-axis for both maps. However, I need to check if there's additional information about which map has larger variance. Let me look at the visual descriptions of the actual figure images.Let me look for the visual description of Figure 11 specifically, which should contain information about the PathMNIST maps.Let me search for image descriptions related to Figure 11's scatter plots to find information about confidence variance.I need to look more carefully at the PathMNIST data in Figure 11. Let me look for visual descriptions of the actual scatter plots. Let me search for image descriptions that might contain the scatter plot information.Looking at the data from PathMNIST in Figure 11:\n- Panel (b) shows \"Data Map by ground truth\" with confidence ranging from 0.0 to 1.0 (as I can see from lines 3831-3873)\n- Panel (c) shows \"Data Map by pseudo-labels\" with confidence ranging from 0.0 to 1.0\n\nThe confidence is on the y-axis. Looking at the values, I see for panel (b) the y-axis goes from 0.0 to ~0.9, and for panel (c) it goes from 0.0 to 1.0 (full range). The question asks about \"confidence variance\" which would be the spread/range of confidence values on the y-axis.\n\nHowever, I need to look more carefully. Let me check if there are image descriptions that describe the actual scatter plot distributions.Let me look at the image descriptions around the PathMNIST figure more carefully. Let me search for image descriptions near Figure 11.Let me look for scatter plot images that are specifically associated with PathMNIST. Let me check around the chunks where PathMNIST appears in Figure 11.Let me look for the actual scatter plot images. Let me search for specific image descriptions that mention PathMNIST scatter plots with confidence and variability axes.Let me search more specifically for scatter plots with confidence and variability axes that relate to PathMNIST.Good! I found a scatter plot description. Let me look for more scatter plots, especially those that might be for PathMNIST ground truth vs pseudo-labels maps. Let me continue searching.Good! I found another scatter plot. Let me continue reading this one.I'm finding scatter plots but I need to identify which ones specifically correspond to PathMNIST panel (b) ground truth and panel (c) pseudo-labels from Figure 11. Let me look for scatter plots with specific axis ranges that match what I saw earlier for PathMNIST.\n\nLooking back at the PathMNIST data from lines 3829-3883, I saw:\n- The y-axis (confidence) goes from 0.0 to at least 0.8\n- Panel (b) shows variability on x-axis with range mentioned as 0.2, 0.4\n- Panel (c) also shows variability on x-axis with range 0.2, 0.4\n\nLet me search for images that might be specifically labeled or that have characteristics matching PathMNIST.I found Im5.jpg which is a scatter plot with confidence 0.0-1.0 and variability 0.0-0.4. This could be one of the PathMNIST maps. Let me look for more scatter plots around Figure 11 area. Let me check if there are descriptions that tell me which image corresponds to which panel of Figure 11.\n\nActually, looking back at the text structure, the figure content is laid out sequentially. From lines 3805-3884, I saw Figure 11 with PathMNIST having:\n- (a) Overall distribution (line 3881)\n- (b) Data Map by ground truth (line 3883)\n- (c) Data Map by pseudo-labels (line 3884)\n\nThe OCR text I saw showed axes values. Let me look more carefully at Im24.jpg and Im29.jpg which I found, as well as other scatter plots, to see if I can determine which represents ground truth vs pseudo-labels.\n\nLet me look for a pattern. Based on the figure caption at line 3805, panels (b) and (e) are ground truth maps, and panels (c) and (f) are pseudo-label maps. Since PathMNIST is (a,b,c) and OrganAMNIST is (d,e,f), I need to find the two scatter plot images that follow the PathMNIST overall distribution.\n\nLet me look at the description of Im24.jpg more carefully (lines 7044-7077) - it shows confidence 0.0-1.0 and variability 0.0-~0.5, with an inverse relationship.\n\nAnd Im29.jpg (lines 7158-7194) shows confidence 0.0-1.0 and variability 0.0-~0.6, with points spread from (0.1, 0.0) to (0.6, 0.7).\n\nNow let me check Im5.jpg (lines 4897-4932) - it shows confidence 0.0-1.0 and variability 0.0-0.4, with a **negative correlation** (upper left high confidence/low variability to lower right low confidence/high variability).\n\nThis is key! The description says Im5.jpg shows data points concentrated in \"Upper left region (low variability, high confidence)\" going to \"Lower right region (high variability, low confidence)\" with \"increased scatter/spread of points in middle and lower portions of curve.\"\n\nNow let me check Im24.jpg and Im29.jpg again to see which shows more spread/variance in confidence.\n\nIm24.jpg (lines 7067-7076): \"Greater scatter/dispersion visible at intermediate confidence values (0.2-0.8)\" and \"Points more tightly clustered near confidence extremes (approaching 0.0 and 1.0)\"\n\nIm29.jpg (lines 7181-7194): \"Some scatter/dispersion around the main curve throughout the distribution\"\n\nLet me look for more scatter plot images that might be the actual PathMNIST panels. Let me check if there are images with specific patterns that would indicate stratification by class (ground truth) vs mixed patterns (pseudo-labels).Perfect! The caption says that ground truth maps have \"data are stratified by classes in the 2D space\" - meaning the points would be grouped/clustered by class. This would create a MORE spread out or varied pattern compared to pseudo-labels which would be more mixed/homogeneous.\n\nBased on the figure captions, the ground truth map shows stratification by classes, which would likely result in **larger variance** in confidence values since different classes cluster at different confidence levels.\n\nLet me search for visual descriptions that mention clustering or stratification patterns in scatter plots.I found very useful information! Looking at lines 5699-5716, there's a description of a clustered scatter plot which might be relevant. But more importantly, I need to focus on the actual PathMNIST dataset maps.\n\nThe key insight from the caption is that ground truth maps show \"data are stratified by classes in the 2D space\" - this means points are separated/grouped by class, which would naturally create **more variance** in confidence values across the entire dataset map, since different classes would cluster at different confidence levels.\n\nIn contrast, pseudo-label maps would show a more homogeneous distribution since pseudo-labels don't perfectly separate classes and mix them together, resulting in **less variance** in confidence values.\n\nBased on the figure caption (line 3805) stating that ground truth maps have data \"stratified by classes\" creating distinct groupings at different confidence levels, the ground truth map would have **larger confidence variance**.\n\nAnswer: ground truth", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 157108, "error": null, "extra": {"thread_id": 1963, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67e301ccb20c47e7a10fd97f2b39b6f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1340, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about Amazon-related datasets mentioned in the paper.\n\nFrom the document, I can identify the following Amazon-related datasets:\n\n1. **Amazon-beauty dataset** - mentioned multiple times throughout the paper as one of the two newly constructed datasets\n2. **Amazon-music dataset** - mentioned multiple times throughout the paper as the second newly constructed dataset\n\nLooking at Table 3 (Dataset Statistics), I can see three datasets are discussed:\n- Amazon-beauty\n- Amazon-music \n- Personality'18\n\nThe Personality'18 dataset is based on MovieLens, not Amazon.\n\nThe paper explicitly states: \"we construct two new domain-specific datasets: an Amazon-beauty and an Amazon-music dataset.\"\n\nTherefore, there are 2 Amazon-related datasets in total.\n\nAnswer: 2", "citations": [], "input_tokens": 26915, "output_tokens": 180, "cost_micros": 83445, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper also mentions Personality'18 dataset with 21,776 items, but this is a MovieLens dataset, not an Amazon dataset.\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 13244, "output_tokens": 132, "cost_micros": 41712, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Statistics of the three datasets used in our study), I can find:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper mentions two Amazon-related datasets: Amazon-beauty and Amazon-music.\n\nTotal items = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 16269, "output_tokens": 113, "cost_micros": 50502, "latency_ms": 5280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Dataset Statistics), I can find information about the datasets used in the study. The paper mentions three datasets:\n\n1. Amazon-beauty\n2. Amazon-music \n3. Personality2018 (which is a MovieLens dataset, not Amazon)\n\nFrom Table 3, I need to find the number of items for the Amazon datasets:\n- The table shows statistics for datasets but I need to locate the specific numbers for items\n\nLooking more carefully at the document, in Section 3.3 \"Dataset Statistics\":\n- It mentions \"Table 3 shows the final statistics of the datasets used in our study\"\n- It states \"The Personality2018 dataset contains the largest number of items and the smallest number of users\"\n- It mentions \"21,776 movies\" for Personality2018\n\nHowever, I cannot see Table 3 clearly displayed with all the numerical values in the provided text. Let me search more carefully...\n\nThe document mentions datasets but the actual Table 3 with complete statistics is not fully visible in the text provided. Without access to the complete Table 3 showing the number of items for Amazon-beauty and Amazon-music datasets, I cannot provide an accurate total.\n\nLooking through the entire document again, I can see references to Table 3 but the actual table with item counts is not included in the extracted text.\n\nSince I cannot find the specific numbers of items for the Amazon datasets in the provided document text, I cannot calculate the total accurately.\n\nAnswer: Unable to determine from the provided text as Table 3 statistics are not fully shown.\n\nWait, let me reconsider - the question asks for an integer answer, so I must find this information. Let me search more carefully for any mention of item counts...\n\nThe document does not provide the specific item counts for Amazon-beauty and Amazon-music datasets in the readable text provided. Without Table 3 being fully visible with those statistics, I cannot provide the accurate total.\n\nAnswer: 0", "citations": [], "input_tokens": 10434, "output_tokens": 451, "cost_micros": 38067, "latency_ms": 11968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study\":\n\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n- Personality'18: 21,776 items (but this is NOT an Amazon dataset, it's a MovieLens dataset)\n\nThe question asks specifically for \"Amazon related datasets\", so I should only count Amazon-beauty and Amazon-music.\n\nTotal items in Amazon datasets = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 14857, "output_tokens": 144, "cost_micros": 46731, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about Amazon-related datasets.Let me search for \"Amazon\" in the document to locate relevant information about datasets.Now let me read the sections that describe the datasets in detail to count the Amazon-related datasets:Let me continue reading to see more details about the datasets:Based on the document, I can see that the paper mentions Amazon-related datasets. Let me check the table more carefully and search for the total number of items:\n\nFrom Table 3 in the document, I can see three datasets are used in the study:\n1. **Amazon-beauty** - with 85 items\n2. **Amazon-music** - with 8,895 items \n3. Personality'18 - (this is not Amazon-related, it's a MovieLens dataset)\n\nThe paper constructed two Amazon-related datasets from the parent Amazon review dataset. Looking at the \"# of items\" row in Table 3:\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41483, "error": null, "extra": {"thread_id": 1964, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a498e50e54164511b090eb63e6f7d154", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the Receptiviti score example shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to find which dataset this corresponds to and what the average words per user is for that dataset.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study,\" I can see:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe User ID format A2GBIFL43U1LKJ appears to be from the Amazon dataset format (as shown in Table 1 with User ID A2SUAM1J3GNN38).\n\nHowever, Table 2 doesn't specify which dataset it's from. Looking at the text in Section 3.2, it states: \"For each user that wrote reviews in either of the two domains, we collect all his/her review texts and concatenate them together into a single document.\"\n\nThe text mentions selecting users for personality inference from both Amazon-beauty and Amazon-music datasets. Since Table 2 is just an example and doesn't specify which dataset, and the question asks about \"the dataset from which a data sample of a user's Receptiviti score is shown,\" I need to determine which dataset the example is from.\n\nGiven that the paper discusses both datasets and Table 2 appears to be a generic example, and considering the context of the paper focuses on both datasets equally, I should look for any other clues. However, without explicit information linking Table 2 to a specific dataset, and since the User ID format matches the Amazon dataset structure, the most reasonable interpretation is that this could be from either Amazon-beauty or Amazon-music.\n\nSince the question asks specifically about \"the dataset\" (singular) and Table 2 is presented as a single example, and given no other specific information, I cannot definitively determine which dataset it's from. However, looking at the document structure and flow, Table 2 appears early in the dataset construction section before domain", "citations": [], "input_tokens": 26939, "output_tokens": 512, "cost_micros": 88497, "latency_ms": 18092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with columns for User ID, AGR, CON, NEU, EXT, and OPEN. The user ID shown is \"A2GBIFL43U1LKJ\".\n\nLooking at Table 4, I can see review text examples with personality labels and scores. The table caption mentions \"The data sample of extreme personality cases to the annotators.\"\n\nNow I need to find which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see:\n\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon datasets (based on the naming convention).\n\nHowever, I need to determine which specific dataset (beauty or music) this example is from. Looking at the context around Table 2, it appears after the discussion of dataset construction and before the detailed statistics. The table is presented as a general example of the Receptiviti API output.\n\nLooking more carefully at the document flow:\n- Section 3.2 discusses dataset construction for both Amazon-beauty and Amazon-music\n- Table 2 is presented as \"An example of Receptiviti score for a specific, anonymized user\"\n- Table 3 immediately follows with statistics for both datasets\n\nSince Table 2 is presented as a general example without specifying which dataset it's from, and given that both Amazon datasets use similar user ID formats, I need to look for more context.\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" - this refers to Table 2. Without explicit identification of which dataset this user is from in Table 2, and given that the paper presents both datasets with their statistics in Table 3, the most reasonable interpretation is that this is asking about one of the Amazon datasets.\n\nSince the question asks for a specific answer and Table 2", "citations": [], "input_tokens": 13268, "output_tokens": 512, "cost_micros": 47484, "latency_ms": 13219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. Which dataset the data sample in Table 2 comes from\n2. The average words per user for that dataset\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nLooking at Table 1, I can see a similar user ID format (A2SUAM1J3GNN38) which appears to be from the Amazon dataset based on the format and fields shown (reviewerID, asin, etc.).\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see the \"Avg. words/user\" for each dataset:\n- Amazon-beauty: 990.48\n- Amazon-music: 466.43\n- Personality'18: - (no data)\n\nThe document mentions in Section 3.2 that they used the Receptiviti API to analyze personality from concatenated review texts. The example user ID format in Table 2 (A2GBIFL43U1LKJ) matches the Amazon dataset format.\n\nSince the paper constructs two Amazon datasets and Table 2 is presented early in the methodology section without specifying which domain, and given that Amazon-beauty is mentioned first throughout the paper and has more prominent results, the sample likely comes from Amazon-beauty dataset.\n\nHowever, looking more carefully at the context, the table appears as the first example when explaining the personality scores, and both datasets use the same methodology. The Amazon-music dataset has 466.43 average words per user.\n\nGiven the ambiguity, but noting that Amazon-beauty is discussed first and more extensively, and the table appears in the general methodology section:\n\nAnswer: 990.48", "citations": [], "input_tokens": 16293, "output_tokens": 400, "cost_micros": 54879, "latency_ms": 11655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find information about the average number of words per user used for experiments in the dataset.\n\nLet me search for relevant information about the dataset construction and word counts.\n\nFrom Section 3.2 \"Dataset Construction\":\n- The paper mentions they \"first extract the user reviews that are between 30 to 80 words\"\n- Then they \"concatenate all the valid review texts of each user and input their concatenation to the Receptiviti API\"\n\nFrom Section 3.2, it also states:\n\"The API can take a long piece of human-written text (more than 300 words), and output a faceted personality score\"\n\nThis indicates that they concatenated multiple reviews (each 30-80 words) to reach more than 300 words total per user.\n\nHowever, the question asks specifically about \"the average number of words per user used for the experiments in the dataset from which a data sample of a user's Receptiviti score is shown.\"\n\nLooking at Table 2, which shows \"User ID\" with personality scores, and Table 4, which shows \"Personality label\" with scores and review texts - these are examples of users with Receptiviti scores.\n\nThe key constraint mentioned is:\n- Individual reviews: 30 to 80 words\n- Concatenated text sent to API: more than 300 words\n\nThe paper doesn't provide an exact average number of words per user. However, it does specify that the concatenated reviews must be \"more than 300 words\" to use the Receptiviti API.\n\nSince the paper doesn't explicitly state the average number of words per user, but only mentions the minimum threshold of 300 words for the API, and the individual review range of 30-80 words, I cannot determine an exact average from the information provided.\n\nLet me re-read more carefully... The paper does not explicitly state the average number of words per user used in the experiments. It only mentions the requirements (30-80 words per review, and >300 words total for API input).\n\nWithout explicit information about the average number of words per user in the dataset, I cannot provide an accurate answer to this question based solely on the document.\n\nAnswer: Cannot be determined from the document provided.", "citations": [], "input_tokens": 10458, "output_tokens": 489, "cost_micros": 38709, "latency_ms": 13437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to identify which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see three datasets:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (not applicable)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon dataset format (starting with \"A\" followed by alphanumeric characters).\n\nLooking at Table 1, which shows \"An example of Receptiviti score for a specific, anonymized user\" and contains a reviewerID \"A2SUAM1J3GNN38\" - this is clearly an Amazon dataset format.\n\nHowever, I need to determine which Amazon dataset (beauty or music) the user in Table 2 belongs to. The document doesn't explicitly state which dataset Table 2's user is from.\n\nLooking more carefully at the document structure:\n- Table 1 shows an Amazon review example\n- Table 2 shows a Receptiviti score example\n- Table 3 shows statistics for all three datasets\n\nSince the document constructs two datasets (Amazon-beauty and Amazon-music) and Table 2 appears early in the dataset construction section (Section 3.2), and considering that both Amazon datasets were constructed using the same methodology, I need to check if there's any indication of which one is being referenced.\n\nLooking at Table 4, which shows \"data sample of extreme personality cases,\" these examples appear to be from the Amazon-beauty dataset based on the product descriptions (shampoo, shower gel, etc.).\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" referring to Table 2. Without explicit indication of which dataset Table 2's user belongs to, and given that the methodology section discusses both datasets together", "citations": [], "input_tokens": 14881, "output_tokens": 512, "cost_micros": 52323, "latency_ms": 13687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about the average number of words per user used for the experiments.Let me search for information about \"Receptiviti\" and \"words per user\" in the document.Let me read more of the document to find the relevant information. I'll search for sections that might contain dataset statistics or experimental setup.I found a key piece of information! Let me read around line 663 where it mentions \"Each user in the Amazon-music dataset has an average of 990.48 review words over all of\".Good! Now let me also check if there's similar information for the Amazon-beauty dataset, and let me look at the table and figure captions to find where Receptiviti scores are shown.Great! I found that Table 1 shows \"An example of Receptiviti score for a specific, anonymized user.\" Let me continue reading this table and find information about the dataset statistics, particularly for the Amazon-beauty dataset.Perfect! Now I need to find the statistics for the Amazon-beauty dataset. Let me look at Table 3 which shows \"the final statistics of the datasets.\"Excellent! I found Table 3 which shows the statistics for all three datasets. Now I need to identify which dataset the Receptiviti score example (Table 1) comes from.\n\nLooking at the document:\n- Table 1 (around line 581) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n- Table 3 (lines 689-732) shows the average words per user for each dataset:\n - Amazon-beauty: 990.48\n - Amazon-music: 466.43\n - Personality'18: -\n\nWait, I notice there's a discrepancy. Let me re-read the section more carefully. The text at line 662-664 says \"Each user in the Amazon-music dataset has an average of 990.48 review words\" but Table 3 shows Amazon-beauty has 990.48 and Amazon-music has 466.43.\n\nLet me read that section again more carefully:There seems to be an inconsistency in the text. Let me check the table more carefully again and see if the columns are correctly labeled:Looking at Table 3, the structure shows:\n- Row 1: Headers - Dataset | Amazon-beauty | Amazon-music | Personality'18\n- The text mentions 1,791 users for Amazon-music (which matches column 2: 1,791)\n- The text mentions 990.48 words/user and 51.01 words/review for Amazon-music\n\nBut in Table 3:\n- Column Amazon-music (2nd data column) shows: 1,791 users, 990.48 avg words/user\n- Wait, I need to re-check. Let me look again.\n\nActually, looking more carefully at the table structure:\n- Line 705: 1,791 is under Amazon-music column for # of users\n- Line 722-723: Avg. words/user shows 990.48 under Amazon-beauty and 466.43 under Amazon-music\n\nThere appears to be a mismatch between the text description and the table. However, the table columns might be swapped. Let me check by cross-referencing other values. The text says \"1,791 active users are selected for the Amazon-music dataset\" and in the table, 1,791 appears in the Amazon-music column.\n\nSo the table seems correct. The text at line 662-664 might have an error, or the columns in the table might be swapped.\n\nNow, to identify which dataset the Receptiviti score sample comes from, let me look more carefully at Table 1:Good! So I can see:\n- Table 1 (line 581) shows an Amazon review example with reviewText mentioning \"piano\" and \"old hymns\" - this appears to be from the music domain\n- Table 2 (line 635) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n\nThe review in Table 1 is clearly about music (piano, hymns), so it's from the Amazon-music dataset.\n\nNow, looking back at the data:\n- The text at lines 662-664 states: \"Each user in the Amazon-music dataset has an average of 990.48 review words\"\n- But Table 3 shows Amazon-music with 466.43 avg words/user\n\nLet me verify by checking if there might be swapped columns. Looking at # of users:\n- Text says 1,791 users for Amazon-music\n- Table shows 1,791 under Amazon-music column\nSo the Amazon-music column is correctly labeled.\n\nLet me check # of items:\n- Amazon-beauty should be smaller (beauty products vs music products)\n- Table shows 85 items for Amazon-beauty and 8,895 for Amazon-music\nThis makes sense - there would be more music items than beauty items.\n\nSo it appears there's an error in the text at line 663, where it says \"990.48\" but should say \"466.43\" based on Table 3. OR the table columns for Avg. words/user might be swapped.\n\nLet me verify using Avg. words/review:\n- Text says Amazon-music has \"averaging 51.01 words for each review\"\n- Table shows Amazon-beauty: 51.01, Amazon-music: 51.18\n\nThis suggests the columns might indeed be swapped for the word statistics! The text matches if we swap the columns.\n\nGiven that:\n1. The Receptiviti score sample (Table 1 and Table 2) shows a music review\n2. The text explicitly states the example is from Amazon-music with 990.48 avg words/user\n3. There appears to be a column swap in Table 3 for the word statistics\n\nThe answer should be **990.48** for the Amazon-music dataset.\n\nAnswer: 990.48", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 89094, "error": null, "extra": {"thread_id": 1965, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a03731abb68747a9a4c0bcb35d9412ce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 777, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the OCEAN personality scores for user A2GBIFL43U1LKJ and determine which personality trait has the highest score, then identify its color in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2 on page 4, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1 on page 5, in the \"Method 2: Soft-labeled personality\" section, the personality embedding matrix shows five rows for the OCEAN traits. The rows are colored differently:\n- Openness: appears to be gray/dark\n- Conscientiousness: appears to be gray/dark\n- Extroversion: appears to be gray/dark\n- Agreeableness: appears to be gray/dark\n- Neuroticism: appears to be gray/dark\n\nHowever, looking more carefully at the figure, the personality embedding matrix shows circles in different shades. For Extroversion (the third row), the circles appear to be a lighter shade or gray.\n\nAnswer: gray", "citations": [], "input_tokens": 26942, "output_tokens": 325, "cost_micros": 85701, "latency_ms": 14828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and determine which has the highest score, then identify its color in the soft-labeled personality embedding matrix shown in Figure 1.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, Method 2 shows the soft-labeled personality embedding matrix. The personality traits are listed in order as:\n- Openness (O) - shown in purple/dark blue\n- Conscientiousness (C) - shown in orange\n- Extroversion (E) - shown in green\n- Agreeableness (A) - shown in red\n- Neuroticism (N) - shown in purple\n\nSince Extroversion has the highest score for this user, I need to identify the color associated with Extroversion (E) in the embedding matrix diagram.\n\nAnswer: green", "citations": [], "input_tokens": 13271, "output_tokens": 301, "cost_micros": 44328, "latency_ms": 6467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "green", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 2, which shows \"An example of Receptiviti score for a specific, anonymized user\":\n\nUser ID: A2GBIFL43U1LKJ\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1, specifically the \"Method 2: Soft-labeled personality\" diagram, it shows the personality embedding matrix with different personality traits represented in different colors. From the figure, Extroversion is shown in purple/violet color in the personality embedding matrix.\n\nAnswer: Purple", "citations": [], "input_tokens": 16296, "output_tokens": 195, "cost_micros": 51813, "latency_ms": 6375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "purple", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and then identify which personality trait has the highest score, and finally determine what color represents that trait in the soft-labeled personality embedding visualization.\n\nFrom Table 2 in the document, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nNow I need to look at Figure 1 which shows the personality embedding methods. In the \"Method 2: Soft-labeled personality\" section, there is a personality embedding vector shown with the OCEAN scores {30,70,50,30,20} for an example user. The figure shows these are arranged in order: Neuroticism, Conscientiousness, Extraversion, Agreeableness, Openness.\n\nHowever, the document does not explicitly show colors associated with each personality trait in the soft-labeled personality embedding matrix visualization. The figure appears to be in grayscale in the provided document, and there is no color legend or color coding system described for the personality traits.\n\nAnswer: The document does not specify colors for personality traits in the embedding matrix.", "citations": [], "input_tokens": 10461, "output_tokens": 337, "cost_micros": 36438, "latency_ms": 8765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "document does not specify colors for personality traits in embedding matrix", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and identify which has the highest score, then determine what color represents that personality in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, specifically at Method 2 (Soft-labeled personality), I can see the personality embedding matrix shows five colored vectors. However, the figure in the document does not clearly show distinct colors for each personality trait in a way that I can definitively identify which color corresponds to Extroversion.\n\nThe figure shows a \"Personality embedding matrix\" but the specific color coding for each of the five OCEAN traits is not explicitly labeled or described in the document text.\n\nWithout clear color labels in the figure or explicit description in the text mapping colors to specific personality traits, I cannot determine the color with certainty.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 14884, "output_tokens": 303, "cost_micros": 49197, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the personality vector color for the specified user.Let me search for information about personality vectors, Receptiviti scores, and soft-labeled personality embeddings in the document.Now let me read the specific sections about the soft-labeled personality embedding matrix and look for figures or tables with user A2GBIFL43U1LKJ:Perfect! I found Table 2 which shows the Receptiviti scores for User A2GBIFL43U1LKJ. The scores are:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extraversion) at 54.39. Now I need to find the figure showing the soft-labeled personality embedding matrix to see what color corresponds to Extraversion.I can see the structure but I need to look at the actual image to identify the colors. Let me search for more information about the figure and its color coding:Let me look at the detailed visual description of Figure 1 with the personality embedding matrix:Perfect! I found the answer. According to the visual description of Figure 1, which shows the soft-labeled personality embedding matrix (Method 2), the personality traits are color-coded as follows:\n\n- Purple circles: Openness\n- Blue circles: Conscientiousness\n- **Brown/orange circles: Extraversion**\n- Green circles: Agreeableness\n- Black/gray circles: Neuroticism\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following Receptiviti scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39 (highest)\n- OPEN (Openness): 42.71\n\nThe highest Receptiviti score for User A2GBIFL43U1LKJ is Extraversion (EXT) at 54.39, and according to the figure's visual description, Extraversion is represented by brown/orange circles.\n\nAnswer: Brown/orange", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58234, "error": null, "extra": {"thread_id": 1966, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f42472e918374f5ab49ea4996d3a5acc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 362, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "brown orange", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores in the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods illustrated:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality \n- Method 3: Hard-coded personality\n\nIn Method 3 (Hard-coded personality), there is a \"Scaling\" step that shows personality scores with blue circles: 0.3 0.7 0.5 0.3 0.2\n\nLooking at Table 4 (data sample of extreme personality cases), I need to find which rows correspond to these scaled scores. The scaled values {0.3, 0.7, 0.5, 0.3, 0.2} represent the normalized OCEAN scores where:\n- Openness (O) = 0.3 \u2192 30\n- Conscientiousness (C) = 0.7 \u2192 70\n- Extroversion (E) = 0.5 \u2192 50\n- Agreeableness (A) = 0.3 \u2192 30\n- Neuroticism (N) = 0.2 \u2192 20\n\nThese are the scaled down values from the original scores {30, 70, 50, 30, 20} mentioned in the figure caption.\n\nSorting these in ascending order: 0.2, 0.3, 0.3, 0.5, 0.7\n\nOr in the original scale: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 26955, "output_tokens": 386, "cost_micros": 86655, "latency_ms": 14437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see that the example shows OCEAN scores of {30,70,50,30,20}. The figure shows different personality traits:\n- Openness (O): 30\n- Conscientiousness (C): 70\n- Extroversion (E): 50\n- Agreeableness (A): 30\n- Neuroticism (N): 20\n\nIn the figure, Conscientiousness is highlighted as the \"most salient personality\" with a score of 70, which appears to be the blue/highlighted vector in the personality embedding.\n\nLooking at Table 4 which shows \"The data sample of extreme personality cases to the annotators,\" I need to find entries related to Conscientiousness (which corresponds to the blue vector in the figure).\n\nThe Conscientiousness entries in Table 4 show personality scores of:\n- 75.38\n- 71.02\n\nArranging these in ascending order:\n\nAnswer: 71.02, 75.38", "citations": [], "input_tokens": 13284, "output_tokens": 265, "cost_micros": 43827, "latency_ms": 7109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "71 02, 75 38", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for a data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods shown:\n1. Method 1: Most salient personality - shows \"Conscientiousness\"\n2. Method 2: Soft-labeled personality - shows a personality embedding matrix with OCEAN scores\n3. Method 3: Hard-coded personality - shows scaled values\n\nIn Method 2 (Soft-labeled personality), there is a personality embedding matrix showing OCEAN scores in what appears to be blue-colored boxes:\n- Openness: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Neuroticism: 20\n\nThe figure caption confirms: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nThese scores in ascending order are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 16309, "output_tokens": 253, "cost_micros": 52722, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. The figure shows three methods for incorporating personality:\n\n1. Method 1: Most salient personality - selects conscientiousness (the highest score of 70)\n2. Method 2: Soft-labeled personality - shows a personality embedding vector with values 20\n3. Method 3: Hard-coded personality - shows scaling with values {0.3, 0.7, 0.5, 0.3, 0.2}\n\nThe figure caption states: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nLooking at the structure, the blue colored elements appear to be in Method 2 (Soft-labeled personality), which shows the personality embedding vector. However, I need to identify which specific values correspond to the blue vector.\n\nFrom the figure description and the example given, the OCEAN scores are:\n- Neuroticism: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Openness: 20\n\nIn ascending order, these scores are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 10474, "output_tokens": 345, "cost_micros": 36597, "latency_ms": 8702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. This appears to be the example being referenced.\n\nNow I need to check Table 4, which shows \"The data sample of extreme personality cases to the annotators.\" I need to identify which entries correspond to the blue personality vector in the model structure.\n\nFrom Figure 1, the example shows:\n- O (Openness): 30\n- C (Conscientiousness): 70\n- E (Extroversion): 50\n- A (Agreeableness): 30\n- N (Neuroticism): 20\n\nThe figure shows that Method 1 selects the maximum score (Conscientiousness = 70), which would be the \"blue\" personality vector in the embedding space.\n\nLooking at Table 4 for Conscientiousness entries, I see:\n- Conscientiousness: 75.38\n- Conscientiousness: 71.02\n\nHowever, I need to match the exact scores from the example in Figure 1. The scores in ascending order from the example {30,70,50,30,20} are:\n\n20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 14897, "output_tokens": 333, "cost_micros": 49686, "latency_ms": 8280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the personality scores for the blue personality vector in the extreme personality cases.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62187, "error": null, "extra": {"thread_id": 1967, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3a604ba7af854be1897689cf0dae5312", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find personality scores for blue personality vector in extreme personality cases", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 on page 6, which shows the distribution of personality traits in Amazon-beauty and Amazon-music datasets, I can see five personality traits displayed: OPEN, CON, EXT, AGR, and NEU.\n\nThe personality trait furthest to the left in the layout is OPEN (Openness).\n\nNow I need to find the highest hit rate (HR) for OPEN across all the datasets and metrics shown in the tables.\n\nLooking at Table 6 on page 8, which shows HR and NDCG results grouped by 5 personality traits:\n- For OPEN in Amazon-beauty: HR = 0.833 (+) and 0.750 (-)\n- For OPEN in Amazon-music: HR = 0.330 (+) and 0.313 (-)\n- For OPEN in Personality2018: HR = 0.535 (+) and 0.547 (-)\n\nThe highest hit rate for OPEN is 0.833 in the Amazon-beauty dataset.\n\nAnswer: 0.833", "citations": [], "input_tokens": 26925, "output_tokens": 268, "cost_micros": 84795, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.833", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate in the results.\n\nLooking at Figure 2 (Distribution of personality traits in Amazon-beauty and Amazon-music datasets), I need to identify which personality trait has its distribution furthest to the left. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU.\n\nFrom the figure description and visual analysis:\n- The median lines (red lines) show the central tendency of each distribution\n- According to the text in Section 5.2: \"agreeable people have the highest median score, and neurotic people have the lowest median score\"\n- This means NEU (Neuroticism) is furthest to the left (lowest scores)\n\nNow I need to find the highest hit rate for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows results grouped by personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty HR: 0.933 (with +) and 0.833 (with -)\n- Amazon-music HR: 0.397 (with +) and 0.254 (with -)\n- Personality2018 HR: 0.489 (with +) and 0.390 (with -)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 13254, "output_tokens": 324, "cost_micros": 44622, "latency_ms": 7905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify the personality trait located furthest to the left in Figure 2, then find its highest hit rate in the results tables.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait appears furthest to the left in the distribution plots. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU for both Amazon-beauty and Amazon-music datasets.\n\nFrom the figure, examining the distributions from left to right, NEU (Neuroticism) appears to have its distribution positioned furthest to the left, with the lowest median scores (indicated by the red line) compared to the other personality traits.\n\nNow I need to find the highest hit rate (HR) value for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows HR and NDCG results grouped by 5 personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (+) and 0.397 (-)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate value for NEU across all datasets is 0.933 (from Amazon-beauty with the + model).\n\nAnswer: 0.933", "citations": [], "input_tokens": 16279, "output_tokens": 313, "cost_micros": 53532, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that appears furthest to the left in Figure 2 (the distribution of personality traits), and then find its highest hit rate from the experimental results.\n\nLooking at Figure 2, I need to identify which personality trait appears furthest to the left. The figure shows distributions for five personality traits: OPEN (Openness), CON (Conscientiousness), EXT (Extroversion), AGR (Agreeableness), and NEU (Neuroticism).\n\nAccording to the text describing Figure 2: \"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nThis indicates that Neuroticism (NEU) has the lowest median score, meaning it would be positioned furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism (NEU) from Table 6, which shows the breakdown by personality traits.\n\nLooking at Table 6 in the document, I need to find the Hit Rate (HR) values for Neuroticism across the three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nHowever, Table 6 is not fully visible in the provided text. But I can see from the discussion in Section 5.4 that it mentions performance changes for different personality traits.\n\nLooking back at Table 5, which shows overall results, I need to check if there's personality-specific data. The table shows H@3, H@5, and H@10 values across different models and datasets.\n\nFrom the text in Section 5.4: \"In the Amazon-music dataset, openness (+27%), agreeableness (+10%), extroversion (+5%) improve while neuroticism (\u201318%) and conscientiousness (\u201312%) decreases.\"\n\nThis suggests neuroticism showed decreases in the Amazon-music dataset. However, I need to find the actual highest hit rate value for neuroticism from the tables.\n\nSince Table 6 details are not fully provided in the visible text, and Table 5 shows overall results rather than personality-specific breakdowns, I need to infer from the context. The highest hit rates in Table 5 are around 0.948-0.977 for H@10 in Amazon-beauty dataset.", "citations": [], "input_tokens": 10444, "output_tokens": 512, "cost_micros": 39012, "latency_ms": 11484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "6.0", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait has the lowest median score (furthest left).\n\nFrom the text in Section 5.2:\n\"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nSo Neuroticism (NEU) is the personality trait furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism in Table 6, which shows results grouped by personality traits.\n\nLooking at Table 6 for NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (both + and -)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for Neuroticism across all datasets is 0.933 (Amazon-beauty with personality information).\n\nAnswer: 0.933", "citations": [], "input_tokens": 14867, "output_tokens": 267, "cost_micros": 48606, "latency_ms": 8485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the distribution of personality traits figure and then identify the corresponding hit rate data.Let me search for figures related to personality traits distribution:Let me read around line 1011 where it mentions \"distribution of users' personalities\":Let me search for Figure 2 which shows the personality distribution:Now let me read Figure 2 in detail:Let me continue reading to see all the personality trait figures:Now let me search for Table 6 which shows the hit rates for different personality traits:Now I need to find which personality trait is \"furthest to the left\" in the distribution figure. Looking back at Figure 2, I need to examine the visual descriptions more carefully. Let me search for more details about each personality trait's distribution:The document states that \"neurotic people have the lowest median score\" (line 1509-1510). This means that NEU (Neuroticism) is the personality trait furthest to the left in the distribution. Now I need to find the highest hit rate for NEU from Table 6.\n\nLooking at Table 6 data I found earlier:\n- NEU (Neuroticism) row shows:\n - Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n - Amazon-music: HR = 0.397 (+) and 0.397 (-)\n - Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 70937, "error": null, "extra": {"thread_id": 1968, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1c93a76cd24f45808d3740ab6074704b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n\n1. Identify the model structure figure - This is Figure 1 on page 5\n2. Find the method at the bottom of this figure - This is \"NCF + Hard-coded personality\"\n3. Look at Table 5 (page 8) which shows results across the three datasets (Amazon-beauty, Amazon-music, and Personality2018)\n4. Find the highest metric value for NCF+Hard-Coded across all metrics\n\nFrom Table 5, for NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value is 0.977 for H@10 in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 26935, "output_tokens": 314, "cost_micros": 85515, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n1. Identify the method at the bottom of the model structure figure (Figure 1)\n2. Find its performance across the three datasets in Table 5\n3. Determine which metric has the highest value\n\nFrom Figure 1, the three methods shown are:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality \n- Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nLooking at Table 5, the row for \"NCF+Hard-Coded\" shows performance across three datasets (Amazon-beauty, Amazon-music, Personality2018) with metrics H@3, H@5, H@10, N@3, N@5, N@10.\n\nThe values for NCF+Hard-Coded are:\n- Amazon-beauty: 0.948, 0.961, 0.977, 0.849, 0.826, 0.848\n- Amazon-music: 0.175, 0.232, 0.345, 0.147, 0.160, 0.189\n- Personality2018: 0.503, 0.622, 0.758, 0.398, 0.447, 0.498\n\nThe highest value across all three datasets for this method is 0.977, which corresponds to H@10 (Hit Rate at 10) in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 13264, "output_tokens": 347, "cost_micros": 44997, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF + Hard-coded Personality model.\n\nFrom Table 5, examining the results for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018) and all metrics (H@3, H@5, H@10, N@3, N@5, N@10):\n\nAmazon-beauty: N@3 = 0.849 (highest value)\nAmazon-music: N@10 = 0.189 (highest value)\nPersonality2018: N@5 = 0.447 (highest value)\n\nThe metric that appears most frequently as the highest is NDCG-related metrics, with N@3 having the highest single value (0.849).\n\nAnswer: N@3", "citations": [], "input_tokens": 16289, "output_tokens": 205, "cost_micros": 51942, "latency_ms": 6843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF+Hard-Coded model.\n\nFrom Table 5, I need to find the highest metric value for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality 2018):\n\nAmazon-beauty: N@3 = 0.849 (highest)\nAmazon-music: N@10 = 0.189\nPersonality 2018: H@10 = 0.758\n\nThe highest value is 0.849 for N@3 (NDCG@3) in the Amazon-beauty dataset.\n\nAnswer: NDCG@3", "citations": [], "input_tokens": 10454, "output_tokens": 171, "cost_micros": 33927, "latency_ms": 6750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ndcg 3", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to identify the method at the bottom of the model structure figure and find its highest evaluation metric across the three datasets.\n\nLooking at Figure 1, the three methods shown are:\n1. Method 1: Most salient personality\n2. Method 2: Soft-labeled personality\n3. Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nNow I need to look at Table 5 to find the highest metric for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nFor NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value across all metrics and datasets for NCF+Hard-Coded is 0.977 for H@10 in Amazon-beauty.\n\nAnswer: H@10", "citations": [], "input_tokens": 14877, "output_tokens": 346, "cost_micros": 49821, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find a model structure figure in this document first, then identify the method at the bottom of that figure, and finally check which evaluation metric has the highest number across three datasets for that method.Let me search for figures in the document more systematically:Great! I found Figure 1 showing the model structure. Now I can see that the three methods are:\n1. Method 1: Most salient personality - at the top\n2. Method 2: Soft-labeled personality - in the middle\n3. Method 3: Hard-coded personality - at the bottom\n\nThe method at the **bottom** is \"Hard-coded personality\" (Method 3). Now I need to find evaluation results for this method across the three datasets to determine which metric has the highest number.Now let me search for the experimental results tables:Let me continue searching for the results table:Perfect! Now I have found Table 5 which shows the evaluation results for all methods including \"NCF+Hard-Coded\" (the method at the bottom of the model structure figure) across the three datasets:\n\nFor **NCF+Hard-Coded** method across the three datasets:\n\n**Amazon-beauty:**\n- H@3: 0.948\n- H@5: 0.961 \n- H@10: 0.977\n- N@3: **0.849** \u2190 Highest\n- N@5: 0.826\n- N@10: 0.848\n\n**Amazon-music:**\n- H@3: 0.175\n- H@5: 0.232\n- H@10: 0.345\n- N@3: 0.147\n- N@5: 0.160\n- N@10: **0.189** \u2190 Highest\n\n**Personality2018:**\n- H@3: 0.503\n- H@5: 0.622\n- H@10: **0.758** \u2190 Highest\n- N@3: 0.398\n- N@5: 0.447\n- N@10: 0.498\n\nLooking at the highest value for each dataset:\n- Amazon-beauty: N@3 = 0.849\n- Amazon-music: N@10 = 0.189\n- Personality2018: H@10 = 0.758\n\nThe evaluation metric with the highest number across all three datasets for the Hard-coded method is **N@3** (with value 0.849 in Amazon-beauty).\n\nAnswer: N@3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 93687, "error": null, "extra": {"thread_id": 1969, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f41f50a7be1a46d59602bb6f59d5d5fd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 515, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3 on page 9, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV 50-shot:\n- Previous SoTA (line 4): 68.5(1.6)\n- The filter-then-rerank methods without ensemble are shown in the rows labeled \"+ Rerank (L)\"\n\nLooking at the three reranker options for 50-shot TACREV:\n- Vicuna-13B + Rerank (L): 70.8(1.6)\n- InstructGPT + Rerank (L): 72.3(1.4)\n- GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which refers to the \"+ Rerank (L)\" rows without the \"+ Ensemble (S)\" component.\n\nSince the question uses singular \"methods\" but there are multiple values, I should identify which one is the main result. Looking at the context, InstructGPT appears to be the primary model discussed in the main experiments (as mentioned in Section 5.3: \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nAnswer: 72.3", "citations": [], "input_tokens": 82081, "output_tokens": 358, "cost_micros": 251613, "latency_ms": 38407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) task, I need to find the 50-shot column.\n\nLooking at the rows for filter-then-rerank without ensemble:\n- Line 6: Vicuna-13B + Rerank (L): 70.8(1.6)\n- Line 7: InstructGPT + Rerank (L): 72.3(1.4)\n- Line 9: GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks about \"filter-then-rerank methods\" in general (plural), but I need to identify which specific method is being asked about. \n\nLooking at the context, when comparing with baseline methods, the paper typically refers to InstructGPT as the main reranker in their comparisons (as mentioned in section 5.3 where they state \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nFor 50-shot TACREV with InstructGPT + Rerank (L) without ensemble:\n\nAnswer: 72.3", "citations": [], "input_tokens": 35348, "output_tokens": 333, "cost_micros": 111039, "latency_ms": 9656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV dataset, I can see columns for 20-shot, 50-shot, and 100-shot settings.\n\nLooking at the 50-shot column for TACREV:\n\nThe rows showing filter-then-rerank methods without ensemble (marked as \"+ Rerank (L)\") are:\n- With Vicuna-13B as reranker: 70.8(1.6)\n- With InstructGPT as reranker: 72.3(1.4)\n- With GPT-4 as reranker: 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which means without the \"+ Ensemble (S)\" prefix. The previous SoTA method (line 4) shows 68.5(1.6) for comparison.\n\nSince there are three different LLM rerankers tested, and the question asks for \"filter-then-rerank methods\" in general, I should identify which specific result is being asked for. Looking at the context, when InstructGPT is used as the reranker (which appears to be the main model discussed in the results section based on Table 4 and other analyses), the performance is 72.3.\n\nAnswer: 72.3", "citations": [], "input_tokens": 46313, "output_tokens": 370, "cost_micros": 144489, "latency_ms": 11125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) at 50-shot setting:\n\nLooking at the rows with filter-then-rerank methods without ensemble:\n- Line 4: \"Previous SoTA\" (this is the baseline SLM)\n- Line 7: \"+ Rerank (L)\" with InstructGPT - this adds reranking to the previous SoTA without ensemble\n\nFrom Table 3, TACREV 50-shot column:\n- Previous SoTA: 68.5(1.6)\n- SLM + Rerank (L) with InstructGPT: 70.8(1.6)\n\nThe filter-then-rerank method without ensemble using InstructGPT shows 70.8.\n\nAnswer: 70.8", "citations": [], "input_tokens": 33414, "output_tokens": 254, "cost_micros": 104052, "latency_ms": 9844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "70.8", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nIn Table 3, under the TACREV (RE) 50-shot column:\n- Previous SoTA (SLM baseline): 72.6(1.5)\n- + Rerank (L) with Vicuna-13B: 73.8(1.7)\n- + Rerank (L) with InstructGPT: 75.4(1.5)\n- + Rerank (L) with GPT-4: 74.5(1.5)\n\nThe question asks for filter-then-rerank methods without ensemble. The \"+ Rerank (L)\" rows show the filter-then-rerank results without ensemble (the ensemble results are shown in the \"+ Ensemble (S) + Rerank (L)\" rows).\n\nSince there are three different rerankers tested, and the question asks for \"filter-then-rerank methods\" in general without specifying which LLM reranker, I should provide the InstructGPT result as it appears to be the primary comparison model used throughout the paper (it's the middle option and shows the best performance).\n\nAnswer: 75.4", "citations": [], "input_tokens": 45083, "output_tokens": 294, "cost_micros": 139659, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.4", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about filter-then-rerank methods performance on the 50-shot TACREV dataset in the provided document.Let me search for tables that contain both TACREV and 50-shot data:Perfect! I found Table 3 which contains the performance data. Let me look at the specific row for filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nFrom the table, I can see:\n- Column headers show: FewNERD (NER) with 5-shot, 10-shot, 20-shot, then TACREV (RE) with 20-shot, 50-shot, 100-shot\n- The filter-then-rerank method without ensemble is the \"+ Rerank (L)\" row (Line 7 as mentioned in the text)\n\nLooking at the data:\n- Under \"SLM\" section, the \"+ Rerank (L)\" row (line 2050-2060) shows the Vicuna-13B results\n- For TACREV 50-shot (5th data column), the value is: 70.8(1.6)\n\nLet me also check the InstructGPT rerank results:\n- \"+ Rerank (L)\" with InstructGPT (line 2080-2090) shows for TACREV 50-shot: 72.3(1.4)\n\nAnd GPT-4 rerank results:\n- \"+ Rerank (L)\" with GPT-4 (line 2111-2121) shows for TACREV 50-shot: 72.3(0.3)\n\nThe question asks for filter-then-rerank methods (w.o. ensemble), which would be the \"+ Rerank (L)\" rows. Looking at line 2084-2085, I can see the InstructGPT version shows 72.3 for 50-shot TACREV.\n\nHowever, I need to verify which specific implementation is being referred to. Let me check the context around line 1903 which mentions \"Lines 4 vs. 7\" for the comparison.\n\nBased on the table structure and the text mentioning \"Lines 4 vs. 7\" where Line 7 is the rerank method, and looking at the TACREV 50-shot column, the filter-then-rerank method (w.o. ensemble) with InstructGPT shows: 72.3\n\nAnswer: 72.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50031, "error": null, "extra": {"thread_id": 1970, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cec9e66ec5254f8aa0ad0e5be252d80b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 338, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left) in the document, which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset,\" the left panel shows \"ChatGPT's performance (F1 Score) across six instruction variants.\"\n\nThe red box in the left panel of Figure 3 highlights the instruction formats labeled I0 through I5 on the x-axis.\n\nBased on the context provided in Appendix E.2, these instruction variants range from simple to complex, with I0 being empty (no instruction) and I1 through I5 being increasingly detailed instructions for the NER task.\n\nAnswer: ChatGPT's performance (F1 Score) across six instruction variants", "citations": [], "input_tokens": 82074, "output_tokens": 166, "cost_micros": 248712, "latency_ms": 34046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "chatgpt s performance f1 score across six instruction variants", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants\" and displays \"ChatGPT's performance (F1 Score) across six instruction variants\" labeled I0 through I5.\n\nThe document provides the full text of these six instruction variants in Section E.2 \"Finding Better Instruction.\"\n\nThe instructions are:\n\n**I0:** [empty]\n\n**I1:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1]: [Definition_1], [Type_2]: [Definition_2], ..., [Type_N]: [Definition_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I3:** Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. The possible entity types are listed as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] identified_entity: [Entity_1]), ([Type_2] identified_entity: [Entity_2])'. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I4:** Assume you are an entity-instance annotator. Your objective is to perform a series of intricate steps for Named Entity Recognition. Firstly, you have to identify a particular word or phrase in the sentence that corresponds to an entity. Following this, classify the entity into one of the potential entity types. The potential entity types are provided as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] identified_entity: [Entity_", "citations": [], "input_tokens": 35341, "output_tokens": 512, "cost_micros": 113703, "latency_ms": 13984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.1702127659574468, "method": "str_norm", "normalised_pred": "type 1 sep identified entity entity", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"ChatGPT's performance (F1 Score) across six instruction variants,\" I need to find the instructions labeled I0 through I5.\n\nFrom Section E.2 \"Finding Better Instruction\" in the document, the six instruction variants are listed. The red box in Figure 3 (left) shows the x-axis labels I0, I1, I2, I3, I4, I5.\n\nThese correspond to:\n\n**I0**: [empty]\n\n**I1**: Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ... , [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2**: Same as I1 but with definitions added for each type\n\n**I3**: Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. [includes format specification]\n\n**I4**: Similar to I3 but with more elaborate language about \"intricate steps\"\n\n**I5**: Combines detailed task description with entity type definitions\n\nAnswer: Six instruction variants from empty (I0) to increasingly complex formats with definitions and detailed task descriptions (I1-I5)", "citations": [], "input_tokens": 46306, "output_tokens": 319, "cost_micros": 143703, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.06779661016949154, "method": "str_norm", "normalised_pred": "six instruction variants from empty i0 to increasingly complex formats with definitions and detailed task descriptions i1 i5", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), I can see it's showing \"Relationship between confidence scores and performance with/without LLM reranking.\"\n\nThe red box in the left figure appears to be highlighting the legend/experimental settings, which shows:\n\n**Answer: RoBERTa-large as filter and InstructGPT as reranker**", "citations": [], "input_tokens": 33407, "output_tokens": 81, "cost_micros": 101436, "latency_ms": 4281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.04081632653061225, "method": "str_norm", "normalised_pred": "roberta large as filter and instructgpt as reranker", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset\" with six instruction variants (I0-I5), I need to find the instruction text from the document.\n\nFrom Section E.2 \"Finding Better Instruction\", the six instruction variants for the NER task are listed. These are the instructions being tested in the red box of Figure 3 (left).\n\nAnswer: The six instruction variants I0 through I5 listed in Section E.2, ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "citations": [], "input_tokens": 45076, "output_tokens": 140, "cost_micros": 137328, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.17910447761194032, "method": "str_norm", "normalised_pred": "six instruction variants i0 through i5 listed in section e 2 ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the instructions corresponding to the settings shown in the red box.Let me search for Figure 3 in the document:Now let me read the section around Figure 3 to see the instruction variants and identify which ones are shown in the red box:I can see that Figure 3 Left shows ChatGPT's performance across six instruction variants labeled I0 through I5. I need to find the details about these instruction variants in Appendix E.2. Let me search for the appendix:Let me search for instruction variants more broadly:Let me search for the appendix sections with \"E.2\" or look for where instruction formats I0-I5 are defined:Let me look at the entire document more systematically. Let me search for sections starting with \"E.\" or \"Appendix\":Great! Now let me read section E.2 which should contain the instruction variants:Perfect! I found the six instruction variants (I0-I5). Now I need to look at Figure 3 more carefully to see which instructions are in the red box. Let me read around the figure again and look for any visual description of the red box:The image X14.png seems to be after Figure 3 in the document. Let me look for other embedded images that might be part of Figure 3:Let me look before Figure 3 to see if there's an image associated with the figure itself:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77483, "error": null, "extra": {"thread_id": 1971, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_538fbe85cd9b43f5a8b0d97c996a7e15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.08301886792452831, "method": "str_norm", "normalised_pred": "i ll read document to find figure 3 and identify instructions corresponding to settings shown in red box let me search for figure 3 in document now let me read section around figure 3 to see instruction variants and identify which ones are shown in red box i can see that figure 3 left shows chatgpt s performance across six instruction variants labeled i0 through i5 i need to find details about these instruction variants in appendix e 2 let me search for appendix let me search for instruction variants more broadly let me search for appendix sections with e 2 or look for where instruction formats i0 i5 are defined let me look at entire document more systematically let me search for sections starting with e or appendix great now let me read section e 2 which should contain instruction variants perfect i found six instruction variants i0 i5 now i need to look at figure 3 more carefully to see which instructions are in red box let me read around figure again and look for any visual description of red box image x14 png seems to be after figure 3 in document let me look for other embedded images that might be part of figure 3 let me look before figure 3 to see if there s image associated with figure itself", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find how many datasets are used for experiments in this paper.\n\nLooking at the document, in Section 3.1 \"Task, Dataset and Evaluation\", it states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe document then lists:\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD (3 datasets)\n- Relation Extraction (RE): TACRED, TACREV (2 datasets)\n- Event Detection (ED): ACE05, MAVEN, ERE (3 datasets)\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS (3 datasets)\n\nHowever, I need to count unique datasets. ACE05 and ERE appear in both ED and EAE tasks.\n\nCounting unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nThis gives us 9 unique datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 82066, "output_tokens": 265, "cost_micros": 250173, "latency_ms": 34217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the datasets used:\n\nFrom the Abstract and Introduction:\n- \"Through extensive experiments on nine datasets across four IE tasks\"\n\nFrom Section 3.1 (Task, Dataset and Evaluation):\n- \"We run experiments on nine widely-used datasets across four IE tasks.\"\n- The nine datasets are listed as:\n 1. CONLL03 (NER)\n 2. OntoNotes (NER)\n 3. FewNERD (NER)\n 4. TACRED (RE)\n 5. TACREV (RE)\n 6. ACE05 (ED)\n 7. MAVEN (ED)\n 8. ERE (ED)\n 9. ACE05, ERE, RAMS (EAE)\n\nWait, let me recount more carefully from the text:\n- NER: CONLL03, OntoNotes, FewNERD = 3 datasets\n- RE: TACRED, TACREV = 2 datasets\n- ED: ACE05, MAVEN, ERE = 3 datasets\n- EAE: ACE05, ERE, RAMS = 3 datasets\n\nHowever, ACE05 and ERE appear in multiple tasks. The document clearly states \"nine datasets\" multiple times.\n\nLooking at Table 6 which shows \"Statistics of nine datasets used\", I can count the distinct dataset names:\n1. CONLL\n2. OntoNotes\n3. FewNERD\n4. TACREV\n5. TACRED\n6. ACE05 (appears for both ED and EAE)\n7. MAVEN\n8. ERE (appears for both ED and EAE)\n9. RAMS\n\nThis confirms 9 distinct datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 35333, "output_tokens": 414, "cost_micros": 112209, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to identify all the datasets used across the four IE tasks.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n\n1. **Named Entity Recognition (NER)**: CONLL03, OntoNotes, FewNERD\n2. **Relation Extraction (RE)**: TACRED, TACREV\n3. **Event Detection (ED)**: ACE05, MAVEN, ERE\n4. **Event Argument Extraction (EAE)**: ACE05, RAMS, ERE\n\nCounting unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE, but counted once)\n- MAVEN\n- ERE (used in both ED and EAE, but counted once)\n- RAMS\n\nTotal unique datasets: 9\n\nThis is confirmed by the abstract which states \"extensive experiments on nine datasets across four IE tasks\" and multiple mentions throughout the paper of \"nine datasets.\"\n\nAnswer: 9", "citations": [], "input_tokens": 46298, "output_tokens": 251, "cost_micros": 142659, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the total number of datasets used in the experiments described in this paper.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\", the paper states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe four tasks and their associated datasets are:\n\n1. **Named Entity Recognition (NER)**: \n - CONLL03\n - OntoNotes\n - FewNERD\n\n2. **Relation Extraction (RE)**:\n - TACRED\n - TACREV\n\n3. **Event Detection (ED)**:\n - ACE05\n - MAVEN\n - ERE\n\n4. **Event Argument Extraction (EAE)**:\n - ACE05\n - ERE\n - RAMS\n\nCounting the unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE)\n- MAVEN\n- ERE (used in both ED and EAE)\n- RAMS\n\nThe paper explicitly states \"nine widely-used datasets\" and when counting unique dataset names, we have 9 distinct datasets (ACE05 and ERE are each used for two different tasks but are still single datasets).\n\nAnswer: 9", "citations": [], "input_tokens": 33399, "output_tokens": 309, "cost_micros": 104832, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for the datasets used in experiments:\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD\n- Relation Extraction (RE): TACRED, TACREV\n- Event Detection (ED): ACE05, MAVEN, ERE\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS\n\nCounting the unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nNote: ACE05 and ERE are used for both Event Detection and Event Argument Extraction tasks, but they are counted once as unique datasets.\n\nAnswer: 9", "citations": [], "input_tokens": 45068, "output_tokens": 207, "cost_micros": 138309, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the datasets used in the experiments.Let me search for a table or figure that lists all the datasets clearly.Perfect! I found Table 6 which clearly lists all the datasets. Let me count them:\n\nFrom the document, the paper uses experiments on **nine datasets** across four IE tasks:\n\n1. **Named Entity Recognition (NER)**: \n - CONLL03\n - OntoNotes\n - FewNERD\n\n2. **Relation Extraction (RE)**:\n - TACRED\n - TACREV\n\n3. **Event Detection (ED)**:\n - ACE05\n - MAVEN\n - ERE\n\n4. **Event Argument Extraction (EAE)**:\n - ACE05 (same dataset used for both ED and EAE)\n - RAMS\n - ERE (same dataset used for both ED and EAE)\n\nCounting unique datasets: CONLL03, OntoNotes, FewNERD, TACRED, TACREV, ACE05, MAVEN, ERE, RAMS = **9 datasets**\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43891, "error": null, "extra": {"thread_id": 1972, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_efab7508547a4ca893cf8d3cb1478856", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 235, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about the filter-then-rerank method and prompt templates...\n\nIn Table 21 (page 29), which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 82080, "output_tokens": 100, "cost_micros": 247740, "latency_ms": 31592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template used for the 'Contact.Meet' label in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 35347, "output_tokens": 142, "cost_micros": 108171, "latency_ms": 4609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for the 'Contact.Meet' event label in the filter-then-rerank paradigm.\n\nIn Table 21 (Templates for ACE05 dataset), which shows the templates used in the filter-then-rerank method, I can find:\n\nContact.Meet: \"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 46312, "output_tokens": 143, "cost_micros": 141081, "latency_ms": 6046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for 'Contact.Meet' used in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 33413, "output_tokens": 106, "cost_micros": 101829, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the template for 'Contact.Meet' in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find:\n\nContact.Meet | The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 45082, "output_tokens": 135, "cost_micros": 137271, "latency_ms": 4452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find information about the prompt template for label 'Contact.Meet' in the filter-then-rerank method.Let me search for information about \"Contact.Meet\" and \"filter-then-rerank\" in this document.Let me search for content with more context:Now I need to find the section that shows the prompt templates. Let me read around line 5446 where Contact.Meet was mentioned, and also look for tables or appendices with prompt templates:Perfect! I found the prompt template for Contact.Meet in Table 21 for ACE05 dataset. According to the document, the template for 'Contact.Meet' in the filter-then-rerank method is:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45328, "error": null, "extra": {"thread_id": 1973, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5466a03f139642c1a7d3c68dd6b01473", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the green module, then look for the 100-shot TACREV setting to calculate the absolute percentage drop.\n\nLooking through the document, I find Figure 6 on page 8, which shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" In this figure, I can see colored modules - orange for \"Easy Sample\" and pink for \"Hard Sample\". The diagram shows a filter (Small LM) and a reranker (Large LM).\n\nHowever, I need to look at Table 5 on page 9, which shows \"Ablation study on three datasets\" and examines the effectiveness of modules in the adaptive filter-then-rerank system. The table shows results for TACREV (100-shot).\n\nLooking at the table columns, I can see:\n- CoT (Chain of Thought)\n- Demo\n- LF (Label Filtering) \n- AD (Adaptive)\n\nThe green box in Figure 6 appears to correspond to the demonstration/demo component based on the visual representation.\n\nFrom Table 5, for TACREV (100-shot):\n- With all modules (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without Demo but with others (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe absolute drop is: 75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 82081, "output_tokens": 344, "cost_micros": 251403, "latency_ms": 36125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance impact of dropping the green module (demonstration) shown in Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can see the ablation results for different components on three datasets including TACREV (100-shot).\n\nThe table shows:\n- Full system (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9(2.4)\n- Without Demo (\u2717 Demo \u2717 CoT \u2713 LF \u2713 AD): 74.9(2.2)\n\nHowever, I need to isolate just the Demo effect. Looking more carefully:\n- With CoT and Demo (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9\n- Without Demo but with CoT (\u2713 CoT \u2717 Demo \u2713 LF \u2713 AD): This line doesn't exist\n\nBut I can see:\n- Without CoT but with Demo (\u2717 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.4\n- Without both CoT and Demo (\u2717 CoT \u2717 Demo \u2713 LF \u2713 AD): 74.9\n\nLet me look at the correct comparison for just dropping Demo:\nFrom the table, comparing rows where only Demo changes:\n- Row 1 (\u2713 CoT \u2713 Demo): 75.9\n- Row 2 (\u2717 CoT \u2713 Demo): 75.4\n- Row 3 (\u2717 CoT \u2717 Demo): 74.9\n\nThe drop from having Demo to not having Demo (when CoT is also removed) is:\n75.4 - 74.9 = 0.5\n\nHowever, to properly isolate the Demo effect with CoT present, I need to estimate. The pattern suggests:\n- Full system: 75.9\n- Without Demo (keeping other factors): approximately 75.4 - 75.9 range\n\nActually, looking at row 2 vs row 3: the difference is 75.4 -", "citations": [], "input_tokens": 35348, "output_tokens": 512, "cost_micros": 113724, "latency_ms": 11858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance drop when removing the green module (demonstration) from Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can find the relevant information:\n- The table shows ablation results on three datasets including TACREV (100-shot)\n- The rows show different configurations with checkmarks (\u2713) and crosses (\u2717)\n\nThe green module in Figure 6 represents the \"Demonstration\" component.\n\nFrom Table 5, for TACREV (100-shot):\n- With CoT \u2713, Demo \u2713, LF \u2713, AD \u2713: 75.9(2.4)\n- With CoT \u2717, Demo \u2717, LF \u2713, AD \u2713 (no demo): 74.9(2.2)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 46313, "output_tokens": 231, "cost_micros": 142404, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the \"green module,\" then look at the 100-shot TACREV setting to determine the performance drop.\n\nLooking at Figure 6 in the document, it shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" The green box is labeled as \"demos\" (demonstration examples).\n\nNow I need to find the ablation study that shows what happens when demos are removed. Looking at Table 5 (Ablation study), I can see different configurations where modules are removed:\n\nThe table shows results for FewNERD (20-shot), TACREV (100-shot), and ACE05 (20-shot).\n\nFor TACREV (100-shot):\n- Full system (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without CoT (\u2717 \u2713 \u2713 \u2713): 75.4(2.4)\n- Without Demo (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe \"Demo\" module corresponds to the green box in Figure 6. When we remove it (going from \u2717 \u2713 \u2713 \u2713 to \u2717 \u2717 \u2713 \u2713), the performance drops from 75.4 to 74.9.\n\nThe absolute drop is: 75.4 - 74.9 = 0.5\n\nAnswer: 0.5", "citations": [], "input_tokens": 33414, "output_tokens": 351, "cost_micros": 105507, "latency_ms": 9827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.5", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the \"green module\" in Figure 6 and its effect on the 100-shot TACREV setting.\n\nFrom Figure 6, the green box represents the **Demonstration** component in the reranker process.\n\nFrom Table 5 (Ablation Study), I can see the results when removing the Demo component:\n- The row \"[ ] [ ] [x] [x]\" indicates: no CoT, no Demo, with LF, with AD\n- For TACREV (100-shot): 74.9(2.2)\n- The full system \"[x] [x] [x] [x]\" achieves: 75.9(2.4)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 45083, "output_tokens": 198, "cost_micros": 138219, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and the information about dropping the green module in the 100-shot TACREV setting.Let me search for Figure 6 in the document:Now let me search for more context around Figure 6:Good! I found Figure 6. It mentions a green box representing \"demos\". Let me read the full content around Figure 6 and look for ablation studies or tables that show what happens when dropping the green module (demos):Perfect! Figure 6 shows that the green box represents \"Demonstration\" (demos). Now I need to find the ablation study or table that shows the performance drop when removing demos. Let me search for ablation or table related to this:Let me search for ablation study or a table showing what happens when demos are removed:Perfect! I found the ablation study in Table 5. Let me read that section:Let me search more specifically for Table 5:Great! I found Table 5. Let me continue reading to see all rows:Perfect! Now let me look for the explanation of what each column means. Let me read the section before Table 5:Let me find the section that explains the ablation study (section 5.5):Let me continue reading the ablation study explanation:I see the explanation was cut off. Let me search for where it continues:Let me look for text after Table 3 that continues the ablation study explanation:Perfect! Now I have the full explanation. Looking at Table 5, I need to identify which row corresponds to dropping the \"Demo\" (green module). Let me re-read Table 5 more carefully:Now let me understand the table structure. The columns are:\n- CoT (Chain of Thought)\n- Demo (Demonstration - the green module)\n- LF (Label Filtering)\n- AD (Adaptive)\n\nThe rows show different configurations with checkmarks (\u2713) meaning present and X (\u2717) meaning removed.\n\nLooking at the table:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 TACREV (100-shot) = 74.9\n- Row 4: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2612 AD \u2192 TACREV (100-shot) = 73.8\n- Row 5: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2717 AD \u2192 TACREV (100-shot) = 59.9\n\nWait, I need to understand the notation better. Let me look at the explanation again. From the text at lines 2189-2194:\n\n\"(2) Demo: We remove all examples, rendering the reranking a zero-shot problem.\"\n\nSo when Demo is removed (\u2717), all examples are removed. Looking at the table structure, I need to find the row where Demo changes from \u2713 to \u2717 while keeping other factors constant.\n\nThe best comparison to isolate the effect of Demo would be:\n- Row 1 (full model with all components): CoT \u2713, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.9\n- Row 2 (without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n\nTo find the effect of dropping Demo specifically, I should compare:\n- Row 2 (with Demo, without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n- Row 3 (without Demo and CoT): CoT \u2717, Demo \u2717, (different LF/AD settings) \u2192 74.9\n\nBut wait, the LF and AD settings change. Let me reconsider. Looking more carefully at the symbols, I see \"\u2612\" which might be different from \"\u2713\" and \"\u2717\".\n\nLet me look for a clearer comparison. The green module in Figure 6 is \"Demo\". To find the effect of dropping it:\n\nFrom the full model (row 1): 75.9\nTo without Demo - I need to find which row removes only Demo while keeping everything else.\n\nActually, looking at row 2 vs row 3:\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 74.9\n\nBut LF and AD also change, so this isn't a clean comparison.\n\nLet me think about this differently. According to the explanation text (lines 2197-2199):\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\"\n\nAnd (line 2199-2200):\n\"(2) Demos without explanations also contribute to performance improvement.\"\n\nSo the comparison should be:\n- With Demo + CoT (row 1): 75.9\n- Without CoT but with Demo (row 2): 75.4 \n- Without both CoT and Demo (row 3 onwards)\n\nThe cleanest way to see the effect of dropping Demo is to compare a configuration with Demo vs without Demo. Looking at row 2 (has Demo but no CoT) = 75.4, and the next logical step would be row 3 (no Demo, no CoT) = 74.9.\n\nBut let me also check if there's a better comparison. Actually, looking at the structure, row 3 onwards all have \"\u2717 \u2612\" for both CoT and Demo columns, meaning both are removed.\n\nSo the drop from having Demo (row 2: 75.4) to not having Demo (row 3: 74.9) = 75.4 - 74.9 = 0.5\n\nBut wait, in row 3, the LF and AD settings also changed (\u2612 instead of \u2713).\n\nLet me reconsider what \u2612 means. It might mean the opposite of the feature (e.g., if \u2713 means \"with\", then \u2612 might mean \"without\" for LF and AD columns).\n\nActually, based on the explanation:\n- LF (label filtering): \u2713 means using top-N labels, \u2717 means retaining all labels\n- AD (adaptive): \u2713 means only hard samples, \u2717 means all samples\n\nSo \u2612 might indicate the opposite setting.\n\nGiven the complexity, let me focus on finding where the Demo is specifically dropped. The clearest comparison would be between:\n- Row 1 (everything included): 75.9\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.9 - 74.9 = 1.0\n\nBut this includes the effect of removing CoT as well. To isolate Demo:\n- Row 2 (no CoT, yes Demo): 75.4\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.4 - 74.9 = 0.5\n\nHowever, LF and AD settings also changed between row 2 and 3.\n\nLet me look at this more systematically. Actually, I think I'm overcomplicating this. The question asks specifically about dropping \"the green module\" which is Demo. Looking at the full system (row 1) vs without demo, I need to find the appropriate ablation.\n\nActually, re-reading the table more carefully with the column headers having 2 cells labeled \"CoT Demo\", I think these might be a single column header spanning 2 rows. Let me re-examine:\n\nLooking at lines 2298-2303:\n```\nCoT Demo\n```\n\nThis suggests \"CoT Demo\" is a single header spanning 2 columns. So the structure might be:\n- Column 1: CoT\n- Column 2: Demo\n- Column 3: LF\n- Column 4: AD\n\nWith this understanding:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, ? LF, ? AD \u2192 74.9\n\nSo dropping Demo (from row 2 to row 3) causes a drop from 75.4 to 74.9 = 0.5\n\nBut the LF and AD settings are unclear in row 3 with the \u2612 symbol.\n\nGiven the question asks specifically for dropping the green module (Demo), and looking at the cleanest comparison while keeping CoT removed:\n- With Demo (row 2): 75.4\n- Without Demo (row 3): 74.9\n- Drop: 75.4 - 74.9 = 0.5\n\nBut if we compare from the full model:\n- Full model (row 1): 75.9\n- Without Demo but with CoT - this row doesn't exist in the table\n\nActually, maybe I should look at row 5 which has everything removed. Let me reconsider all rows:\n1. All components: 75.9\n2. No CoT: 75.4\n3. No CoT, No Demo: 74.9\n4. No CoT, No Demo, No LF: 73.8\n5. Nothing (all removed): 59.9\n\nThe progression suggests cumulative removal. To isolate Demo's effect:\nRow 2 (75.4) - Row 3 (74.9) = 0.5\n\nWait, but I should verify this interpretation by looking at the explanation text again. From lines 2196-2200:\n\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\n(2) Demos without explanations also contribute to performance improvement.\"\n\nThis suggests:\n- Row 1 has Demo WITH CoT (explanations)\n- Row 2 has Demo WITHOUT CoT (no explanations) \n- Row 3 onwards has NO Demo\n\nSo the effect of dropping Demo entirely (going from row 2 to row 3): 75.4 - 74.9 = 0.5\n\nBut wait, there might be more to consider. Let me check if there's another row that might represent dropping just Demo while keeping CoT. Looking at the table again... no, there isn't such a row.\n\nGiven the structure and the question asking about dropping the green module (Demo), the most reasonable answer based on the ablation study is:\n\nFrom row 1 (full model with all components) = 75.9\nTo the configuration without Demo... I need to find which row that is.\n\nIf I assume row 3 still has other components but not Demo:\nRow 1: 75.9\nRow 3: 74.9\nDifference: 1.0\n\nBut this seems to also remove CoT. Let me think about what makes sense given Figure 6. The green module is specifically the \"Demonstration\" box shown feeding into the reranker.\n\nActually, perhaps the cleanest interpretation is:\n- Best system with Demo (row 1 or 2): max is 75.9 (row 1)\n- Best system without Demo (row 3, 4, or 5): max is 74.9 (row 3)\n- Absolute drop: 75.9 - 74.9 = 1.0\n\nBut this conflates CoT and Demo effects.\n\nHmm, let me reconsider the table structure once more. Maybe the header \"CoT Demo\" with colspan=\"2\" means there are two sub-columns, one for each. And the checkmarks below indicate their presence/absence.\n\nGiven the typical ablation study structure, each row progressively removes one more component. So:\n- Row 1: Everything (baseline) = 75.9\n- Row 2: Remove CoT = 75.4 (drop of 0.5)\n- Row 3: Remove Demo (CoT already removed) = 74.9 (additional drop of 0.5)\n- Row 4: Remove LF (CoT and Demo already removed) = 73.8 (additional drop of 1.1)\n- Row 5: Remove AD (everything removed) = 59.9 (additional drop of 13.9)\n\nIf we want the isolated effect of Demo, we need to compare configurations that differ only in Demo. But there's no row with CoT present and Demo absent.\n\nHowever, if we accept that row 2 \u2192 row 3 transition shows the effect of removing Demo (with CoT already removed), then the drop is 0.5.\n\nAlternatively, if the question wants the total contribution of Demo to the best system, we could argue:\n- Best with Demo: 75.9 (row 1)\n- Best without Demo: 74.9 (row 3)\n- Total drop attributable to absence of Demo: 1.0\n\nBut this includes the removal of CoT's contribution when combined with Demo removal.\n\nGiven typical ablation study interpretation and the question phrasing \"if I drop the green module,\" I believe the answer is the difference from the full system (row 1: 75.9) to the first row without Demo which is row 3 (74.9):\n\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 147812, "error": null, "extra": {"thread_id": 1974, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b54490e8642341dd973e7bd446513622", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1457, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to locate the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, Figure 1 appears on page 2. It shows \"An example of our SCITAB dataset\" with a reasoning graph on the right side.\n\nIn the reasoning graph on the right side of Figure 1, I can see:\n- Green rectangles (boxes): I can count \"Closed-domain knowledge: Table caption\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker: Supported\" - that's 5 green boxes\n- Grey rectangles: I can see grey/neutral colored boxes containing text like \"Productivity corresponds to Prod. column\", \"The number of random chance is 50%\", \"A's productivity is 57.5%\", and \"The subtraction result between 57.5% and 50% is 7.5%\" - that's 4 grey boxes\n\nWait, let me recount more carefully by looking at the colors:\n- Green boxes appear to be the operation/reasoning step boxes\n- Grey boxes appear to be the intermediate result/statement boxes\n\nCounting again in Figure 1's reasoning graph:\n- Green rectangles: 5 (the labeled reasoning steps)\n- Grey rectangles: 4 (the intermediate statements)\n\nIn descending order: [\"5\", \"4\"]\n\nAnswer: 5, 4", "citations": [], "input_tokens": 68164, "output_tokens": 313, "cost_micros": 209187, "latency_ms": 35953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4, 5", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1.\n\nLooking at Figure 1 on the first page, I can see a reasoning graph on the right side of the figure. Let me count the rectangles by color:\n\nGreen rectangles (these appear to represent supported claims or correct reasoning):\n1. \"A's productivity is 57.5%\"\n2. \"Productivity corresponds to Prod. column\"\n3. \"The number of random chance is 50%\"\n4. \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 4 green rectangles.\n\nGrey rectangles (these appear to represent refuted or incorrect claims):\n1. \"A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n\nThat's 1 grey rectangle.\n\nIn descending order: [\"4\", \"1\"]\n\nAnswer: 4, 1", "citations": [], "input_tokens": 31797, "output_tokens": 222, "cost_micros": 98721, "latency_ms": 7155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "1, 4", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the document, which is Figure 1 on the first page.\n\nLooking at Figure 1, I can see a reasoning graph on the right side. Let me count the rectangles by color:\n\nGreen rectangles (which appear to represent certain types of nodes in the reasoning graph):\n- \"Closed-domain knowledge: Table caption\"\n- \"Productivity corresponds to Prod. column\"\n- \"Commonsense knowledge\"\n- \"The number of random chance is 50%\"\n- \"Simple lookup\"\n- \"A's productivity is 57.5%\"\n- \"Subtraction\"\n- \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 8 green rectangles.\n\nGrey rectangles (which appear to represent the claims):\n- \"Supported Claim: A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n- \"Refuted Claim: A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n- \"Not Enough Info Claim: The low performance of \"to\" can be explained by the fact that it is responsible for only 4.6% of the inference in the training set.\"\n\nThat's 3 grey rectangles.\n\nIn descending order: 8, 3\n\nAnswer: 8, 3", "citations": [], "input_tokens": 44352, "output_tokens": 333, "cost_micros": 138051, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "3, 8", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, the first figure is Figure 1, which shows \"An example of our SCITAB dataset (left) and its corresponding reasoning graph (right).\"\n\nIn the reasoning graph (right side of Figure 1), I can see:\n- The graph contains nodes connected by arrows\n- There are rectangular boxes of different colors\n- Looking at the reasoning graph structure, I can identify green rectangles and grey rectangles\n\nCounting the rectangles in the reasoning graph:\n- Green rectangles: These appear to be the operation/function nodes in the graph. I can count: \"Lookup\", \"Background knowledge\", \"Commonsense knowledge\", \"Subtract\" - that's 4 green rectangles\n- Grey rectangles: These appear to be the value/result nodes. I can count: \"57.5%\", \"Prod.\", \"50%\", \"7.5%\" - that's 4 grey rectangles\n\nWait, let me recount more carefully by examining the figure description and structure:\n- Green rectangles (operations): 4\n- Grey rectangles (values): 4\n\nIn descending order: [\"4\", \"4\"]\n\nAnswer: 4, 4", "citations": [], "input_tokens": 28779, "output_tokens": 281, "cost_micros": 90552, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1 on page 1.\n\nLooking at Figure 1, I can see it contains:\n- A table on the left side\n- A reasoning graph on the right side with various nodes and connections\n\nIn the reasoning graph (right side), I can identify:\n- Grey rectangles: These appear to be nodes like \"Closed-domain knowledge\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker\"\n- Green rectangles: I need to look for any green-colored rectangles\n\nLet me count carefully:\n- Grey rectangles in the reasoning graph: I can see multiple grey nodes representing different types of operations and knowledge\n- Green rectangles: Looking at the figure, I don't see distinctly green rectangles in the traditional sense, but there appear to be some lighter/different colored elements\n\nActually, examining the figure more carefully:\n- There appear to be approximately 5 grey rectangular nodes in the reasoning graph\n- I don't clearly see green rectangles that are distinctly separate from the grey ones\n\nHowever, looking more carefully at the color scheme and the structure, it appears there are:\n- Grey rounded rectangles: approximately 5\n- There don't appear to be clearly distinct green rectangles\n\nBased on my examination, I count approximately 5 grey rectangles and 0 distinctly green rectangles in Figure 1.\n\nAnswer: 5, 0", "citations": [], "input_tokens": 36689, "output_tokens": 320, "cost_micros": 114867, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "0, 5", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper. Let me continue reading to locate Figure 1.Perfect! I found Figure 1. Now I need to examine this figure carefully to count the green and grey rectangles. Based on the content I can see, Figure 1 shows a reasoning graph with different colored rectangles representing different steps in the reasoning process. Let me read more carefully to identify all the rectangles and their colors.\n\nFrom what I can see in the figure description:\n- There's a table on the left\n- A reasoning graph on the right\n- The reasoning graph contains several boxes/rectangles with different steps\n\nLooking at the text content that describes the figure structure:\n1. \"Closed-domain knowledge: Table caption\" - appears to be one rectangle\n2. \"Commonsense knowledge\" - appears to be another rectangle\n3. \"Productivity corresponds to Prod. column\" - part of the reasoning\n4. \"Simple lookup\" - appears to be a step\n5. \"The number of random chance is 50%\" - another element\n6. \"Supported Claim\" - labeled element\n7. \"A's productivity is 57.5%\" - text element\n8. Various reasoning steps with operators (+, -, =, \u00d7, ?)\n9. \"Refuted Claim\" - labeled element\n10. \"Not Enough Info Claim\" - labeled element\n\nLet me search for more details about the actual visual representation or read the next section to see if there's a clearer description.Let me go back and read the figure description more carefully to understand the visual elements:It seems the content around line 632-646 is describing a green checkmark icon, which is not Figure 1. Let me look more carefully at the actual Figure 1 content. Let me re-read around the figure caption area:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55655, "error": null, "extra": {"thread_id": 1975, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b73a2e9dd2a74761aa49d98419f10801", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 323, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me search for more details about actual visual representation or read next section to see if there s clearer description let me go back and read figure description more carefully to understand visual elements it seems content around line 632 646 is describing green checkmark icon, which is not figure 1 let me look more carefully at actual figure 1 content let me re read around figure caption area", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to count the reasoning steps shown in the reasoning graph on the right side of the figure.\n\nThe reasoning graph shows the following steps:\n1. Closed-domain knowledge: Table caption\n2. Commonsense knowledge\n3. Simple lookup\n4. Subtraction\n\nThese are the four distinct reasoning steps required to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nAnswer: 4", "citations": [], "input_tokens": 68131, "output_tokens": 119, "cost_micros": 206178, "latency_ms": 30733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see a reasoning graph on the right side that shows the steps needed to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nThe reasoning graph shows the following steps:\n\n1. \"Closed-domain knowledge: Productivity corresponds to Prod. column\" (from table caption)\n2. \"Commonsense knowledge: The number of random chance is 50%\"\n3. \"Simple lookup: A's productivity is 57.5%\"\n4. \"Subtraction: The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese steps lead to verifying whether the claim is \"Supported\" or \"Refuted\".\n\nCounting the reasoning steps in the graph, I can identify 4 distinct reasoning steps.\n\nAnswer: 4", "citations": [], "input_tokens": 31764, "output_tokens": 225, "cost_micros": 98667, "latency_ms": 8390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the reasoning steps shown in the reasoning graph in Figure 1.\n\nLooking at Figure 1's reasoning graph (on the right side), I can identify the following reasoning steps:\n\n1. \"Closed-domain knowledge: Table caption\" - Productivity corresponds to Prod. column\n2. \"Commonsense knowledge\" - The number of random chance is 50%\n3. \"Simple lookup\" - A's productivity is 57.5%\n4. \"Subtraction\" - The subtraction result between 57.5% and 50% is 7.5%\n5. Final verification step - comparing the claim with the calculated result\n\nThe reasoning graph shows 5 distinct reasoning steps from the initial knowledge extraction to the final verification of the claim.\n\nAnswer: 5", "citations": [], "input_tokens": 44319, "output_tokens": 174, "cost_micros": 135567, "latency_ms": 6969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see it contains:\n- A table with columns: Cue, App., Prod., Cov.\n- A claim about \"A's productivity of 57.5%\"\n- A reasoning graph on the right side\n\nThe reasoning graph shows the verification process with several nodes connected by arrows. Let me count the reasoning steps by tracing through the graph:\n\n1. Start with the claim about A's productivity of 57.5%\n2. Look up \"A's productivity\" \u2192 refers to cell at last row and \"Prod.\" column\n3. Background knowledge: \"productivity\" corresponds to \"Prod.\" column\n4. Simple lookup: Get value 57.5% from the table\n5. Commonsense knowledge: \"random chance\" means 50% accuracy\n6. Numerical reasoning: Calculate difference 57.5% - 50% = 7.5%\n7. Compare calculated result with claimed value (7.5%)\n\nCounting the nodes in the reasoning graph shown in Figure 1, I can see there are 7 distinct reasoning steps involved in verifying this claim.\n\nAnswer: 7", "citations": [], "input_tokens": 28746, "output_tokens": 277, "cost_micros": 90393, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I need to count the reasoning steps shown in the reasoning graph on the right side.\n\nThe reasoning graph shows the following steps to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\":\n\n1. **Closed-domain knowledge**: Productivity corresponds to Prod. column (from table caption)\n2. **Commonsense knowledge**: The number of random chance is 50%\n3. **Simple lookup**: Finding the value 57.5% in the table (A's productivity)\n4. **Subtraction**: The subtraction result between 57.5% and 50% is 7.5%\n\nAnswer: 4", "citations": [], "input_tokens": 36656, "output_tokens": 169, "cost_micros": 112503, "latency_ms": 7671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned as most relevant to find Figure 1 and count the reasoning steps.Let me continue reading to find Figure 1:Now let me read the section around line 562 where Figure 1 is described:Let me read more to see the complete figure with all reasoning steps:Based on the document content, I can see Figure 1 shows a reasoning graph with the following steps:\n\n1. **Closed-domain knowledge: Table caption** - \"Productivity corresponds to Prod. column\"\n2. **Simple lookup** - \"A's productivity is 57.5%\"\n3. **Commonsense knowledge** - \"The number of random chance is 50%\"\n4. **Subtraction** - \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese 4 reasoning steps lead to verifying the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\" which is marked as \"Supported\".\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37062, "error": null, "extra": {"thread_id": 1976, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6471c23a58a448bd8c1bac8c5ba5da64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 190, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from the document.\n\nFrom Table 1 on page 4, I can see:\n\n**Wiki Table datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Table claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 68140, "output_tokens": 175, "cost_micros": 207045, "latency_ms": 33358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the number of claims in Wiki Table datasets versus Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 31773, "output_tokens": 182, "cost_micros": 98049, "latency_ms": 6016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 44328, "output_tokens": 174, "cost_micros": 135594, "latency_ms": 6858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 28755, "output_tokens": 183, "cost_micros": 89010, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 36665, "output_tokens": 174, "cost_micros": 112605, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about claims in Wiki Table datasets compared to scientific articles datasets.Perfect! I found the table with the statistics. Let me extract the specific information:\n\nFrom the table:\n- **Wiki Tables datasets**: \n - TabFact: 117,854 claims\n - FEVEROUS: 87,026 claims\n - Total: 117,854 + 87,026 = 204,880 claims\n\n- **Scientific Articles datasets**:\n - SEM-TAB-FACTS: 5,715 claims\n - SCITAB: 1,225 claims\n - Total: 5,715 + 1,225 = 6,940 claims\n\nDifference: 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36890, "error": null, "extra": {"thread_id": 1977, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eedb3672a2cb45bf898fdd6ba6d9a71c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 152, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the total number of claims in the dataset and the payment per claim annotation.\n\nFrom the document:\n\n1. Total number of claims in SCITAB: 1,225 claims (mentioned in the abstract and Table 1)\n\n2. Payment per claim annotation: 0.37 USD (mentioned in Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\")\n\nHowever, I need to consider that the annotation process involves multiple rounds and different types of claims:\n\nFrom Section 2.3, the manual verification process involves:\n- Verifying 872 false claims (counter-claims)\n- Verifying 900 NEI claims\n- Reviewing 872 real-world scientific claims\n\nThis suggests a two-round annotation process where each claim is annotated by two different annotators.\n\nLooking more carefully at the construction process:\n- 872 original claims were extracted\n- For each original claim, a counter-claim was generated (872 counter-claims)\n- 900 NEI claims were generated\n- After verification, the final dataset has 1,225 claims total\n\nThe annotation process in Section 2.3 states: \"We assign two different annotators to perform a two-round annotation for each claim\"\n\nSo the total annotations would be:\n- For supported/refuted claim pairs: 872 pairs need verification (2 rounds each)\n- For NEI claims: 900 claims need verification (2 rounds each)\n\nTotal annotations = (872 \u00d7 2) + (900 \u00d7 2) = 1,744 + 1,800 = 3,544 annotations\n\nTotal cost = 3,544 \u00d7 0.37 = 1,311.28 USD\n\nAnswer: 1311.28", "citations": [], "input_tokens": 68152, "output_tokens": 408, "cost_micros": 210576, "latency_ms": 38546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1311.28", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the annotation cost and the total number of claims in the dataset.\n\nFrom the document:\n\n1. **Annotation cost**: \"Each claim annotation is reimbursed at a rate of 0.37 USD\" (Section 2.3)\n\n2. **Total number of claims in SCITAB**: Looking at Table 1, the total number of claims in SCITAB is 1,225.\n\nHowever, I need to determine how many claims actually needed annotation. According to Section 2:\n\n- Section 2.1: 872 real-world scientific claims were filtered from SciGen\n- Section 2.2: They generated 872 false claims (counter-claims) and 900 NEI claims\n- Section 2.3: Manual verification was done on all these claims\n\nFrom Section 2.3 and Appendix D:\n- 872 false claims were verified\n- 900 NEI claims were verified (which resulted in 355 final NEI claims)\n- 872 original claims were also reviewed\n\nSo the total annotations performed:\n- False claim verification: 872 claims\n- NEI claim verification: 900 claims\n- Total annotations: 872 + 900 = 1,772 claims\n\nTotal cost = 1,772 claims \u00d7 0.37 USD per claim = 655.64 USD\n\nAnswer: 655.64", "citations": [], "input_tokens": 31785, "output_tokens": 318, "cost_micros": 100125, "latency_ms": 9562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "655.64", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. The total number of claims in the SCITAB dataset\n2. The payment rate per claim annotation\n\nFrom the document:\n\n**Total number of claims:**\nFrom Table 1 and Section 2.3: \"Total # of Claims: 1,225\"\n\n**Payment per claim:**\nFrom Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n**Calculation:**\nTotal cost = 1,225 claims \u00d7 0.37 USD per claim = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 44340, "output_tokens": 145, "cost_micros": 135195, "latency_ms": 5389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost for annotating all claims in the SCITAB dataset.\n\nFrom the document, I can find:\n\n1. **Total number of claims in SCITAB**: According to the abstract and Section 2, SCITAB contains **1,225 claims** (also written as \"1.2K expert-verified scientific claims\").\n\n2. **Cost per claim annotation**: According to Section 2.3 under \"Annotator Recruitment\":\n - \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n Total cost = Number of claims \u00d7 Cost per claim\n Total cost = 1,225 \u00d7 0.37\n Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 28767, "output_tokens": 183, "cost_micros": 89046, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost of annotating all claims in the SCITAB dataset.\n\nFrom the document:\n\n1. **Total number of claims in SCITAB**: According to Table 1, SCITAB has 1,225 total claims.\n\n2. **Payment per claim annotation**: From Section 2.3, it states \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n Total cost = Number of claims \u00d7 Cost per claim\n Total cost = 1,225 \u00d7 0.37\n Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 36677, "output_tokens": 152, "cost_micros": 112311, "latency_ms": 5469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant to find information about annotation costs for the proposed dataset.Let me search for information about annotation costs, dataset size, and pricing in the document.Let me read through the document more systematically. I'll start with sections that typically discuss dataset details and costs.Let me search for sections that discuss the dataset size and annotation costs. Let me look for keywords like \"price\", \"annotator\", \"pay\", etc.Let me search for numbers that might relate to costs:Let me read sections that might contain appendix or details about annotation costs:Let me look in later sections of the document where appendix or cost information might be:Let me search for \"Appendix\" section which might contain cost details:Let me try searching with content mode to find where these terms appear:Great! I found a reference to annotation cost. Let me read around line 974:Perfect! I found the annotation cost information. Now I need to find the total number of claims in the dataset. Let me search for that information:Perfect! Now I have all the information I need:\n- Total number of claims: 1,225\n- Cost per claim annotation: 0.37 USD\n\nLet me calculate the total cost:\nTotal cost = 1,225 \u00d7 0.37 = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97266, "error": null, "extra": {"thread_id": 1978, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77659594d74045e99872a87f698ab753", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 502, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about the distribution of reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3 on page 5, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset\":\n\nThe figure shows the percentage of claims for each number of reasoning steps (1-11).\n\nFrom the histogram, I can read the following percentages:\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20% (highest)\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1 on page 4, the total number of claims in SCITAB is 1,225.\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 68134, "output_tokens": 299, "cost_micros": 208887, "latency_ms": 34843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of claims with the highest reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the distribution of reasoning steps.\n\nThe x-axis shows reasoning steps from 1 to 11, and the y-axis shows the percentage.\n\nFrom the figure, I can read the following percentages:\n- 1 step: 1%\n- 2 steps: 7%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 6%\n- 9 steps: 5%\n- 10 steps: 3%\n- 11 steps: 2%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nThe document states there are 1,225 total claims in SCITAB (from Table 1).\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 31767, "output_tokens": 277, "cost_micros": 99456, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step percentage distribution in the SCITAB dataset from Figure 3.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 2%\n- 2 steps: 3%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 7%\n- 9 steps: 6%\n- 10 steps: 5%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nSince the dataset contains 1,225 claims total (as stated in the abstract and throughout the document), I need to calculate 20% of 1,225:\n\n1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 44322, "output_tokens": 243, "cost_micros": 136611, "latency_ms": 6812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the highest percentage of reasoning steps in the SCITAB dataset and determine how many claims fall into that category.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see:\n\n- The x-axis represents the reasoning steps in each claim\n- The y-axis represents the frequency for each reasoning step\n- The figure distinguishes between shallow claims (1-2 reasoning steps, highlighted in red) and deep claims (3+ reasoning steps, highlighted in blue)\n\nFrom the histogram in Figure 3, I need to identify which reasoning step has the highest frequency (highest bar).\n\nExamining the histogram carefully:\n- The highest bar appears to be at 4 reasoning steps\n- The frequency at 4 reasoning steps appears to be approximately 350-400 claims\n\nLooking more carefully at the visual representation, the bar at 4 reasoning steps is the tallest in the histogram, with a frequency of approximately 350 claims.\n\nAnswer: 350", "citations": [], "input_tokens": 28749, "output_tokens": 223, "cost_micros": 89592, "latency_ms": 7078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "350", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step with the highest percentage in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20%\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1, the total number of claims in SCITAB is 1,225.\n\nNumber of claims with 5 reasoning steps = 1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 36659, "output_tokens": 266, "cost_micros": 113967, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the most relevant document to find information about claims and reasoning steps in the author's proposed dataset.Let me continue reading to find information about the dataset and reasoning steps distribution.Great! I found a reference to a distribution histogram of reasoning steps. Let me read that specific section.Let me look at the earlier part where the figure caption was mentioned to understand the chart better.Perfect! Now I can see the histogram data. Let me read more to see all the percentage values clearly:From the histogram data I can see, the percentages are listed vertically and the reasoning steps go from 2 to 11. Looking at the data:\n\n- The percentages shown are: 20, 18, 20, 15, 15, 15, 6, 8, 7, 5, 3, 2, 1, 1\n\nThe highest percentages appear to be 20% which occurs twice. Let me look for more context to understand the exact mapping between reasoning steps and percentages. Let me search for more information about the total number of claims.Good! So SCITAB has 1,225 total claims. Now I need to figure out which reasoning step number has the highest percentage (20%), and calculate how many claims that represents. Looking at the histogram data again, I need to understand which reasoning steps correspond to the 20% values. Let me look for more details about the distribution.Now let me carefully re-examine the histogram data. The data shows reasoning steps 2-11 and their corresponding percentages. Looking at the visual structure again:I see the structure now. Lines 1182-1198 show \"2 3 4 5 6 7 8 9 10 11\" which are the reasoning steps on the x-axis. The percentages must be reading from the bars. Based on the structure, it appears:\n\nFrom the data, I can see percentages: 20, 18, 20, 15, 15, 15, 10, 6, 8, 7, 5, 5, 3, 2, 1, 1\n\nThe highest percentage is 20%, which appears twice in the data. With 1,225 total claims and 20% being the highest:\n- 20% of 1,225 = 0.20 \u00d7 1,225 = 245 claims\n\nHowever, I see 20% appears twice, which could mean there are two different reasoning step counts that each have 20%. Let me verify by checking if there's a table or more precise description.Based on the figure caption, the y-axis shows \"frequency\" which is shown as \"Percentages (%)\". The highest percentage is 20%, and since this appears to occur for specific reasoning step(s), I need to calculate the number of claims.\n\nWith 1,225 total claims in SCITAB and the highest percentage being 20%:\n20% \u00d7 1,225 = 245 claims\n\nAnswer: 245", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77500, "error": null, "extra": {"thread_id": 1979, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0b34f104b1a04155b78b4d63f4b337dd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the number of figures in the Appendix section of the paper.\n\nLooking through the document, I can see the Appendix starts at section A on page 13. Let me go through each appendix section and count the figures:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task (page 14)\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task (page 15)\n- Figure 7: The user interface for the refuted claim verification task (page 16)\n- Figure 8: Examples of post-annotation survey questions (page 17)\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: Refuted claims cases A to C (page 22)\n- Figure 10: Refuted claims cases D and E (page 23)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT (page 24)\n- Figure 12: Error Cases 4-6 for InstructGPT (page 25)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts (page 26)\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts (page 27)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures in the Appendix.\n\nAnswer: 10", "citations": [], "input_tokens": 68138, "output_tokens": 472, "cost_micros": 211494, "latency_ms": 35156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the paper.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- A.2 Claim Extraction Interface: Figure 5\n\n**Appendix B (Manual Claim Verification Procedure):**\n- B.2 NEI Claim Verification Interface: Figure 6\n- B.3 Refuted Claim Verification Interface: Figure 7\n- B.4 Annotation Post-Survey: Figure 8\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9 (refuted cases A to C)\n- Figure 10 (refuted cases D and E)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11 (Error Cases 1-3)\n- Figure 12 (Error Cases 4-6)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13 (Error Cases 1-3)\n- Figure 14 (Error Cases 4 and 5)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 31771, "output_tokens": 373, "cost_micros": 100908, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 44326, "output_tokens": 415, "cost_micros": 139203, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure)**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure)**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset)**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration)**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims)**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT)**\n- Figure 11: Error Cases 1-3 for InstructGPT\n- Figure 12: Error Cases 4-6 for InstructGPT\n\n**Appendix G (Error Cases for Program-of-Thoughts)**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts)**\n- No figures\n\nTotal figures in Appendix:\nFigure 5, 6, 7, 8, 9, 10, 11, 12, 13, 14\n\nAnswer: 10", "citations": [], "input_tokens": 28753, "output_tokens": 380, "cost_micros": 91959, "latency_ms": 8592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLet me go through the Appendix sections:\n\n**Appendix A** (Claim Extraction Procedure):\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B** (Manual Claim Verification Procedure):\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C** (Analysis of Refuted Reasons in the Sci-Fact dataset):\n- No figures\n\n**Appendix D** (Discussions on Human-Machine Collaboration):\n- No figures\n\n**Appendix E** (Case Study for Refuted Claims):\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F** (Error Cases for InstructGPT):\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G** (Error Cases for Program-of-Thoughts):\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H** (Prompts):\n- No figures\n\nTotal figures in Appendix:\n- Figure 5\n- Figure 6\n- Figure 7\n- Figure 8\n- Figure 9\n- Figure 10\n- Figure 11\n- Figure 12\n- Figure 13\n- Figure 14\n\nAnswer: 10", "citations": [], "input_tokens": 36663, "output_tokens": 427, "cost_micros": 116394, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find out how many figures are in the Appendix.Let me search for the Appendix section in the document.Now let me search for figures in the Appendix sections. Let me read the end of the document where appendices are typically located.Let me continue reading to find the Appendix section.Let me search for all figures in the appendix sections. Let me look for \"Figure\" in the context of Appendix.Let me read the document more systematically to find all appendix sections and count the figures. Let me search for specific appendix sections.Now I can see the appendix sections starting. Let me continue reading to find all figures in the appendices.Let me continue reading to find more figures in the appendix.Let me continue to look for all figures in the appendix. Let me read further.Let me continue reading to find all the figures.Let me continue reading to find more figures.Now let me search for the end of the document to make sure I've found all figures.Perfect! Now let me count all the figures I found in the Appendix sections. Based on my reading, I found the following figures in the appendices:\n\n**Appendix A:**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B:**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix E:**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F:**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G:**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\nTotal figures in the Appendix: 10\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 85187, "error": null, "extra": {"thread_id": 1980, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0161632b18b34ebfa7ee0421186ab320", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 490, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly at Figures 3, 7, and 9 which show the relative sizes of Swp (information flow from text part to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\n- In shallow layers, Swp is high while Spq is low\n- In deep layers, Spq becomes dominant and exceeds Swp\n\nThe document explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis means that Swp is NOT always greater than Spq - it depends on the layer depth.\n\nAnswer: no", "citations": [], "input_tokens": 41233, "output_tokens": 158, "cost_micros": 126069, "latency_ms": 32250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the relative sizes of Swp (information flow from text to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\nIn shallow layers (early layers), Swp is greater than Spq.\nIn deep layers (later layers), Spq becomes dominant and is greater than Swp.\n\nThe document explicitly states: \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince Swp is not always greater than Spq across all layers (it depends on which layer), the answer is no.\n\nAnswer: no", "citations": [], "input_tokens": 17763, "output_tokens": 200, "cost_micros": 56289, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 and Figure 7 in the document, which show the relative sizes of S_wp (information flow from text to label words), S_pq (information flow from label words to target position), and S_ww across different layers:\n\nIn shallow layers (layers close to input), S_wp is high while S_pq is low.\n\nIn deep layers (layers close to output), S_pq becomes dominant and exceeds S_wp.\n\nThe document explicitly states: \"In shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low, while S_wp, the information flow from the text part to label words is high; (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince S_wp is greater in shallow layers but S_pq becomes greater in deep layers, S_wp is NOT always greater than S_pq.\n\nAnswer: no", "citations": [], "input_tokens": 20121, "output_tokens": 222, "cost_micros": 63693, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly Section 2.1 and Figure 3, the metrics Swp (information flow from text part to label words) and Spq (information flow from label words to target position) are compared across different layers.\n\nThe text states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Swp is larger in shallow layers but Spq becomes larger in deep layers. Therefore, Swp is not always greater than Spq across all layers.\n\nAnswer: No", "citations": [], "input_tokens": 13762, "output_tokens": 132, "cost_micros": 43266, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the data presented in the document, particularly in Figure 3 and related figures showing $S_{wp}$, $S_{pq}$, and $S_{ww}$ values across different layers:\n\nIn shallow layers (e.g., layers 0-10), $S_{wp}$ (information flow from text to label words) is greater than $S_{pq}$ (information flow from label words to target position).\n\nHowever, in deep layers (e.g., layers 15-48), $S_{pq}$ becomes much larger than $S_{wp}$.\n\nFor example, in Figure 3(a) for SST-2:\n- Layer 0: $S_{wp}$ = 0.72, $S_{pq}$ = 0.15\n- Layer 20: $S_{wp}$ = 0.10, $S_{pq}$ = 0.90\n\nSince the relationship changes across layers, $S_{wp}$ is not **always** greater than $S_{pq}$.\n\nAnswer: no", "citations": [], "input_tokens": 23820, "output_tokens": 243, "cost_micros": 75105, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about information flow between text parts, label words, and target positions.Let me search for information about information flow between text parts, label words, and target positions.Let me search for specific terms related to the comparison:Let me read through the document more systematically to find tables or figures that might contain this information:Let me read Figure 3 and related sections since it mentions Swp, Spq, and Sww which seem to be related to information flow:Good! I found relevant information. Let me read more to understand the definitions and comparisons better:Perfect! Now I have the key information. Let me look at Figure 3 more carefully to see the actual values:Based on my reading of the document, I can now answer the question. The document clearly states:\n\nFrom the text (lines 842-851):\n- \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while **Swp, the information flow from the text part to label words is high**\"\n- \"in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one\"\n\nThe document also states in the figure caption (line 943): \"Initially, **Swp occupies a significant proportion**, but it gradually decays over layers, while **Spq becomes the dominant one**.\"\n\nThis indicates that:\n- In shallow layers: Swp (text part to label words) > Spq (label words to target position)\n- In deep layers: Spq (label words to target position) > Swp (text part to label words)\n\nSince Swp starts high and decays while Spq starts low and increases, across ALL layers (taking the mean), Swp is NOT always greater than Spq. In fact, Spq becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59108, "error": null, "extra": {"thread_id": 1981, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_31954715cb0b491eade4006731d43b22", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the related discussion in Section 2.1:\n\nThe text states: \"Figure 3 reveals that: (1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 41220, "output_tokens": 149, "cost_micros": 125895, "latency_ms": 31437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe information flow from label words to the target position (S_pq) is LOW in shallow layers, but becomes DOMINANT in deep layers. The document states: \"(1) in shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 17750, "output_tokens": 119, "cost_micros": 55035, "latency_ms": 4890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe text states: \"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 20108, "output_tokens": 140, "cost_micros": 62424, "latency_ms": 5203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and Section 2.1, I need to examine when the information flow from label words to the target position (Spq) dominates.\n\nFrom Figure 3 and the text in Section 2.1:\n- Initially (in shallow layers), Swp (information flow from text to label words) \"occupies a significant proportion\"\n- Spq (information flow from label words to target position) \"gradually\" becomes dominant\n- Spq becomes \"the dominant one\" but this happens over layers, not in all layers\n\nThe text explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Spq does NOT dominate in all layers - only in the deeper layers after the information aggregation phase in shallow layers is complete.\n\nAnswer: No", "citations": [], "input_tokens": 13749, "output_tokens": 198, "cost_micros": 44217, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the paper's findings in Section 2.1 and the results shown in Figures 3, 7, and 9:\n\nIn shallow layers, $S_{wp}$ (information flow from text to label words) is high while $S_{pq}$ (information flow from label words to target position) is low.\n\nIn deep layers, $S_{pq}$ becomes dominant while $S_{wp}$ decreases.\n\nThe paper explicitly states: \"in shallow layers, $S_{pq}$, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, $S_{pq}$, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 23807, "output_tokens": 171, "cost_micros": 73986, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to answer your question.Let me search for content related to \"information flow\" and \"label words\" in the document.Now let me read the specific sections that discuss whether information flow from label words dominates in all layers.Perfect! I found the answer. Let me read a bit more to confirm the complete picture.Based on the document, the answer is clear. The text states:\n\n\"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis shows that information flow from label words to the target position does NOT dominate in all layers - it is low in shallow layers and only becomes dominant in deep layers.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38452, "error": null, "extra": {"thread_id": 1982, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_91e767a2b44d476aa25ee429c43c2924", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 4, which shows the impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers:\n\nThe figure shows that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - has minimal impact on both Label Loyalty and Word Loyalty metrics for both GPT2-XL and GPT-J models. The bars for \"Random (Last)\" show loyalty values close to 100%, similar to the \"No Isolation\" baseline.\n\nThis is further confirmed in the text on page 4 which states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 41222, "output_tokens": 170, "cost_micros": 126216, "latency_ms": 33032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - shows loyalty values very close to the \"No Isolation\" baseline (around 100%). This indicates almost no impact on the model's behavior.\n\nAnswer: Yes", "citations": [], "input_tokens": 17752, "output_tokens": 101, "cost_micros": 54771, "latency_ms": 3967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that:\n\n- \"Random (Last)\" (randomly isolating non-label words in the last 5 layers) shows very high loyalty scores (close to 100%), nearly identical to \"No Isolation\"\n- This indicates minimal impact on the model's predictions\n\nThe text also confirms: \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 20110, "output_tokens": 134, "cost_micros": 62340, "latency_ms": 6030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 and the related text in section 2.2, the document shows that isolating label words within the first 5 layers has substantial impact, but isolation within the last 5 layers has negligible impact. The text also states that \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nFigure 4 and Figure 8 show that randomly isolating non-label words (represented by lighter colored lines) in the last layers results in very high loyalty scores (close to 100%), indicating almost no impact on the model's behavior.\n\nAnswer: yes", "citations": [], "input_tokens": 13751, "output_tokens": 139, "cost_micros": 43338, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see the results for \"Random (Last)\".\n\nThe table shows:\n- Label Loyalty (GPT2-XL) with Random (Last): 98\n- Word Loyalty (GPT2-XL) with Random (Last): 98\n- Label Loyalty (GPT-J) with Random (Last): 98\n- Word Loyalty (GPT-J) with Random (Last): 98\n\nThese values are nearly identical to the \"No Isolation\" baseline (100), indicating minimal impact.\n\nThe document states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 23809, "output_tokens": 191, "cost_micros": 74292, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about randomly isolating non-label words within the last 5 layers.Perfect! I found the answer. The document states that randomly isolating non-label words within the last 5 layers has \"inconsequential\" impact, which means \"almost no impact.\"\n\nAnswer: Yes", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40125, "error": null, "extra": {"thread_id": 1983, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c2144cf872eb43e78cc21f804b3432de", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with Vanilla In-Context Learning (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 41209, "output_tokens": 115, "cost_micros": 125352, "latency_ms": 29484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find the average improvement from the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 on the page discussing Anchor Re-weighting results:\n\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nThis is also confirmed in the text which states: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.7", "citations": [], "input_tokens": 17739, "output_tokens": 149, "cost_micros": 55452, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.7", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with the baseline vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract that mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 20097, "output_tokens": 141, "cost_micros": 62406, "latency_ms": 5483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the average improvement of the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 in the document, I can see the following results:\n\n**Vanilla In-Context Learning (1-shot per class):**\n- SST-2: 61.28\n- TREC: 57.56\n- AGNews: 73.32\n- EmoC: 15.44\n- Average: 51.90\n\n**Anchor Re-weighting (1-shot per class):**\n- SST-2: 90.07\n- TREC: 60.92\n- AGNews: 81.94\n- EmoC: 41.64\n- Average: 68.64\n\nThe improvement is calculated as:\n68.64 - 51.90 = 16.74\n\nThe document also states in the text: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 13738, "output_tokens": 229, "cost_micros": 44649, "latency_ms": 5717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to find the performance improvement of Anchor Re-weighting over vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract and Section 3.1 which mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 23796, "output_tokens": 148, "cost_micros": 73608, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the Anchor Re-weighting method and its performance improvement over vanilla ICL.Let me search for \"Anchor Re-weighting\" in the document to find the relevant information.Let me read the section with the results table to find the specific improvement values.Perfect! I found Table 1 which shows the performance comparison. Let me calculate the improvement:\n\nFrom the table:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement = 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35969, "error": null, "extra": {"thread_id": 1984, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25b28dfa8e5c4fbeb8c80f7d7650cb08", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 144, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I need to find information about anchor-only context compression speedup for GPT2-XL on AGNews.\n\nLooking at Table 3 on page 8, which shows \"Acceleration ratios of the Hiddenanchor method\":\n\nThe table shows:\n- Model: GPT2-XL\n- AGNews: 2.5\u00d7\n\nThis indicates that using the anchor-only context compression method (Hiddenanchor), GPT2-XL achieves a 2.5\u00d7 speedup on the AGNews dataset.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 41212, "output_tokens": 129, "cost_micros": 125571, "latency_ms": 31171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews using anchor-only context compression (Hiddenanchor method):\n\nIn Table 3 on the topic of \"Acceleration ratios of the Hiddenanchor method,\" the speedup ratio for GPT2-XL on AGNews is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 17742, "output_tokens": 90, "cost_micros": 54576, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup for GPT2-XL on AGNews...\n\nIn Table 3 (page 7), the acceleration ratios of the Hiddenanchor method are provided:\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 20100, "output_tokens": 86, "cost_micros": 61590, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews with anchor-only context compression...\n\nIn Table 3 (on page 8), which shows \"The speed-up ratio compared to the original ICL\", I can find the speedup values for different models and datasets.\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 13741, "output_tokens": 100, "cost_micros": 42723, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup on AGNews with GPT2-XL...\n\nIn Table 3 on page 11, the acceleration ratios of the Hidden_anchor method are presented:\n\n| Model | SST-2 | TREC | AGNews | EmoC |\n| ------- | ----- | ---- | ------ | ---- |\n| GPT2-XL | 1.1\u00d7 | 1.5\u00d7 | 2.5\u00d7 | 1.4\u00d7 |\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 23799, "output_tokens": 134, "cost_micros": 73407, "latency_ms": 5157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I'll search for information about anchor-only context compression and GPT2-XL speed up on AGNews in the provided document.Let me search for \"anchor\" and \"AGNews\" in the document to find the relevant information.Now let me look at Table 3 which shows acceleration ratios for the Hiddenanchor method:Perfect! I found the answer in Table 3. The table shows acceleration ratios of the Hiddenanchor method, and for GPT2-XL on AGNews, the speedup is 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36062, "error": null, "extra": {"thread_id": 1985, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c1692b1f5bfe4ec6aa79ada899da40bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 139, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the most confusing category of Abbreviation in the document.\n\nLooking at the confusion matrices in the document, particularly in Section 3.3 (Anchor Distances for Error Diagnosis) and the appendices, I can see confusion matrices for the TREC dataset.\n\nIn Figure 6 on page 8, there are two confusion matrices shown:\n- (a) Confusion matrix of Confusion^pred_ij\n- (b) Confusion matrix of Confusion_ij\n\nLooking at the Abbreviation row in both matrices, I need to identify which category Abbreviation is most confused with (the lowest confusion score, or highest confusion).\n\nFrom Figure 6(b), looking at the Abbreviation row:\n- Abbreviation-Abbreviation: 1 (diagonal, set to 1)\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n- Abbreviation-Person: 1\n- Abbreviation-Location: 1\n- Abbreviation-Number: 0.87\n\nThe lowest value (indicating highest confusion) in the Abbreviation row is with Description (0.76), followed by Entity (0.84).\n\nThis is also confirmed in the text on page 8 which states: \"Figure 6 shows that the proposed approximation metric, Confusion^pred_ij, can identify the most confusing case (Description-Entity) and performs reasonably well for highly confusing categories (Entity-Abbreviation, Description-Abbreviation).\"\n\nAnswer: Description", "citations": [], "input_tokens": 41202, "output_tokens": 351, "cost_micros": 128871, "latency_ms": 26937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the confusion matrix of Confusion_ij on the TREC dataset, I need to find which category has the highest confusion score with Abbreviation.\n\nFrom the confusion matrix in Figure 6(b), the values for Abbreviation's confusion with other categories are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nThe lowest value (highest confusion) is with Entity at 0.84.\n\nAnswer: Entity", "citations": [], "input_tokens": 17732, "output_tokens": 125, "cost_micros": 55071, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the actual confusion matrix (Confusionij) on the TREC dataset, I need to find which category Abbreviation is most confused with.\n\nFrom the confusion matrix in Figure 6(b), the Abbreviation row shows the following confusion scores with other categories:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nLower confusion scores indicate higher confusion between categories (as stated in the paper). The lowest score is 0.76 with Description, followed by 0.84 with Entity.\n\nAnswer: Description", "citations": [], "input_tokens": 20090, "output_tokens": 147, "cost_micros": 62475, "latency_ms": 5234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices in Figure 14, I need to identify which category is most confused with \"Abbreviation.\"\n\nIn the confusion matrices, lower values indicate higher confusion (as stated in the document: \"The closer Confusion_{ij} approximates 1, the less pronounced the confusion\").\n\nLooking at the \"Abbreviation\" row across the different matrices (M=5, 10, 20, 50, 100, 200), the lowest values (highest confusion) are:\n- Description: 0.31, 0.45, 0.52\n- Entity: 0.44, 0.49, 0.51\n\nThe \"Description\" category consistently shows the lowest values when compared with Abbreviation, indicating the highest degree of confusion.\n\nAnswer: Description", "citations": [], "input_tokens": 13731, "output_tokens": 174, "cost_micros": 43803, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices provided in the document, I need to find which category is most confused with \"Abbreviation.\"\n\nIn Figure 6(a), which shows the predicted confusion matrix ($\\text{Confusion}_{ij}^{\\text{pred}}$), the values for Abbreviation with other categories are:\n- Entity: 0.49\n- Description: 0.45\n- Person: 0.90\n- Location: 1.00\n- Number: 0.83\n\nLower values indicate higher confusion (as stated in the document: \"0 indicating the highest degree of category confusion\").\n\nIn Figure 6(b), which shows the actual confusion matrix ($\\text{Confusion}_{ij}$), the values for Abbreviation are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1.00\n- Location: 1.00\n- Number: 1.00\n\nIn both matrices, the lowest value (highest confusion) for Abbreviation is with \"Description\" (0.45 predicted, 0.76 actual), followed closely by \"Entity\" (0.49 predicted, 0.84 actual).\n\nAnswer: Description", "citations": [], "input_tokens": 23789, "output_tokens": 270, "cost_micros": 75417, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the most confusing category of abbreviation.Let me search for information about \"confusing\" and \"abbreviation\" categories in the document.Let me read the specific chunk that mentions confusing categories and abbreviations:Perfect! I found the answer. Let me read a bit more context to confirm:Based on the document, the text clearly states that \"the most confusing case (Description-Entity)\" was identified. However, the question asks specifically about \"the most confusing category of Abbreviation.\"\n\nLooking at the confusion matrix data and the text, it mentions that the highly confusing categories involving Abbreviation are:\n- Entity-Abbreviation\n- Description-Abbreviation\n\nLet me check if there's more specific information about which is THE most confusing for Abbreviation:\n\nLooking at the confusion matrix values shown:\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n\nThe lower the value in a confusion matrix, the more confusing the pair. So Entity (0.84) is more confusing with Abbreviation than Description (0.76), but actually looking at this as a similarity matrix (as the caption says \"heatmaps display similarity\"), higher values mean more similar/confusing.\n\nBased on the text stating \"highly confusing categories (Entity-Abbreviation, Description-Abbreviation)\" with Entity-Abbreviation listed first, and the confusion matrix showing 0.84 for Abbreviation-Entity vs 0.76 for Abbreviation-Description:\n\nAnswer: Entity", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40344, "error": null, "extra": {"thread_id": 1986, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_052d25ab92e746d681f311675087569d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 227, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2, which shows the MIMIC-IT overview tree diagram, I can see three main branches: Perception, Reasoning, and Planning.\n\nAt the top of the Planning branch (shown in red/pink on the right side), the task labeled is \"Indoor Event Planning\".\n\nAnswer: Indoor Event Planning", "citations": [], "input_tokens": 55843, "output_tokens": 76, "cost_micros": 168669, "latency_ms": 49328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "indoor event planning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the MIMIC-IT overview with different branches representing fundamental capabilities, I need to identify the task at the top of the planning branch.\n\nIn the planning section (rightmost branch of the tree diagram), examining from top to bottom, the topmost task shown is \"Commonsense Reasoning\" with the example question \"What might this young boy learn from this experience? (LA)\"\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 21188, "output_tokens": 104, "cost_micros": 65124, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, which shows the MIMIC-IT overview with three main branches (Perception, Reasoning, and Planning), I can see the Planning branch on the right side.\n\nAt the top of the Planning branch, the task shown is \"Indoor Event Planning (IEP)\" with the question \"Can you suggest any recreational pursuits that can be enjoyed within this room?\"\n\nAnswer: Indoor Event Planning (IEP)", "citations": [], "input_tokens": 23513, "output_tokens": 95, "cost_micros": 71964, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "indoor event planning iep", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a planning branch or tree structure with tasks.\n\nIn Figure 1 (MIMIC-IT overview), I can see different capabilities organized into three main branches: Perception, Reasoning, and Planning.\n\nUnder the Planning branch, I can see two tasks listed:\n1. Egocentric Planning (E4D)\n2. Episode Memory (DC)\n\nLooking at the visual layout in Figure 1, \"Egocentric Planning\" appears to be positioned at the top of the Planning branch.\n\nAnswer: Egocentric Planning", "citations": [], "input_tokens": 20245, "output_tokens": 129, "cost_micros": 62670, "latency_ms": 5657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "egocentric planning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the task at the top of the Planning branch.\n\nThe Planning section shows three items:\n1. At the top: \"Is it safe to walk on the floor while the woman is cleaning? E4D\" \u2192 EVR [Egocentric Video Reasoning]\n2. In the middle: \"Can you suggest any ways to brighten up the room I'm in? E4D\" \u2192 EP [Egocentric Planning]\n3. At the bottom: \"Can you suggest any recreational pursuits that can be enjoyed within this room? IEP\" \u2192 IEPP [Indoor Event Planning]\n\nThe topmost task in the Planning branch is Egocentric Video Reasoning (EVR).\n\nAnswer: Egocentric Video Reasoning", "citations": [], "input_tokens": 24261, "output_tokens": 176, "cost_micros": 75423, "latency_ms": 6139, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "egocentric video reasoning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the planning branch of the tree and identify the task at the top.Let me search for content related to \"planning branch\" or \"tree\" to find the relevant figure or diagram:Let me search more specifically for \"tree\" and look at figures:Let me look at Figure 1 which mentions perception, reasoning, and planning:Good! I can see \"Planning\" section. Let me continue reading to see what tasks are under the Planning branch:Perfect! I can see the Planning section with its tasks. Looking at the structure, under \"Planning\" I can see:\n\n1. \"Commonsense Reasoning\" - \"What might this young boy learn from this experience? (LA)\"\n2. \"General Scene Difference\" \n3. \"Egocentric Planning\"\n4. \"Indoor Event Planning\"\n\nBut I need to see the visual tree structure more carefully. Let me look at the earlier part to understand the tree layout better:Now I can see the structure clearly. Looking at Figure 1, there's a tree structure with three main branches:\n1. Perception\n2. Reasoning\n3. Planning\n\nUnder the \"Planning\" branch, the first task/question that appears is: \"What might this young boy learn from this experience? (LA)\" which is labeled as \"Commonsense Reasoning\".\n\nSo the task at the top of the Planning branch is \"Commonsense Reasoning\".\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51937, "error": null, "extra": {"thread_id": 1987, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f3cc3b28e854493c927ab87e43d7d1d0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 285, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for bar plots.\n\nLooking through the figures:\n\n**Figure 1** (page 2): This shows example images and questions from the MIMIC-IT dataset. No bar plots here.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text. No bar plots here.\n\n**Figure 3** (page 5): This shows the Syphus pipeline overview. No bar plots here.\n\n**Figure 4** (page 7): This figure shows \"The data statistics of multi-modal in-context instruction-response pairs\" with:\n- (a) Instructions - shown as a sunburst/radial chart (not a bar plot)\n- (b) Responses - shown as a sunburst/radial chart (not a bar plot)\n- (c) Statistics of instructions and responses - This contains **4 bar plots**:\n 1. Instruction Length distribution\n 2. Response Length distribution\n 3. # Images distribution\n 4. # Related Instructions distribution\n\n**Figure 5** (page 8): This shows example responses from Otter with images and text. No bar plots here.\n\n**Figure 6** (page 10): This shows three subplots:\n- (a) Video understanding - **2 bar plots** (MSVD 0-shot and MSRVTT 0-shot)\n- (b) Vision-language model alignment - **1 bar plot** (Elo Rating)\n- (c) COCO caption - This is a line graph, not a bar plot\n\nTotal bar plots: 4 (from Figure 4c) + 2 (from Figure 6a) + 1 (from Figure 6b) = **7 bar plots**\n\nAnswer: 7", "citations": [], "input_tokens": 55838, "output_tokens": 405, "cost_micros": 173589, "latency_ms": 40905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots, including any subplots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text - no bar plots.\n\n**Figure 2**: Shows example images and text comparisons - no bar plots.\n\n**Figure 3**: Shows a pipeline diagram - no bar plots.\n\n**Figure 4**: This figure contains multiple subplots showing data statistics:\n- (a) Shows two circular/sunburst plots for instructions - not bar plots\n- (b) Shows two circular/sunburst plots for responses - not bar plots\n- (c) Shows four plots with statistics:\n 1. \"Instruction Length\" - appears to be a histogram/bar plot\n 2. \"Response Length\" - appears to be a histogram/bar plot\n 3. \"# Images\" - appears to be a histogram/bar plot\n 4. \"# Related Instructions\" - appears to be a histogram/bar plot\n\nSo Figure 4(c) contains 4 bar plots.\n\n**Figure 5**: Shows example images and responses - no bar plots.\n\n**Figure 6**: This figure contains three subfigures:\n- (a) \"Video understanding\" - shows a grouped bar chart with multiple bars\n- (b) \"Vision-language model alignment\" - shows a bar chart\n- (c) \"COCO caption\" - shows a line plot with points, not a bar plot\n\nSo Figure 6 has 2 bar plots (a and b).\n\nTotal count: 4 (from Figure 4c) + 2 (from Figure 6) = 6 bar plots.\n\nAnswer: 6", "citations": [], "input_tokens": 21183, "output_tokens": 367, "cost_micros": 69054, "latency_ms": 11281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count the bar plots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text describing different capabilities - no bar plots.\n\n**Figure 2**: Shows data format comparison with images and text - no bar plots.\n\n**Figure 3**: Shows the Sythus pipeline overview - no bar plots.\n\n**Figure 4**: This is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst plot showing root verb-noun pairs of instructions\n- (b) A circular/sunburst plot showing root verb-noun pairs of responses \n- (c) Four separate plots labeled \"Statistics of instructions and responses\":\n - Top left: A histogram/bar plot showing \"# Instructions\" vs \"Instruction Length\"\n - Top right: A histogram/bar plot showing \"# Responses\" vs \"Response Length\"\n - Bottom left: A histogram/bar plot showing \"# Instructions\" vs \"# Images\"\n - Bottom right: A histogram/bar plot showing \"# Instructions\" vs \"# Related Instructions\"\n\nSo in Figure 4(c), there are **4 bar plots**.\n\n**Figure 5**: Shows example responses with images and text - no bar plots.\n\n**Figure 6**: Contains three subplots:\n- (a) \"Video understanding\" - shows bar plots comparing VideoChatGPT and Otter on MSVD and MSRVTT for QA and Captioning. This contains **4 grouped bar comparisons** (which I count as bar plots).\n- (b) \"Vision-language model alignment\" - shows an Elo rating comparison with bars for different models - this is **1 bar plot**.\n- (c) \"COCO caption\" - shows a line plot, not a bar plot.\n\nSo Figure 6 has: 4 (from 6a) + 1 (from 6b) = **5 bar plots**.\n\n**Figure 7**: Shows text examples in multiple languages - no bar plots.\n\nTotal bar plots: 4 (from Figure 4c) + 5 (from Figure 6) = **9 bar plots**.\n\nAnswer: 9", "citations": [], "input_tokens": 23508, "output_tokens": 506, "cost_micros": 78114, "latency_ms": 14187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count bar plots.\n\nLooking through the figures:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with three main branches (Perception, Reasoning, Planning). Not a bar plot.\n\n**Figure 2**: This shows data format comparisons with images and text examples. Not a bar plot.\n\n**Figure 3**: This is a flowchart showing the Sythus overview. Not a bar plot.\n\n**Figure 4**: This figure is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst chart showing instructions (verb-noun pairs)\n- (b) A circular/sunburst chart showing responses (verb-noun pairs)\n- (c) \"Statistics of instructions and responses\" which contains 4 separate bar plots:\n 1. Instruction Length vs # Instructions (bar plot)\n 2. Response Length vs # Responses (bar plot)\n 3. # Images vs # Instructions (bar plot)\n 4. # Related Instructions vs # Instructions (bar plot)\n\nSo Figure 4(c) contains **4 bar plots**.\n\n**Figure 5**: This shows example responses from Otter with images and text. Not a bar plot.\n\n**Figure 6**: This contains three parts:\n- (a) A table showing video understanding results. Not a bar plot.\n- (b) A table showing vision-language model alignment. Not a bar plot.\n- (c) A table showing COCO caption results. Not a bar plot.\n\nTotal count: 4 bar plots (all from Figure 4c)\n\nAnswer: 4", "citations": [], "input_tokens": 24256, "output_tokens": 385, "cost_micros": 78543, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count all bar plots (including subplots).Let me search for figures and plots in the document:Now let me read the actual content around the figures to see the descriptions of plots:Let me read more sections to find all the figures:Now let me look at Figure 4 more carefully to see if it contains bar plots:Good! I can see Figure 4 (c) has bar plots. Let me continue looking at Figure 6:Now I need to look at the actual visual description to understand what kind of plots Figure 6 has. Looking at the data in lines 1693-1823, I can see Figure 6 has three subplots (a), (b), and (c). Let me check if these are bar plots by examining the structure:\n\n- Figure 6(a): Shows \"MSVD 0-shot\" and \"MSRVTT 0-shot\" with \"QA\" and \"Captioning\" categories, comparing VideoChatGPT and Otter - this appears to be bar plots\n- Figure 6(b): Shows \"Elo Rating\" comparison among MM-GPT, Inst. BLIP, LLaVA, MiniGPT, and Otter - this appears to be a bar plot\n- Figure 6(c): Shows \"COCO caption\" with \"0-shot 4-shot 8-shot 16-shot\" comparing Otter and OpenFlamingo - this appears to be a line or bar plot\n\nNow let me search for more figures to make sure I don't miss any:Figure 7 is mentioned but let me check if it's a plot or just examples. From line 2292, Figure 7 seems to be just text examples for multi-lingual translation, not a plot.\n\nLet me search for any appendix figures or additional figures:Great! I found references to bar charts. Let me read those sections more carefully:These descriptions refer to the charts/bar plots that appear in Figure 4(c). Let me now carefully read to understand which images correspond to which figures. The Im1.png through Im4.png appear to be the four subplots in Figure 4(c).\n\nLet me search for Figure 6 visual descriptions:Let me look for the visual description of Figure 6:Let me search for visual descriptions that mention bar charts or plots more systematically by searching around the figure areas:Now let me check if there are any additional figures by searching for all figure captions:Good! So we have:\n- Figure 1: Overview (not a bar plot)\n- Figure 2: Data format comparison (not a bar plot)\n- Figure 3: Sythus overview (not a bar plot)\n- Figure 4: Data statistics with (a) and (b) showing verb-noun pairs, and (c) showing statistics with multiple subplots\n- Figure 5: Response examples (not a bar plot)\n- Figure 6: With (a), (b), and (c) subplots\n- Figure 7: Multi-lingual translation examples (not a bar plot)\n\nNow I need to count the bar plots more carefully. Let me re-examine Figure 4(c) and Figure 6:\n\nFrom my reading:\n- Figure 4(a): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles\n- Figure 4(b): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles \n- Figure 4(c): Contains 4 bar plot histograms based on the visual descriptions I found:\n 1. Instructions vs Instruction Length (Im1.png) - bar plot\n 2. Responses vs Response Length (Im2.png) - bar plot\n 3. Instructions vs # Images (Im3.png) - bar plot\n 4. Instructions vs # Related Instructions (Im4.png) - bar plot\n\nFor Figure 6, I need to examine what type of plots they are. Let me look at the text around Figure 6 more carefully. From the data I saw (lines 1693-1823), Figure 6 appears to have:\n- Figure 6(a): Shows QA and Captioning comparisons for MSVD and MSRVTT - likely bar plots (2 groups x 2 bars = 4 bars total, but this counts as 2 bar plots for the two datasets)\n- Figure 6(b): Shows Elo Rating comparison - likely a bar plot (1 bar plot)\n- Figure 6(c): Shows COCO caption with 0-shot, 4-shot, 8-shot, 16-shot - this could be a line plot or bar plot\n\nLet me search for more specific information about these plots. Let me check if there are visual descriptions for Figure 6 plots:The file ends at line 5199. Let me search systematically for any remaining bar plot descriptions:Based on my thorough analysis of the document, let me now count all the bar plots:\n\n**Figure 4(c)** contains 4 bar plot histograms:\n1. Instructions vs Instruction Length\n2. Responses vs Response Length \n3. Instructions vs # Images\n4. Instructions vs # Related Instructions\n\n**Figure 6** - I need to determine how many bar plots are in each subplot. Looking at the data structure:\n- Figure 6(a): Video understanding evaluation with MSVD 0-shot and MSRVTT 0-shot, each showing QA and Captioning - this appears to be 2 grouped bar charts (one for MSVD, one for MSRVTT)\n- Figure 6(b): Elo Rating comparison showing multiple models - this is 1 bar chart\n- Figure 6(c): COCO caption showing 0-shot through 16-shot results comparing Otter and OpenFlamingo - this could be either a grouped bar chart or line chart\n\nLooking at the structure in the text (lines 1816-1817: \"0-shot 4-shot 8-shot 16-shot\"), this suggests it's showing a progression, which is more typical of a line chart. However, I need to be careful.\n\nLet me reconsider the counting. The question asks for \"bar plots (including subplots)\". This means I should count each separate bar plot panel, not individual bars.\n\nFrom the evidence:\n- **Figure 4(c)**: 4 bar plot histograms (clearly described as histograms/bar charts in the visual descriptions)\n- **Figure 6(a)**: 2 bar plots (one for MSVD 0-shot, one for MSRVTT 0-shot, each comparing models on QA and Captioning)\n- **Figure 6(b)**: 1 bar plot (Elo Rating comparison)\n- **Figure 6(c)**: Likely 1 chart but unclear if it's a bar plot or line plot based on the \"0-shot 4-shot 8-shot 16-shot\" progression\n\nTotal count depends on whether Figure 6(c) is a bar plot. Given the context of comparing few-shot performance across different numbers of shots, and looking at typical machine learning papers, this is often shown as either a line chart or grouped bar chart.\n\nHowever, examining the OCR text more carefully from chunk 152307 (lines 1816-1822), the layout shows:\n- (a) Video understanding\n- (b) Vision-language model alignment \n- (c) COCO caption\n\nAnd earlier in the caption it mentions \"CIDEr\" metric with values like \"60.8\", \"55\", etc. with labels for different shot counts.\n\nGiven the uncertainty, let me make my best assessment: Figure 6(c) showing few-shot learning progression is more likely to be a line plot given it shows a progression/trend across shot counts.\n\n**Final count:**\n- Figure 4(c): 4 bar plots\n- Figure 6(a): 2 bar plots\n- Figure 6(b): 1 bar plot\n- Figure 6(c): Not a bar plot (likely line plot)\n\n**Total: 7 bar plots**\n\nWait, let me reconsider Figure 6(a). Looking at the text structure again (lines 1693-1806), it shows:\n- MSVD 0-shot with QA and Captioning\n- MSRVTT 0-shot with QA and Captioning\n- These are comparing VideoChatGPT vs Otter\n\nThis could be:\n- 2 separate bar plots (one for MSVD, one for MSRVTT), OR\n- 1 bar plot with 2 groups\n\nTypically in papers, when multiple datasets are shown side-by-side in subfigure (a), they would be considered separate bar plots. So I'll count them as 2.\n\n**Final answer: 7**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 164109, "error": null, "extra": {"thread_id": 1988, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_98fcc3ce4d2f4d98b3c04b41720c35ca", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1170, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 titled \"Sythus overview\" on page 5, I can see the cold start process.\n\nThe figure shows a \"Cold Start\" stage that comes before the main Sythus pipeline. The cold start stage shows:\n- In-context examples (document icon at top)\n- An arrow pointing down to \"Cold Start\"\n- ChatGPT being queried during this stage\n- This then leads to \"Step 1: System Message + visual annotation\"\n\nThe cold start stage is described in the text as being used to \"identify the optimal system message and in-context example for querying instruction-response pairs in a given dataset.\"\n\nAnswer: In-context examples", "citations": [], "input_tokens": 55843, "output_tokens": 153, "cost_micros": 169824, "latency_ms": 41358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "in context examples", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage appears before the main pipeline steps (Step 1-4).\n\nThe cold start stage shows interactions with ChatGPT to identify the optimal system message and in-context examples. The figure shows arrows connecting to \"System Message\" and \"In-context examples\" components.\n\nAnswer: Identify optimal system message and in-context example", "citations": [], "input_tokens": 21188, "output_tokens": 91, "cost_micros": 64929, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "identify optimal system message and in context example", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage shows a flow diagram where the first step involves \"System Message\" with an arrow pointing to ChatGPT.\n\nAnswer: System Message", "citations": [], "input_tokens": 23513, "output_tokens": 50, "cost_micros": 71289, "latency_ms": 4468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 (Sythus overview), I can see the pipeline stages.\n\nThe cold start stage is shown at the top, and the first step within it involves prompting ChatGPT to generate system messages and in-context examples.\n\nAnswer: System Message", "citations": [], "input_tokens": 20245, "output_tokens": 62, "cost_micros": 61665, "latency_ms": 4858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, I can see the cold start process and the subsequent steps.\n\nThe cold start section shows \"In-context examples\" which feeds into Step 1.\n\nStep 1 is labeled as \"System Message + visual annotation\" which leads to the \"Prompt\".\n\nAnswer: System Message + visual annotation", "citations": [], "input_tokens": 24261, "output_tokens": 80, "cost_micros": 73983, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message visual annotation", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure about cold start and identify the first step.Let me search for \"cold start\" in the document to locate the relevant figure.Good! I found a reference to \"Cold Start\" with steps. Let me read the section around line 759-798 (chunk 152250) where the figure appears.Perfect! I can see the figure clearly shows the steps of the cold start process. According to the figure, the steps are:\n\n- Step 1: System Message\n- Step 2: Generate instruction-response pairs\n- Step 3: Filtering\n- Step 4: Translation\n\nThe first step of cold start is \"System Message\".\n\nAnswer: System Message", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 29859, "error": null, "extra": {"thread_id": 1989, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39c9f325d0c8483fb88a1b48780b1918", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figures in the document, particularly Figure 1 and Figure 5 which show examples from the MIMIC-IT dataset and Otter's capabilities:\n\nIn Figure 1, there are examples showing egocentric video reasoning with what appears to be soccer/football scenes.\n\nIn Figure 5, the egocentric visual assistant examples at the bottom show scenes from what appears to be soccer/football, including references to passing the ball, wearing jerseys, and the goalkeeper saving the ball.\n\nAnswer: Soccer/Football", "citations": [], "input_tokens": 55848, "output_tokens": 115, "cost_micros": 169269, "latency_ms": 35250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples showing sports.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an \"Egocentric Visual Assistant\" example with text that says \"Hey Otter, what should I do?\" and the response mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\"\n\nThis clearly refers to soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 21193, "output_tokens": 102, "cost_micros": 65109, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, particularly in Figure 5 under \"Egocentric Visual Assistant,\" I can see an image showing a soccer/football scene from a first-person perspective.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 23518, "output_tokens": 52, "cost_micros": 71334, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, I need to identify what sport is shown in the MIMIC-IT dataset examples and Otter's response examples.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an example labeled \"Egocentric Visual Assistant\" at the bottom. The text mentions \"guiding users to land a small aircraft.\"\n\nAdditionally, in the Ego4D (E4D) section's in-context examples (Table 5), there are questions about:\n- \"What color of jersey does my teammates wear?\" with the response \"Black, as you pass the ball to a player wearing black jersey\"\n- \"Does our team goal?\" with the response about a goalkeeper saving the ball\n\nThis indicates soccer/football is being discussed.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 20250, "output_tokens": 180, "cost_micros": 63450, "latency_ms": 7734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples that show sports.\n\nIn Figure 5, under the \"Egocentric Visual Assistant\" section, there are two screenshots. The right screenshot shows \"A person playing soccer with a chat overlay\" and includes the text \"User: Hey Otter, what should I do? OTTER-E: Pass the ball *to the middle* and run to the gate, looking for a shooting opportunity.\"\n\nAnswer: Soccer", "citations": [], "input_tokens": 24266, "output_tokens": 102, "cost_micros": 74328, "latency_ms": 6890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "soccer", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document you mentioned to find information about the visual examples in MIMIC-IT and Otter.Let me search for visual examples and figures in the document to find information about sports shown in MIMIC-IT and Otter examples.Let me read more of the document to find figures and visual examples:Let me continue reading to find specific visual examples that show sports:Found \"tennis\"! Let me search for the context around this mention:Perfect! Let me read that section to get more context about the tennis visual example:Now let me search for where this image appears in relation to MIMIC-IT or Otter examples. Let me look earlier in the document to understand the context of these figures:Let me search backwards to find where these images are part of a figure showing examples:Let me search for \"Otter\" in the document to find where these visual examples are shown:Now let me look at Figure 5, which shows \"Otter's response examples\":Great! I found it. In the Egocentric Visual Assistant section of Figure 5, there's a conversation where Otter-E says \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\" This clearly refers to soccer/football. Let me verify by looking at the visual description of the image that accompanies this text:Perfect! I found the answer. The visual description clearly states this is an \"Outdoor soccer/football field with green grass\" with players wearing blue athletic shirts. The Otter-E response also mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity,\" which confirms this is soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 123078, "error": null, "extra": {"thread_id": 1990, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e493d9b5d4804d67aac4ae04786b3ef7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at Figure 7 on page 16, which shows \"In-context examples for multi-lingual translation query,\" I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 55842, "output_tokens": 108, "cost_micros": 169146, "latency_ms": 37125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nAnswer: 8", "citations": [], "input_tokens": 21187, "output_tokens": 113, "cost_micros": 65256, "latency_ms": 4327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 23512, "output_tokens": 102, "cost_micros": 72066, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 7 which shows \"In-context examples for multi-lingual translation query\", I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 20244, "output_tokens": 108, "cost_micros": 62352, "latency_ms": 4578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7 on page 16, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 24260, "output_tokens": 107, "cost_micros": 74385, "latency_ms": 4422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about multi-lingual translation query and count the languages in the in-context examples.Now I can see references to the multi-lingual translation query. Let me read the specific section that mentions Figure 7 and the languages.Perfect! I found the information. Let me now look at Figure 7 to see the actual in-context examples and count the languages.Perfect! I can now see Figure 7 which shows the in-context examples for multi-lingual translation query. Let me count the languages shown:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nThe document clearly shows 8 languages in the in-context examples for multi-lingual translation query in Figure 7.\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44640, "error": null, "extra": {"thread_id": 1991, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25a64843d98447e9baa03cf538d93723", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 212, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll go through the document to count all the line plots shown.\n\nLooking through the figures in the document:\n\n**Figure 1** (page 2): This shows a complex diagram with multiple examples of different tasks and image types, but does not appear to contain line plots.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text, but no line plots.\n\n**Figure 3** (page 5): This shows the Sythus pipeline overview with icons and flowchart elements, but no line plots.\n\n**Figure 4** (page 7): This contains:\n- (a) A sunburst chart showing instructions\n- (b) A sunburst chart showing responses \n- (c) Multiple bar charts/histograms and what appears to be **line plots** showing statistics\n\nLooking at Figure 4(c) more carefully, there appear to be line plots showing \"# Instructions\" and \"# Responses\" over \"Instruction Length\" and \"Response Length\", plus what looks like a line plot for \"# Related Instructions\".\n\n**Figure 5** (page 8): Shows example images with text responses, not line plots.\n\n**Figure 6** (page 10): This contains three subfigures:\n- (a) Bar charts for video understanding\n- (b) Bar chart for vision-language model alignment\n- (c) Contains **line plots** showing CIDEr scores across different shot settings (0-shot, 4-shot, 8-shot, 16-shot) comparing Otter and OpenFlamingo - this appears to show 2 line plots\n\n**Figure 7** (page 16): Shows text examples in multiple languages, no line plots.\n\nCounting the line plots:\n- Figure 4(c): appears to show 2-3 line plots\n- Figure 6(c): shows 2 line plots (one for Otter, one for OpenFlamingo)\n\nTotal: approximately 4-5 line plots visible in Figure 4(c) and 2 in Figure 6(c) = approximately 6-7 total.\n\nLooking more carefully at Figure 4(c), I can see there are line plots for instruction length distribution, response length distribution, and related instructions. Figure 6(c) clearly shows 2 ", "citations": [], "input_tokens": 55833, "output_tokens": 512, "cost_micros": 175179, "latency_ms": 42171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots.\n\nLooking through the figures in the document:\n\n**Figure 1**: This shows example images with text descriptions - not line plots.\n\n**Figure 2**: Shows example data formats with images and text - not line plots.\n\n**Figure 3**: Shows a pipeline/flowchart diagram - not line plots.\n\n**Figure 4**: This figure contains multiple subplots:\n- (a) A circular/sunburst chart showing root verb-noun pairs\n- (b) Another circular/sunburst chart showing root verb-noun pairs\n- (c) Four separate line plots showing:\n 1. Instruction Length distribution\n 2. Response Length distribution\n 3. # Images distribution\n 4. # Related Instructions distribution\n\nSo Figure 4(c) contains **4 line plots**.\n\n**Figure 5**: Shows example images with text responses - not line plots.\n\n**Figure 6**: This figure contains three subplots:\n- (a) Two grouped bar charts for \"Video understanding\" (MSVD 0-shot and MSRVTT 0-shot)\n- (b) A bar chart showing \"Human evaluation comparison\" with Elo Ratings\n- (c) A line plot showing \"COCO caption\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\nSo Figure 6(c) contains **1 line plot**.\n\n**Total line plots**: 4 (from Figure 4c) + 1 (from Figure 6c) = **5 line plots**\n\nAnswer: 5", "citations": [], "input_tokens": 21178, "output_tokens": 363, "cost_micros": 68979, "latency_ms": 10016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing data points connected by lines).\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows sample images with text descriptions - not a line plot.\n\n**Figure 2**: Shows a comparison of data formats with images and text - not a line plot.\n\n**Figure 3**: Shows a flowchart/pipeline diagram - not a line plot.\n\n**Figure 4**: Contains multiple subfigures:\n- (a) A circular/sunburst diagram showing verb-noun pairs\n- (b) Another circular/sunburst diagram showing verb-noun pairs\n- (c) Contains **4 line plots**: \n 1. \"# Instructions\" vs \"Instruction Length\"\n 2. \"# Responses\" vs \"Response Length\"\n 3. \"# Instructions\" vs \"# Images\"\n 4. \"# Instructions\" vs \"# Related Instructions\"\n\n**Figure 5**: Shows example images with captions and chat interfaces - not a line plot.\n\n**Figure 6**: Contains three subfigures:\n- (a) Bar chart for video understanding - not a line plot\n- (b) Bar chart for vision-language model alignment - not a line plot\n- (c) Shows **1 line plot**: \"CIDEr\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\n**Figure 7**: Shows text examples in multiple languages - not a line plot.\n\nTotal line plots found:\n- Figure 4(c): 4 line plots\n- Figure 6(c): 1 line plot\n\nAnswer: 5", "citations": [], "input_tokens": 23503, "output_tokens": 370, "cost_micros": 76059, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to search through the document for line plots/graphs.\n\nLooking through the document, I can identify the following figures with line plots:\n\n1. Figure 4 - This shows multiple visualizations including what appears to be bar charts and word clouds showing verb-noun structures and dataset statistics. The caption mentions \"Fig. 4 (a)\" for instructions and \"Fig. 4 (b)\" for responses, and \"Fig. 4 (c)\" for diversity metrics. These appear to be bar charts and word frequency visualizations, not line plots.\n\n2. Figure 6 - The text references \"Fig. 6(b)\" and \"Fig. 6(c)\" where:\n - Fig. 6(b) is described as showing Elo ratings (likely a bar chart or ranking visualization)\n - Fig. 6(c) is described as showing \"COCO caption (CIDEr) few-shot evaluation\" - this is explicitly described as comparing performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot, 32-shot), which would typically be shown as a line plot\n\nBased on the text descriptions, Figure 6(c) showing the few-shot learning performance across different numbers of shots (0, 4, 8, 16, 32) would be presented as a line plot to show the progression of performance.\n\nHowever, I cannot actually see the figures in this document to count them definitively. Based on the textual descriptions alone, there appears to be at least 1 line plot (Figure 6c showing few-shot performance).\n\nAnswer: 1", "citations": [], "input_tokens": 20235, "output_tokens": 361, "cost_micros": 66120, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing lines connecting data points over continuous axes).\n\nLooking through the figures in the document:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with different capabilities (Perception, Reasoning, Planning) - not a line plot.\n\n**Figure 2**: Shows data format comparisons with images and text examples - not a line plot.\n\n**Figure 3**: Shows a flowchart of the Syphus pipeline - not a line plot.\n\n**Figure 4**: This figure shows multiple visualizations of data statistics:\n- (a) Shows a circular/sunburst chart for Instructions (verb-noun pairs)\n- (b) Shows a circular/sunburst chart for Responses (verb-noun pairs)\n- (c) Shows \"Statistics of instructions and responses\" with what appears to be 4 small plots showing:\n - Instruction Length vs # Instructions\n - Response Length vs # Responses\n - # Images vs # Instructions\n - # Related Instructions vs # Instructions\n\nThese plots in Figure 4(c) appear to be line plots showing distributions/relationships.\n\n**Figure 5**: Shows example responses from Otter with images and text - not a line plot.\n\n**Figure 6**: Shows three tables labeled (a), (b), and (c) with numerical results - these are tables, not line plots.\n\n**Figure 7**: Shows text examples for multi-lingual translation - not a line plot.\n\nCounting the line plots in Figure 4(c): There are 4 small line plots showing different statistics.\n\nAnswer: 4", "citations": [], "input_tokens": 24251, "output_tokens": 364, "cost_micros": 78213, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to count the line plots.Let me search for figures and plots in the document by reading through it systematically:Now let me read the actual content of these figures to understand their structure and count line plots:Let me continue to check all figures systematically:Let me search more specifically for line plots or line charts:Let me look at Figure 6 more carefully as it seems to contain multiple subfigures with plots:Based on the data I see in Figure 6(c), it appears to show different shot settings (0-shot, 4-shot, 8-shot, 16-shot) which suggests this is a line plot. Let me search for more visual descriptions to understand all the plots:Let me read more around Figure 4 to understand what types of plots it contains:Now let me look for visual descriptions and embedded images to understand the structure of figures better. Let me check if there are visual descriptions that mention line plots or graphs:Let me search for embedded images and their visual descriptions:Let me look at the visual descriptions of the image that corresponds to Figure 6:This visual description seems to be of a different image not related to Figure 6. Let me look for a chart or graph description by searching through the document more systematically. Let me read sections with image data to find charts:The visual descriptions I'm seeing are for photographs, not charts. Let me search for keywords related to charts or visualizations more carefully:Good! Let me read this section to find charts and visualizations:Good! I found the section with chart visual descriptions. These appear to be histograms/bar charts for Figure 4. Now let me search for descriptions that mention line graphs or line plots:Let me specifically look for \"line\" combined with visualization terms:Now let me search specifically for Figure 6's visualization descriptions by looking near the Figure 6 location more carefully. Let me look for the embedded images around Figure 6:Let me check for chart/plot related embedded images. Looking back at Figure 6, I saw it has three subfigures (a), (b), and (c). Let me search for embedded images that might show line plots by looking at descriptions with \"axis\":I found histograms/bar charts. Now let me look for any line-based visualizations. Let me search for \"CIDEr\" which was mentioned in Figure 6(c) about COCO caption evaluation:I can see Figure 6(c) shows data with labels like \"0-shot 4-shot 8-shot 16-shot\" and \"CIDEr\", which strongly suggests it's a line plot showing performance across different shot settings. Let me look for a visualization description that mentions this, or look for visual descriptions that discuss multiple data points connected:The text \"0-shot 4-shot 8-shot 16-shot\" followed by \"(c) COCO caption\" suggests Figure 6(c) is indeed a line plot or chart. However, the embedded image Im14.jpg description seems unrelated (clinical setting). Let me look at the data more carefully and count what we know so far. Let me check if there are visual descriptions specifically for charts with \"shot\":", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 191452, "error": null, "extra": {"thread_id": 1992, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_48224411a27b457b894b9bc78d679631", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 942, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "1"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 9:**\n- Table 5: SFT annotation examples\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nAnswer: 6", "citations": [], "input_tokens": 80762, "output_tokens": 144, "cost_micros": 244446, "latency_ms": 6391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count all the tables on pages 5-10.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Figure 5 (not a table)\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 8:**\n- Table 5: SFT annotation - example of helpfulness and safety annotation\n\n**Page 9:**\n- No tables (contains Figures 11 and discussion)\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nTotal count: 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 109911, "output_tokens": 181, "cost_micros": 332448, "latency_ms": 8235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables that appear in pages 5-10 of the document.\n\nPage 5: \n- Table 1: Llama 2 family of models (comparing parameters, context length, etc.)\n\nPage 6:\n- No tables\n\nPage 7:\n- Table 3: Overall performance on grouped academic benchmarks\n\nPage 8:\n- Table 4: Comparison to closed-source models on academic benchmarks\n- Table 5: SFT annotation example (shown at top of page)\n\nPage 9:\n- No tables (this page discusses supervised fine-tuning details)\n\nPage 10:\n- Table 6: Statistics of human preference data for reward modeling\n\nCounting all tables in pages 5-10:\n- Page 5: 1 table\n- Page 6: 0 tables\n- Page 7: 1 table\n- Page 8: 2 tables\n- Page 9: 0 tables\n- Page 10: 1 table\n\nTotal: 1 + 0 + 1 + 2 + 0 + 1 = 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 80961, "output_tokens": 265, "cost_micros": 246858, "latency_ms": 7983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\nTotal: 1 table\n\n**Page 6:**\n- Table 2: CO\u2082 emissions during pretraining\n- Figure 5: Training Loss for LLAMA 2 models (this is a figure, not a table)\nTotal: 1 table\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\nTotal: 1 table\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\nTotal: 1 table\n\n**Page 9:**\n- Table 5: SFT annotation examples\nTotal: 1 table\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\nTotal: 1 table\n\nTotal tables on pages 5-10: 1 + 1 + 1 + 1 + 1 + 1 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 105634, "output_tokens": 249, "cost_micros": 320637, "latency_ms": 8264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document that was flagged as most relevant (2307.09288v2.pdf.xml) and check pages 5-10 for tables.I need to search for page markers to identify pages 5-10. Let me search for page indicators in the document.Let me read more of the document to understand its structure and find page markers.Good! I can see page numbers are indicated with ``. Let me search for these page markers to find pages 5-10.Perfect! Now I know that:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo I need to read from line 1660 to line 2460 to cover pages 5-10. Let me search for table markers in this range.Great! I can see the content from pages 5-10. Let me continue reading to make sure I capture all tables in this range. I've found several tables so far. Let me count them systematically:\n\nFrom line 1660 to 2460 (pages 5-10), I've seen:\n1. Table 1 (line 1664) - LLAMA 2 family of models\n2. Table 2 (line 1862) - CO2 emissions during pretraining\n3. Table 3 (line 1959) - Overall performance on grouped academic benchmarks\n4. Table 4 (line 2131) - Comparison to closed-source models\n5. Table 5 (line 2209) - SFT annotation\n6. Table 6 (line 2336) - Statistics of human preference data\n\nLet me verify by reading the remaining portion up to line 2460 to make sure I haven't missed any tables:Perfect! Now I can see all the `` tags in the document. Based on the PageNumber markers I found earlier:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo pages 5-10 cover lines 1660 to 2460 (just before page 11).\n\nThe tables that fall within this range are:\n1. Line 1664: `
` - Table 1 (on page 5)\n2. Line 1862: `
` - Table 2 (on page 6)\n3. Line 1959: `
` - Table 3 (on page 7)\n4. Line 2131: `
` - Table 4 (on page 8)\n5. Line 2209: `
` - Table 5 (on page 8)\n6. Line 2336: `
` - Table 6 (on page 10)\n\nTables at lines 2479 and 2544 are on page 11 or later, so they don't count.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54500, "error": null, "extra": {"thread_id": 1993, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d18e0e208d4c44ad9ccab3815ed12b04", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 413, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart in Figure 3, the models with the lowest violation percentages are:\n\n1. LLAMA 2-70b-chat (lowest violation %)\n2. ChatGPT 0301 (second lowest)\n3. LLAMA 2-34b-chat (third lowest)\n\nAnswer: Llama-2 70b-chat, ChatGPT 0301, Llama-2 34b-chat", "citations": [], "input_tokens": 80773, "output_tokens": 157, "cost_micros": 244674, "latency_ms": 6141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart, the three models with the lowest violation percentages are:\n1. Llama-2-70b-chat (approximately 0.5%)\n2. Llama-2-34b-chat (approximately 1%)\n3. Llama-2-13b-chat (approximately 1.5%)\n\nAnswer: Llama-2-70b-chat, Llama-2-34b-chat, Llama-2-13b-chat", "citations": [], "input_tokens": 109922, "output_tokens": 165, "cost_micros": 332241, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and close-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the figure, the models are ordered from left to right with their violation percentages. The three models with the lowest violation percentages are:\n\n1. Llama 2-Chat (70B) - appears to have the lowest violation percentage\n2. ChatGPT (0301) - appears to have the second lowest\n3. Falcon (40B-instruct) - appears to have the third lowest\n\nAnswer: Llama 2-Chat (70B), ChatGPT (0301), Falcon (40B-instruct)", "citations": [], "input_tokens": 80972, "output_tokens": 176, "cost_micros": 245556, "latency_ms": 7233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, falcon 40b instruct, llama 2 chat 70b", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the top-3 models.Let me search more specifically for Figure 3 content:Now let me read the area around line 1363 to see the actual figure data:Based on the document, Figure 3 shows safety violation percentages where \"lower is safer\". Let me look for the actual values to determine the top-3 (lowest violation %) models:The visual description indicates that the first 4 bars (dark blue) have the lowest values (approximately 3-7), which are the safest models. Based on the model order from lines 1391-1420, the models are listed in order:\n\n1. Llama-2 7b-chat\n2. Llama-2 13b-chat\n3. Llama-2 34b-chat\n4. Llama-2 70b-chat\n5. MPT 7b-chat\n6. Vicuna 13b-v1.1\n7. Vicuna 33b-v1.3\n8. Falcon 40b-instruct\n9. PaLM Bison\n10. ChatGPT 0301\n\nThe description states the first 4 bars (dark blue) have values approximately 3-7, which are the lowest (safest). So the top-3 safest models would be the first three models.\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 34b-chat", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48187, "error": null, "extra": {"thread_id": 1994, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8f47a07752394e8d87ac6ac816505c2a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 for QUAC (f1) results in the 0-shot setting:\n\nThe table shows:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 199228, "output_tokens": 210, "cost_micros": 600834, "latency_ms": 79592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the 0-shot QUAC results are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting for QUAC is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 80777, "output_tokens": 227, "cost_micros": 245736, "latency_ms": 7592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the 0-shot results for the QUAC dataset.\n\nIn the QUAC (f1) 0-shot column, the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot QUAC column is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 109926, "output_tokens": 271, "cost_micros": 333843, "latency_ms": 8594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 \"Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\", I need to find the QUAC 0-shot column.\n\nThe table shows QUAC results with 0-shot and 1-shot columns. For the 0-shot setting on QUAC:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nThe highest 0-shot score on QUAC is 44.1, achieved by Llama 1 33B.\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 80976, "output_tokens": 284, "cost_micros": 247188, "latency_ms": 8469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table 23 and identify the best performing model in the 0-shot setting on the QUAC dataset.Let me search for \"Table 23\" in the document.Now let me read the section containing Table 23 to see the complete table.Perfect! I can see Table 23 completely now. Looking at the QUAC (f1) 0-shot column (column 7 in the table), the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting on QUAC is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40921, "error": null, "extra": {"thread_id": 1995, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1765040c73804b7381cc9d57c302f53d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 202, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find Table 21 in the document and count how many slots (cells) are missing data.\n\nLooking through the document, I can find Table 21 on page 49. Let me examine it:\n\nTable 21 is titled \"Code generation results on Human-Eval and MBPP.\"\n\nThe table has the following structure:\n- Rows for different models: MPT (7B, 30B), Falcon (7B, 40B), Llama 1 (7B, 13B, 33B, 65B), and Llama 2 (7B, 13B, 34B, 70B)\n- Columns for: Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80)\n\nLet me count the missing entries (shown as \"-\" or blank):\n\nLooking at the table:\n- MPT 7B: has values for pass@1 under Human-Eval (18.3) and MBPP (22.6), but \"-\" for pass@100 and pass@80\n- MPT 30B: has values for pass@1 (25.0, 32.8), but \"-\" for pass@100 and pass@80\n- Falcon 7B: has values for pass@1 (0.0, 11.2), but \"-\" for pass@100 and pass@80\n- Falcon 40B: has values for pass@1 (0.6, 29.8), but \"-\" for pass@100 and pass@80\n- All Llama 1 and Llama 2 models have complete data (no missing slots)\n\nMissing slots:\n- MPT 7B: 2 missing (pass@100 for both benchmarks)\n- MPT 30B: 2 missing\n- Falcon 7B: 2 missing\n- Falcon 40B: 2 missing\n\nTotal: 8 missing slots\n\nAnswer: 8", "citations": [], "input_tokens": 199211, "output_tokens": 461, "cost_micros": 604548, "latency_ms": 75532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 in the document, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Rows: Different models (MPT 7B, Falcon 7B/40B, LLAMA 1 7B/13B/33B/65B, LLAMA 2 7B/13B/34B/70B)\n- Columns: Human-Eval pass@1, Human-Eval pass@100, MBPP pass@1, MBPP pass@80\n\nLet me count the missing entries (marked with \"-\"):\n\nMPT 7B: pass@100 for Human-Eval is missing (1 slot)\nMPT 30B: pass@100 for Human-Eval is missing (1 slot)\nFalcon 40B: pass@1 and pass@100 for Human-Eval are missing, pass@1 and pass@80 for MBPP are missing (4 slots)\n\nTotal missing slots: 1 + 1 + 4 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 80760, "output_tokens": 280, "cost_micros": 246480, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count the missing slots.\n\nLooking at Table 21, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model name, size, and then 4 metric columns (pass@1, pass@100 for Human-Eval, and pass@1, pass@80 for MBPP)\n- Row groups for: MPT (2 sizes), Falcon (2 sizes), LLAMA 1 (4 sizes), and LLAMA 2 (4 sizes)\n\nLet me count the cells systematically:\n\nFor MPT:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 30B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor Falcon:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 40B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor LLAMA 1 (4 sizes: 7B, 13B, 33B, 65B):\n- All sizes have all 4 metrics filled = 0 missing\n\nFor LLAMA 2 (4 sizes: 7B, 13B, 34B, 70B):\n- All sizes have all 4 metrics filled = 0 missing\n\nTotal missing slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 109909, "output_tokens": 429, "cost_micros": 336162, "latency_ms": 10592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model, Human-Eval (pass@1, pass@100), MBPP (pass@1, pass@80)\n- Row headers: MPT, Falcon, Llama 1, Llama 2\n\nLet me count the cells:\n- MPT row: 7B size is listed, then has values 18.3, -, 22.6, -\n- Falcon row: 7B size is listed, then has values 0.0, -, 11.2, -\n- Llama 1 row: 7B size is listed, then has values 15.8, 52.5, 22.0, 64.0\n- Llama 2 row: 7B size is listed, then has values 18.3, 60.2, 30.6, 69.0\n\nThe \"-\" symbols indicate missing data.\n\nCounting the dashes:\n- MPT: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Falcon: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Llama 1: 0 dashes (all values present)\n- Llama 2: 0 dashes (all values present)\n\nTotal missing slots: 2 + 2 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 80959, "output_tokens": 385, "cost_micros": 248652, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 to count the missing slots (empty cells).\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model | Size | Human-Eval pass@1 | Human-Eval pass@100 | MBPP pass@1 | MBPP pass@80\n- Rows for: MPT (7B, 30B), Falcon (7B, 40B), LLAMA 1 (7B, 13B, 33B, 65B), LLAMA 2 (7B, 13B, 34B, 70B)\n\nLet me count the cells:\n- Total rows with data: 12 (excluding header)\n- Total columns with data: 6 (including Model and Size)\n- So for the data columns (excluding Model column): 12 rows \u00d7 4 data columns = 48 potential data cells\n\nNow counting the filled cells in the data columns:\n- MPT 7B: 4 values (18.3, -, 22.6, -)\n- MPT 30B: 4 values (25.0, -, 32.8, -)\n- Falcon 7B: 4 values (0.0, -, 11.2, -)\n- Falcon 40B: 4 values (0.6, -, 29.8, -)\n- LLAMA 1 7B: 4 values (10.5, 36.5, 17.7, 56.2)\n- LLAMA 1 13B: 4 values (15.8, 52.5, 22.0, 64.0)\n- LLAMA 1 33B: 4 values (21.7, 70.7, 30.2, 73.4)\n- LLAMA 1 65B: 4 values (23.7, 79.3, 37.7, 76.8)\n- LLAMA 2 7B: 4 values (12.8, 45.6, 20.8, 62.", "citations": [], "input_tokens": 105632, "output_tokens": 512, "cost_micros": 324576, "latency_ms": 13625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Table 21 and count the missed slots.Let me search for Table 21 in the document:Now let me search for where Table 21 appears with more context:Let me read the area around line 7837 where Table 21 is located:Now I have Table 21. Let me analyze it carefully. The table shows code generation results with columns for Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80). I can see that some entries have \"-\" which indicates missing data. Let me count all the \"-\" entries:\n\nLooking at the table structure:\n- MPT 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- MPT 30B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 40B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- LLAMA 1 models (7B, 13B, 33B, 65B): All have values, no \"-\"\n- LLAMA 2 models (7B, 13B, 34B, 70B): All have values, no \"-\"\n\nTotal missed slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42437, "error": null, "extra": {"thread_id": 1996, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b8bd21d9aaba4efda0a40946ca2b4039", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine pages 49-50 to count the datasets reported.\n\nOn page 49 (Table 20), I can see results for several standard benchmarks including:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets on page 49.\n\nOn page 50 (Table 21), I can see code generation results on:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets on page 50.\n\nTotal datasets reported across pages 49-50: 10 + 2 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 199213, "output_tokens": 177, "cost_micros": 600294, "latency_ms": 43437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the datasets mentioned.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: NaturalQuestions and TriviaQA results\n- Table 23: SQUAD and QUAC results\n- Table 24: AGI Eval comparison (mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count all unique datasets mentioned across these tables on pages 49-50:\n\nFrom Table 20 (page 49):\n1. BoolQ\n2. PIQA\n3. SIQA\n4. HellaSwag\n5. WinoGrande\n6. ARC-e\n7. ARC-c\n8. OBQA\n9. CSQA\n10. MMLU\n\nFrom Table 21 (page 49):\n11. Human-Eval\n12. MBPP\n\nFrom Table 22 (page 50):\n13. NaturalQuestions\n14. TriviaQA\n\nFrom Table 23 (page 50):\n15. SQUAD\n16. QUAC\n\nFrom Table 24 (page 50):\n17. AGI Eval (which includes multiple subtasks: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nCounting the subtasks of AGI Eval separately:\n17. AQuA-RAT\n18. LogiQA\n19. LSAT-AR\n20. LSAT-LR\n21.", "citations": [], "input_tokens": 80762, "output_tokens": 512, "cost_micros": 249966, "latency_ms": 10782, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the number of datasets reported.\n\nLooking at page 49, I can see:\n- Table 20: \"Performance on standard benchmarks\" - this discusses various benchmarks but appears to be evaluation results rather than listing distinct datasets\n- Table 21: \"Code generation results on Human-Eval and MBPP\" - mentions 2 datasets: Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: \"NaturalQuestions\" and \"TriviaQA\" - 2 datasets\n- Table 23: \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\" - 2 datasets: SQUAD and QUAC\n- Table 24: \"Comparison to open source models on AGI Eval (English)\" - 1 dataset: AGI Eval (with multiple subtasks listed: AQUA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count the distinct datasets mentioned:\n1. Human-Eval\n2. MBPP\n3. NaturalQuestions\n4. TriviaQA\n5. SQUAD\n6. QUAC\n7. AGI Eval (this is one dataset with multiple subtasks)\n\nAdditionally, in Table 20, there are multiple benchmarks mentioned: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU - that's 10 more datasets.\n\nTotal distinct datasets across pages 49-50:\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10)\nFrom Table 21: Human-Eval, MBPP (2)\nFrom Table 22: NaturalQuestions, TriviaQA (2)\nFrom Table 23: SQUAD,", "citations": [], "input_tokens": 109911, "output_tokens": 512, "cost_micros": 337413, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "23", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 to count the datasets reported there.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n- Table 22: NaturalQuestions and TriviaQA Performance\n\nLooking at page 50, I can see:\n- Table 23: Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\n- Table 24: Comparison to Open Source Models on AGI Eval (English) - mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math\n\nNow let me count all unique datasets mentioned across pages 49-50:\n\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10 datasets)\n\nFrom Table 21: Human-Eval, MBPP (2 datasets)\n\nFrom Table 22: NaturalQuestions, TriviaQA (2 datasets)\n\nFrom Table 23: SQUAD, QUAC (2 datasets)\n\nFrom Table 24: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math (8 datasets, noting that SAT-en and SAT-en (w/o Psg.) are variants of the same dataset but listed separately)\n\nTotal unique datasets: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 80961, "output_tokens": 493, "cost_micros": 250278, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to carefully examine pages 49-50 to count the number of datasets reported.\n\nLooking at page 49 (Table 20), I can see:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets in Table 20.\n\nLooking at page 49 (Table 21), I can see:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets in Table 21.\n\nLooking at page 50 (Table 22), I can see:\n- NaturalQuestions\n- TriviaQA (Wiki)\n\nThat's 2 datasets in Table 22.\n\nLooking at page 50 (Table 23), I can see:\n- SQUAD\n- QUAC\n\nThat's 2 datasets in Table 23.\n\nLooking at page 50 (Table 24), I can see:\n- AQuA-RAT\n- LogiQA\n- LSAT-AR\n- LSAT-LR\n- LSAT-RC\n- SAT-en\n- SAT-en (w/o Psg.)\n- SAT-math\n\nThat's 8 datasets in Table 24.\n\nTotal datasets across pages 49-50: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 105634, "output_tokens": 355, "cost_micros": 322227, "latency_ms": 11062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the information on pages 49-50.Let me search for page numbers in the document to locate pages 49-50.Let me search for page markers more broadly:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35907, "error": null, "extra": {"thread_id": 1997, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db129915abb34a60aeaa6083e2f2fd47", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 109, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "49", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which is titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure:\n- Shows a conversation about \"Always answer with emojis\"\n- The response \"Who are you?\" appears to have text but I need to count emojis\n- Looking at the left side, I can see there are some emojis in the responses\n\nRight subfigure:\n- Shows the same conversation structure with GAtt improvement\n- The response appears to have more emojis\n\nLet me count more carefully by examining the figure:\n\nLeft side conversation:\n- \"Who are you?\" response appears to have some emojis\n- \"How to go from Paris to NY?\" response has text with some emojis\n\nRight side conversation:\n- \"Who are you?\" response has emojis\n- \"How to go from Paris to NY?\" response has more emojis\n- There appears to be an additional question about Italy\n\nLooking at the visual representation in Figure 9, the right subfigure clearly shows more emoji usage throughout the conversation compared to the left subfigure. \n\nCounting the visible emojis:\n- Left: appears to have fewer emojis (approximately 2-3 visible)\n- Right: appears to have more emojis throughout (approximately 10-12 visible)\n\nThe difference appears to be approximately 8-9 emojis.\n\nAnswer: 8", "citations": [], "input_tokens": 80772, "output_tokens": 344, "cost_micros": 247476, "latency_ms": 10140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\n**Left subfigure (without GAtt):**\n- Shows a conversation with \"Who are you?\" followed by \"How to go from Paris to NY?\" and \"What caused the extinction of the dinosaurs?\"\n- I can see there are emoji circles (\ud83d\ude0a symbols) used in the conversation\n- Counting the emojis: I see 3 emoji symbols\n\n**Right subfigure (with GAtt):**\n- Shows the same conversation structure with \"Who are you?\" followed by \"How to go from Paris to NY?\" \n- Counting the emojis: I see 3 emoji symbols as well\n\nWait, let me recount more carefully by looking at the figure description and structure:\n\nLeft side: I count 3 emoji/circle symbols in the conversation flow\nRight side: I count 3 emoji/circle symbols in the conversation flow\n\nActually, looking more carefully at the figure, both sides appear to have the same number of emoji symbols (3 each) representing the conversation turns.\n\nThe difference would be: 3 - 3 = 0\n\nAnswer: 0", "citations": [], "input_tokens": 109921, "output_tokens": 294, "cost_micros": 334173, "latency_ms": 9984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 in the document, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure (without GAtt):\n- Shows a conversation about acting as Oscar Wilde\n- I can see emoji-like elements in the visualization\n\nRight subfigure (with GAtt):\n- Shows the same type of conversation\n- Also has emoji-like elements in the visualization\n\nLet me count more carefully. The figure appears to show attention visualization patterns with what look like emoji or icon representations in the conversation bubbles.\n\nFrom the image description and visual elements:\n- Left side: Shows conversation elements with some emoji representations\n- Right side: Shows conversation elements with emoji representations\n\nLooking at the actual figure more carefully, the right subfigure appears to have more emoji icons visible in the conversation compared to the left subfigure. \n\nCounting the visible emojis/icons in each:\n- Left: appears to have fewer emoji elements\n- Right: appears to have more emoji elements\n\nBased on the visual comparison, the right subfigure has approximately 2 more emojis than the left subfigure.\n\nAnswer: 2", "citations": [], "input_tokens": 80971, "output_tokens": 281, "cost_micros": 247128, "latency_ms": 10141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 (titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\"):\n\n**Left subfigure (Generic Preprompt):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcbb\u2328\ufe0f\" (3 emojis)\n- User: \"How to go from Paris to NY?\"\n- Assistant: Long text response with no emojis\nTotal emojis in left: 3\n\n**Right subfigure (Preprompt with Answer Template):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcac...\" (2 emojis shown, plus \"...\")\n- User: \"How to go from Paris to NY?\"\n- Assistant: \"\ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3\" (4 emojis)\n- User: \"What caused the extinction of the dinosaurs?\"\n- Assistant: \"\u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80\" (4 emojis)\n- User: \"Italy?\"\n- Assistant: \"\ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f\" (4 emojis)\n\nLet me recount the right side more carefully:\n- First response: \ud83e\udd16\ud83d\udcac (2 emojis, the \"...\" indicates continuation but not additional emojis)\n- Second response: \ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3 (4 emojis)\n- Third response: \u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80 (4 emojis)\n- Fourth response: \ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f (4 emojis)\nTotal emojis in right: 2 + 4 + 4 + 4 = 14\n\nDifference: 14 - 3 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 105644, "output_tokens": 462, "cost_micros": 323862, "latency_ms": 13312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and count the emojis in each subfigure.Let me search for Figure 9 in the document:Now let me read the section around line 3147 to see Figure 9:Let me continue reading to see the complete Figure 9 with emoji counts:I can see Figure 9 is described, but it mentions an embedded image. Let me look at the embedded image description more carefully. The text shows numbers like \"8\", \"0\" which might be emoji counts. Let me search for the actual image description or look for more context:The image description I saw was for a different figure (a scatter plot). Let me look back at the text content of Figure 9 more carefully. I can see there are conversations shown with numbers like \"8\" and \"0\". Let me search for the actual figure content more carefully:The text I'm seeing doesn't show the actual figure properly. Let me look for an embedded image associated with Figure 9. The page header says \"Always answer with emojis\" which is the instruction. Let me search for the actual visual description or OCR of the image:Let me go back and look more carefully at the figure. The text between lines 3146-3181 should contain the figure content. Let me look for an image file or better description:The visual description starting at line 3190 describes a scatter plot, which is NOT Figure 9. That appears to be a different figure (the embedded image X6.png). The actual Figure 9 content is in the text between lines 3147-3180. Let me look for an image file that might contain the actual Figure 9. Let me search for embedded images near Figure 9:I notice the page headers say \"Always answer with emojis\" twice (lines 3142-3143). This suggests the page has two columns - left and right subfigures. Looking at the figure text:\n\nLeft subfigure appears to show:\n- Line 3149: \"Who are you?\" \n- Line 3151: \"Who are you?\"\n- Line 3153: \"8\" (could indicate 8 emojis)\n- Line 3157: \"How to go from Paris to NY?\"\n- Line 3159: \"How to go from Paris to NY?\"\n- Line 3161: \"8\" (could indicate 8 emojis)\n- Line 3163: \"0\" (could indicate 0 emojis)\n- Line 3165-3168: Text response without emojis\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (could indicate 0 emojis)\n\nLet me search for an image description specifically for Figure 9. Let me look before line 3146:Let me look for an image file associated with Figure 9. Let me search for embedded images around this area:So there's an embedded image X6.png at line 3185, which is right after Figure 9 ends at line 3181. But the visual description for X6.png shows it's a scatter plot, not Figure 9. Let me look for an image BEFORE Figure 9:Let me search for image descriptions that might actually be Figure 9. Let me look for an OCR or description of dialogues with emojis:Great! I found relevant information. Let me look at lines 2729-2760 which describe emoji exchanges:Excellent! This is describing an image related to Figure 9. Looking at the OCR text at lines 2729-2737:\n- \"Always answer with emojis\" (the header/instruction)\n- \"8\" (appears after \"Who are you?\")\n- \"...\" \n- \"How to go from Paris to NY?\"\n\nAnd the visual description mentions:\n1. First exchange: emojis (thinking face, eyes, and speech bubble) - that's 3 emojis\n2. Second exchange: transportation-themed emojis (airplane, boat, landmark, car, globe) - that's 5 emojis\n3. Third exchange: dinosaur-themed emojis (dinosaur, calendar/document, ocean wave, volcano, fire) - that's 5 emojis\n4. Fourth exchange: Italy-related emojis (flag, food items, landmark) - that's at least 3 emojis\n\nBut wait, the OCR shows \"8\" which might mean 8 emojis. Let me look at lines 1795-1835 which also mention \"Always answer with emojis\":This describes a different version showing the LEFT subfigure (without GAtt) - it shows the model failing to follow the \"always answer with emojis\" instruction in the third exchange (text response instead of emojis).\n\nNow let me look more carefully at the actual Figure 9 structure. Looking back at the figure text I saw earlier, the caption says \"Issues with multi-turn memory (left) can be improved with GAtt (right).\" The page has TWO column headers \"Always answer with emojis\" (lines 3142-3143), suggesting left and right subfigures.\n\nLet me count the emojis more carefully. Looking at the embedded image X1.png descriptions:\n\n**LEFT subfigure (lines 1795-1835)**: Model FAILS to always use emojis\n- First exchange: 2 emojis (robot face and laptop)\n- Second exchange: (question only shown, no response visible in description)\n- Third exchange: TEXT response (not emojis) - this shows the failure!\n\n**RIGHT subfigure (lines 2726-2760)**: Model SUCCEEDS with emojis\n- First exchange: 3 emojis (thinking face, eyes, speech bubble)\n- Second exchange: 5 emojis (airplane, boat, landmark, car, globe)\n- Third exchange: 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n- Fourth exchange: 3+ emojis (flag, food items, landmark)\n\nLet me count the \"food items\" more carefully. It says \"flag, food items, landmark\" - \"food items\" is plural, so at least 2 food emojis. That would be at least 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis minimum for the fourth exchange.\n\nTotal for RIGHT: 3 + 5 + 5 + (at least 4) = at least 17 emojis\n\nBut wait, let me look at the OCR text again at line 2730 - it shows \"8\" right after \"Always answer with emojis\". This might indicate 8 emojis in a single response or might be part of the interface.\n\nLet me search for more specific information about counting emojis in each subfigure:The document doesn't use the term \"subfig\". Let me look more carefully at the actual textual representation of Figure 9 between lines 3146-3181. The numbers \"8\", \"0\", \"8\" might be emoji counts:I see it! The pattern shows:\n- Line 3149: \"Who are you?\" (question appears twice - probably left and right)\n- Line 3151: \"Who are you?\" (duplicate)\n- Line 3153: \"8\" (LEFT subfigure response)\n- Line 3157: \"How to go from Paris to NY?\" (appears twice)\n- Line 3159: \"How to go from Paris to NY?\" (duplicate)\n- Line 3161: \"8\" (RIGHT subfigure response)\n- Line 3163: \"0\" (LEFT subfigure response - no emojis, text instead)\n- Lines 3165-3169: Text response (LEFT fails to use emojis)\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (LEFT subfigure - no emojis)\n- Line 3177: \"Italy?\"\n- Line 3179: \"8\" (response)\n\nThis is confusing. Let me look for an actual image file description. Let me check if there's a better description by looking at different chunks:Let me try a different approach. Let me look for the actual visual description or image file that corresponds to Figure 9. Let me search around page 15 for embedded images:The image X6.png after Figure 9 is NOT Figure 9 - it's a scatter plot. The actual Figure 9 content must be represented in the text between lines 3146-3181. But this text representation is unclear.\n\nLet me look BEFORE Figure 9 to see if there's an image file that IS Figure 9. Looking at line 3139, it says \"PageNumber 15\" and then the two page headers \"Always answer with emojis\". This must be a two-column layout. Let me search backwards for an embedded image that might be Figure 9:So Figure 9 starts immediately after the page break at line 3140. There's no embedded image file BEFORE it. The figure content itself must be in the text, or the image description is missing.\n\nBased on what I've found, let me piece together what I know from the two image descriptions I found (X1.png from lines 1795 and 2726):\n\n**LEFT subfigure** (lines 1795-1835 - model WITHOUT GAtt):\n- First response: 2 emojis\n- Later responses: TEXT (no emojis) - shows failure\n\n**RIGHT subfigure** (lines 2726-2760 - model WITH GAtt):\n- First response: 3 emojis \n- Second response: 5 emojis\n- Third response: 5 emojis \n- Fourth response: \"flag, food items, landmark\"\n\nFor the fourth response, \"food items\" is plural, indicating at least 2 items. So minimum: 1 flag + 2 food + 1 landmark = 4 emojis.\n\nLet me count more carefully. Looking at the visual description at lines 2750-2753 again:\n1. \"thinking face, eyes, and speech bubble emojis\" - that's 3 emojis\n2. \"airplane, boat, landmark, car, globe\" - that's 5 emojis\n3. \"dinosaur, calendar/document, ocean wave, volcano, fire\" - that's 5 emojis\n4. \"flag, food items, landmark\" - the term \"food items\" suggests multiple food emojis\n\nActually, let me check if there's a more specific count in the OCR at line 2730:OK! The OCR text at line 2730 shows \"8\". This might mean 8 emojis total for the first response, OR it might be labeling something else. But the description clearly lists:\n1. 3 emojis (thinking face, eyes, speech bubble)\n2. 5 emojis (airplane, boat, landmark, car, globe) \n3. 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n4. At least 3+ emojis (flag, food items [plural = 2+], landmark)\n\nNow for the LEFT subfigure. Let me find its details:So the LEFT subfigure shows:\n- First exchange: 2 emojis (robot face and laptop) \n- Second exchange: (no response details given)\n- Third exchange: TEXT response (not emojis) - this is the FAILURE\n\nSo the LEFT has only 2 emojis total before it fails.\n\nThe RIGHT has: 3 + 5 + 5 + (at least 3) = at least 16 emojis\n\nBut the question asks specifically about Figure 9. Let me verify these two X1.png images at lines 1795 and 2726 are actually the left and right parts of Figure 9. They both have the same instruction \"Always answer with emojis\" at the top, which matches the page headers for Figure 9.\n\nActually, looking more carefully, let me check how many food items specifically. The text says \"flag, food items, landmark\". If \"food items\" means 2 items, then the fourth response would be: 1 + 2 + 1 = 4 emojis.\n\nSo RIGHT would be: 3 + 5 + 5 + 4 = 17 emojis\nLEFT has: 2 emojis\n\nDifference: 17 - 2 = 15\n\nWait, but I need to verify this is correct. Let me think about this differently. The description says \"food items\" (plural). The minimum plural is 2. But it could be more. Let me see if I can find more specific information.\n\nLooking at the specific items listed for Italy: \"flag, food items, landmark\". Given the context is Italy, common food emojis might be pizza and pasta (2 items), or there could be 3+ food items. Without more specific information, I'll assume \"food items\" means at least 2.\n\nActually, looking at the pattern of the other responses which are very specific:\n- Response 1: lists 3 specific emojis\n- Response 2: lists 5 specific emojis \n- Response 3: lists 5 specific emojis\n- Response 4: says \"flag, food items, landmark\"\n\nIf the describer was being consistent, and they specifically didn't count the food items individually but said \"food items\" plural, it likely means 2 food items (pizza and pasta would be typical for Italy).\n\nSo: 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis\n\nRIGHT subfigure total: 3 + 5 + 5 + 4 = 17 emojis\nLEFT subfigure total: 2 emojis\n\nAnswer: 17 - 2 = 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 179203, "error": null, "extra": {"thread_id": 1998, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_913336fbec024206afbfe0bdfdc22736", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1666, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "15"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find Figure 122 in the document and look for information about the color of OSCARb in the prompt.\n\nLooking at the document, I can find Figure 122 which is titled \"Self-Reflection to Improve Coding\" in Section 10.3.\n\nIn Figure 122, there are two prompts shown. The first prompt asks to \"Generate python code to draw similar curves\" and shows a reference image with a graph. The second prompt asks \"Can you double-check the code and align it better with the reference image?\"\n\nLooking at the graphs in Figure 122, I can see there are plots comparing \"OSCarB\" and \"MiniVLM (ours)\". In the visualizations shown, OSCarB appears to be represented in orange/red color in the legend and plot lines.\n\nAnswer: orange", "citations": [], "input_tokens": 117434, "output_tokens": 184, "cost_micros": 355062, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in Section 10.3 on Self-Reflection to Improve Coding, I can see the prompt includes a reference image with a comparison chart.\n\nIn the chart showing \"Comparison of OSCarB and MiniVLM\", OSCarB is represented by the orange/coral colored bars.\n\nAnswer: Orange", "citations": [], "input_tokens": 133651, "output_tokens": 77, "cost_micros": 402108, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see the prompt mentions \"OSCARb\" in the context of generating Python code to draw similar curves.\n\nIn the figure, there is a bar chart showing \"OSCARb\" and \"MiniVLM (ours)\" with bars for \"Base,\" \"Large,\" and \"Huge\" configurations. The bars for OSCARb appear to be orange/amber colored in the visualization.\n\nAnswer: orange", "citations": [], "input_tokens": 103918, "output_tokens": 116, "cost_micros": 313494, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see two side-by-side examples.\n\nIn the right example (the bar chart comparison), the prompt asks to \"Write python codes to generate similar figures.\" The reference image shows a bar chart comparing OSCAR_B and MiniVLM across different categories.\n\nLooking at the bar chart, OSCAR_B is represented by blue bars, while MiniVLM (ours) is represented by orange bars.\n\nAnswer: Blue", "citations": [], "input_tokens": 139293, "output_tokens": 120, "cost_micros": 419679, "latency_ms": 7781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 122 and identify the color of OSCARb in the prompt.Let me search for \"Figure 122\" in the document:Now let me search for where Figure 122 appears with more context:Let me read the section around line 32131 and 32550 to see Figure 122:I can see the figure content. Let me continue reading to see more details about the colors:Let me continue reading to see the color information for the bars:I need to look more carefully at the actual figure. Let me search for more specific information about the figure 122 visual description or color information. Let me read around line 32300-32350 more carefully where the bar chart appears:The image described starting at line 32358 (Image7.jpg) is showing a Golden Retriever dog, which is not the chart. Let me look at the figure that appears before that - the bar chart figure starting at line 32302. This shows the data but doesn't explicitly state colors. Let me search for any embedded image descriptions that might be the actual bar chart:Great! I found it. Let me read the section around line 19587 where it says \"Blue bars represent OSCarB\":However, this is describing a different figure. Let me check if this is Figure 122 or another figure. Let me look back at the context around line 32300 where the chart with OSCarB appears in the \"Prompt: Write python codes to generate similar figures\" section, which is part of Figure 122. Let me look for the visual description of the image that corresponds to the reference image in the prompt:Good! This shows that OSCarB is represented by blue bars. Now let me verify this is indeed related to Figure 122. Let me check if Figure 122's prompt section has this same image. Let me look around where the figure caption for Figure 122 is:This is showing a different chart (the left side of Figure 122 with CIDEr curves). Let me look at the right side which should be the bar chart. The section at line 32297-32354 shows the bar chart prompt. Let me look for embedded images near that area. Based on the structure, the embedded images have visual descriptions. Let me check what comes just before line 32297:I see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interrupting the flow. The actual bar chart reference image in the prompt should be described elsewhere. Let me look for where Figure 122 starts and what images are in it. Let me search for the image that appears right before the \"Prompt: Write python codes to generate similar figures\":Let me look at the first occurrence at line 16349 to see if it has a visual description:This looks like it's showing both charts mixed together in the OCR text. Let me look for the embedded image before this section to find the visual description:So this is Figure 47, not Figure 122. Let me go back to the Figure 122 area and look more carefully. The prompt section starting at line 32299 shows the reference image that contains the bar chart. Let me look for what image comes right before or is part of that prompt section. Let me search more systematically around the Figure 122 area to find embedded images:Perfect! So Figure 122 \"demonstrates the application of self-reflection to improve the results shown in Figure 47.\" This means Figure 122 is showing the same charts as Figure 47, but with self-reflection applied. Since Figure 47 had the bar chart with OSCarB and MiniVLM, and I found earlier at line 19587 that \"Blue bars represent OSCarB\", this should apply to Figure 122 as well since it's based on Figure 47.\n\nLet me verify by checking if there's a specific visual description for the reference image in the right-side prompt of Figure 122. Let me look for any embedded image that might be between lines 32286-32302:Let me look at Figure 47 more carefully. Let me search for where Figure 47 begins with an embedded image:Let me look for embedded images near Figure 47. Let me search backwards from line 16333 to find the embedded images that are part of Figure 47:Let me now search for where Figure 47 contains the bar chart reference image. Since Figure 47 shows both charts (the curve chart and the bar chart), let me look for embedded images in the Figure 47 section. Let me search for \"Embedded image\" near Figure 47:These embedded images are from Section 5, not Figure 47. Let me search more systematically. Let me look for embedded images that appear right before or in Figure 47 section. Let me check around line 15900-16350:Let me search for \"Embedded image\" directly:There are many embedded images. Based on what I've seen, the visual description at line 19587 clearly states \"Blue bars represent OSCarB\" for a bar chart showing \"Comparison of OSCarB and MiniVLM.\" This is the same chart that appears in both Figure 47 and Figure 122 (since Figure 122 is showing improvements to Figure 47 via self-reflection).\n\nLet me verify once more by looking at line 39070 which also mentioned OSCarB color:Interesting! This description says \"Red/coral bars: Represent OSCarB method\" while the earlier one at line 19587 said \"Blue bars represent OSCarB\". These might be describing different versions or different embedded images of the same chart. Let me check if these are in different contexts. Let me look at what figure this is part of (line 39070):Let me check what figure or section this is in:This is Image10.jpg. Let me look back to see what figure this is part of. Let me search backwards for \"Figure\" before line 38980:These are Instagram Reels images, not related to Figure 122. Let me search more directly. Let me look for what section/figure is near line 39000-39100. Let me search backwards for \"Figure\" or \"Sec\":These are all Instagram food images. Let me search for which figure or section contains line 39070. Let me look much earlier before these Instagram images:Still Instagram images. Let me just search for \"Figure 122\" to look for its caption and boundaries:So Figure 122's caption is at line 32550. The figure shows two parts - Left (curves) and Right (bar chart). The GPT-4V output for the right side starts at line 32552. This is within Figure 122. Now I need to find the reference image (the prompt input) for the right side, which should show the original bar chart with OSCarB colors. The prompt for the right side is at line 32299 \"Write python codes to generate similar figures.\" The reference image data is at lines 32302-32354. But this doesn't tell me colors directly since it's just OCR text.\n\nBased on my findings:\n1. Figure 122 is based on Figure 47 (as stated at line 32131-32132)\n2. The bar chart in the figures compares OSCarB and MiniVLM\n3. At line 19587, the visual description clearly states \"Blue bars represent OSCarB\"\n4. At line 39070, another visual description states \"Red/coral bars: Represent OSCarB\"\n\nThese seem to be describing different embedded images. Let me check if the one at 19587 is specifically part of Figure 47:This is Section 7.3, not Figure 47. Let me look for what this section is:This is Section 7.3. So the embedded image with \"Blue bars represent OSCarB\" is in Section 7.3, not in Figure 47. Let me search for what figures are in the Sec 10.3 Self-Reflection area where Figure 122 is. Let me look for embedded images right before or around the prompt at line 32299. Actually, the reference image for the prompt should be immediately before it. Let me look:The
starting at line 32302 is the reference image for the prompt \"Write python codes to generate similar figures.\" This figure contains the bar chart data (OSCARB, MiniVLM, etc.) shown as OCR text from lines 32304-32354. But this OCR text doesn't tell me the colors. The actual visual description of this embedded image should be before or after the figure content. Let me check line 32354 onwards:That's the Golden Retriever image (Image7.jpg), which is not the bar chart. So the bar chart figure (lines 32302-32354) doesn't have a separate \"Embedded image\" marker. This means the figure content IS the embedded image content, just displayed as the
OCR text.\n\nLet me look for Image4.jpg or other images that might be before line 32302:I can see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interspersed in the document but not part of Figure 122's chart. The actual chart images in Figure 122 are shown as
blocks without \"Embedded image\" labels.\n\nLooking at the document structure, I notice that Figure 122 references Figure 47. Since I know that:\n1. Figure 122 is improving upon Figure 47\n2. They show the same charts (left: curves, right: bar chart)\n3. The color of the bars should be consistent between them\n\nLet me look more carefully at Figure 47 to find which embedded image is the bar chart. Let me search around Figure 47's location (around line 16333):This is still in Section 4.5 Multilingual, before Figure 47. Let me search for images closer to Figure 47. Actually, let me take a different approach. Since Figure 47 appears around line 16333 and the caption says it shows examples, the actual embedded images for Figure 47 should have visual descriptions. But since Figure 47 shows generated output rather than original images, the
blocks ARE the actual content.\n\nGiven the conflicting information (line 19587 says \"Blue\" for OSCarB, line 39070 says \"Red/coral\" for OSCarB), let me check which one is actually related to Figure 122 or Figure 47. Let me look at the context more carefully. The document says Figure 122 improves Figure 47. The prompt in Figure 122 at line 32299 is \"Write python codes to generate similar figures\" and the reference image is at lines 32302-32354 showing the bar chart data.\n\nSince Figure 122's caption (line 32550) says \"Right: the percentage is added to align with the reference image\", the reference image (the prompt image) is the ORIGINAL one that needs improvement. This original should be from Figure 47.\n\nLet me look at the data itself. At line 32312, it says \"OSCARB\" (all caps), and at line 32553, in the GPT-4V output, it says \"OSCarB\" (mixed case). Based on the color descriptions I found, and given that there are multiple embedded images of this chart throughout the document, I need to determine which color appears in the specific prompt image in Figure 122.\n\nGiven the two conflicting descriptions and the difficulty in determining which applies to Figure 122's prompt image specifically, let me check if there's any pattern. The one at line 19587 explicitly lists the color scheme at lines 19514-19518:\n- \"Bright cyan/blue for first method\" \n- \"Orange-red for second method\"\n\nAnd it says at line 19587: \"Blue bars represent OSCarB\"\n\nThe one at line 39070 says:\n- \"Red/coral bars: Represent OSCarB method\"\n- \"Teal/cyan bars: Represent MiniVLM method\"\n\nThese are OPPOSITE! Let me check the legend text. At line 32312-32314 in Figure 122's prompt image, it shows:\n\"OSCARB\"\n\"MiniVLM (ours)\"\n\nAnd at line 32553-32561 in the GPT-4V output for Figure 122:\n\"OSCarB\"\n\"MiniVLM (ours)\"\n\nLet me check the earlier descriptions for legend position. At line 19505-19506: \"Legend in upper right distinguishes the two methods\"\n\nGiven that standard chart conventions typically list legend items in order, and OSCarB is listed first (before MiniVLM), OSCarB would typically be the first color in the color scheme.\n\nLooking at line 19515-19516:\n- \"Bright cyan/blue for first method\"\n- \"Orange-red for second method\"\n\nIf OSCarB is the first method (listed first in the legend), then it should be \"Bright cyan/blue\" which matches line 19587: \"Blue bars represent OSCarB\"\n\nAnswer: blue", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 238671, "error": null, "extra": {"thread_id": 1999, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e4d50e05962f4bf6af3a2d9a2bff8f0a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2074, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 113 to identify the music app shown in the computer screen.\n\nLooking at the document, Figure 113 is described in the section on \"Notification Understanding\" (Section 9.8, page 143).\n\nIn the image caption and description for Figure 113, I can see a computer desktop screenshot. Looking at the desktop icons visible in the image, I can see several application icons including:\n- YouTube\n- Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown on the desktop is labeled as \"Music\" in the icon.\n\nAnswer: Music", "citations": [], "input_tokens": 117435, "output_tokens": 134, "cost_micros": 354315, "latency_ms": 8593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in Section 9.8 (GUI Navigation - Notification Understanding), I can see the computer desktop screen.\n\nOn the desktop, there are several icons visible on the left side, including:\n- YouTube Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown is YouTube Music.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 133652, "output_tokens": 80, "cost_micros": 402156, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I can see a computer desktop screenshot with a notification that says \"Kevin Lin is calling you.\" \n\nThe figure shows several application icons on the desktop and in the taskbar. However, I cannot clearly identify a specific music app from the image description or details provided in the document about this figure.\n\nThe document states: \"The image on the computer screen is a desktop background of a bridge over a body of water. There are several icons on the desktop, including a YouTube icon, a Recycle Bin icon, and a Terminal icon. At the bottom of the screen, there is a taskbar with several application icons, such as Microsoft Edge and File Explorer.\"\n\nAnswer: Not clearly identifiable from the provided information", "citations": [], "input_tokens": 103919, "output_tokens": 160, "cost_micros": 314157, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "not clearly identifiable from provided information", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I need to examine the desktop icons shown in the computer screen image.\n\nThe figure shows a Windows desktop with several application icons on the left side. Among these icons, I can see \"YouTube Music\" listed as one of the desktop shortcuts.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 139294, "output_tokens": 67, "cost_micros": 418887, "latency_ms": 6266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 113 and identify the music app shown in the computer screen.Let me search for \"Figure 113\" in the document.Now let me read the section around line 29414 to see Figure 113 and its description.I can see Figure 113 is referenced, but I need to see the actual image content. Let me look a bit earlier to see the full figure with the image description.I need to look at the actual figure content for Figure 113. Let me search for where the figure starts by looking earlier.Perfect! I found Figure 113. Looking at the content around lines 29326-29350, I can see the computer screen showing desktop icons. Line 29330-29331 clearly shows:\n\n\"You Tube\nMusic\"\n\nThis appears to be \"YouTube Music\" split across two lines in the OCR text. This is the music app shown on the computer screen in Figure 113.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52515, "error": null, "extra": {"thread_id": 2000, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_26bb070ab7624a93b3c5c955dc0af2c0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 106921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and look for the price of the keyboard shown in the screenshot.\n\nLooking at the document, Figure 111 is described in the \"List of Figures\" section as:\n\"Figure 111: Section 9.8: online shopping.\"\n\nLet me search for Figure 111 in the actual content. It appears in Section 9.8 on \"Online Shopping (Ergonomic Keyboard)\".\n\nLooking at the prompt description for Figure 111:\n\"Prompt:\nImagine that you are a robot operating a phone. Like how humans operate the phone, you can move your finger to an icon, click an icon with your finger, scroll down a screen, or type some texts with the keyboard.\n\nYou are asked to shop for an ergonomic keyboard, your budget is between $50 and $100.\n\nPrevious action: \" My next move would be to move my finger to the \"Add to Cart\" button, which is located below the \"Buy New\" button and above the \"Ships from Amazon\" section. I would then click on the \"Add to Cart\" button to add the item to my shopping cart. \"\n\nYou have now completed the previous action as described above. Below is what you see on the phone screen. What will you do next?\"\n\nThe screenshot shows a cart with:\n\"Subtotal $4999\"\n\nand the product listed is:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540 ...\n$4999\"\n\nThe price shown is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 117431, "output_tokens": 353, "cost_micros": 357588, "latency_ms": 12750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 111 in the document, which shows a phone screenshot of a shopping cart.\n\nThe screenshot displays:\n- A product: \"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\"\n- The price shown is: $49.99\n- There's a subtotal of $49.99\n- The item shows \"In Stock\" status\n\nAnswer: 49.99", "citations": [], "input_tokens": 103915, "output_tokens": 97, "cost_micros": 313200, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in that screenshot.\n\nLooking through the document for Figure 111...\n\nFound it on page 140:\n\nFigure 111 shows a smartphone screen displaying an Amazon shopping cart. The image shows:\n- A Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\n- Price: $49.99\n- The button shows \"Proceed to checkout (1 item)\"\n- Subtotal: $49.99\n\nThe price of the keyboard shown in the screenshot is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 139290, "output_tokens": 144, "cost_micros": 420030, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find Figure 111 and identify the keyboard price.Let me search for \"Figure 111\" in the document to locate it.Now let me search for where Figure 111 appears in the content with context.Let me read around line 29062 where Figure 111 is located to see the actual content and price information.Perfect! I found Figure 111 which shows a screenshot of shopping for an ergonomic keyboard. The price is clearly shown in the screenshot as $49.99 (displayed as \"$4999\" without the decimal in multiple places in lines 29088 and 29095).\n\nAnswer: 49.99", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52062, "error": null, "extra": {"thread_id": 2001, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd7392a113cf44358e67b4c74d3afe31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 172, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and look for the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107...\n\nFound it in Section 9.8 GUI Navigation under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" in the top left corner, and the battery percentage \"76\" is displayed in the top right corner of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 133646, "output_tokens": 109, "cost_micros": 402573, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at Figure 107 to find the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107, I can see it's in the \"Sec. 9.8 GUI Navigation\" section under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" displayed at the top left, and the battery percentage \"76\" shown at the top right of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 103913, "output_tokens": 112, "cost_micros": 313419, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 107 in Section 9.8 (GUI Navigation - Online Shopping), I need to find the battery percentage shown in the screenshot.\n\nIn the top-right corner of the screenshot shown in Figure 107, the status bar displays:\n- Time: 5:27\n- Battery percentage: 76\n\nAnswer: 76", "citations": [], "input_tokens": 139288, "output_tokens": 76, "cost_micros": 419004, "latency_ms": 6592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 107 and identify the battery percentage shown in the screenshot.Let me search for \"Figure 107\" in the document to locate it quickly.Now let me search for the specific line with more context:Let me read around line 28504 to see Figure 107:I can see Figure 107 shows a phone screenshot. I can see \"5:27\" as the time at line 28507, and at line 28511 I see \"76\". Let me check if this is the battery percentage. Looking at the structure, it appears to show:\n- 5:27 (time)\n- 76 (likely battery percentage)\n\nLet me read a bit more to see if there's additional context or if the battery percentage is shown elsewhere:Based on the document content, Figure 107 shows a phone screenshot with the time \"5:27\" at the top, and the number \"76\" appears right after it. In typical phone screenshots, the battery percentage is displayed in the status bar at the top of the screen, usually near the time. The number 76 appears to be the battery percentage.\n\nAnswer: 76", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64703, "error": null, "extra": {"thread_id": 2002, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_281e7f398f814d8a9e02d3b21485af45", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 226, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 110562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Figure 101 to find information about the Politico website news cover.\n\nLooking at the document, Figure 101 is described in Section 9.8 \"GUI Navigation - Web Browsing (Today's News)\" on page 131.\n\nThe figure shows a Google search results page with news stories. The prompt asks GPT-4V about what happens after reading the first news article. Looking at the \"Top stories\" section in the image, I can see several news sources listed including:\n\n- **Politico**: \"State Department doubles down after Zelenskyy slams reluctance...\"\n- CNN: \"Catastrophic flooding swamped Vermont's capital...\"\n- Reuters: \"US says it no longer deems Donald Trump immune from E. Jean...\"\n- DCist: \"D.C. Council Approves Emergency Crime Bill\"\n- The New York Times: \"Hard Right Presses Culture War Fights on Defense Bill...\"\n- NPR: \"The Anthropocene began in 1950s, scientists say\"\n\nThe Politico headline mentions \"Zelenskyy,\" which refers to Volodymyr Zelenskyy, the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 117439, "output_tokens": 268, "cost_micros": 356337, "latency_ms": 11250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in Section 9.8 (Web Browsing - Today's News), I can see the Google search results page showing several news headlines.\n\nThe top news item from Politico shows: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThis refers to Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 133656, "output_tokens": 87, "cost_micros": 402273, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in the document, I can see a screenshot showing a news article from Politico. The article is titled \"State Department doubles down after Zelenskyy slams reluctance...\" \n\nThe article discusses Ukrainian President Volodymyr Zelenskyy and Ukraine's desire to join NATO. Zelenskyy is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 103923, "output_tokens": 85, "cost_micros": 313044, "latency_ms": 7108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101, I can see a Google search results page showing news articles. The figure description mentions \"Top stories\" and lists several news sources including Politico.\n\nThe first story listed under Politico is: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 139298, "output_tokens": 88, "cost_micros": 419214, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant (2309.17421v2.pdf.xml) to find Figure 101 and identify the information about the Politico website cover.Let me search for \"Figure 101\" in the document to find the specific location:Let me search for \"politico\" to find the relevant section:I can see mentions of Politico with information about Zelenskyy. Let me search more specifically for \"Figure 101\":Great! I found the reference to Figure 101 at line 27715. Let me read that section:Let me look earlier in the document to see the actual Figure 101 image content. I need to search backwards from line 27715 to find the visual description:Now I can see the context. Figure 101 shows a Google News page with various news articles. Looking at the content around lines 27625-27631, I can see:\n\n- Politico news source\n- Article title: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy (also spelled Zelensky in other parts of the document), who is the President of Ukraine. Let me verify this is correct by looking at the article content earlier:\n\nFrom lines 27419-27421 and 27445-27446, I can confirm:\n- \"State Department doubles down after Zelenskyy slams reluctance on Ukraine's entry to NATO\"\n- \"In a statement posted to Twitter on Tuesday, Ukrainian President Volodymyr Zelenskyy took issue with NATO's reluctance...\"\n\nSo the person on the cover of the news on the Politico website in Figure 101 is Zelenskyy (Volodymyr Zelenskyy), who is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84875, "error": null, "extra": {"thread_id": 2003, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6c36693d9bfb44369693c25b87e59141", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 305, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Figure 89 to identify the brand name of the coffee machine.\n\nLooking at the document, Figure 89 is in Section 9.7 \"Embodied Agent\" under \"Operating Machine\". The figure shows a coffee machine's operating panel with buttons and includes text that mentions operating menu instructions.\n\nIn the image shown in Figure 89, I can see a coffee machine operating panel. Looking at the visible text and branding on the machine, I can see \"Keurig\" appears to be the brand name based on the styling and layout typical of Keurig coffee makers.\n\nAnswer: Keurig", "citations": [], "input_tokens": 117434, "output_tokens": 139, "cost_micros": 354387, "latency_ms": 7641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine's operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 133651, "output_tokens": 61, "cost_micros": 401868, "latency_ms": 6500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 103918, "output_tokens": 60, "cost_micros": 312654, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7, which shows the coffee machine operating panel with button menus, I can see the brand name displayed on the machine.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 139293, "output_tokens": 47, "cost_micros": 418584, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Figure 89 and identify the coffee machine brand.Let me search for \"Figure 89\" in the document:Now let me search more specifically to find where Figure 89 appears:Let me read the section around line 25761 to see Figure 89 and its image:I can see Figure 89 references a coffee machine. Let me look a bit earlier to see the actual image content:Let me continue reading to see the menu and coffee machine images:Let me continue to find the actual coffee machine image with the brand visible:I see that line 25763 shows \"JoooDeee\" which appears to be OCR text from the coffee machine image. Let me look at the earlier Figure 88 section which also shows the coffee machine, as it might have clearer brand information:I can see the OCR text on line 25609 and 25763 shows \"JoooDeee\". Let me search for Figure 88 which is mentioned just before to see if there's more context about the brand:Let me look before line 25584 to see if there's an image with better OCR or visual description for Figure 88:Let me search more specifically around where Figure 88 might have its embedded image:Let me look earlier to find where Figure 88 actual image appears. Let me search backwards from where Figure 88 was mentioned:Let me search more systematically. Let me look for where the coffee machine images are actually embedded. Let me search around where both Figure 88 and 89 would appear together:Let me try a different approach and look for pages around 116-117 where Figures 88 and 89 are mentioned, searching for embedded coffee machine images. Let me search for \"Joo\" which appeared in the OCR text:Great! Let me look at line 34358 and chunk 155184 which has a better visual description with \"JoooDeee\":Perfect! I found it. According to the OCR text on line 34358, the coffee machine has \"JoooDeee\" visible on it. Looking at the visual description starting at line 34363, it describes the device as a water dispenser/cooler, but based on the context of the document discussing coffee machines and the OCR text showing \"JoooDeee\", this appears to be the brand name visible on the coffee machine in Figure 89.\n\nHowever, \"JoooDeee\" seems like it might be an OCR misreading. Let me check if there's any other reference that might clarify the brand. Looking at the OCR pattern \"JoooDeee\", this could potentially be \"Jura\" (a well-known coffee machine brand) misread by OCR, but I should report what the document actually says.\n\nBased on the document, the OCR text from the coffee machine image shows \"JoooDeee\" with \"DI\" below it.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 148609, "error": null, "extra": {"thread_id": 2004, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f9e89fe20d454f6c90a127387a939523", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 624, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 1, I need to identify the relation arrows (edges) in the knowledge graph and determine which ones do not point to specific leaf nodes.\n\nThe figure shows a knowledge graph centered around Artemisia Gentileschi (Q212657) and Orazio Gentileschi (Q367560). The relations shown include:\n\n- **child** (from Orazio to Artemisia)\n- **father** (from Artemisia to Orazio)\n- **citizen** (from both to Italy Q38)\n- **occupation** (pointing to \"Painter\")\n- **style** (from Artemisia to Baroque Q37853)\n- **movement** (from Artemisia to Baroque Q37853)\n- **place of birth** (from Artemisia to Rome Q220)\n- **member** (from Artemisia, appears to point to another entity)\n\nThe arrows that do NOT point to specific leaf nodes (i.e., they point to other entities that are themselves nodes in the graph rather than terminal values) are:\n\n- child (points to another person entity)\n- father (points to another person entity)\n\nAnswer: child, father", "citations": [], "input_tokens": 60197, "output_tokens": 266, "cost_micros": 184581, "latency_ms": 12796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify which relation arrows do not point to specific leaf nodes (entities with specific values or names).\n\nLet me examine each relation arrow from the entities shown:\n\nFrom Q367560 (Orazio Gentileschi):\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- movement \u2192 Q37853 (Baroque) - points to a class/type node\n- child \u2192 Artemisia Gentileschi Q212657 - points to a specific person\n\nFrom Artemisia Gentileschi Q212657:\n- father \u2192 Orazio Gentileschi Q367560 - points to a specific person\n- birth \u2192 [date shown] - points to specific value\n- place of birth \u2192 [location] - points to specific value\n- Citizen \u2192 Italy Q38 - points to a specific country\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- style \u2192 Q37853 (Baroque) - points to a class/type node\n- member \u2192 [organization] - appears to point to a specific entity\n\nThe relations that point to class/type nodes rather than specific leaf nodes are:\n- occupation (points to the class \"Painter\")\n- movement/style (points to the class \"Baroque\")\n\nAnswer: occupation, movement, style", "citations": [], "input_tokens": 22214, "output_tokens": 311, "cost_micros": 71307, "latency_ms": 7718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "movement, occupation, style", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the relation arrows (edges) in the knowledge graph that do not point to specific leaf nodes (literal values).\n\nIn the knowledge graph shown in Figure 1, I can see the following relationships:\n\n- \"Painter\" (Q1028181) - this is an entity node, not a leaf\n- \"Baroque\" (Q37853) - this is an entity node, not a leaf\n- \"Italy\" (Q38) - this is an entity node, not a leaf\n- \"Rome\" (Q212657) - this is an entity node, not a leaf\n- Orazio Gentileschi (Q367560) - this is an entity node, not a leaf\n- Artemisia Gentileschi - the central entity\n\nThe relations that point to these non-leaf entity nodes (rather than to literal values like dates or strings) are:\n\n- occupation (pointing to Painter)\n- style (pointing to Baroque)\n- movement (pointing to Baroque)\n- citizen (pointing to Italy)\n- place of birth (pointing to Rome)\n- father (pointing to Orazio Gentileschi)\n- child (pointing to Artemisia Gentileschi)\n- member (appears to be a relation as well)\n\nAnswer: occupation, style, movement, citizen, place of birth, father, child, member", "citations": [], "input_tokens": 25539, "output_tokens": 310, "cost_micros": 81267, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "child, citizen, father, member, movement, occupation, place of birth, style", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows in the Knowledge Graph that do not point to leaf nodes (specific values).\n\nIn the Knowledge Graph shown, the relations are labeled on the arrows connecting entities. Let me trace each relation:\n\n- \"occupation\" \u2192 points to \"Painter\" (Q37853) - this is a leaf node\n- \"movement\" \u2192 points to \"Baroque\" (Q1028181) - this is a leaf node\n- \"style\" \u2192 appears to connect nodes but the endpoint is not clearly a specific leaf value\n- \"place of birth\" \u2192 points to a specific location\n- \"child\" \u2192 points to \"Artemisia\" (another person entity, not a leaf value)\n- \"father\" \u2192 points to \"Orazio\" (another person entity, not a leaf value)\n- \"member\" \u2192 connects to citizenship information\n- \"citizen\" \u2192 points to \"Italy\" (a leaf node)\n\nThe relations that point to other entities rather than specific attribute values (leaf nodes) are:\n\nAnswer: child, father", "citations": [], "input_tokens": 17550, "output_tokens": 231, "cost_micros": 56115, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows that do not point to specific leaf nodes (entity or property nodes).\n\nFrom the Knowledge Graph Context cloud, I can see the following relationships:\n\n- E1 (OrazioGentileschi) -- occupation --> P1 (Painter) - points to leaf node P1\n- E1 -- movement --> P2 (Baroque) - points to leaf node P2\n- E1 -- birth --> E1 - points back to itself, not a leaf node\n- E1 -- child --> E2 - points to E2, which has further connections\n- E2 (ArtemisiaGentileschi) -- father --> E1 - points to E1, which has further connections\n- E2 -- occupation --> P1 - points to leaf node P1\n- E2 -- style --> P2 - points to leaf node P2\n- E2 -- place of birth --> E3 - points to E3, which has further connections\n- E2 -- member --> E2 - points back to itself, not a leaf node\n- E2 -- Citizen --> E3 - points to E3, which has further connections\n- E1 -- Citizen --> E3 - points to E3, which has further connections\n\nThe relations that do not point to specific leaf nodes are those pointing to entities that have their own outgoing connections or pointing back to themselves:\n\nAnswer: birth, child, father, place of birth, member, Citizen", "citations": [], "input_tokens": 23598, "output_tokens": 341, "cost_micros": 75909, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "birth, child, citizen, father, member, place of birth", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify which relation arrows do not point to specific leaf nodes.Let me search for \"Figure 1\" in the document to locate it:Let me read more to see the complete Figure 1 description:I can see Figure 1 shows a knowledge graph. Let me look more carefully at the structure. The text shows entities and relations. Let me search for more context about the graph structure:Based on the text extracted from Figure 1, I can see the knowledge graph structure. The entities and relations listed are:\n\n- Q1028181 (Painter)\n- Q37853 (Baroque)\n- Q367560 (Orazio Gentileschi)\n- Q212657 (with \"Artemisia Gentileschi Citizen\" below it)\n- Q38 (Italy)\n\nRelations shown:\n- occupation\n- style\n- movement\n- occupation (appears twice)\n- child\n- birth\n- place of birth\n- father\n- member\n\nLooking at the structure, the relations that appear to connect between entities but may not point to specific leaf nodes would be those that connect to higher-level entities or categories. From the layout, I need to identify which relation arrows don't terminate at leaf nodes.\n\nLet me look for any figures or images that might show the actual graph structure more", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59797, "error": null, "extra": {"thread_id": 2005, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c3fdd69258514ce3917c73b31fe40d3e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 249, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me look for any figures or images that might show actual graph structure more", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to locate Figure 5 in the document and examine the lines shown in it.\n\nLooking through the document, I can find Figure 5 on page 8. The figure is titled \"Retrieval Analysis\" and shows citation evaluation (Micro) of generated texts using knowledge graphs with retrieval accuracy 100 (gold), 80, 60, 40, and 20.\n\nThe figure contains four lines:\n- A yellow line (precision)\n- A green line (recall)\n- A blue line (F1_Score)\n- A red line (correctness)\n\nLooking at the graph, I need to identify which line does not intersect with any other line.\n\nThe red line (correctness) starts at the highest point and remains consistently above all other lines throughout the graph. It descends from left to right but never crosses or intersects with any of the other three lines (yellow, green, or blue).\n\nAnswer: red", "citations": [], "input_tokens": 60196, "output_tokens": 207, "cost_micros": 183693, "latency_ms": 12141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, which shows \"Retrieval Analysis\" with multiple lines plotted against Retrieval Accuracy on the x-axis:\n\nThe lines shown are:\n- precision (yellow)\n- recall (green)\n- F1_Score (appears to be another color)\n- correctness (red/orange)\n\nThe correctness line (red/orange colored line at the top of the graph) maintains a consistently high value around 80-90 and does not intersect with any of the other lines throughout the plot.\n\nAnswer: red (or orange/correctness line)", "citations": [], "input_tokens": 22213, "output_tokens": 134, "cost_micros": 68649, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "red or orange correctness line", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 titled \"Retrieval Analysis\", I need to identify which line does not intersect with any other line.\n\nThe figure shows four lines representing different metrics (precision, recall, F1_Score, and correctness) plotted against retrieval accuracy from gold (100) to 20.\n\nExamining the lines:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines \n- The blue line (F1_Score) intersects with other lines\n- The red line (correctness) stays at the top of the graph and does not intersect with any of the other three lines\n\nAnswer: red", "citations": [], "input_tokens": 25538, "output_tokens": 150, "cost_micros": 78864, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I need to identify the line that has no intersection with any other line.\n\nFigure 5 shows citation evaluation metrics (Micro) with retrieval accuracy from 100 (gold) down to 20. There are four lines in the graph representing:\n- Correctness (blue)\n- Precision (yellow) \n- Recall (green)\n- F1 (red)\n\nExamining the lines, the blue line (Correctness) remains at the top of the graph and does not intersect with any of the other three lines throughout the entire range of retrieval accuracy values.\n\nAnswer: Blue", "citations": [], "input_tokens": 17549, "output_tokens": 144, "cost_micros": 54807, "latency_ms": 5610, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I can see a chart showing citation evaluation metrics (precision, recall, F1_score, and correctness) plotted against retrieval accuracy levels (20, 40, 60, 80, and gold/100).\n\nExamining the lines in the chart:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines\n- The blue line (F1_score) intersects with other lines\n- The red line (correctness) runs above all other lines and does not intersect with any of them\n\nAnswer: red", "citations": [], "input_tokens": 23597, "output_tokens": 138, "cost_micros": 72861, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 5 and identify the line with no intersections.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25967, "error": null, "extra": {"thread_id": 2006, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a9d66758d1df48679fe6def6615062f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 40, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document to find figure 5 and identify line with no intersections", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many tables in the document include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1 (page 3) - Shows an example data entry, not evaluation metrics\n2. Table 2 (page 3) - Human Evaluation on dataset quality - no F1\n3. Table 3 (page 7) - Citation Quality evaluation - shows \"F1.\" column (both Micro and Macro)\n4. Table 4 (page 7) - Text quality evaluation - no F1\n5. Table 5 (page 7) - Citation quality with knowledge removed - shows \"F1.\" column\n6. Table 6 (page 8) - Human evaluation alignment - no F1\n7. Table 7 (page 9) - Comparison of General and Specific questions - shows \"F1.\" column\n8. Table 8 (page 15) - Human annotation instructions - no F1\n9. Tables 9-17 (pages 16-21) - Prompts and instructions - no F1\n10. Table 18 (page 22) - Evolutionary question generation example - no F1\n11. Tables 19-20 (pages 23-24) - Example outputs - no F1\n\nAdditionally, I can see:\n- Figure 4 (page 8) shows \"F1_Score\" in a graph\n- Figure 5 (page 8) shows \"F1_Score\" in a graph\n\nBut the question asks specifically about tables, not figures.\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 60181, "output_tokens": 381, "cost_micros": 186258, "latency_ms": 25483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all tables in the document that include \"F1\" as a metric.\n\nLet me go through the tables systematically:\n\n1. Table 1 (Example data entry) - No F1 metric\n2. Table 2 (Human Evaluation on BioKaLMA dataset) - No F1 metric\n3. Table 3 (Citation Quality) - Yes, has \"F1.\" columns (both Micro and Macro)\n4. Table 4 (Text quality evaluation) - No F1 metric\n5. Table 5 (Citation quality with removed knowledge) - Yes, has \"F1.\" column\n6. Table 6 (Human Evaluation on alignment) - No F1 metric\n7. Table 7 (Comparison of General and Specific questions) - Yes, has \"F1.\" column\n\nNow let me check if there are more tables in the appendices:\n\n8. Table 8 (Human Evaluation Instructions) - No F1 metric\n9. Table 9 (Instruction for evolutionary question construction) - No F1 metric\n10. Table 10 (Instruction for round 2-5) - No F1 metric\n11. Table 11 (General question generation) - No F1 metric\n12. Table 12 (Specific question generation) - No F1 metric\n13. Table 13 (Answer generation instruction) - No F1 metric\n14. Table 14 (GPT-EVAL Coherence) - No F1 metric\n15. Table 15 (GPT-EVAL Consistency) - No F1 metric\n16. Table 16 (GPT-EVAL Fluency) - No F1 metric\n17. Table 17 (GPT-EVAL Relevance) - No F1 metric\n18. Table 18 (Evolutionary question construction example) - No F1 metric\n19. Table 19 (Example from ChatGPT) - No F1 metric\n20. Table 20 (Example from GPT-4) - No F1 metric\n\nTables with F1 metric: Table 3, Table 5, and Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 22198, "output_tokens": 502, "cost_micros": 74124, "latency_ms": 9109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1: Shows an example data entry - no F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - no F1 metric\n3. Table 3: Citation Quality evaluation - includes \"F1.\" column (both Micro and Macro F1)\n4. Table 4: Evaluation on generated text quality - no F1 metric\n5. Table 5: Citation quality evaluation with knowledge removed - includes \"F1.\" column\n6. Table 6: Human Evaluation on text-citation alignment - no F1 metric\n7. Table 7: Comparison of evaluation results on General and Specific question setting - includes \"F1.\" column\n8. Table 8: Instruction for human annotators - not a data table with metrics\n9. Tables 9-17: These are prompt/instruction tables - no F1 metrics\n10. Table 18: Example of generated questions - no F1 metric\n11. Tables 19-20: Examples of attributed answers - no F1 metrics\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 25523, "output_tokens": 289, "cost_micros": 80904, "latency_ms": 8578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for tables that include \"F1\" as a metric.\n\nLet me identify all tables in the document:\n\n1. Table 1: Shows an example data entry in BioKaLMA - does not contain F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - contains metrics: Authenticity, Relevance, Naturalness, Significance - no F1\n3. Table 3: Citation Quality evaluation - contains columns including \"F1.\" in both Micro and Macro sections - **YES, contains F1**\n4. Table 5: Citation quality evaluation for generated texts - contains columns: Corr., Prec., Rec., F1. - **YES, contains F1**\n5. Table 6: Result of Human Evaluation on text-citation alignment - contains Alignment and Human Avg. - no F1\n6. Table 7: Comparison between general and specific questions - contains columns including F1. - **YES, contains F1**\n7. Table 8: Instruction for human annotators - no metrics table\n8. Table 9: Not clearly visible as a numbered table in the content shown\n9. Table 10: Instruction and demonstration - not a metrics table\n10. Table 11: Instruction and demonstration - not a metrics table\n11. Table 12: Instruction and demonstration - not a metrics table\n12. Table 13: Instruction for answer generation - not a metrics table\n13. Tables 14-17: Evaluation criteria descriptions - not metrics tables\n14. Table 18: Example of generated questions - not a metrics table\n15. Table 19: Stephen Crane biographical information - not a metrics table with F1\n\nCounting tables with F1 metric:\n- Table 3: YES\n- Table 5: YES\n- Table 7: YES\n\nAnswer: 3", "citations": [], "input_tokens": 17534, "output_tokens": 420, "cost_micros": 58902, "latency_ms": 11342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. **Table 1**: Shows an example data entry - no F1 metric\n2. **Table 2**: Human Evaluation metrics (Authenticity, Relevance, Naturalness, Significance) - no F1\n3. **Table 3**: Citation Quality evaluation - includes \"Micro F1.\" and \"Macro F1.\" columns - **YES**\n4. **Table 4**: Text quality evaluation (Coh., Con., Flu., Rel.) - no F1\n5. **Table 5**: Citation quality with removed knowledge - includes \"F1.\" column - **YES**\n6. **Figure 4**: Shows precision, recall, and F1_Score for [NA] - **YES** (this is labeled as a figure but formatted as a table)\n7. **Figure 5**: Citation evaluation with retrieval accuracy - includes \"F1_score\" column - **YES** (also labeled as figure but formatted as a table)\n8. **Table 6**: Human evaluation on alignment - no F1\n9. **Table 7**: Comparison of General and Specific questions - includes \"Citation Eval. F1.\" column - **YES**\n10. **Table 8**: Human annotation instructions - no F1\n11. Tables 9-17: Various prompts and instructions - no F1 metrics\n12. **Table 18**: Example of evolutionary question construction - no F1\n13. **Table 19**: Example output from ChatGPT - no F1\n14. **Table 20**: Example output from GPT-4 - no F1\n\nTables/Figures with F1 metric:\n- Table 3\n- Table 5\n- Figure 4 (table format)\n- Figure 5 (table format)\n- Table 7\n\nAnswer: 5", "citations": [], "input_tokens": 23582, "output_tokens": 429, "cost_micros": 77181, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned (2310.05634v2.pdf.xml) to find all tables that include \"F1\" as a metric.Let me continue reading the document to search for all tables:Now let me search for \"F1\" to find which tables contain this metric:Let me read the specific sections containing the tables to identify which ones include F1 as a metric:Now let me check all the other tables to see if they contain F1. Let me search more systematically:Let me also check Table 6:Now let me check the tables in the appendix to ensure I haven't missed any:Now let me scan through all the remaining content to ensure I have checked all tables. Let me list out what I've found so far:\n\nTables with F1:\n1. Table 3 - Has F1 (both Micro and Macro F1)\n2. Table 5 - Has F1 \n3. Table 7 - Has F1\n\nTables without F1:\n1. Table 1 - Does not have F1 (has Authenticity, Relevance, Naturalness, Significance)\n2. Table 2 - This appears to be just a caption reference, same as Table 1\n3. Table 4 - Does not have F1 (has Coh., Con., Flu., Rel.)\n4. Table 6 - Does not have F1 (has Alignment and Human Avg.)\n\nLet me continue checking the rest of the tables (8-20) mentioned in the document:Based on my thorough examination of the document, I have identified all tables and checked which ones include \"F1\" as a metric. Let me verify my findings:\n\nTables with F1 as a metric:\n1. **Table 3**: Citation Quality evaluation - includes both Micro F1 and Macro F1 columns\n2. **Table 5**: Citation quality evaluation for generated texts using a KG with N pieces of knowledge removed - includes F1 column\n3. **Table 7**: Comparison of evaluation results on General and Specific question setting - includes F1 in the Citation Evaluation columns\n\nTables without F1 as a metric:\n- Table 1: Example data entry (no F1)\n- Table 2: Human Evaluation on BioKaLMA dataset (no F1)\n- Table 4: Evaluation on generated text quality (no F1, only Coh., Con., Flu., Rel.)\n- Table 6: Result of Human Evaluation on text-citation alignment (no F1, only Alignment and Human Avg.)\n- Tables 8-20: These are instruction tables, prompt demonstrations, and examples (no F1 metrics)\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 116094, "error": null, "extra": {"thread_id": 2007, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d5e6c0fe45c9431aaf087f3d70bed847", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 545, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the paper, which shows the citation quality evaluation results for different models:\n\nThe alignment scores for ChatGPT at different temperatures are:\n- ChatGPT (0.1): 85.9\n- ChatGPT (0.5): 84.5\n- ChatGPT (0.9): 84.1\n\nThe highest alignment score is at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 60189, "output_tokens": 103, "cost_micros": 182112, "latency_ms": 22187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nFrom Table 3 (Citation Quality evaluation), the alignment scores for ChatGPT are:\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 22206, "output_tokens": 129, "cost_micros": 68553, "latency_ms": 5421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from the paper.\n\nLooking at Table 3 in the paper, which shows \"Citation Quality OpenAI models and LLaMA family models\" including alignment scores:\n\n- ChatGPT (0.1): Alignment = 85.9(2.5)\n- ChatGPT (0.5): Alignment = 84.5(1.1)\n- ChatGPT (0.9): Alignment = 84.1(0.5)\n\nThe highest alignment score among the ChatGPT models is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 25531, "output_tokens": 159, "cost_micros": 78978, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which presents the citation quality evaluation results including alignment scores for different models:\n\nFor ChatGPT at different temperatures:\n- ChatGPT (0.1): Alignment = 85.9\n- ChatGPT (0.5): Alignment = 84.5\n- ChatGPT (0.9): Alignment = 84.1\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 17542, "output_tokens": 125, "cost_micros": 54501, "latency_ms": 5592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nLooking at Table 3 (Citation Quality evaluation), I can see the alignment scores for different ChatGPT temperatures:\n\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 for ChatGPT at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 23590, "output_tokens": 139, "cost_micros": 72855, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about temperature and ChatGPT alignment scores.Let me search for temperature settings and ChatGPT results more specifically:Now let me read the specific section where temperature experiments with ChatGPT are discussed:Perfect! I found the table with alignment scores for ChatGPT at different temperatures. From Table 3, I can see:\n\n- ChatGPT (0.1): Alignment score = 85.9(2.5)\n- ChatGPT (0.5): Alignment score = 84.5(1.1)\n- ChatGPT (0.9): Alignment score = 84.1(0.5)\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78952, "error": null, "extra": {"thread_id": 2008, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_826b46b364ee4f52853639bf7b854cb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Section A and subsections A.1, A.2, A.3), I need to compare the length of descriptions for each step:\n\n**A.1 Person Selection**: Approximately 150 words - describes selecting name pairs from the biographical database and filtering criteria.\n\n**A.2 Name Disambiguation**: Approximately 200 words - describes using SPARQL queries to map names to WikiData entities, handling duplicate names, and extracting one-hop sub-graphs.\n\n**A.3 Evolutionary Question Generation**: Approximately 450-500 words - provides extensive detail about the iterative process, knowledge injection, specificity and coherence scoring, the formula for scoring, and implementation details across five iterations.\n\nThe Evolutionary Question Generation step clearly contains the most detailed explanation, including mathematical formulas, specific algorithms for knowledge selection, and detailed descriptions of the iterative process.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 60191, "output_tokens": 204, "cost_micros": 183633, "latency_ms": 21312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Appendix A), I need to compare the lengths of descriptions for each step:\n\n1. **Person Selection (A.1)**: Describes selecting name pairs from biographical database - relatively brief, about 1 paragraph\n\n2. **Name Disambiguation (A.2)**: Describes mapping names to WikiData entities using SPARQL queries - moderate length, about 1-2 paragraphs\n\n3. **Evolutionary Question Generation (A.3)**: Describes the iterative process of extending paragraphs, selecting knowledge, calculating scores with formulas, and generating questions - significantly longer, spanning multiple paragraphs with detailed explanations of specificity scores, coherence scores, the scoring formula, iteration process, and implementation details\n\nThe Evolutionary Question Generation step clearly contains the most extensive description with the most detail about the methodology.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 22208, "output_tokens": 193, "cost_micros": 69519, "latency_ms": 6781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "To determine which step takes the most words to describe in the dataset construction process, I need to examine the three main steps described in Section 2.2 and detailed in Appendix A:\n\n1. **Person Selection** (Section A.1): This section describes filtering and selecting people from the biographical database. It's relatively brief.\n\n2. **Name Disambiguation** (Section A.2): This section explains mapping names to WikiData entities using SPARQL queries and extracting one-hop sub-graphs. It's moderately detailed.\n\n3. **Evolutionary Question Generation** (Section A.3): This section provides extensive detail about the iterative process of extending paragraphs, knowledge selection algorithms, scoring mechanisms (specificity and coherence scores), and the multiple rounds of generation with specific templates and prompts.\n\nThe Evolutionary Question Generation section is significantly longer and more detailed than the other two steps, including mathematical formulas, multiple iteration explanations, and extensive procedural details.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 25533, "output_tokens": 217, "cost_micros": 79854, "latency_ms": 7764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at section 2.2 Dataset Construction and Appendix A Dataset Construction, the three main steps are:\n\n1. Person Selection\n2. Name Disambiguation \n3. Evolutionary Question Generation\n\nComparing the word counts in Appendix A where these steps are described in detail:\n\n- A.1 Person Selection: approximately 150 words\n- A.2 Name Disambiguation: approximately 180 words\n- A.3 Evolutionary Question Generation: approximately 130 words (before the subsections)\n\nHowever, the Evolutionary Question Generation section has significantly more content when including all the detailed prompt tables (Tables 9-12) and examples that follow it, making it the most extensively described step overall.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 17544, "output_tokens": 165, "cost_micros": 55107, "latency_ms": 5703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the dataset construction section (2.2), I need to compare the length of descriptions for each of the three steps:\n\n1. **Person Selection** (A.1): Approximately 150 words\n2. **Name Disambiguation** (A.2): Approximately 200 words \n3. **Evolutionary Question Generation** (A.3): Approximately 450+ words (includes detailed explanation of the iterative process, knowledge selection algorithm with formula, scoring mechanisms, etc.)\n\nThe Evolutionary Question Generation step is described in significantly more detail than the other two steps, taking up the most space in the paper.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 23592, "output_tokens": 143, "cost_micros": 72921, "latency_ms": 5844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about dataset construction steps and compare their descriptions.Let me search for \"dataset construction\" in the document:Let me look at the detailed dataset construction section:Let me read the Dataset Construction section starting at line 506:Good! I can see the three steps mentioned at line 521-523. Now let me read the Appendix A which has the detailed dataset construction process:Perfect! I can now see the three steps of dataset construction in detail. Let me count the words in each section to determine which takes the most words to describe.\n\nLet me read each section more carefully to count:Now let me see where the Evolutionary Question Generation section ends:Perfect! Now I have all three sections. Let me analyze the word count for each step:\n\n**A.1 Person Selection** (lines 2089-2110): Approximately 22 lines of content\n**A.2 Name Disambiguation** (lines 2113-2145): Approximately 33 lines of content \n**A.3 Evolutionary Question Generation** (lines 2148-2222): Approximately 75 lines of content\n\nBy visual inspection and line count, the \"Evolutionary Question Generation\" section is significantly longer than the other two steps. It contains detailed explanations about:\n- The evolutionary approach\n- Iteration process\n- Knowledge annotation\n- Specificity and coherence scoring\n- Formula for scoring\n- Implementation details\n\nThis is clearly the step that takes the most words to describe.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78750, "error": null, "extra": {"thread_id": 2009, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b0d2d82267f4e5eab9a849c3924465e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document's definition of the \"Conscious Incompetence\" setting in Section 2.1:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 22222, "output_tokens": 71, "cost_micros": 67731, "latency_ms": 4952, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\nAnswer: It can be partially verified by the knowledge graph G", "citations": [], "input_tokens": 25547, "output_tokens": 40, "cost_micros": 77241, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "it can be partially verified by knowledge graph g", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of when a sentence can map to both [NA] and knowledge citations.\n\nIn Section 2.1 \"Task Formulation,\" under \"Setting of Conscious Incompetence,\" the document states:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can.\"\n\nHowever, this is incomplete. Looking at the full context, the document explains that [NA] is used when a sentence \"includes some knowledge to be verified, but the knowledge is absent in the knowledge graph G.\"\n\nThe key insight comes from the demonstration in Table 13, which shows sentences containing both specific knowledge citations from the graph AND [NA] citations. This occurs when a sentence contains both:\n1. Knowledge that IS present in the knowledge graph (cited with specific knowledge)\n2. Knowledge that IS NOT present in the knowledge graph (cited with [NA])\n\nAnswer: When it contains both knowledge present in and absent from the knowledge graph", "citations": [], "input_tokens": 17558, "output_tokens": 214, "cost_micros": 55884, "latency_ms": 7110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2727272727272727, "method": "str_norm", "normalised_pred": "when it contains both knowledge present in and absent from knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, in the \"Setting of Conscious Incompetence\" section (2.1), a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph $G$.\"\n\nAnswer: When it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 23606, "output_tokens": 91, "cost_micros": 72183, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the author's definition of conscious incompetence and when a sentence can map to both [NA] and a list of sub-graph knowledge.Let me search for \"conscious incompetence\" in the document:Now let me read the section around line 485 and 514 where \"conscious incompetence\" is defined:Perfect! I found the answer. According to the document, in the section on \"Setting of Conscious Incompetence\", a sentence can map to both [NA] and a list of sub-graph knowledge when it can be partially verified by the knowledge graph G.\n\nAnswer: when it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34875, "error": null, "extra": {"thread_id": 2010, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6035a4bba19d42d88826ef02286ce3c9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure of question-guided reasoning and count the numbers displayed in blue color.\n\nLooking at Figure 1 on page 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims,\" I can see:\n\n- Q1: What is the greatest depth of the Black Sea?\n- A1: Black sea has a maximum depth of **2,212** meters.\n- Q2: How far can sunlight penetrate water?\n- A2: Sunlight does not penetrate water below **1,000** meters.\n- At the bottom: \"2,212 is greater than 1,000. Therefore, the claim is\"\n\nIn the figure, I can see numbers that appear to be highlighted or emphasized:\n- 2,212 (appears twice - once in A1 and once in the reasoning)\n- 1,000 (appears twice - once in A2 and once in the reasoning)\n\nLooking at the visual representation in the figure, the numbers that are displayed in blue/highlighted color are:\n- 2,212 (in A1)\n- 1,000 (in A2)\n- 2,212 (in the final reasoning line)\n- 1,000 (in the final reasoning line)\n\nSo there are 4 instances of numbers shown in blue color.\n\nAnswer: 4", "citations": [], "input_tokens": 27152, "output_tokens": 309, "cost_micros": 86091, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n1. The claim about the Black Sea\n2. Q1 asking about the greatest depth of the Black Sea\n3. A1 stating \"Black sea has a maximum depth of 2,212 meters\" - the number **2,212** appears in blue\n4. Additional text showing \"Average depth\" with what appears to be another number\n5. Q2 asking about how far sunlight can penetrate water\n6. A2 stating \"Sunlight does not penetrate water below 1,000 meters\" - the number **1,000** appears in blue\n7. At the bottom, there's reasoning that states \"2,212 is greater than 1,000\" - both **2,212** and **1,000** appear in blue here as well\n\nLooking carefully at the figure, I can identify the following blue-colored numbers:\n- 2,212 (appears multiple times)\n- 1,000 (appears multiple times)\n\nHowever, the question asks for how many numbers with blue color are mentioned, not how many times they appear. The distinct blue numbers are:\n1. 2,212\n2. 1,000\n\nAnswer: 2", "citations": [], "input_tokens": 11640, "output_tokens": 300, "cost_micros": 39420, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n- The claim about sunlight and the Black Sea\n- Q1 asking about the greatest depth of the Black Sea\n- A1 stating \"Black sea has a maximum depth of **2,212 meters**\" (2,212 appears to be in blue)\n- Q2 asking about how far sunlight can penetrate water\n- A2 stating \"Sunlight does not penetrate water below **1,000 meters**\" (1,000 appears to be in blue)\n- At the bottom, there's text saying \"2,212 is greater than 1,000\" where both numbers appear to be in blue\n\nSo the blue-colored numbers mentioned are:\n1. 2,212 (in A1)\n2. 1,000 (in A2)\n3. 2,212 (in the comparison at bottom)\n4. 1,000 (in the comparison at bottom)\n\nHowever, if we count unique numbers, there are 2 distinct numbers (2,212 and 1,000). But if we count each mention, there are 4 total mentions of numbers in blue.\n\nLooking more carefully at the figure, the numbers that appear to be highlighted/emphasized in blue are:\n- 2,212 (appears twice)\n- 1,000 (appears twice)\n\nCounting total mentions: 4\n\nAnswer: 4", "citations": [], "input_tokens": 12756, "output_tokens": 349, "cost_micros": 43503, "latency_ms": 8500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure that shows question-guided reasoning and count the blue-colored numbers in it.\n\nLooking at the document, Figure 1 is mentioned in the text as showing an example of question-guided reasoning for the claim \"Sunlight can reach the deepest part of the Black Sea.\" The text states: \"This reasoning process can be formulated as question-guided reasoning, where the verification of the claim is guided by asking and answering a series of relevant questions, as shown in Figure 1.\"\n\nHowever, Figure 1 itself is not shown in the provided document content. \n\nThe document does show Figure 4, which displays the QACHECK user interface with an example about \"Lars Onsager won the Nobel prize when he was 30 years old.\" In this figure, I can see:\n- \"Reasoning depth: 0\" and \"Reasoning depth: 1\" labels\n- \"1968\" (the year Lars Onsager won the Nobel Prize)\n- \"1903\" (the year Lars Onsager was born)\n- \"65\" (mentioned in the final prediction: \"He was 65 when he won the Nobel prize\")\n\nLooking at the visible figures in the document, Figure 4 shows the question-guided reasoning process example. The numbers that appear to be highlighted or emphasized in the context of the reasoning are: 1968, 1903, and 65.\n\nWithout being able to see the actual colors in Figure 1 (which is referenced but not displayed), and based on Figure 4 which shows a complete example of question-guided reasoning, I can count 3 distinct numbers that would typically be highlighted in such a demonstration.\n\nAnswer: 3", "citations": [], "input_tokens": 8625, "output_tokens": 374, "cost_micros": 31485, "latency_ms": 9280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nLooking at Figure 1, I can see:\n- The claim about sunlight reaching the deepest part of the Black Sea\n- Step 1 with Q1 and A1, where A1 mentions \"2,212 meters\" \n- Step 2 with Q2 and A2, where A2 mentions \"1,000 meters\"\n- The conclusion states \"2,212 is greater than 1,000\"\n\nThe numbers mentioned are:\n1. 2,212 (meters - maximum depth of Black Sea)\n2. 1,000 (meters - how far sunlight penetrates)\n\nBoth numbers appear to be in the same color as the rest of the text in the boxes. Looking at the figure description and the actual content, I need to identify which numbers are specifically in blue color.\n\nFrom the document's Figure 1, examining the text carefully, the numbers that appear are 2,212 and 1,000. The question asks specifically about numbers with \"blue color.\"\n\nBased on the visual representation in Figure 1, I can identify 2 numbers: 2,212 and 1,000.\n\nAnswer: 2", "citations": [], "input_tokens": 12525, "output_tokens": 288, "cost_micros": 41895, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the example figure related to \"question-guided reasoning\" and count the blue-colored numbers.Let me search for \"question-guided reasoning\" in the document to locate the relevant section.Now let me read the section around line 217 where Figure 1 is mentioned, which shows \"An example of question-guided reasoning\":Perfect! I can see Figure 1 which shows the question-guided reasoning example. Let me continue reading to see the complete figure description and identify all blue-colored numbers:Based on the document content I've read, I can see Figure 1 which shows the question-guided reasoning example. The figure contains:\n\n1. The text mentions \"2,212 m\" (Black Sea depth)\n2. The text mentions \"2,212 meters\" in the answer A1\n3. The text mentions \"1,000 meters\" in the answer A2\n4. The visual description mentions \"Blue accent colors for interactive elements (icons, links)\"\n\nLet me look more carefully at the OCR text section which lists the specific numbers visible in the figure:\n\nFrom lines 288-298, the OCR text shows:\n- 2,212 m (appears multiple times)\n- 1,253 m\n- 7,257 ft\n- 547,000 km\u00b3\n- 131,200 cu mi\n- 10+\n\nAnd from the main figure text (lines 219-270):\n- 2,212 m (Black Sea depth)\n- 1,000 meters (sunlight penetration)\n- 200 meters (mentioned in the evidence text)\n\nThe key information states: \"Blue accent colors for interactive elements (icons, links)\" which suggests that numbers appearing in blue would be those that are interactive or emphasized.\n\nLooking at the structure of Figure 1, the numbers that appear to be in blue color based on the context are:\n1. **2,212** (the answer to Q1 about Black Sea depth)\n2. **1,000** (the answer to Q2 about sunlight penetration)\n\nThese are the two key numerical answers that appear in the question-guided reasoning process.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45156, "error": null, "extra": {"thread_id": 2011, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ee68d3622e54a41be76708c8f52b00e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 300, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system are implemented using Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in Section 3:\n\n1. **Claim Verifier (Section 3.1)**: \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator (Section 3.2)**: \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Model (Section 3.3)**: This module has three different implementations:\n - Retriever-Reader: Uses BM25 retriever and RoBERTa-large reader (not primarily LLM-based)\n - FLAN-T5: This is an LLM\n - GPT Reciter-Reader: Uses InstructGPT (LLM)\n \n The text states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default implementation uses LLM.\n\n4. **QA Validator (Section 3.4)**: \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner (Section 3.5)**: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nCounting the modules that are implemented with LLM backbone:\n1. Claim Verifier - Yes (InstructGPT)\n2. Question Generator - Yes (InstructGPT)\n3. Question Answering Model - Yes (default is GPT Reciter-Reader with InstructGPT)\n4. QA Validator - Yes (InstructGPT)\n5. Reasoner - Yes (FLAN-T5", "citations": [], "input_tokens": 11637, "output_tokens": 512, "cost_micros": 42591, "latency_ms": 10953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): Uses InstructGPT with in-context learning\n - LLM-based \u2713\n\n2. **Question Generator** (Section 3.2): Uses InstructGPT for in-context learning\n - LLM-based \u2713\n\n3. **Question Answering Model** (Section 3.3): Has three implementations:\n - Retriever-Reader: Uses BM25 + RoBERTa-large fine-tuned on SQUAD\n - FLAN-T5: Uses FLAN-T5 model\n - GPT Reciter-Reader: Uses InstructGPT\n - Since the system uses one of these at a time, and the default is GPT Reciter-Reader (mentioned in the text), this module uses LLM \u2713\n\n4. **QA Validator** (Section 3.4): Uses InstructGPT with demonstrations\n - LLM-based \u2713\n\n5. **Reasoner** (Section 3.5): Has two implementations:\n - FLAN-T5 (end-to-end QA model)\n - InstructGPT\n - Both options use LLMs \u2713\n\nAll five modules are implemented with Large Language Models as their backbone (either InstructGPT or FLAN-T5, both of which are LLMs).\n\nAnswer: 5", "citations": [], "input_tokens": 12753, "output_tokens": 375, "cost_micros": 43884, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Module** (Section 3.3): This module has three different implementations:\n - Retriever-Reader: Uses RoBERTa-large (which is a language model)\n - FLAN-T5: \"we also incorporate the FLAN-T5 model\" - This is an LLM\n - GPT Reciter-Reader: Uses InstructGPT - This is an LLM\n \n The default implementation uses GPT Reciter-Reader (LLM-based).\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nAll five modules are implemented with Large Language Models as their backbone (either as the primary or available implementation).\n\nAnswer: 5", "citations": [], "input_tokens": 8622, "output_tokens": 425, "cost_micros": 32241, "latency_ms": 10062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - Uses LLM \u2713\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - Uses LLM \u2713\n\n3. **Question Answering Model** (Section 3.3): This module has three different implementations:\n - Retriever-Reader: Uses RoBERTa-large fine-tuned on SQuAD (this is a reader model, but the main approach is retrieval-based)\n - FLAN-T5: This is an LLM \u2713\n - GPT Reciter-Reader: Uses InstructGPT as the reciter \u2713\n \n The document states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default uses LLM.\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - Uses LLM \u2713\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs \u2713\n\nCounting the modules that use LLMs:\n1. Claim Verifier - LLM\n2. Question Generator - LLM\n3. Question Answering Model - LLM (default implementation)\n4. QA Validator - LLM\n5. Reasoner - LLM\n\nAll 5 modules use LLMs in their implementation (either exclusively or as the default/primary option).\n\nAnswer: 5", "citations": [], "input_tokens": 12522, "output_tokens": 503, "cost_micros": 45111, "latency_ms": 11405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find information about modules implemented with Large Language Models backbone.Let me search for information about modules implemented with LLM backbone in the document.Now let me search for information about which modules use LLM backbone:Let me read more of the document to find details about the system architecture and which modules use Large Language Models:Now let me search for information specifically about which modules are implemented with Large Language Models:Based on the document, I can now identify which modules are implemented with Large Language Models (specifically InstructGPT). Let me verify this information:\n\nFrom the document, I can see that the QACHECK system has **five modules**:\n1. **Claim Verifier** - implemented with InstructGPT (Large Language Model)\n2. **Question Generator** - implemented with InstructGPT (Large Language Model)\n3. **Question Answering Model** - has three implementations, one of which (GPT Reciter-Reader) uses InstructGPT (Large Language Model)\n4. **QA Validator** - implemented with InstructGPT (Large Language Model)\n5. **Reasoner** - has two implementations, one of which uses InstructGPT (Large Language Model)\n\nHowever, I need to count how many modules are implemented with LLM backbone. Looking at the text more carefully:\n\n- **Claim Verifier**: Uses InstructGPT (LLM) \u2713\n- **Question Generator**: Uses InstructGPT (LLM) \u2713\n- **Question Answering Model**: Has 3 options (Retriever-Reader, FLAN-T5, GPT Reciter-Reader), but the QA module itself is not purely LLM-based in all implementations\n- **QA Validator**: Uses InstructGPT (LLM) \u2713\n- **Reasoner**: Has 2 implementations (FLAN-T5 and InstructGPT), one uses LLM \u2713\n\nLet me re-read to get the precise count of modules that ARE implemented with LLM backbone (not just have an option for it):\n\nFrom the text:\n- Claim Verifier: \"We build the claim verifier based on InstructGPT\" - **LLM**\n- Question Generator: \"we also leverage InstructGPT for in-context learning\" - **LLM**\n- QA Validator: \"We again implement the validator by prompting InstructGPT\" - **LLM**\n- Reasoner: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - has LLM option but also non-LLM option\n\nThe Question Answering Model has multiple implementations, and the document states the system uses \"GPT Reciter-Reader as the default implementation\" which uses InstructGPT.\n\nSo counting modules that are implemented with LLM backbone: Claim Verifier (1), Question Generator (2), QA Validator (3), and considering the default implementations, the Reasoner could be 4.\n\nLet me check if there's a clearer statement in the document about this specific question.Let me read the sections more carefully to understand which modules specifically use LLM backbone:\n\nBased on my reading of the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT\" - Uses LLM \u2713\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning\" - Uses LLM \u2713\n3. **Question Answering Model** (Section 3.3): Multiple implementations, NOT all use LLM\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT\" - Uses LLM \u2713\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Has LLM option\n\nThe modules that are definitively implemented with LLM (InstructGPT) backbone are:\n1. Claim Verifier\n2. Question Generator \n3. QA Validator\n\nThat's **3 modules** that are implemented with Large Language Models backbone.\n\nHowever, if we consider that the Reasoner also has an InstructGPT implementation and FLAN-T5 is also a Large Language Model, it could be 4. Let me verify if FLAN-T5 counts as an LLM.\n\nFLAN-T5 is described in the document as \"a Seq2Seq model pre-trained on more than 1.8K tasks with instruction tuning\" - this is indeed a Large Language Model.\n\nSo if the question is asking about modules that ARE implemented (not optionally, but actually) with LLM backbone:\n- Claim Verifier: InstructGPT (LLM) \u2713\n- Question Generator: InstructGPT (LLM) \u2713\n- QA Validator: InstructGPT (LLM) \u2713\n- Reasoner: Has two implementations, both are LLMs (FLAN-T5 and InstructGPT) \u2713\n\nThat would be **4 modules**.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 100172, "error": null, "extra": {"thread_id": 2012, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5a2a7a1ab0194a9ba5958368d2c54734", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 686, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 5 and identify the final step shown in it.\n\nLooking at page 5, I can see Figure 4, which shows a screenshot of the QACHECK user interface with numbered annotations.\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) is clearly labeled as \"The final prediction result with rationale\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 27168, "output_tokens": 141, "cost_micros": 83619, "latency_ms": 10530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 5, which shows the QACHECK user interface screenshot, the final step (labeled as \"4\") shows:\n\n\"The final prediction result with rationale\"\n\nThis section displays:\n- \"Prediction with rationale:\"\n- The reasoning explanation\n- \"Finish\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 11656, "output_tokens": 80, "cost_micros": 36168, "latency_ms": 4530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 (the screenshot at the top of page 5), I can see the interface is annotated with 4 numbered sections:\n\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe fourth and final step shows \"Prediction with rationale:\" followed by the reasoning and final answer.\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 12772, "output_tokens": 111, "cost_micros": 39981, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figure at the top of page 5 (Figure 4), I can see the system interface showing the fact-checking process for the claim \"Lars Onsager won the Nobel prize when he was 30 years old.\"\n\nThe final step shown in the figure is labeled \"The final prediction result with rationale\" which displays:\n\n\"Lars Onsager won the Nobel prize in 1968. Lars Onsager was born in 1903. He was 65 when he won the Nobel prize. Therefore, the final answer is: False.\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 8641, "output_tokens": 135, "cost_micros": 27948, "latency_ms": 5422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 5, I need to identify the final step in the architecture diagram.\n\nThe flow shows:\n1. Claim \u2192 Claim Verifier\n2. Question Generator \u2192 QA Model \u2192 Validator \u2192 Relevant Context (in a loop)\n3. Claim Verifier \u2192 Reasoner\n4. Reasoner \u2192 Label\n\nThe final step in the process is when the Reasoner outputs the Label.\n\nAnswer: Label", "citations": [], "input_tokens": 12541, "output_tokens": 104, "cost_micros": 39183, "latency_ms": 4155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "label", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 5 and identify the final step.Let me search for page 5 content in the document:Let me search for page markers and figures in the document:Let me read around line 819 where Figure 4 is mentioned, as this might be on page 5:Perfect! I can see Figure 4 which appears to be on page 5 (after the PageBreak marker at line 815). Looking at the figure, I can see it shows a step-by-step process. Let me examine the final step more carefully:\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) shows: \"Prediction with rationale:\" followed by the text and ending with \"**Finish**\".\n\nLooking at the structure, the final step in the figure is marked as \"**Finish**\" at line 888.\n\nAnswer: Finish", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33953, "error": null, "extra": {"thread_id": 2013, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca5f20bedd764740aef245cbc4ae41c8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 195, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finish", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 2 and Figure 3 on page 4:\n\nIn Figure 2 (the system architecture), the \"Reasoner\" module is shown in a blue rectangle.\n\nIn Figure 3 (showing the three QA model implementations), the \"Reader\" component appears in orange/coral rectangles in parts (a) and (c).\n\nHowever, the question asks about a model rectangle that appears in BOTH the QA model and Reasoner modules. Looking at the figures more carefully:\n\n- In Figure 3(b), the \"FLAN-T5\" box appears in yellow\n- In Figure 3(c), the \"Reader\" boxes appear in orange\n- The \"Reasoner\" in the main architecture uses similar model components\n\nThe \"Reader\" model component appears in an orange/coral colored rectangle in both the QA module implementations and is used in the reasoning process.\n\nAnswer: Orange", "citations": [], "input_tokens": 27172, "output_tokens": 206, "cost_micros": 84606, "latency_ms": 10108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 that shows the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 4, which shows \"The architecture of our QACHECK system,\" I can see the system architecture with various modules including:\n- Claim Verifier\n- Question Generator\n- QA Model\n- Validator\n- Reasoner\n\nThe figure shows these modules connected with arrows. However, this figure does not clearly show colored rectangles that would indicate a specific model appearing in both the QA Model and Reasoner modules.\n\nLooking more carefully at the document, Figure 3 on page 5 shows \"Illustrations of the three different implementations of the Question Answering module in QACHECK\" with three subfigures (a), (b), and (c). In subfigure (a) \"Retriever-Reader\" and subfigure (c) \"GPT Reciter-Reader\", there is a \"Reader\" component shown.\n\nAccording to the text in Section 3.5, the Reasoner \"is a special question-answering model\" and the text mentions that both the QA module (Figure 3) and potentially the Reasoner use a \"Reader\" component.\n\nHowever, the document is in grayscale/black and white, so I cannot determine colors from the PDF provided.\n\nAnswer: Cannot determine - document appears to be in grayscale", "citations": [], "input_tokens": 11660, "output_tokens": 312, "cost_micros": 39660, "latency_ms": 9907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine document appears to be in grayscale", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 4, I need to identify the rectangles that appear in both the \"QA Model\" and \"Reasoner\" modules.\n\nIn the architecture diagram (Figure 2), the QA Model outputs \"(Q, A)\" pairs, and the Reasoner outputs \"Label\". \n\nThe component that connects both modules is the \"Wikipedia Corpus\" which is shown as a cylinder/database shape that provides evidence to the QA Model, and the accumulated context from Q&A pairs feeds into the Reasoner.\n\nHowever, looking more carefully at the diagram structure, both the QA Model and Reasoner are shown as rectangular boxes in the flowchart. The rectangles representing these modules themselves appear to be gray/neutral colored boxes in the system architecture.\n\nBut examining the actual visual elements more carefully, there isn't a colored rectangle that appears in both specific modules as separate components. The modules themselves are represented as boxes in the flow diagram.\n\nAnswer: gray", "citations": [], "input_tokens": 12776, "output_tokens": 212, "cost_micros": 41508, "latency_ms": 7203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 to identify the color of the model rectangle that appears in both the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 3 (which shows the system architecture), I can see the general architecture diagram. However, the question asks specifically about page 4.\n\nOn page 4, I can see Figure 3, which shows three different QA model implementations:\n(a) Retriever-Reader\n(b) FLAN-T5\n(c) GPT Reciter-Reader\n\nLooking at the diagram, I need to identify which model appears in both the QA module and the Reasoner module. According to the text in section 3.5, the Reasoner has two implementations: \"1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model.\"\n\nIn Figure 3, I can see rectangles representing different models. The FLAN-T5 model is shown in Figure 3(b), and this same model is mentioned as being used in the Reasoner module.\n\nLooking at the visual representation in Figure 3(b), the FLAN-T5 rectangle appears to be blue in color.\n\nAnswer: blue", "citations": [], "input_tokens": 8645, "output_tokens": 287, "cost_micros": 30240, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Figure 2 on page 4, which shows \"The architecture of our QACHECK system.\"\n\nLooking at the architecture diagram, I can see several modules represented as rectangles. The question asks about a rectangle that appears in both the QA Model and Reasoner modules.\n\nIn the diagram, I can see:\n- A \"QA Model\" box in the loop section\n- A \"Reasoner\" box after the loop\n- Both of these modules appear to have the same visual representation\n\nHowever, looking more carefully at the figure description and the actual diagram components, the modules that represent model components (like the Question Answering module and Reasoner) are shown as rectangles in the flowchart.\n\nThe document shows these as standard flowchart boxes, but I cannot definitively determine a specific color from the description provided. Looking at typical diagram conventions and the figure as presented, these model components appear to be represented in standard boxes.\n\nWithout being able to see the actual colors in the PDF image clearly described in the text, I cannot make a definitive statement about the specific color.\n\nAnswer: Cannot be determined from the provided text", "citations": [], "input_tokens": 12545, "output_tokens": 247, "cost_micros": 41340, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided text", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the figure on page 4 and identify the color of the model rectangle that appears in both the QA model and Reasoner module.Let me search for page 4 content and figures in the document:Let me search for figure-related content and read more of the document to find page 4:Let me read the section containing Figure 3 which appears to show different implementations of the QA module:This is Figure 3. Let me continue reading to find more figures and look for page markers:Let me search for page numbers or section breaks to locate page 4:Let me read around the fourth PageBreak (line 815) which should be around page 4:This is Figure 4, which shows a user interface. Let me continue reading to see if there are more visual elements described:Now I need to go back and look at Figure 2, which shows the architecture of the QACHECK system. That figure should show both the QA model and Reasoner module. Let me read around line 416 where Figure 2 is mentioned:I see Figure 2 is described, but I need to look at more details about the visual representation. Let me search for information about colors in the figure or read earlier to see the full figure description:The embedded image description in Figure 2 only describes a yellow/golden amber circular icon with a question mark. But the question asks about a model rectangle that appears in both QA model and Reasoner modules. Let me search for more complete visual descriptions or look for other figures. Let me check if there's more detailed architecture visualization:Let me read that chunk:Let me read that section:This doesn't seem to be the right figure. Let me search more specifically for information about the architecture diagram or look for mentions of colors in model components:Let me search for \"green\" or other colors that might appear in the architecture diagram:The green icons appear to be information icons. Let me look at the text structure of Figure 2 more carefully. The figure shows \"QA Model\" and \"Reasoner\" as separate boxes. Let me search for \"orange\" or \"red\" colors:Let me try searching for \"model\" and \"reasoner\" together or look at the complete Figure 2 text more carefully:Let me look at a different approach. The question mentions \"page 4\", so let me count the PageBreaks more carefully. Let me look between the 3rd and 4th PageBreak which should be page 4:So Figure 3 is on page 4 (after the 3rd PageBreak at line 648). Figure 3 shows the three different implementations of the Question Answering module. The visual description mentions a green circular icon with \"i\". But this doesn't show both QA model and Reasoner module together.\n\nLet me look for if there are more images or visual descriptions in Figure 3:I see in Figure 3 there are three parts (a), (b), and (c) showing different QA implementations. Each shows \"Reader\" as a component, and there's a green circular icon. But I need to understand the question better - it asks about a \"model rectangle\" that appears in both QA model and Reasoner module.\n\nLet me re-read Figure 2 which shows the overall architecture, and look for visual elements that might represent both components. Let me search for mentions of \"Reader\" since that appears in both:I notice that \"Reader\" appears in Figure 3 as a component. In the figure, the description says there's a green circular icon with \"i\". The \"Reader\" component in the Retriever-Reader, FLAN-T5, and GPT Reciter-Reader architectures likely uses the same visual element. But the question asks about something that appears in both \"QA model\" and \"Reasoner module\".\n\nWait - let me re-read the architecture description. The Reasoner is described as \"a special question-answering model\". So both might use the same underlying reader model. Let me look at where the reader model is used in Figure 3. The green \"i\" icon might be the answer indicator. But the question asks about a \"model rectangle\" not an icon.\n\nLet me think about this differently. Perhaps the document has better visual descriptions elsewhere. Let me search for all embedded images:There are multiple embedded images. The question specifically mentions Figure on page 4. Based on the PageBreaks, page 4 would be between line 648 and 815. Let me look more carefully at what figures or diagrams might show architectural components with rectangles:\n\nLooking back at Figure 3 (lines 651-707), it shows three sub-figures (a), (b), and (c) representing different QA implementations. Each has components like \"Retriever\", \"Reader\", \"FLAN-T5\", \"InstructGPT\". These might be shown as rectangles in the original figure.\n\nIn Figure 3, all three QA implementations show a \"Reader\" component. And the text mentions that the Reasoner module (section 3.5, lines 919-933) is \"a special question-answering model\" that can be implemented using \"FLAN-T5\" or \"InstructGPT model\".\n\nSo FLAN-T5 appears in both:\n- In Figure 3(b) as the QA model implementation \n- In the Reasoner as one of the two implementation options\n\nLet me check if there's a visual description that mentions FLAN-T5 as a colored rectangle. Let me search for \"FLAN\" in visual descriptions:Perfect! So FLAN-T5 is used in BOTH:\n1. Figure 3(b) - as a QA Model implementation\n2. Section 3.5 (Reasoner) - as one of the two reasoner implementations\n\nNow I need to find the visual description showing what color the FLAN-T5 rectangle is. Let me look more carefully at the embedded images around Figure 3. However, the visual description at line 719 only describes the green \"i\" icon, not the model boxes themselves.\n\nLet me check if there are more visual descriptions or embedded images in the Figure 3 section. The document might have multiple embedded images for the same figure. Let me read more around that area:These are descriptions of other images. Let me look at the specific section where Im3.png first appears (around line 711) and see if there are additional visual descriptions I missed:Still showing the green \"i\" icon. Let me look at Im5.jpg which mentions GPT3 - this might be describing the model boxes:Im5.jpg shows a GPT3 logo with purple and pink/magenta colors. But I need to find descriptions of the model rectangles/boxes in Figure 3. Let me search for descriptions that mention \"rectangle\" or \"box\" with colors:Purple appears in the GPT3 logo. Since the question asks about a component that appears in BOTH QA Model and Reasoner, and we know FLAN-T5 is used in both, let me look for visual descriptions that might show FLAN-T5 box. Let me search for \"yellow\" since that was mentioned earlier:Yellow appears in question mark icons. Let me try a different approach. Let me look at all the visual descriptions more systematically. Let me check descriptions that mention model names or rectangular shapes directly. Let me search for visual descriptions that mention boxes or rectangles with specific colors:Not helpful. Let me think differently. The embedded images are specific parts of the figure. Maybe there are multiple embedded images within Figure 3 that I haven't found yet. Let me look at the embedded images that appear chronologically around Figure 3 content. Figure 3 is around lines 651-720. Let me check what other images might be part of it:\n\nActually, wait - let me reconsider. Looking at the OCR text, Im3.png shows \"i\", Im5.jpg shows \"GPT3\". These are components within the figures. Let me look for images that might show \"FLAN-T5\" or \"FLAN\" text:FLANonly appears as text, not in embedded image OCR. Let me look for embedded images with OCR text that might contain model names. Let me check if there are images showing \"T5\" or looking at what other embedded images show:All I'm finding are green \"i\" icons. Let me try a completely different approach. Perhaps I need to look at the actual figure diagram structure more carefully. In Figure 3, there are three sub-diagrams showing different QA implementations. Each has boxes/rectangles for components. Looking at the text structure:\n\nFigure 3(a): Retriever-Reader has \"Retriever\" and \"Reader\" boxes\nFigure 3(b): FLAN-T5 has \"FLAN-T5\" box \nFigure 3(c): GPT Reciter-Reader has \"InstructGPT\" and \"Reader\" boxes\n\nThe common element between Figure 3 and the Reasoner would be the model itself. Since both QA module and Reasoner can use FLAN-T5, I should look for a visual description of the FLAN-T5 box/rectangle.\n\nLet me search through all the visual descriptions more systematically for anything mentioning model boxes or FLAN. Actually, let me look for embedded images that have model-related OCR text. Let me look for embedded images around the areas discussing models:\n\nWait, I should look for the actual image file names and descriptions that might show text boxes or model components. Let me search for descriptions that mention \"text\" and colors:Turquoise/cyan blue! Let me read that chunk:This turquoise/cyan blue button shows \"Finish\" - that's from Figure 4 (the UI screenshot). Let me look systematically for all visual descriptions that mention rectangular shapes or boxes. Actually, let me search for specific model component descriptions. Let me look for \"RoBERTa\" which is mentioned as the Reader model:RoBERTa is used in the Reader model but there's no visual description mentioning it. Let me try to systematically go through the embedded images and look for any that might show architectural component boxes. Let me look at the images that haven't been described yet. Let me check Im2.png more thoroughly since that's Figure 2:These are still just the icons. Let me try a completely different approach. Maybe the XML parser extracted images separately from the diagram. Let me look at the beginning of the document to see if there are more detailed figure descriptions I'm missing:This is Figure 1. The visual content shows search icons and question marks. Let me consider: maybe the XML extraction treats architectural diagram components differently. Perhaps the model boxes themselves don't have detailed color descriptions, or the color information is embedded in a way I haven't found yet.\n\nLet me reconsider what the question is actually asking. It says \"the model rectangle in the figure of page 4 that appears both in QA model and Reasoner module\". If Figure 3 is on page 4, and Figure 3 shows three different QA implementations, then the component that would also appear in the Reasoner would be the UNDERLYING MODEL.\n\nLooking back at the text:\n- Section 3.3 (QA module) describes three implementations, one being FLAN-T5 (line 675-686)\n- Section 3.5 (Reasoner) says it can be implemented using \"the end-to-end QA model based on FLAN-T5\" (line 931-932)\n\nSo FLAN-T5 is the model that appears in both. Now I need to find what color the FLAN-T5 rectangle is shown in Figure 3. Since the visual descriptions I've found so far only show icons (green \"i\", yellow \"?\", orange \"?\"), maybe the model boxes themselves are not described with specific colors in the extracted XML, or I need to look elsewhere.\n\nLet me try to find if there's any systematic description of the figure architecture. Maybe there's a legend or color scheme description:Turquoise appears as a \"Finish\" button and footer element. But wait, let me read chunk 155824 which mentions a layout with multiple colored sections:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 328655, "error": null, "extra": {"thread_id": 2014, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4736292b32d6461e98620f394ccee4da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2072, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "let me try to find if there s any systematic description of figure architecture maybe there s legend or color scheme description turquoise appears as finish button and footer element but wait let me read chunk 155824 which mentions layout with multiple colored sections", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompt instruction examples shown in the main body of the paper (not in the appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction example:\n - \"Claim = CLAIM / We already know the following: CONTEXT / Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instruction examples:\n - Initial question: \"Claim = CLAIM / To verify the above claim, we can first ask a simple question:\"\n - Follow-up question: \"Claim = CLAIM / We already know the following: CONTEXT / To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction example:\n - \"Claim = CLAIM / We already know the following: CONTEXT / Now we further know: NEW QA PAIR / Does the QA pair have additional knowledge useful for verifying the claim?\"\n\n4. **Section 3.5 (Reasoner)** - The text mentions prompts are in \"Appendix A.4\" but doesn't show the full prompt in the main body.\n\nTotal prompts shown in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 27159, "output_tokens": 335, "cost_micros": 86502, "latency_ms": 9625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper.\n\nLooking through the document, I can find the following prompt instructions in the main body (not in the Appendix):\n\n1. **Claim Verifier prompt** (Section 3.1):\n```\nClaim = CLAIM We already know the following:\nCONTEXT\nCan we know whether the claim is true or false now? Yes or no?\n```\n\n2. **Question Generator - Initial question prompt** (Section 3.2):\n```\nClaim = CLAIM\nTo verify the above claim, we can first ask a simple question:\n```\n\n3. **Question Generator - Follow-up question prompt** (Section 3.2):\n```\nClaim = CLAIM\nWe already know the following: CONTEXT\nTo verify the claim, what is the next question we need to know the answer to?\n```\n\n4. **QA Validator prompt** (Section 3.4):\n```\nClaim = CLAIM\nWe already know the following:\nCONTEXT\nNow we further know: NEW QA PAIR\nDoes the QA pair have additional knowledge useful for verifying the claim?\n```\n\nThat gives us 4 prompt instruction examples in the main body of the paper.\n\nAnswer: 4", "citations": [], "input_tokens": 11647, "output_tokens": 289, "cost_micros": 39276, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompts/instruction examples that are explicitly shown in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction:\n ```\n Claim = CLAIM\n We already know the following:\n CONTEXT\n Can we know whether the claim is true or false now? Yes or no?\n ```\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instructions:\n - For initial question generation:\n ```\n Claim = CLAIM\n To verify the above claim, we can first ask a simple question:\n ```\n \n - For follow-up questions:\n ```\n Claim = CLAIM\n We already know the following:\n CONTEXT\n To verify the claim, what is the next question we need to know the answer to?\n ```\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction:\n ```\n Claim = CLAIM\n We already know the following:\n CONTEXT\n Now we further know:\n NEW QA PAIR\n Does the QA pair have additional knowledge useful for verifying the claim?\n ```\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12763, "output_tokens": 336, "cost_micros": 43329, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (excluding the Appendix).\n\nLet me go through the main body of the paper systematically:\n\n1. **Section 3.1 (Claim Verifier)**: The text mentions \"We prompt InstructGPT with ten distinct in-context examples as detailed in Appendix A.1\" - but no examples are shown in the main body, only referenced.\n\n2. **Section 3.2 (Question Generator)**: Two prompts are shown:\n - Initial question generation prompt (1 prompt)\n - Follow-up question generation prompt (1 prompt)\n\n3. **Section 3.3 (Question Answering Model)**: No complete prompt examples are shown in the main body.\n\n4. **Section 3.4 (QA Validator)**: One prompt instruction is shown for the validator module (1 prompt)\n\n5. **Section 3.5 (Reasoner)**: The text mentions \"the InstructGPT model with the prompts given in Appendix A.4\" - but no examples are shown in the main body.\n\nCounting the prompts actually displayed in the main body:\n- Section 3.2: 2 prompts (initial question generation + follow-up question generation)\n- Section 3.4: 1 prompt (QA validator)\n\nTotal: 3 prompts in the main body\n\nAnswer: 3", "citations": [], "input_tokens": 8632, "output_tokens": 331, "cost_micros": 30861, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - 1 prompt instruction:\n - \"Claim = CLAIM... Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - 2 prompt instructions:\n - Initial question generation: \"Claim = CLAIM... To verify the above claim, we can first ask a simple question:\"\n - Follow-up questions: \"Claim = CLAIM... We already know the following: CONTEXT... To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - 1 prompt instruction:\n - \"Claim = CLAIM... We already know the following: CONTEXT... Now we further know: NEW QA PAIR... Does the QA pair have additional knowledge useful for verifying the claim?\"\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12532, "output_tokens": 279, "cost_micros": 41781, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about instruction examples in the main body of the paper.Let me continue reading to find information about instruction examples in the main body of the paper.Let me read the Appendix section to find the prompts and instruction examples.Perfect! I can now see the prompts section clearly. Let me count the instruction examples mentioned. Looking at the document, I can see:\n\n1. **A.1 Prompts for Claim Verifier** - Shows \"<10 demonstrations in total>\"\n2. **A.2 Prompts for Question Generation** (initial question) - Shows \"<10 demonstrations in total>\"\n3. **A.2 Prompts for follow-up question generation** - Shows \"<10 demonstrations in total>\"\n4. **A.3 Prompts for Validator** - Shows \"<10 demonstrations in total>\"\n5. **A.4 Prompts for Reasoner** - Shows \"<10 demonstrations in total>\"\n\nHowever, the question asks about \"instruction examples listed in the main body of the paper.\" Let me check if there's more specific information in the main body about how many examples are actually shown.Let me search more specifically for mentions of how many examples are shown in the main body.Great! I found it. Let me read line 582 more carefully. The main body states: \"We prompt InstructGPT with **ten distinct in-context examples** as detailed in Appendix A.1\"\n\nLet me verify this is the answer by checking if there are other mentions of specific numbers in the main body.Perfect! I found another mention in line 790: \"with a suite of **ten demonstrations** shown in Appendix A.3\"\n\nLet me search for all instances where numbers of examples/demonstrations are mentioned in the main body.Based on my reading of the document, I found that in the main body of the paper, there are **two explicit mentions** of the number of instruction examples/demonstrations:\n\n1. Line 582: \"We prompt InstructGPT with **ten distinct in-context examples**\" (for the Claim Verifier)\n2. Line 790: \"with a suite of **ten demonstrations**\" (for the Validator)\n\nHowever, the question asks specifically \"How many prompts instruction examples are listed in the main body of the paper?\" - it asks for the COUNT/NUMBER that is mentioned, not how many times it's mentioned.\n\nAccording to the main body:\n- 10 examples for Claim Verifier (mentioned on line 582)\n- 10 demonstrations for Validator (mentioned on line 790)\n\nSince both mention \"10\" and the question asks for the number in float format and wants a single integer answer, the answer is:\n\n**10**\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77358, "error": null, "extra": {"thread_id": 2015, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_302989d449c9437a85f8635948950b78", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 438, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance scores for ProgramFC and QACheck (the proposed system) in the HOVER dataset from Table 1.\n\nFrom Table 1 on page 6:\n\n**HOVER dataset:**\n- ProgramFC scores: 54.27 (2-hop), 54.18 (3-hop), 52.88 (4-hop)\n- QACheck scores: 55.67 (2-hop), 54.67 (3-hop), 52.35 (4-hop)\n\nNow I'll calculate the performance gap for each:\n- 2-hop: 55.67 - 54.27 = 1.40\n- 3-hop: 54.67 - 54.18 = 0.49\n- 4-hop: 52.35 - 52.88 = -0.53\n\nAverage performance gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.453333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 27174, "output_tokens": 268, "cost_micros": 85542, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nLooking at Table 1 in the document, I can find the F1 scores for both systems on HOVER:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nTo calculate the average performance gap across all three HOVER subsets:\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 11662, "output_tokens": 275, "cost_micros": 39111, "latency_ms": 5983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12778, "output_tokens": 247, "cost_micros": 42039, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1 in the document, I can see the performance results:\n\nFor HOVER dataset:\n- ProgramFC: 54.27\n- QACheck: 55.67\n\nFor 2-hop:\n- ProgramFC: 54.18\n- QACheck: 54.67\n\nFor 3-hop:\n- ProgramFC: 52.88\n- QACheck: 52.35\n\nFor 4-hop:\n- ProgramFC: 59.66\n- QACheck: 59.47\n\nThe HOVER dataset has three subsets (2-hop, 3-hop, 4-hop) based on reasoning depth.\n\nTo calculate the average performance gap:\n- 2-hop gap: 54.67 - 54.18 = 0.49\n- 3-hop gap: 52.35 - 52.88 = -0.53\n- 4-hop gap: 59.47 - 59.66 = -0.19\n\nAverage gap = (0.49 + (-0.53) + (-0.19)) / 3 = -0.23 / 3 = -0.0767\n\nRounded to 2 decimal places: -0.08\n\nAnswer: -0.08", "citations": [], "input_tokens": 8647, "output_tokens": 319, "cost_micros": 30726, "latency_ms": 6530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-0.08", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck in the HOVER dataset from Table 1.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12547, "output_tokens": 251, "cost_micros": 41406, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about ProgramFC performance and the proposed system on the HOVER dataset.Let me search for tables or sections containing performance comparisons on the HOVER dataset.Let me read more of the document to find the tables with performance results.Let me search for tables with results and performance metrics.Perfect! I found Table 1 with the performance results. Let me extract the exact values:\n\nFrom Table 1, for the HOVER dataset:\n- **ProgramFC**: \n - 2-hop: 54.27\n - 3-hop: 54.18\n - 4-hop: 52.88\n\n- **QACheck (proposed system)**:\n - 2-hop: 55.67\n - 3-hop: 54.67\n - 4-hop: 52.35\n\nNow I need to calculate the average performance gap between QACheck and ProgramFC:\n\nFor each hop level:\n- 2-hop gap: 55.67 - 54.27 = 1.40\n- 3-hop gap: 54.67 - 54.18 = 0.49\n- 4-hop gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51640, "error": null, "extra": {"thread_id": 2016, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b7dbcb3c5c4d4293862871c529bf39fc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 231, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify the main tasks the authors conducted experiments on by examining the document.\n\nFrom the abstract and introduction (page 1), the authors state:\n\"More in detail, we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 PRELIMINARIES (page 2), the document explicitly describes two main tasks:\n- Section 2.1: Event Relation Extraction\n- Section 2.2: Deductive Reasoning\n\nThese are consistently mentioned throughout the paper as the primary experimental tasks:\n- The pilot study (Section 3.1.1) uses \"MAVEN-ERE and ProofWriter\" \n- Main experiments (Section 4.1) evaluate on \"MAVEN-ERE, Causal-TimeBank, and ProofWriter\"\n- MAVEN-ERE and Causal-TimeBank are event relation extraction datasets\n- ProofWriter is a deductive reasoning dataset\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 65638, "output_tokens": 233, "cost_micros": 200409, "latency_ms": 34842, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks that the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This is extensively discussed throughout the paper, with experiments on datasets like MAVEN-ERE and Causal-TimeBank. The task involves identifying coreference, temporal, causal, and subevent relations between events.\n\n2. **Deductive Reasoning** - This is the second main task, with experiments conducted on the ProofWriter dataset. The task involves logical reasoning to prove, disprove, or determine if statements are unknown based on given facts and rules.\n\nThese two tasks are explicitly mentioned in the abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 28196, "output_tokens": 189, "cost_micros": 87423, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks where experiments were conducted.\n\nFrom the Abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 (Preliminaries): \"In this section, we first introduce two tasks that this paper mainly explores.\" followed by Section 2.1 \"Event Relation Extraction\" and Section 2.2 \"Deductive Reasoning\"\n\nFrom the experimental setup sections, both tasks are consistently used throughout the paper for evaluation (Tables 1, 2, 3, and various figures reference MAVEN-ERE/Causal-TimeBank for ERE and ProofWriter for deductive reasoning).\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 34236, "output_tokens": 175, "cost_micros": 105333, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for the main experimental tasks:\n\nFrom the Introduction and throughout the paper, the authors clearly state they conduct investigations on two main tasks:\n\n1. **Event Relation Extraction (ERE)** - mentioned extensively, including datasets MAVEN-ERE and Causal-TimeBank\n2. **Deductive Reasoning** - mentioned alongside ERE, including the ProofWriter dataset\n\nThis is confirmed in multiple places:\n- Section 2 \"PRELIMINARIES\" has subsections 2.1 \"Event Relation Extraction\" and 2.2 \"Deductive Reasoning\"\n- Section 3.1.1 states: \"We conduct a manual evaluation on MAVEN-ERE and ProofWriter\"\n- The abstract mentions \"event relation extraction and deductive reasoning\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 26309, "output_tokens": 193, "cost_micros": 81822, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This task aims to identify relations (Coreference, Temporal, Causal, and Subevent) between two events in text. The authors used datasets like MAVEN-ERE and Causal-TimeBank for this task.\n\n2. **Deductive Reasoning** - This task involves beginning with known facts and rules, then iteratively making new inferences. The authors used the ProofWriter dataset for this task.\n\nThese two tasks are explicitly mentioned in Section 2 (PRELIMINARIES) where the authors state: \"In this section, we first introduce two tasks that this paper mainly explores.\" The section then proceeds with subsections 2.1 Event Relation Extraction and 2.2 Deductive Reasoning.\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 34535, "output_tokens": 211, "cost_micros": 106770, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify the two main tasks the author conducted experiments on.Let me continue reading to find the specific tasks mentioned:Based on the document, I can see that the author evaluated LLMs in two practical scenarios:\n\n1. Event relation extraction (lines 473)\n2. Deductive reasoning tasks (lines 480-481)\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57891, "error": null, "extra": {"thread_id": 2017, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fc47d9b3789248779051669f324d1694", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 114, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about the number of possible combinations of relationships between two events.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the task involves identifying four types of relations between two events:\n\n1. **Coreference relations**: 2 possible labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 possible labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 possible labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 possible labels (NO_SUBEVENT, SUBEVENT)\n\nThe total number of combinations would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, let me verify this with Figure 1, which shows an example calculation. In Figure 1, it states:\n\n\"Logical Inconsistency (LI) = Number of Conflicts (1) / Number of Combinations (6) = 1/6 \u2248 16.7%\"\n\nThe text explains: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis is referring to pairwise combinations between the 4 relation types, not the total number of possible relationship combinations.\n\nFor the total number of possible combinations of relationships, the calculation is:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nAnswer: 84", "citations": [], "input_tokens": 65637, "output_tokens": 393, "cost_micros": 202806, "latency_ms": 38250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the paper defines 4 types of relations between two events:\n\n1. **Coreference relations**: 2 labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 labels (NO_SUBEVENT, SUBEVENT)\n\nThe document also explicitly mentions in the logical inconsistency calculation (Figure 1 and Section 2.1):\n\n\"Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis refers to combinations between the 4 relation types (choosing 2 from 4 types to check for conflicts), not the total possible relationship combinations.\n\nHowever, looking more carefully at the document, particularly in Figure 1, it states:\n\"Number of Combinations (+ O) = 6\"\n\nThis is the number of pairwise combinations when checking for conflicts between the 4 different relation types: C(4,2) = 4!/(2!\u00d72!) = 6.\n\nBut the question asks about \"combinations of relationships between two events,\" which would be the total number of possible relationship combinations (one label from each of the 4 types):\n\nTotal combinations = 2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, re-reading the context in Figure 1 more carefully, the \"6\" refers to checking consistency between pairs of the 4 relation types, not the total possible labelings.\n\nLooking at the question again and the context in the paper where they discuss logical inconsistency, the relevant number appears to be **6** - the number of combinations when checking pairs of relation types for conflicts", "citations": [], "input_tokens": 28195, "output_tokens": 512, "cost_micros": 92265, "latency_ms": 12843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper states that for each pair of events, there are 4 types of relations to determine:\n\n1. **Coreference relations**: {NO_COREFERENCE, COREFERENCE} - 2 options\n2. **Temporal relations**: {NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON} - 7 options\n3. **Causal relations**: {NO_CAUSAL, PRECONDITION, CAUSE} - 3 options\n4. **Subevent relations**: {NO_SUBEVENT, SUBEVENT} - 2 options\n\nThe paper also provides a specific formula in Figure 1 for calculating the \"total number of combinations\" when computing logical inconsistency:\n\nIn the example, it states: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis formula 4*(4-1)/2 = 6 represents the number of pairs of relation types (combinations of 2 from 4 types), which is used to check for conflicts between different relation types.\n\nHowever, if the question asks about the total possible combinations of relationship assignments (not pairs for conflict checking), it would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84 possible combinations\n\nBut based on the context in Figure 1 where they explicitly calculate \"total number of combinations\" as 6 using the formula 4*(4-1)/2, the paper is referring to the number of pairs of relation types that need to be checked for logical consistency.\n\nAnswer: 6", "citations": [], "input_tokens": 34235, "output_tokens": 428, "cost_micros": 109125, "latency_ms": 11375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper.\n\nFrom Section 2.1 (Event Relation Extraction), the paper identifies four types of relations between two events:\n\n1. **Coreference relations**: 2 options\n - NO_COREFERENCE\n - COREFERENCE\n\n2. **Temporal relations**: 7 options\n - NO_TEMPORAL\n - BEFORE\n - OVERLAP\n - CONTAINS\n - SIMULTANEOUS\n - ENDS-ON\n - BEGINS-ON\n\n3. **Causal relations**: 3 options\n - NO_CAUSAL\n - PRECONDITION\n - CAUSE\n\n4. **Subevent relations**: 2 options\n - NO_SUBEVENT\n - SUBEVENT\n\nThe paper states in Section 2.1: \"We formulate ERE as a multi-classification problem, determining one label (relation) for each of these four relation types.\"\n\nSince we need to determine one label for each of the four relation types, and these are independent choices, the total number of possible combinations is:\n\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nThis is also confirmed in Section 2.1 where it mentions calculating logical inconsistency as \"the ratio of the number of conflicts to the total number of combinations (i.e., all combinations of each two relations).\"\n\nAnswer: 84", "citations": [], "input_tokens": 26308, "output_tokens": 335, "cost_micros": 83949, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper discusses the Logical Inconsistency (LI) metric. In the example shown in Figure 1, there's a specific calculation:\n\n\"Among these, \"SIMULTANEOUS\" and \"CAUSE\" are identified as conflicting with each other based on the logical constraints we have defined, resulting in a single conflict. Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: $4 * (4 - 1) / 2 = 6$.\"\n\nThis calculation shows that with 4 types of relations (Coreference, Temporal, Causal, and Subevent), the number of combinations between pairs of these relations is 6.\n\nThis is confirmed in the definition of Logical Inconsistency in Section 2.1:\n\"logical inconsistency is calculated as the ratio of the number of conflicts (i.e., the answers that conflict with the given logical constraints) to the total number of combinations (i.e., all combinations of each two relations).\"\n\nThe example explicitly states: \"Hence, the logical inconsistency in this example is computed as LI = 1/6 (or approximately 16.7%).\"\n\nThe denominator 6 represents the total number of possible combinations of relationships between two events.\n\nAnswer: 6", "citations": [], "input_tokens": 34534, "output_tokens": 346, "cost_micros": 108792, "latency_ms": 10032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the definition of relationships between two events.Let me search for more specific terms related to event relationships:Perfect! I found the answer. Let me read that specific chunk to get the complete information:Based on the document, I can see that for each pair of events, there are 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36141, "error": null, "extra": {"thread_id": 2018, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_45c25fd9bb164100939e531a52487f98", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 140, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows \"Vicuna-13B and Llama2-13B's performance on MAVEN-ERE and Causal-TimeBank after pre-training on LLM-LR\":\n\nFor Vicuna-13B-PT on MAVEN-ERE, the results are:\n- vanilla ICL: 15.3% Micro-F1\n- vanilla ICL w. CoT: 15.8% Micro-F1\n- CoT w. logical constraints: 18.0% Micro-F1\n- w. all logical constraints: 16.3% Micro-F1\n- w. retrieved logical constraints: 16.1% Micro-F1\n- w. post-processing: 11.0% Micro-F1\n\nThe best performance is 18.0% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 65644, "output_tokens": 212, "cost_micros": 200112, "latency_ms": 35562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B after pre-training (Vicuna-13B-PT) on MAVEN-ERE:\n\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest Micro-F1 score is 18.0%.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 28202, "output_tokens": 149, "cost_micros": 86841, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, I need to find the best performance of Vicuna-13B-PT on MAVEN-ERE.\n\nThe table shows the following Micro-F1 scores for Vicuna-13B-PT on MAVEN-ERE:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34242, "output_tokens": 168, "cost_micros": 105246, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, which shows the performance of Vicuna-13B-PT (pre-trained) on MAVEN-ERE:\n\nThe results show:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest micro-F1 score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 26315, "output_tokens": 151, "cost_micros": 81210, "latency_ms": 4937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B and Llama2-13B after pre-training on LLM-LR:\n\nFor Vicuna-13B on MAVEN-ERE, the Micro-F1 scores are:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest performance is 18.0% with CoT w. logical constraints.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34541, "output_tokens": 175, "cost_micros": 106248, "latency_ms": 5140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about Vicuna-13B's performance on MAVEN-ERE.Let me search for information about MAVEN-ERE and Vicuna-13B performance:Let me read the table that shows the performance results:Perfect! I found the table showing Vicuna-13B's performance on MAVEN-ERE. Looking at the table, I can see three different approaches for Vicuna (Vicuna-13B):\n\n1. vanilla ICL: 13.8% Micro-F1\n2. vanilla ICL w. CoT: 11.6% Micro-F1\n3. CoT w. logical constraints: 14.9% Micro-F1\n\nThe best performance was with \"CoT w. logical constraints\" at 14.9% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38983, "error": null, "extra": {"thread_id": 2019, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b286a9a39b954fac8755dc6ce4c285c7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 166, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for information about the Sociology subject...\n\nFrom Figure 3 on page 3, I can see that Sociology is listed under \"Humanities & Social Sci. (9%)\" with \"Sociology (287, 2.48%)\".\n\nFrom Table 8 on page 18 (Humanities & Social Science results), I can see that Sociology has 252 samples in the test set.\n\nAdding the validation and test samples:\n- Validation set appears to have a portion of the 947 total H&SS samples\n- Test set for Sociology: 252 samples\n\nLooking more carefully at the statistics in Table 1 on page 4, the total is 11,550 questions with a Dev:Validation:Test split of 150:900:10500.\n\nFrom the detailed breakdown in Figure 3, Sociology shows (287, 2.48%), which represents 287 total samples across all splits.\n\nAnswer: 287", "citations": [], "input_tokens": 259120, "output_tokens": 216, "cost_micros": 1559580, "latency_ms": 77812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in MMMU that belong to the sociology subject.\n\nLooking at the document, I can find this information in Figure 3, which shows the breakdown of questions by disciplines, subjects, and subfields.\n\nIn Figure 3, under \"Humanities & Social Sci. (9%)\", I can see:\n\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Sociology (287, 2.48%)\n- Psychology (340, 2.94%)\n\nThe number in parentheses after \"Sociology\" shows 287 questions, which represents 2.48% of the total.\n\nAnswer: 287", "citations": [], "input_tokens": 79424, "output_tokens": 155, "cost_micros": 240597, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in the MMMU dataset that belong to the sociology subject.\n\nLooking through the document, I can find information about the distribution of samples across subjects in Figure 3 and Table 10.\n\nFrom Figure 3, which shows \"MMMU contains 11.5K multimodal questions covering six broad disciplines, 30 subjects, and 183 subfields,\" I can see the breakdown of subjects with their sample counts.\n\nUnder \"Humanities & Social Sci. (9%)\", I can see:\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- Sociology (287, 2.48%)\n\nThe number shown for Sociology is 287 samples.\n\nAnswer: 287", "citations": [], "input_tokens": 109175, "output_tokens": 183, "cost_micros": 330270, "latency_ms": 8484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the number of samples in the Sociology subject.\n\nIn Figure 3 on page 3, there is a breakdown table showing the distribution of samples across different subjects. Under \"Humanities & Social Sci. (9%)\", I can see:\n\nv Sociology (287, 2.48%)\n\nThe number 287 represents the number of samples in the Sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 83138, "output_tokens": 100, "cost_micros": 250914, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about MMMU and sociology samples in the provided documents.Let me search for information about sociology and sample counts in the document:I found the information! Let me read the specific section with the table that shows the sample counts:Perfect! I found the answer. According to line 1572, Sociology has 287 samples in MMMU, which represents 2.48% of the dataset.\n\nAnswer: 287", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 63671, "error": null, "extra": {"thread_id": 2020, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6e9ac9023c84b0284ac8c1e7870f526", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 141, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and compare it to the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: According to Figure 3 on page 3, Public Health has **544 samples** (shown as \"Public Health (544, 4.7%)\").\n\n2. **Public Health samples showcased**: I need to count the case study figures related to Public Health from Table 10 on page 21. According to this table, Public Health has the following case study figures:\n - Correct Case: 63\n - Lack of Knowledge: 65\n - Textual Understanding Error: 64\n\n This gives us **3 showcased samples** from Public Health.\n\nThe ratio would be: 3/544 = 0.0055147...\n\nRounding to a reasonable number of decimal places: 0.0055\n\nAnswer: 0.0055", "citations": [], "input_tokens": 259140, "output_tokens": 214, "cost_micros": 1559655, "latency_ms": 83546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the ratio to the total Public Health samples.\n\nFrom Table 7 (Health & Medicine results), I can see that Public Health has 509 samples in the test set.\n\nNow I need to count how many Public Health samples are showcased in the paper (Appendix B - Case Study).\n\nLooking through the Case Study figures list and the actual figures:\n\nPublic Health samples shown:\n- Figure 63: Public Health 1 (Epidemiology) - Correct Case\n- Figure 64: Public Health 2 (Epidemiology) - Textual Understanding Error\n- Figure 65: Public Health 3 (Epidemiology) - Lack of Knowledge\n\nSo there are 3 Public Health samples showcased.\n\nThe ratio is: 3 / 509 = 0.005892...\n\nRounding to a reasonable number of decimal places:\n\nAnswer: 0.0059", "citations": [], "input_tokens": 79444, "output_tokens": 220, "cost_micros": 241632, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the percentage of Public Health samples that are showcased in the paper.\n\nFrom the document, I can find:\n\n1. Total number of Public Health samples in the test set:\n - From Table 7, Public Health has 509 samples in the test set\n\n2. Number of Public Health samples showcased in the paper:\n - I need to count the figures in Appendix B that are from Public Health\n - From the List of Case Study Figures (pages 20-21):\n - Figure 63: Public Health 1: Correct Case\n - Figure 64: Public Health 2: Textual Understanding Error\n - Figure 65: Public Health 3: Lack of Knowledge\n \n So there are 3 Public Health samples showcased.\n\n3. Calculate the percentage:\n - Percentage = (3 / 509) \u00d7 100 = 0.589...\n\nWait, let me verify the total number. Looking at Table 1 (page 4), Public Health has 544 samples total. But the question asks about samples \"in this paper\", which could mean the test set.\n\nFrom Table 7 on page 17, Public Health (test set) = 509 samples.\n\nThe ratio would be: 3/509 = 0.00589...\n\nIf we want this as a percentage: 0.589%\n\nBut the question asks for \"a ratio\" as a decimal number, so:\n\n3/509 = 0.005893\n\nAnswer: 0.0059", "citations": [], "input_tokens": 109195, "output_tokens": 347, "cost_micros": 332790, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the percentage.\n\nFrom the document, I can find information about Public Health samples:\n\n1. First, let me find the total number of Public Health samples in the dataset.\nFrom Table 1 (Key statistics), I can see the total questions is 11,550.\n\nLooking at Figure 3 or the breakdown tables, I need to find Public Health information:\n- From Table 7 (Health & Medicine results), I can see Public Health is listed with 509 samples in the test set.\n- The validation set has 150 samples total for Health & Medicine.\n\nActually, looking more carefully at the tables:\n- Table 7 shows Health & Medicine breakdown with Public Health having 509 samples in the test set\n- There's also validation data\n\nLet me look at the case study section (Appendix B):\nFrom Table 10 (Table index of case study figures by subjects), I can count the Public Health cases:\n- Public Health 1: Correct Case (Figure 63)\n- Public Health 2: Textual Understanding Error (Figure 64)\n- Public Health 3: Lack of Knowledge (Figure 65)\n\nSo there are 3 Public Health cases showcased in the paper.\n\nNow I need to find the total number of Public Health samples:\nFrom Table 7, the Public Health row shows 509 samples in the test set.\n\nThe ratio would be: 3/509 = 0.00589...\n\nWait, let me verify this is asking for the percentage of showcased samples. \n\n3/509 \u00d7 100 = 0.589%\n\nAs a ratio (not percentage): 3/509 = 0.00589\n\nAnswer: 0.00589", "citations": [], "input_tokens": 83158, "output_tokens": 399, "cost_micros": 255459, "latency_ms": 11937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.00589", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and divide by the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: Looking at Figure 3, Public Health has 544 samples (4.7% of total).\n\n2. **Public Health samples showcased in the paper**: Looking at Table 10 (Case Study Figures by subjects), I can find the Public Health figures:\n - Figure 63: Public Health 1: Correct Case\n - Figure 64: Public Health 2: Textual Understanding Error\n - Figure 65: Public Health 3: Lack of Knowledge\n\nSo there are 3 Public Health case study figures shown in the paper.\n\nThe ratio is: 3 / 544 = 0.00551...\n\nAnswer: 0.0055", "citations": [], "input_tokens": 103252, "output_tokens": 191, "cost_micros": 312621, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about 'Public Health' samples and calculate the percentage.Let me search for tables and figures that might contain information about Public Health samples:Now let me look at Figure 3 and tables that show the breakdown of subjects:Perfect! I found the information. Let me read more to make sure I have all the details:Perfect! I found the information in the table. From line 1505, I can see:\n\n\"Public Health (544, 4.7%)\"\n\nThis indicates that:\n- Public Health has 544 samples\n- This represents 4.7% of all samples in the dataset\n\nThe answer to the question \"Among all samples belonging to 'Public Health' subject, how many percentage samples showcased in this paper?\" is 4.7%.\n\nSince the question asks for a ratio (percentage) and wants a single decimal number, the answer is:\n\nAnswer: 4.7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53796, "error": null, "extra": {"thread_id": 2021, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8dc9bfdb2b5b47d48d7a6fa4dc0574a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "4.7", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics-related figures with error categories.\n\nLooking through the case study figures in Appendix B:\n\n1. Figure 22 - Economics: Econometrics - This is a CORRECT case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n This case shows GPT-4V making an error in correctly identifying numbers in the table.\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n This case shows GPT-4V failing to correctly interpret the intersection point in the graph.\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n\nFrom Table 10, I can see:\n- Economics has entries for figures 22, 23, 24\n- Figure 22 is marked as \"Correct Case\"\n- Figure 23 is marked as \"Perceptual Error\"\n- Figure 24 is marked as \"Perceptual Error\"\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 79431, "output_tokens": 298, "cost_micros": 242763, "latency_ms": 8734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\n1. **Figure 23** - Economics: Macroeconomics - Error Category: Perceptual Error\n - GPT-4V incorrectly attributes numbers to the correct objects for production\n\n2. **Figure 24** - Economics: Macroeconomics - Error Category: Perceptual Error\n - GPT-4V fails to interpret the image correctly regarding the equilibrium interest rate\n\nLooking through the List of Case Study Figures table (Table 10), I can see:\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nSo there are **2** Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 109182, "output_tokens": 227, "cost_micros": 330951, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the case study section and count how many fall into the \"Perceptual Error\" category.\n\nLooking through the case study figures, I'll identify Economics-related cases:\n\n1. Figure 22 - Economics: Econometrics - This is a correct case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n\nNow let me verify these are all the Economics cases by checking the Table of Contents in the case study section (Table 10):\n\nFrom Table 10:\n- Economics 1: Correct Case (Figure 22/36)\n- Economics 2: Perceptual Error (Figure 23/37)\n- Economics 3: Perceptual Error (Figure 24/38)\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 83145, "output_tokens": 237, "cost_micros": 252990, "latency_ms": 7797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in Appendix B (Case Study) that are categorized as \"Perceptual Error\".\n\nLet me search through the Case Study section for Economics cases:\n\n1. **Figure 22** - Economics: Econometrics - This is a **correct case**, not an error case.\n\n2. **Figure 23** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\n3. **Figure 24** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nThat's all the Economics cases listed.\n\nTherefore, there are 2 Economics-related error cases that fall into the Error Category of Perceptual Error.\n\nAnswer: 2", "citations": [], "input_tokens": 103239, "output_tokens": 242, "cost_micros": 313347, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about Economics-related error cases and Perceptual Error categories in the provided documents.Let me search for content related to \"Economics\" and \"Perceptual Error\" in this document.Let me search for tables or error categories in the document:Now let me look for tables with error categories and subject areas. Let me search more broadly:Let me look at the specific area around line 7247 where there's a question about stocks (Economics-related):Let me search for a table that shows the distribution of error categories by subject area:Let me search for tables that might show error distribution:Let me look for error analysis tables. Let me search around the beginning of the document for methodology or error analysis sections:Let me search for \"Figure\" to find error analysis figures/tables:Great! Let me read the section with Figure 6 which shows error distribution:Good! I can see Figure 6 shows the overall error distribution, but I need to find a breakdown by subject area. Let me look for tables that show error categories by discipline:I can see there's a table showing different subjects and error cases. Let me read around line 5808 where there's an Accounting entry:Perfect! I can see the list of case studies. I can see:\n- Economics 1: Correct Case\n- Economics 2: Perceptual Error\n- Economics 3: Perceptual Error\n\nSo there are 2 Economics cases with Perceptual Error. Let me verify this is the complete list by checking if there are more Economics cases:Perfect! I found Table 10 which shows the case study figures by subjects with associated error categories. Looking at the Economics row:\n\n- Subject:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94360, "error": null, "extra": {"thread_id": 2022, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ce7c6d6aee6641609ca2f09db6ff3175", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 531, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the subfields for the Energy & Power subject in the MMMU dataset.\n\nLooking at the document, I can find this information in Table 11 on page 111, which lists \"Subfields of each subject.\"\n\nUnder the \"Tech & Engineering\" discipline, I can see the \"Energy & Power\" subject with its subfields listed.\n\nFrom Table 11:\nEnergy & Power: Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 259135, "output_tokens": 118, "cost_micros": 1557465, "latency_ms": 60233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists subfields of each subject:\n\nFor Energy & Power under the Tech & Engineering discipline, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 79439, "output_tokens": 52, "cost_micros": 239097, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists all subfields by subject:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 109190, "output_tokens": 35, "cost_micros": 328095, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document, which lists \"Subfields of each subject,\" I can find the subfields for Energy & Power.\n\nAccording to the table:\n\n**Energy & Power:** Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 103247, "output_tokens": 73, "cost_micros": 310836, "latency_ms": 4766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the subfields in the Energy & Power subject.I found two subfields mentioned for Energy and Power. Let me search for more information to ensure I have a complete list.Let me search more specifically for all Energy and Power subfield mentions:Based on the document, I found two subfields explicitly mentioned for Energy and Power:\n1. Fluid Mechanics (Figure 89)\n2. Thermodynamics (Figure 90)\n\nLet me verify if there are any other subfields by looking at tables or lists that might contain more comprehensive information:Perfect! I found the information. In line 1545, it shows the subfields for Energy Power:\n\n\"Fluid Mechanics, Heat Transfer ...\"\n\nLet me search for more complete information about all the subfields:Excellent! I found the complete list. Let me verify this is in a table showing all subfields:Perfect! I found the complete list of subfields for Energy & Power in the table. The subfields are:\n- Thermodynamics\n- Heat Transfer\n- Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 66234, "error": null, "extra": {"thread_id": 2023, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db9765522b274b60ae516c6ef2df0d73", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 314, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU benchmark.\n\nLooking at the detailed subject breakdown tables in the appendix:\n\nFrom Table 9 (Tech & Engineering results on page 19):\n- Computer Science (Comp. Sci.) column shows GPT-4V(ision) (Playground) achieved **57.1%**\n\nFrom Table 6 (Science results on page 16):\n- Math column shows GPT-4V(ision) (Playground) achieved **45.0%**\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 259141, "output_tokens": 158, "cost_micros": 1558401, "latency_ms": 61328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can find Computer Science:\n- Computer Science (371): 57.1%\n\nLooking at Table 6 (Science results), I can find Math:\n- Math (505): 45.0%\n\nThe difference is: 57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 79445, "output_tokens": 114, "cost_micros": 240045, "latency_ms": 5546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU dataset.\n\nLooking through the tables in the document, I need to find the breakdown by subject for GPT-4V.\n\nFrom Table 6 (Science results), I can see:\n- Math: GPT-4V(ision) (Playground) achieves 45.0%\n\nFrom Table 9 (Tech & Engineering results), I can see:\n- Computer Science (Comp. Sci.): GPT-4V(ision) (Playground) achieves 57.1%\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 109196, "output_tokens": 167, "cost_micros": 330093, "latency_ms": 7889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science (CS) and Math subjects in the MMMU dataset.\n\nLooking through the document for GPT-4V results by subject:\n\nIn Table 2 (page 6), I can see overall results, but I need to find the breakdown by specific subjects.\n\nLooking at the appendix sections for detailed subject breakdowns:\n\nFrom Table 9 (page 19) - Tech & Engineering results:\n- Computer Science is listed under Tech & Engineering\n- GPT-4V(ision) (Playground) shows: Comp. Sci. = 57.1%\n\nFrom Table 6 (page 16) - Science results:\n- Math is listed under Science\n- GPT-4V(ision) (Playground) shows: Math = 45.0%\n\nLet me verify these numbers are correct by checking the table again:\n\nTable 6 (Science results):\n- GPT-4V(ision) (Playground): Math column shows 45.0\n\nTable 9 (Tech & Engineering results):\n- GPT-4V(ision) (Playground): Comp. Sci. column shows 57.1\n\nThe performance difference is:\n57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 83159, "output_tokens": 300, "cost_micros": 253977, "latency_ms": 9016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can see Computer Science is listed with GPT-4V achieving **57.1%**.\n\nLooking at Table 6 (Science results), I can see Math is listed with GPT-4V achieving **45.0%**.\n\nThe performance difference is: 57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 103253, "output_tokens": 119, "cost_micros": 311544, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find GPT-4V's performance on CS and Math subjects in the MMMU dataset.Let me search for performance tables or results sections that would contain GPT-4V's performance by subject.Let me search for tables with performance metrics:Let me look for a results table that shows performance by subject. Let me read around line 5873 and 5978 where I saw Computer Science and Math mentioned:This appears to be a table of figure references. Let me search for actual performance data tables. Let me look for \"accuracy\" or percentage data:Let me read the beginning of the paper to find the main results table:Let me search for a table with actual performance numbers. Let me look for \"GPT-4V\" with numbers:Let me look for Table 2 which should contain the main results broken down by subject:Good! I found Table 2. Now I need to find the breakdown by individual subjects. The table shows broad disciplines but I need specific subjects like Computer Science and Math. Let me search for another table with subject-level breakdown:Table 9 should have Tech & Engineering which includes Computer Science. Let me read that table:Perfect! I found GPT-4V's Computer Science performance at 57.1% (line 5254). Now I need to find Math performance. Let me check Table 6 for Science subjects:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 132796, "error": null, "extra": {"thread_id": 2024, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b9c01f114dd641199d8ac115ae26b4c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 506, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-4.0", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays various categories:\n\n1. **Comprehensive Disciplines** - showing a pie chart with percentages for different disciplines (Engineering 26%, Art & Design 11%, Business 14%, Medicine 17%, Science 23%, Humanities & Social Sci. 9%)\n\n2. **Heterogeneous Image Types** - listing various types: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\n\n3. **Interleaved Text and Images** - showing an example with 3 medical images\n\n4. **Expert-level Skills Test** - showing three categories:\n - Perception (with an icon)\n - Knowledge (with an icon)\n - Reasoning (with an icon)\n\nThe question asks about \"different icons shown as different image types\" in Figure 1. Looking at the heterogeneous image types section, I can count the distinct image type categories mentioned (not just listed as text, but representing different types):\n\nFrom the visible list and description: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics (and the ellipsis suggests more).\n\nThat's 12 explicitly mentioned types, but the question asks about icons shown. The figure shows examples and visual representations rather than just listing them.\n\nLooking more carefully at Figure 1, I need to count the actual different image type examples/icons displayed, not just mentioned in text.\n\nAnswer: 12", "citations": [], "input_tokens": 79427, "output_tokens": 403, "cost_micros": 244326, "latency_ms": 11984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which is titled \"Overview of the MMMU dataset,\" I can see it displays four main sections:\n\n1. **Comprehensive Disciplines** - showing a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - showing various image type icons\n3. **Interleaved Text and Images** - showing a sample question with images\n4. **Expert-level Skills Test** - showing three overlapping circles for Perception, Knowledge, and Reasoning\n\nIn the \"Heterogeneous Image Types\" section, I can count the following distinct icons representing different image types:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Chemical (structures)\n5. Photos/Photographs\n6. Paintings\n7. Geometric (shapes)\n8. Music (sheet music)\n9. Medical (images)\n\nLet me recount more carefully by examining the visual elements shown in that section. The figure shows various small icons/symbols representing different image types in a grid-like arrangement.\n\nCounting the distinct image type icons shown: 9 different icons are displayed representing different image types (Diagrams, Tables, Plots and Charts, Chemical Structures, Photographs, Paintings, Geometric shapes, Music/Sheet Music, and Medical Images).\n\nAnswer: 9", "citations": [], "input_tokens": 109178, "output_tokens": 314, "cost_micros": 332244, "latency_ms": 10828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to look at Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays four main characteristics:\n\n1. **Comprehensive Disciplines** - showing 6 disciplines with percentages:\n - Engineering (26%)\n - Art & Design (11%)\n - Business (14%)\n - Science (23%)\n - Humanities & Social Sci. (9%)\n - Medicine (17%)\n\n2. **Heterogeneous Image Types** - listing various types like: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Music, Geometric, Pathology images, Microscopic Images, Comics, \u2026\n\n3. **Interleaved Text and Images** - showing an example question with images\n\n4. **Expert-level Skills Test** - listing various types of reasoning and knowledge\n\nThe figure shows icons/visual representations for these categories, but the question asks specifically about \"different icon shown as different image types.\"\n\nLooking at the \"Heterogeneous Image Types\" section in Figure 1, I can count the distinct image type categories mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nAnd there's an ellipsis (\u2026) indicating more types.\n\nHowever, the question asks about icons shown in Figure 1. Looking at the visual representation, the heterogeneous image types section appears to show example icons or representations of these different types.\n\nBased on the visible list in the \"Heterogeneous Image Types\" section of Figure 1, I can count 12 distinct image type categories explicitly listed before the ellipsis.\n\nAnswer: 12", "citations": [], "input_tokens": 83141, "output_tokens": 438, "cost_micros": 255993, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine Figure 1 to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see four main sections:\n\n1. **Comprehensive Disciplines** - Shows a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - This section lists various image types\n3. **Interleaved Text and Images** - Shows an example question with images\n4. **Expert-level Skills Test** - Shows a diagram with arrows\n\nIn the \"Heterogeneous Image Types\" section, I can see a list of different image types mentioned:\n- Diagrams\n- Tables\n- Plots and Charts\n- Photographs\n- Chemical Structures\n- Paintings\n- Medical Images\n- Sheet Music\n- Geometric\n- Pathology images\n- Microscopic Images\n- Comics\n- (and the list continues with \"...\")\n\nHowever, the question asks about \"icons shown as different image types.\" Looking at the figure more carefully, I don't see actual icons representing each image type - rather, I see text labels listing the image types.\n\nLet me reconsider - perhaps the question is asking about visual representations or examples shown. In Figure 1, I can see:\n- A pie chart (1 type of visualization)\n- Example images in the \"Interleaved Text and Images\" section (3 medical scan images)\n- A flow diagram in the \"Expert-level Skills Test\" section\n\nBut this doesn't seem to match what the question is asking either.\n\nRe-reading the question: \"How many different icon are shown as different image types in Figure 1?\"\n\nLooking at the figure description more carefully, in the \"Heterogeneous Image Types\" section, there appears to be a list of image types. The text mentions: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nCounting the explicitly listed items before the \"...\": 12 different image types are mentioned.\n\nAnswer: 12", "citations": [], "input_tokens": 103235, "output_tokens": 475, "cost_micros": 316830, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and count the different icons shown as different image types.Let me search for \"Figure 1\" in the document:Now let me read the beginning of the document content to find Figure 1:Good, I can see Figure 1 information. Let me continue reading to see the complete description of the heterogeneous image types shown in Figure 1:Perfect! I can see the \"Heterogeneous Image Types\" section in Figure 1. The document lists the following image types:\n\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThe text shows \"...\" after Comics, but I need to verify if there are icons shown for each type. Let me search for more information about Figure 1 to see if there's a visual description that tells us how many icons are actually displayed:Looking back at the text from Figure 1, I can see it lists these image types under \"Heterogeneous Image Types\":\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nLet me count the distinct types mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts (this appears to be one category)\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nHowever, I need to verify if the figure actually shows icons for each. Let me look for embedded images or visual descriptions around Figure 1 to see if there are actual icon images:I see that Im1.png is an embedded image showing a hierarchical flowchart. That's not the heterogeneous image types. Let me search more carefully through the document. Let me look for more embedded images around Figure 1:Let me look more carefully at the beginning of the document. Im1.png appeared right after the figure caption for Figure 1. Let me look at Figure 1 more systematically. Let me check if there are multiple images that are part of Figure 1:I see. The Figure 1 section ends at line 1160 with `
`, and then there are embedded images. The text shows , , within the figure. Let me search more systematically. The figure lists:\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nBut I need to see if there are icons displayed. Let me search for where the actual icons might be described. Let me look for \"icon\" in the document:Perfect! I found it. At line 1265, there's a reference to a camera icon. Let me read that section more carefully since it's right after Figure 1:Good! So now I can see there are embedded images that are icons representing different image types. Let me systematically go through and look for all the icons that are part of Figure 1. The document structure shows Im11.png is a camera icon and Im12.png is a geometric icon. Let me look back at the text of Figure 1 to understand the layout better. Looking at the document, it seems the figure starts at line 1097 and the text lists image types. Let me search for more icon descriptions nearby:I see that after the icon descriptions (Im11.png - camera, Im12.png - geometric), the document moves to Figure 3 (a table). So the icons are between Figure 1 and Figure 3. Let me go back and look more carefully at the images embedded right after Figure 1 ends. Let me look for all embedded images between line 1160 (where Figure 1 ends) and line 1455 (where Figure 3 starts):I see Im13.png is a germ/pathogen icon. That's another icon in the series. Let me systematically look at all embedded images between Figure 1 ending and where we transition to other figures. Let me search systematically from line 1160 to 1860:\n\nBased on what I've seen:\n- Im1.png (line 1164): Flowchart diagram\n- Im11.png (line 1256): Camera icon (Photographs)\n- Im12.png (line 1428): Geometric icon\n- Im13.png (line 1863): Germ/pathogen icon\n\nLet me continue looking for more icons. Let me search for the next embedded images:Good! Im14.png is a microscope icon. Let me continue to find more icons. Let me look at the next embedded images. Let me search more systematically. Based on what the abstract says: \"comprising 30 highly heterogeneous image types\" - but Figure 1 specifically lists these types. Let me look at the section with the image types more carefully. I need to look back at line 2501 which says \"Music\" and \"Medical\" - these might be labels for icons:Ah! Figure 5 shows different types of images: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical. That's 9 types. But this is Figure 5, not Figure 1. Let me go back and look very carefully at what exactly is in Figure 1. Let me re-read the text in Figure 1:\n\nFrom lines 1134-1138, the text explicitly states under \"Heterogeneous Image Types\":\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nSo let me count these carefully:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nBut the question asks about \"different icons shown as different image types in Figure 1\". I need to look back at Figure 1 itself more carefully. Let me check if there are actually icons displayed IN Figure 1, not just listed. Let me re-examine the structure. The figure tag starts at 1097 and ends at 1160. Let me look at what images are referenced within that figure:I see! Within the figure (lines 1097-1160), there are references to ``, ``, and `` (lines 1113-1115 as part of a question, and again at lines 1154-1158). But the key section is \"Heterogeneous Image Types\" which lists the types. However, the question asks about icons shown as different image types. \n\nThe text lists image types but doesn't specify how many icons are actually displayed visually. Based on the embedded images that come right after Figure 1:\n- Im1.png (flowchart - but this appears to be an example diagram, not an icon representing a type)\n- Im11.png (camera icon - represents Photographs)\n- Im12.png (geometric shapes icon - represents Geometric)\n- Im13.png (germ icon - could represent Medical/Pathology/Microscopic)\n- Im14.png (microscope icon - represents Microscopic/Medical)\n\nBut I need to find ALL the icons that are specifically showing different image types in Figure 1. Let me look more systematically at what comes after Figure 1 but before Figure 2 (which starts at line 1289). All embedded images between line 1160 and line 1289 might be icons for Figure 1.\n\nFrom my reading:\n- Line 1164: Im1.png (flowchart diagram - this might be part of the figure illustration)\n- Line 1256: Im11.png (camera icon)\n- Line 1428: Im12.png (geometric shapes)\n\nLet me check if there are other embedded images I missed before Figure 2:\n\nLooking at the embedded image list I got earlier, between lines 1160 and 1289 I have:\n- Im1.png at line 1164\n- Im11.png at line 1256\n- Im12.png at line 1428 (but line 1289 is Figure 2, so this is after Figure 2 starts)\n\nWait, let me recheck. Figure 2 caption is at line 1289. So Im12.png at line 1428 is after Figure 2. Let me look more carefully:So between Figure 1 (ending at line 1160) and Figure 2 (starting at line 1288), there are only 2 embedded images:\n- Im1.png at line 1164\n- Im11.png at line 1256\n\nBut actually, looking at the visual descriptions, Im1.png is described as a \"Hierarchical flowchart/organizational diagram\" which seems to be an example diagram that's PART of Figure 1, not an icon representing an image type.\n\nIm11.png is clearly described as a \"Stylized camera icon\" which represents the Photographs image type.\n\nLet me think about this differently. Maybe the icons are all embedded within the actual Figure 1 structure, but the XML parsing has extracted them as separate images. Looking at the text in Figure 1, it lists types under \"Heterogeneous Image Types\". Perhaps in the original PDF, there are visual icons shown alongside or above these text labels, and those icons have been extracted as separate embedded images.\n\nGiven the timeframe and what I can see, let me search for a clearer pattern. Looking at Figure 5 (line 2475) which shows \"Performance of models on different types of images\" and lists: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical - that's 9 types.\n\nBut I need to answer specifically about Figure 1. Let me think about the structure: Figure 1 has a section called \"Heterogeneous Image Types\" that lists multiple types. The question asks how many different icons are shown. \n\nLooking at the abstract again (line 1212): \"comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures.\"\n\nBut in Figure 1's \"Heterogeneous Image Types\" section, it explicitly lists: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nIf I count unique types (treating \"Plots and Charts\" as one type):\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThat's 12 distinct types listed (plus \"...\" suggesting more).\n\nHowever, Figure 5 later in the paper shows icons for 9 types. Perhaps Figure 1 shows icons for these 9 main types as well. Let me verify Figure 5 shows exactly 9 by re-reading:\n\nFrom line 2487-2503 in Figure 5:\n1. Diagrams\n2. Tables\n3. Charts\n4. Chemical\n5. Photos\n6. Paintings\n7. Geometric\n8. Music\n9. Medical\n\nThat's exactly 9 types in Figure 5.\n\nGiven the typical structure of such figures and the pattern I see, my best answer is that Figure 1 shows icons for **9** different image types.\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 192280, "error": null, "extra": {"thread_id": 2025, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f360703be6ba4537aef7760373d79d2f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1273, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts included in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Contains text and references but no charts visible\n\n**Page 6:**\n- Table 2: \"Overall results of different models on the MMMU validation and test set\" - This is a table, not a chart\n\n**Page 7:**\n- Figure 5: \"Performance of models on different types of images\" - This is a bar chart\n- Table 3: \"Result decomposition across question difficulty levels\" - This is a table, not a chart\n\n**Page 8:**\n- Figure 6: \"Error distribution over 150 annotated GPT-4V errors\" - This is a pie chart\n- Figure 7: Shows an example case with images and text - Not a chart\n\n**Page 9:**\n- Contains text discussion, no charts\n\n**Page 10:**\n- Contains text and references, no charts\n\nCounting the charts:\n- Page 7: Figure 5 (bar chart) = 1 chart\n- Page 8: Figure 6 (pie chart) = 1 chart\n\nTotal charts in pages 5-10: 2\n\nAnswer: 2", "citations": [], "input_tokens": 79424, "output_tokens": 276, "cost_micros": 242412, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll carefully count all charts in Pages 5-10.\n\n**Page 5:**\n- Figure 2: This shows sample MMMU examples in a table format, not a chart\n- No charts on this page\n\n**Page 6:**\n- Table 2: This is a table showing model results, not a chart\n- No charts on this page\n\n**Page 7:**\n- Figure 5: This is a bar chart showing \"Performance of models on different types of images\"\n- Table 3: This is a table, not a chart\n- Count: 1 chart\n\n**Page 8:**\n- Figure 6: This is a pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\n- Figure 7: This shows an example with illustrations but is not a chart\n- Count: 1 chart\n\n**Page 9:**\n- No figures or charts on this page (only references text)\n\n**Page 10:**\n- No figures or charts on this page (only references text)\n\nTotal charts in Pages 5-10: 1 (Figure 5) + 1 (Figure 6) = 2\n\nAnswer: 2", "citations": [], "input_tokens": 109175, "output_tokens": 264, "cost_micros": 331485, "latency_ms": 9110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts (plots, graphs, tables, diagrams) that appear in pages 5-10 of the document.\n\nLet me go through pages 5-10:\n\n**Page 5:**\n- Table 2: Overall results of different models (this is a large table)\n- Count: 1\n\n**Page 6:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a bar chart)\n- Count: 2\n\n**Page 7:**\n- Figure 6: Error analysis pie chart showing distribution of error types\n- Figure 7: Example error case with text and an image (not a chart)\n- Count: 1\n\n**Page 8:**\n- Continuation of references, no charts\n\n**Page 9:**\n- Continuation of references, no charts\n\n**Page 10:**\n- References continue, no charts\n\nTotal charts in pages 5-10:\n- Page 5: 1 (Table 2)\n- Page 6: 2 (Table 3, Figure 5)\n- Page 7: 1 (Figure 6)\n- Pages 8-10: 0\n\nTotal: 1 + 2 + 1 + 0 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 300, "cost_micros": 253914, "latency_ms": 8781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count all charts appearing in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Table 1: Key statistics of the MMMU benchmark\n\n**Page 6:**\n- Table 2: Overall results of different models on the MMMU validation and test set\n\n**Page 7:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a table/chart)\n\n**Page 8:**\n- Figure 6: Error distribution over 150 annotated GPT-4V errors (this is a table/chart)\n- Figure 7: A sample error case (this is an example, not a chart)\n\n**Page 9:**\n- No charts (this page discusses conclusion and references begin)\n\n**Page 10:**\n- No charts (continues references section)\n\nLet me recount more carefully by looking at what constitutes a \"chart\":\n\nPages 5-10:\n- Page 5: Table 1 (1 chart)\n- Page 6: Table 2 (1 chart)\n- Page 7: Table 3 (1 chart), Figure 5 which shows a table of performance data (1 chart)\n- Page 8: Figure 6 showing error distribution as a table (1 chart), Figure 7 is an example case study, not a chart\n- Pages 9-10: References, no charts\n\nTotal charts: 5\n\nAnswer: 5", "citations": [], "input_tokens": 103232, "output_tokens": 346, "cost_micros": 314886, "latency_ms": 10516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll read the document to count the charts in pages 5-10.Let me search for page markers to identify pages 5-10:Let me read through the document to find page markers:I can see page markers. Let me continue searching for pages 5-10:Perfect! Now I know the line numbers for pages 5-10. Let me read those pages to count the charts:Now let me search for charts/figures systematically in pages 5-10. Let me look for figure markers:Now let me carefully count the charts in pages 5-10. Let me search for \"Figure\" markers in this range:\n\nBased on my reading of pages 5-10 (lines 2071-3184), I can identify the following charts:\n\n**Page 5** (starts at line 2071):\n- No charts (only text and Table 2)\n\n**Page 6** (starts at line 2462):\n- **Figure 5**: \"Performance of models on different types of images\" - this is a bar chart showing performance metrics\n\n**Page 7** (starts at line 2687):\n- **Figure 6**: \"Error distribution over 150 annotated GPT-4V errors\" - this is a pie chart showing error percentages\n- **Figure 7**: Shows a perceptual error example (not a chart, but a diagram/illustration)\n\n**Page 8** (starts at line 2912):\n- No charts (only text starting with Conclusion section)\n\nLet me continue reading pages 9 and 10:**Page 9** (starts at line 3045): References section - no charts\n\n**Page 10** (starts at line 3184): References section - no charts\n\nNow let me verify my count by carefully reviewing what I found:\n\n**Summary of charts in Pages 5-10:**\n\n- **Page 5**: 0 charts (contains Table 2, which is a table, not a chart)\n- **Page 6**: 1 chart (Figure 5 - bar chart)\n- **Page 7**: 1 chart (Figure 6 - pie chart) + Figure 7 (which is an illustration/example, not a chart)\n- **Page 8**: 0 charts (text only)\n- **Page 9**: 0 charts (references)\n- **Page 10**: 0 charts (references)\n\nTotal charts in pages 5-10: **2 charts**\n\n(Figure 5 is a bar chart and Figure 6 is a pie chart. Figure 7 is not a chart but an illustrative example showing a perceptual error case.)\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79750, "error": null, "extra": {"thread_id": 2026, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8497190a9f7e4c40b3088d1ce4cbc759", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 453, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across two dimensions - Breadth (Knowledge) and Depth (Reasoning).\n\nThe figure shows various datasets plotted on these two axes:\n- VQA, GQA, VisWiz (basic breadth and depth)\n- TextVQA, MMBench, SEED (moderate breadth)\n- MM-Vet, ScienceQA (moderate breadth)\n- OKVQA (moderate breadth)\n- MMMU (positioned highest on both breadth and depth)\n\nAccording to the figure and the accompanying table, MMMU covers:\n- 11.5K questions\n- 30 Types of images\n- 6 disciplines, 30 subjects, and 183 subfields\n\nThis is explicitly described in the caption as excelling \"in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nAnswer: MMMU", "citations": [], "input_tokens": 259139, "output_tokens": 228, "cost_micros": 1559964, "latency_ms": 60530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the paper, I need to examine which dataset covers the broadest range of disciplines and knowledge areas.\n\nFrom the figure, the datasets compared are:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nThe figure shows that MMMU covers:\n- 6 disciplines\n- 30 subjects\n- 183 subfields\n- 11.5K questions\n\nIn comparison, other datasets are more limited in scope. For example, ScienceQA covers specific subjects but is noted as being at elementary to middle school level. The other datasets (VQA, GQA, etc.) focus on specific task types rather than broad disciplinary coverage.\n\nThe paper explicitly states that MMMU \"covers 30 subjects across 6 disciplines\" and emphasizes its \"breadth\" across multiple disciplines including Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.\n\nAnswer: MMMU", "citations": [], "input_tokens": 79443, "output_tokens": 256, "cost_micros": 242169, "latency_ms": 8655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across breadth (knowledge) and depth (reasoning) dimensions:\n\nThe figure shows various datasets plotted, with MMMU positioned to excel in both breadth and depth. Among the nine different datasets shown (VQA, GQA, VizWiz, TextVQA, OKVQA, SEED, MMBench, MM-Vet, ScienceQA, and MMMU), MMMU is positioned highest on the breadth axis.\n\nAnswer: MMMU", "citations": [], "input_tokens": 109194, "output_tokens": 132, "cost_micros": 329562, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset covers the most breadth of knowledge.\n\nThe figure shows a comparison of different datasets along two axes:\n- Breadth (Knowledge) - horizontal axis\n- Depth (Reasoning) - vertical axis\n\nFrom the figure, the datasets shown include:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nLooking at the horizontal positioning (breadth), MMMU is positioned furthest to the right, indicating it has the greatest breadth of knowledge coverage among the datasets shown. This is also supported by the text which states MMMU covers \"30 Types\" of images and spans across 6 disciplines, 30 subjects, and 183 subfields.\n\nAnswer: MMMU", "citations": [], "input_tokens": 83157, "output_tokens": 210, "cost_micros": 252621, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset represents the most breadth (knowledge) on the x-axis.\n\nThe scatter plot shows various datasets plotted with \"Breadth (Knowledge)\" on the x-axis and \"Depth (Reasoning)\" on the y-axis. MMMU is shown as a star at the highest position for both breadth and depth.\n\nAmong the datasets shown in Figure 4, MMMU is positioned furthest to the right on the Breadth axis, indicating it has the most breadth of knowledge coverage.\n\nAnswer: MMMU", "citations": [], "input_tokens": 103251, "output_tokens": 132, "cost_micros": 311733, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which dataset reflects the most breadth of knowledge.Let me search for Figure 4 in the document:Good! I found the caption for Figure 4. Now let me read the actual content around Figure 4 to see the details about which dataset reflects the most breadth of knowledge:Now let me look at the actual Figure 4 to see the visual comparison. Let me search for more context about breadth of knowledge in the figure:Let me read the section that shows the actual Figure 4 visualization:Perfect! I can see Figure 4 shows a scatter plot with \"Breadth (Knowledge)\" on one axis and \"Depth (Reasoning)\" on the other axis. The figure shows the following datasets plotted:\n- MMMU\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- MMBench\n- SEED\n- MM-Vet\n- ScienceQA\n- OKVQA\n\nFrom the figure structure (lines 1834-1858), I can see \"Breadth (Knowledge)\" is labeled as one of the axes. The caption states \"MMMU excels in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nLooking at the text in chunk 156205 (lines 2022-2029), it explicitly states: \"From the breadth perspective, the prior benchmarks are heavily focused on daily knowledge and common sense. The covered image format is also limited. Our benchmark aims to cover college-level knowledge with 30 image formats...\"\n\nThis indicates that **MMMU** reflects the most breadth of knowledge among the nine datasets shown in Figure 4.\n\nAnswer: MMMU", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57452, "error": null, "extra": {"thread_id": 2027, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e1415f7fe2384e1ba85595cd09d85b89", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 282, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I need to look at Figure 6, which shows the error distribution over 150 annotated GPT-4V errors.\n\nFrom Figure 6, I can see the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 79448, "output_tokens": 174, "cost_micros": 240954, "latency_ms": 5937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 in the document, which shows the error analysis of 150 GPT-4V error cases:\n\nThe distribution shows:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding Error: 6%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 83162, "output_tokens": 122, "cost_micros": 251316, "latency_ms": 5827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 which shows the error distribution over 150 annotated GPT-4V errors:\n\n1. Perceptual Error: 35%\n2. Lack of Knowledge: 29%\n3. Reasoning Error: 26%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 103256, "output_tokens": 77, "cost_micros": 310923, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and identify the top-3 error types over 150 annotated GPT-4V errors.Let me search for Figure 6 in the document:Now let me read the section around line 2692 where Figure 6 is located:Perfect! I found Figure 6 which shows the error distribution over 150 annotated GPT-4V errors. Looking at the data:\n\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33125, "error": null, "extra": {"thread_id": 2028, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c246c6154fa7469892ccf49cfee50145", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 158, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 (Figures 82-87) to identify all error types mentioned in the case studies.\n\nLooking at each figure in the specified range:\n\n**Figure 82** (Architecture and Engineering: Civil Engineering) - Page 96\n- Error Category: Reasoning Error\n\n**Figure 83** (Computer Science: Data Structure and Algorithm) - Page 97\n- This is a correct case, no error category\n\n**Figure 84** (Computer Science: Compiler Principle) - Page 98\n- Error Category: Perceptual Error, Lack of Knowledge\n\n**Figure 85** (Computer Science: Operating System) - Page 99\n- Error Category: Perceptual Error\n\n**Figure 86** (Computer Science: Compiler Principle) - Page 100\n- Error Category: Perceptual Error\n\n**Figure 87** (Electronics: Signal Processing) - Page 101\n- This is a correct case, no error category\n\nSo the error types mentioned in pages 95-100 are:\n- Reasoning Error\n- Perceptual Error\n- Lack of Knowledge\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 79440, "output_tokens": 265, "cost_micros": 242295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 of the document to identify all error types mentioned in the case studies.\n\nLooking through the figures on these pages:\n\n**Page 95 (Figure 81):** Architecture and Engineering - Correct case (no error type)\n\n**Page 96 (Figure 82):** Architecture and Engineering - **Reasoning Error**\n\n**Page 97 (Figure 83):** Computer Science - Correct case (no error type)\n\n**Page 98 (Figure 84):** Computer Science - **Perceptual Error, Lack of Knowledge**\n\n**Page 99 (Figure 85):** Computer Science - **Perceptual Error**\n\n**Page 100 (Figure 86):** Computer Science - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 109191, "output_tokens": 192, "cost_micros": 330453, "latency_ms": 8281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 of the document for error types mentioned in case studies.\n\nLooking at the case studies in this range:\n\nPage 95 (Figure 81 - Architecture and Engineering): No error type specified (correct case)\n\nPage 96 (Figure 82 - Architecture and Engineering): **Reasoning Error**\n\nPage 97 (Figure 83 - Computer Science): No error type specified (correct case)\n\nPage 98 (Figure 84 - Computer Science): **Perceptual Error, Lack of Knowledge**\n\nPage 99 (Figure 85 - Computer Science): **Perceptual Error**\n\nPage 100 (Figure 86 - Computer Science): **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 83154, "output_tokens": 184, "cost_micros": 252222, "latency_ms": 6655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "Looking through pages 95-100 of the document (Figures 82-86 in the case studies):\n\nFigure 82 (Architecture and Engineering: Civil Engineering) - **Reasoning Error**\n\nFigure 83 (Computer Science: Data Structure and Algorithm) - No error type mentioned (this is a correct case)\n\nFigure 84 (Computer Science: Compiler Principle) - **Perceptual Error, Lack of Knowledge**\n\nFigure 85 (Computer Science: Operating System) - **Perceptual Error**\n\nFigure 86 (Computer Science: Compiler Principle) - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 103248, "output_tokens": 158, "cost_micros": 312114, "latency_ms": 11889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll read the document to find the error types mentioned in the case studies on pages 95-100.Let me search for page numbers to locate pages 95-100:Let me search for \"case stud\" to find the case studies section:Let me read around line 5765 where Table 10 mentions case study figures with error categories:Good! I found Table 10 which shows the error categories. The columns are: Correct Case, Perception, Lack of Knowledge, Reasoning, and Other. Let me continue reading to see the complete table:Good! I found that the error categories are: Perception, Lack of Knowledge, Reasoning, and Other (plus \"Correct Case\" which is not an error). Now I need to find the actual case study pages 95-100. Let me search for figure 95 or case 95:Let me search for the case studies section more specifically. Let me look for the pattern of case numbers 95-100:Figure 96 is something different. Let me check the structure - from the earlier search, I saw \"90 Energy and Power 2: Reasoning Error 104\" and \"95 Mechanical Engineering 3: Re\". Let me search for these patterns directly:Good! So these are Figure numbers 90-95. Let me read that section where it lists figures 90-100:I can see figures 90-95 here. Now I need to find what comes after figure 95. Let me search earlier in the document to see the complete list:This appears to be a table of contents or index. I need to understand better - these numbers (95-100) might refer to page numbers or figure numbers. Let me look at this list more carefully. I can see:\n\n- Page 95: Figure 81 Architecture and Engineering 1: Correct Case\n- Page 96: Figure 83 Computer Science 1: Correct Case \n- Page 97: ?\n- Page 98: Figure 84 Computer Science 2: Perceptual Error, Lack of Knowledge\n- Page 99: Figure 85 Computer Science 3: Perceptual Error\n- Page 100: Figure 86 Computer Science 4: Perceptual Error\n\nBut I need to verify this interpretation. Let me search for where these actual figures appear in the document:Perfect! Now let me read from page 95 to page 100. Let me find line numbers around page 95:Good, I'm at page 95. Now let me continue reading through pages 95-100:Good! I'm now at page 97. Let me continue reading to get to page 100:Good! I'm at page 99. Let me continue to page 100:Good! Now let me continue to see the rest of page 100:Perfect! Now I have reviewed pages 95-100. Let me compile all the error types I found in the case studies on these pages:\n\nFrom the case studies I read on pages 95-100:\n\n**Page 95:**\n- Figure 81: Correct Case (not an error)\n\n**Page 96:**\n- Figure 82: Reasoning Error\n\n**Page 97:**\n- Figure 83: Correct Case (not an error)\n\n**Page 98:**\n- Figure 84: Perceptual Error, Lack of Knowledge\n\n**Page 99:**\n- Figure 85: Perceptual Error\n\n**Page 100:**\n- Figure 86: Perceptual Error\n- Figure 87: Correct Case (not an error)\n\nNow let me verify by looking back at the table of contents I saw earlier to ensure I have the right mapping. Let me check once more:Perfect! This confirms my interpretation. The table shows:\n- Figure 81 is on pages 94-95\n- Figure 82 is on page 96 (spanning from before)\n- Figure 83 is on pages 96-97\n- Figure 84 is on page 98\n- Figure 85 is on page 99\n- Figure 86 is on page 100\n- Figure 87 is on page 101 (starting at page 100)\n\nSo the error types mentioned in case studies on pages 95-100 are:\n\n1. **Reasoning Error** (Figure 82 - Architecture and Engineering)\n2. **Perceptual Error** (Figure 84, 85, 86 - Computer Science)\n3. **Lack of Knowledge** (Figure 84 - Computer Science)\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 114937, "error": null, "extra": {"thread_id": 2029, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77fb85ac92354e66b7280742dc24bcdb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 755, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to examine pages 65-70 to count the successful cases (marked with a checkmark \u2713).\n\n**Page 65** (Figure 51): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 66** (Figure 52): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 67** (Figure 53): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 68** (Figure 54): Clinical Medicine: Pathology - **Error case** \u2717 (Perceptual Error)\n\n**Page 69** (Figure 55): Clinical Medicine: Heart Disease - **Error case** \u2717 (Lack of Knowledge)\n\n**Page 70** (Figure 56): Diagnostics and Laboratory Medicine: Radiology - **Correct case** \u2713\n\nCounting the successful cases (those with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 259120, "output_tokens": 295, "cost_micros": 1561358, "latency_ms": 64484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful (correct) cases in pages 65-70 of the document.\n\nLet me go through each figure in this range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine: Pathology - \"A sample **error** case\" \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine: Heart Disease - \"A sample **error** case\" \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Laboratory Medicine: Radiology - \"A sample **correct** case\" \u2713\n\nCounting the correct cases: Pages 65, 66, 67, and 70 each have one correct case.\n\nTotal successful cases = 4\n\nAnswer: 4", "citations": [], "input_tokens": 79424, "output_tokens": 257, "cost_micros": 242127, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (marked with \u2713 or \"V\") in Pages 65-70.\n\nLet me go through each figure in this page range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine 1: Correct Case \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine 2: Correct Case \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine 3: Correct Case \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine 4: Perceptual Error \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine 5: Lack of Knowledge \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Lab Medicine 1: Correct Case \u2713\n\nCounting the successful cases (marked with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 109175, "output_tokens": 280, "cost_micros": 331725, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases in Pages 65-70 of the document.\n\nLet me go through each page:\n\n**Page 65 (Clinical Medicine 1):**\n- Figure 51: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 66 (Clinical Medicine 2):**\n- Figure 52: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 67 (Clinical Medicine 3):**\n- Figure 53: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 68 (Clinical Medicine 4):**\n- Figure 54: \"A sample error case of Clinical Medicine (subfield: Pathology). Error category: Perceptual Error\"\n- This is an ERROR case \u2717\n\n**Page 69 (Clinical Medicine 5):**\n- Figure 55: \"A sample error case of Clinical Medicine (subfield: Heart Disease). Error category: Lack of Knowledge\"\n- This is an ERROR case \u2717\n\n**Page 70 (Diagnostics and Laboratory Medicine 1):**\n- Figure 56: \"A sample correct case of Diagnostics and Laboratory Medicine (subfield: Radiology)\"\n- This is a CORRECT case \u2713\n\nTotal successful (correct) cases in pages 65-70: 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 348, "cost_micros": 254634, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list and look at pages 65-70 to find information about successful cases.I need to search for content related to pages 65-70. Let me search for page markers in the document.Let me search for page numbers in a different format:Let me search for the word \"successful\" or \"cases\" to understand the document structure:Let me search for \"cases\" or look for page numbers differently. Let me read more of the document to understand its structure:This is not helpful. Let me search for the actual page numbers in the document. PDF documents often have page numbers embedded. Let me try different patterns:I can see there's a \"Case Study\" section. Let me look for the appendix section that might have pages 65-70. Let me read the entire document more systematically. First, let me check how long the document is:Great! I found a list of case studies with page numbers. I can see this is a table mapping case numbers to their descriptions and page numbers. Let me continue reading to see pages 65-70:Perfect! I found the information. Looking at pages 65-70, I can see:\n\n- Page 65: Case 51 - Clinical Medicine 1: **Correct Case**\n- Page 66: Case 52 - Clinical Medicine 2: **Correct Case**\n- Page 67: Case 53 - Clinical Medicine 3: **Correct Case**\n- Page 68: Case 54 - Clinical Medicine 4: Perceptual Error\n- Page 69: Case 55 - Clinical Medicine 5: Lack of Knowledge\n- Page 70: Case 56 - Diagnostics and Lab Medicine 1: **Correct Case**\n\nCounting the \"Correct Case\" entries in pages 65-70:\n1. Page 65: Clinical Medicine 1: Correct Case\n2. Page 66: Clinical Medicine 2: Correct Case\n3. Page 67: Clinical Medicine 3: Correct Case\n4. Page 70: Diagnostics and Lab Medicine 1: Correct Case\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54718, "error": null, "extra": {"thread_id": 2030, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77d82c91b9624d83aeec575d8461194f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 347, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many key steps the authors divided causal inference into.\n\nLooking through the document, I can see in Section 3.2 \"Generating the Explanations\" on page 6, there is a discussion of step-by-step explanations and subskills:\n\n\"Apart from the question-answer pairs, we also generate the step-by-step explanations. Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis clearly states that they identified **six subskills** for causal inference.\n\nAdditionally, in Figure 1 on page 2, I can see these six steps are illustrated in the example.\n\nIn Section 4 \"Our CAUSALCOT Model\" on page 7, it also mentions \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: \u2460 identifying the causal graph structure; \u2461 determining the causal query type; \u2462 formulating the query symbolically precisely; and \u2463 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: \u2464 correctly deducing the estimand using causal inference techniques; and finally \u2465 evaluating the estimand to answer the question.\"\n\nThis confirms the six steps again.\n\nAnswer: 6", "citations": [], "input_tokens": 75261, "output_tokens": 410, "cost_micros": 231933, "latency_ms": 16531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Figure 1, there is a section titled \"Correct steps to lead to the ground-truth answer:\" which shows 6 numbered steps:\n\n1) Parse the causal graph\n2) Classify the query type\n3) Formulate the query to its symbolic form\n4) Collect the available data\n5) Derive the estimand using causal inference\n6) Solve for the estimand by plugging in the relevant data\n\nThis is also confirmed in Section 3.2 where they discuss generating explanations: \"We identify the following six subskills: 1 causal graph extraction; 2 correct query type interpretation; 3 symbolic formalization of the query; 4 semantic parsing to compile the available data; 5 estimand derivation; and 6 arithmetic calculation to solve the estimand\"\n\nAdditionally, in Section 4 describing their CAUSALCOT model, they state: \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps... Then... the formal solution: 5 correctly deducing the estimand... and finally 6 evaluating the estimand\"\n\nAnswer: 6", "citations": [], "input_tokens": 31749, "output_tokens": 291, "cost_micros": 99612, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Section 4, \"Our CAUSALCOT Model,\" the authors describe their approach based on the causal inference engine. On page 7, they explicitly state:\n\n\"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nThis clearly describes 6 steps:\n1. Identifying the causal graph structure\n2. Determining the causal query type\n3. Formulating the query symbolically precisely\n4. Extracting relevant data from the prompt\n5. Correctly deducing the estimand using causal inference techniques\n6. Evaluating the estimand to answer the question\n\nThis is also shown in Figure 1 and Figure 4, where 6 steps are illustrated.\n\nAnswer: 6", "citations": [], "input_tokens": 38818, "output_tokens": 294, "cost_micros": 120864, "latency_ms": 8828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the key steps of causal inference.\n\nIn Section 3.2 \"Generating the Explanations,\" the document states:\n\n\"Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis is also reflected in Figure 4 which shows the CAUSALCOT model with 6 steps, and in Section 4 where they describe their prompting strategy that \"breaks down a causal reasoning problem into multiple symbolically-grounded, simpler steps.\"\n\nAnswer: 6", "citations": [], "input_tokens": 33630, "output_tokens": 227, "cost_micros": 104295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can identify the key steps that the authors divided causal inference into.\n\nThe figure clearly shows 6 numbered steps:\n\n1. Parse the causal graph (Causal Relation Extraction)\n2. Classify the query type (Causal Question Classification)\n3. Formulate the query to its symbolic form (Formalization)\n4. Collect the available data (Semantic Parsing)\n5. Derive the estimand using causal inference (Formal Causal Inference)\n6. Solve for the estimand by plugging in the relevant data (Arithmetics)\n\nThis is further confirmed in Section 3.2 where the explanation generation is described, and in Section 4 where the CAUSALCOT model is introduced, which follows these same 6 steps.\n\nAnswer: 6", "citations": [], "input_tokens": 39832, "output_tokens": 211, "cost_micros": 122661, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the provided document to find information about the key steps the author divided causal inference into.Let me search for information about how the author divided the process or steps:Perfect! I found the answer. The text clearly states that the author divided causal inference into 6 steps. Let me verify this by reading a bit more context:Perfect! The document clearly shows that the author divided causal inference into **6 steps**:\n\n1. Extract the causal graph\n2. Determine the query type\n3. Formalize the query\n4. Gather all relevant data\n5. Deduce the estimand using causal inference\n6. Calculate the estimand\n\nThe text explicitly states: \"first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then... 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44937, "error": null, "extra": {"thread_id": 2031, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b14a0eb01624709bf2cbb4b451f998a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about subskills related to collecting available data...\n\nIn Figure 1 on page 2, I can see the step-by-step reasoning process for answering a causal inference question. Step 4 is labeled \"Collect the available data\" and it has an associated subskill label.\n\nThe subskill associated with Step 4 \"Collect the available data\" is **Semantic Parsing**.\n\nThis is also confirmed in Section 3.2 where it mentions the six subskills:\n- \u2460 causal graph extraction\n- \u2461 correct query type interpretation\n- \u2462 symbolic formalization of the query\n- \u2463 semantic parsing to compile the available data\n- \u2464 estimand derivation\n- \u2465 arithmetic calculation\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 75267, "output_tokens": 185, "cost_micros": 228576, "latency_ms": 32921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the step-by-step process for answering a causal inference question, Step 4 is labeled as \"Collect the available data:\" and the corresponding subskill is clearly marked as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 31755, "output_tokens": 68, "cost_micros": 96285, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation for solving a causal inference question:\n\nStep 4 is labeled \"Collect the available data\" and the subskill required for this step is identified as \"Semantic Parsing\" (shown in the colored box in the figure).\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 38824, "output_tokens": 79, "cost_micros": 117657, "latency_ms": 5264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation, and Section 3.2 which discusses \"Generating the Explanations,\" I can identify the subskills mentioned.\n\nStep 4 in Figure 1 is labeled \"Collect the available data\" and the document describes this as requiring \"semantic parsing to compile the available data\" in Section 3.2.\n\nAnswer: semantic parsing", "citations": [], "input_tokens": 33636, "output_tokens": 96, "cost_micros": 102348, "latency_ms": 5125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can see that Step 4 is labeled \"Collect the available data\" and the subskill associated with it is \"Semantic Parsing.\"\n\nThis is also confirmed in Section A.8 (Explanation Template) where Step \u2463 states: \"Collect all the available data: The available data are: '$d$'.\"\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 39838, "output_tokens": 109, "cost_micros": 121149, "latency_ms": 5516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about what subskill is needed to collect the available data.Let me search for more specific terms related to data collection:Perfect! I found the answer. Let me read that section more carefully to get the complete context:Based on the document, I can now provide the answer. The document clearly shows that step 4 \"Collect the available data\" has the subskill listed as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32687, "error": null, "extra": {"thread_id": 2032, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_46f68e1513d341d0b0ee21cfa0dae2f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 137, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the rungs of the Ladder of Causation...\n\nFrom Section 2.1 \"The Ladder of Causation\" on page 3:\n\nThe document describes three distinct rungs:\n- Rung 1 (\"seeing\"): Statistical associations\n- Rung 2 (\"doing\"): Interventions\n- Rung 3 (\"imagining\"): Counterfactuals\n\nRung 2 is specifically described as dealing with interventions and the do-operator.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 75267, "output_tokens": 131, "cost_micros": 227766, "latency_ms": 31077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, Section 2.1 \"The Ladder of Causation\" describes the three rungs:\n\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nThe document states that Rung 2 \"enables us to formalize the concept of actively intervening in the world.\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 31755, "output_tokens": 111, "cost_micros": 96930, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the second rung of the ladder of causation...\n\nFrom Section 2.1 \"The Ladder of Causation\":\n\n\"Rung 2 (\"doing\"). This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"If I take an aspirin now, will my headache subside?\"). Interventions can be formalized using the do-operator [24] and Causal Bayesian Networks [67]...\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 38824, "output_tokens": 126, "cost_micros": 118362, "latency_ms": 6250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 which describes \"The Ladder of Causation\":\n\nThe three rungs are:\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nRung 2 is explicitly described as dealing with interventions, using the do-operator to formalize the concept of actively intervening in the world.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 33636, "output_tokens": 122, "cost_micros": 102738, "latency_ms": 5062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 \"The Ladder of Causation\":\n\n**Rung 2 (\"doing\").** This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"*If I take an aspirin now, will my headache subside?*\"). Interventions can be formalized using the *do-operator* [24] and *Causal Bayesian Networks* [67] to represent, for example, the distribution over $Y$ when intervening on $X$ to set its value to $x$ as $P(Y = y|do(X = x))$.\n\nAnswer: Intervention or \"doing\"", "citations": [], "input_tokens": 39838, "output_tokens": 159, "cost_micros": 121899, "latency_ms": 8094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about the second ladder of causation.Let me search for \"ladder of causation\" in the document:Let me search for \"second rung\" or information about the rungs:Good! I found a reference at line 553. Let me read that section:Perfect! I found the answer. The document clearly states that the three rungs of the Ladder of Causation are:\n- Rung 1: associational\n- Rung 2: interventional\n- Rung 3: counterfactual\n\nAnswer: interventional", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43718, "error": null, "extra": {"thread_id": 2033, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_7af68403347145c59b6ebb40237510ed", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 215, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.0, "method": "str_norm", "normalised_pred": "interventional", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the chain-type causal graph and count its directed edges.\n\nLooking through the document, I can find the causal graph structures in Figure 6 on page 19, which shows \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\"\n\nIn Figure 6, I can see a \"Chain\" graph structure. The Chain graph shows:\n- X \u2192 (middle node) \u2192 Y\n\nThis represents a chain with three nodes connected by two directed edges:\n1. One edge from X to the middle node\n2. One edge from the middle node to Y\n\nTherefore, a chain-type causal graph has 2 directed edges.\n\nAnswer: 2", "citations": [], "input_tokens": 75271, "output_tokens": 156, "cost_micros": 228153, "latency_ms": 30437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\", I can see the \"Chain\" graph structure.\n\nThe Chain graph shows: X \u2192 M \u2192 Y\n\nThis is a simple chain structure where:\n- X has an arrow to M (1 edge)\n- M has an arrow to Y (1 edge)\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 31759, "output_tokens": 133, "cost_micros": 97272, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, there is a table titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\" which shows:\n\nFor the \"Chain\" causal graph:\n- CGTE Alias: Chain\n- Treatment-Effect Pair: X \u2192 Y\n\nThe diagram shows a simple chain structure: X \u2192 Z \u2192 Y\n\nThis is a chain with 3 nodes (X, Z, Y) connected sequentially.\n\nCounting the directed edges:\n1. X \u2192 Z (one edge)\n2. Z \u2192 Y (one edge)\n\nTotal: 2 directed edges\n\nAnswer: 2", "citations": [], "input_tokens": 38828, "output_tokens": 165, "cost_micros": 118959, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 18, there is a \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\" The table shows:\n\n| Causal Graph | CGTE Alias | Treatment-Effect Pair |\n| ------------ | ----------- | --------------------- |\n| Chain | Chain | X Y |\n\nThe visual representation shows: X \u2192 M \u2192 Y\n\nThis indicates a chain structure where X points to M (a mediator), and M points to Y.\n\nTherefore, there are 2 directed edges in a chain-type causal graph: one from X to M, and one from M to Y.\n\nAnswer: 2", "citations": [], "input_tokens": 33640, "output_tokens": 174, "cost_micros": 103530, "latency_ms": 6796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of a chain-type causal graph.\n\nIn Section A.3 \"Collection of Causal Graphs,\" there is a table (Figure 6) that lists all causal graph types with their structures.\n\nFor the \"Chain\" graph, the table shows:\n- Causal Graph Alias: Chain\n- CGTE Alias: Chain \n- Treatment-Effect Pair: X \u2192 Z \u2192 Y (X and Y are treatment-effect pair)\n\nThe notation X \u2192 Z \u2192 Y indicates:\n- One edge from X to Z\n- One edge from Z to Y\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 39842, "output_tokens": 164, "cost_micros": 121986, "latency_ms": 7343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the definition of a chain-type causal graph and count the directed edges.Let me search for \"chain-type\" or \"causal graph\" in the document:Let me search for \"causal graph\" to find the definition:Let me read the section around line 2387 where the causal graph definition appears:Now let me search for \"chain\" with \"graph\" or look for specific graph types. Let me check Figure 6 which shows all the causal graphs:Good! I can see \"Chain\" is listed as one of the causal graphs at line 2101-2104. Now let me look for the actual visualization or definition of the Chain graph. Let me check if there's a figure showing these graphs:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48766, "error": null, "extra": {"thread_id": 2034, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca8eb7f650264585a1c5b7287fc4aa99", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 247, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2387", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 on page 8, which shows \"Performance of all models on our CLADDER dataset v1.5\":\n\nThe \"Overall Acc.\" column shows:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe highest overall accuracy is achieved by CAUSALCOT with 70.40%.\n\nLooking at the document, CAUSALCOT is described as \"GPT-4 + CAUSALCOT\" in the context, which means it's GPT-4 enhanced with their causal chain-of-thought prompting strategy.\n\nThe best performing model is CAUSALCOT (which uses GPT-4 as the base model) with an accuracy of 70.40%.\n\nAnswer: 70", "citations": [], "input_tokens": 75259, "output_tokens": 336, "cost_micros": 230817, "latency_ms": 24141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in Table 2 (on page 8), I need to find which model achieves the highest overall accuracy on the CLADDER dataset v1.5.\n\nFrom Table 2:\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- **+ CAUSALCOT: 70.40%**\n\nThe model with the highest overall accuracy is **CAUSALCOT** (which is GPT-4 with the CausalCoT prompting strategy) at 70.40%.\n\nAnswer: 4", "citations": [], "input_tokens": 31747, "output_tokens": 263, "cost_micros": 99186, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in the document:\n\nFrom Table 2 (CLADDER v1.5 - the main/current version):\n- GPT-4 + CAUSALCOT achieves 70.40% accuracy, which is the highest performance among all models tested\n\nFrom Table 6 (CLADDER v1.0 - earlier version):\n- GPT-4 + CAUSALCOT achieves 66.64% accuracy, which is also the highest\n\nThe document clearly states on page 3: \"our CAUSALCOT achieves an accuracy of 70.40%, which substantially improves the performance of vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nAnd on page 8: \"And amongst all, our CAUSALCOT achieves the highest performance of 70.40%\"\n\nCAUSALCOT is described as a chain-of-thought prompting strategy used with GPT-4, so the best performing model is GPT-4 with CAUSALCOT.\n\nSince GPT-4 is mentioned to have approximately 1T (1 trillion) parameters, and CAUSALCOT is a prompting strategy applied to GPT-4:\n\nAnswer: 1000000000000", "citations": [], "input_tokens": 38816, "output_tokens": 288, "cost_micros": 120768, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1000000000000", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset according to the document.\n\nLooking at Table 2 (page 8), which shows \"Performance of all models on our CLADDER dataset v1.5\", I can see the overall accuracy for each model:\n\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- + CAUSALCOT: 70.40%\n\nThe model with the highest overall accuracy is \"GPT-4 + CAUSALCOT\" with 70.40%.\n\nThe text also states: \"our CAUSALCOT achieves the highest performance of 70.40%, which is substantially better than the vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nSince CAUSALCOT is based on GPT-4 (as stated in the document: \"We build our CAUSALCOT prompting strategy using GPT-4\"), the best performing model is GPT-4 when using the CAUSALCOT prompting strategy.\n\nThe overall accuracy is 70.40%, which rounds to 70%.\n\nAnswer: 70", "citations": [], "input_tokens": 33628, "output_tokens": 390, "cost_micros": 106734, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 (Performance of all models on CLADDER dataset v1.5), which shows the main results:\n\nThe models and their Overall Accuracy are:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe model with the highest overall accuracy is \"+ CAUSALCOT\" at 70.40%.\n\nCAUSALCOT is described in the paper as a chain-of-thought prompting strategy built on top of GPT-4, so it's GPT-4 enhanced with their CAUSALCOT method.\n\nThe accuracy is 70.40%, which as a percentage would be approximately 70 (rounding to the nearest integer).\n\nAnswer: 70", "citations": [], "input_tokens": 39830, "output_tokens": 325, "cost_micros": 124365, "latency_ms": 8687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about model performance on Cladder.Let me search for \"Cladder\" in the document to find the relevant information:Now let me search for tables or results sections that might contain performance metrics:Perfect! I found Table 2 which shows the performance of all models on the CLADDER dataset. Let me read that section:Perfect! I can see Table 2 which shows the performance of all models on the CLADDER dataset. Looking at the \"Overall Acc.\" column:\n\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- +CAUSALCOT: 70.40\n\nThe model that performs the best on Cladder is \"+CAUSALCOT\" with an overall accuracy of 70.40.\n\nAnswer: 70", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36610, "error": null, "extra": {"thread_id": 2035, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e78cd64845f341ad917f431928ce6d64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 165, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor (human) is above/smarter than the student (model).\n\nThe second subfigure shows \"Superalignment\" where the supervisor (human) is below/weaker than the student (superhuman model).\n\nThe difference is the relative capability levels are reversed - in Traditional ML the supervisor is stronger, while in Superalignment the student is stronger.\n\nAnswer: Supervisor is stronger than student (vs. student stronger than supervisor)", "citations": [], "input_tokens": 121916, "output_tokens": 121, "cost_micros": 367563, "latency_ms": 32577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe figure shows three scenarios comparing Traditional ML, Superalignment, and \"Our Analogy.\"\n\nIn the first subfigure (Traditional ML), the supervisor (human) is above the human level line, while the student (model) is below the human level line.\n\nIn the second subfigure (Superalignment), the supervisor (human) is at the human level line, while the student (model) is far above the human level line.\n\nThe key difference is that in Traditional ML, the supervisor is stronger than/above the student, whereas in Superalignment, the student is much stronger than the supervisor.\n\nAnswer: Supervisor is stronger than student (versus student stronger than supervisor)", "citations": [], "input_tokens": 45459, "output_tokens": 162, "cost_micros": 138807, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student versus student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the **supervisor is stronger than the student** (supervisor is above the human level line, student is below it).\n\nThe second subfigure shows \"Superalignment\" where the **supervisor is weaker than the student** (supervisor is at human level, student is above it - superhuman).\n\nAnswer: Supervisor is stronger than student (vs weaker than student)", "citations": [], "input_tokens": 49232, "output_tokens": 103, "cost_micros": 149241, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.25, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs weaker than student", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor is at human level and the student is below human level.\n\nThe second subfigure shows \"Superalignment\" where the supervisor is at human level and the student is above human level (superhuman).\n\nThe key difference is the student's capability relative to human level.\n\nAnswer: Student is weaker than human vs student is stronger than human", "citations": [], "input_tokens": 47601, "output_tokens": 99, "cost_micros": 144288, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2222222222222222, "method": "str_norm", "normalised_pred": "student is weaker than human vs student is stronger than human", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to compare the first subfigure (Traditional ML) with the second subfigure (Superalignment).\n\nIn the first subfigure (Traditional ML):\n- Supervisor: Human\n- Student: Robot\n- The human is stronger/smarter than the robot\n\nIn the second subfigure (Superalignment):\n- Supervisor: Human \n- Student: Superhuman Robot\n- The human is weaker/less capable than the superhuman robot\n\nThe key difference is the relative capability between supervisor and student.\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 89555, "output_tokens": 133, "cost_micros": 270660, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and analyze the difference between the first and second subfigures regarding supervisor-student relationships.Let me search for Figure 1 in the document:Now let me read the actual Figure 1 caption and description more carefully:Based on the figure caption and structure, I can see that Figure 1 has three subfigures:\n1. Traditional ML\n2. Superalignment \n3. Our Analogy\n\nThe question asks about the difference between the first subfigure (Traditional ML) and the second subfigure (Superalignment) regarding the supervisor-student relationship.\n\nFrom the caption: \"Traditional ML focuses on the setting where humans supervise models that are weaker than humans. For the ultimate superalignment problem, humans will have to supervise models much smarter than them.\"\n\nThe key difference is about the relative capability/intelligence level between supervisor and student:\n- In Traditional ML (first subfigure): Supervisor is stronger/smarter than student\n- In Superalignment (second subfigure): Supervisor is weaker than student (students are \"much smarter\" than supervisors)\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43967, "error": null, "extra": {"thread_id": 2036, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e51022c429054652ae2e24b4002aba31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the figures from Page 3 to Page 10 in this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 2: Shows weak-to-strong generalization results with three panels (NLP Tasks, Chess Puzzles, ChatGPT Reward Modeling)\n\n**Page 4:**\n- No figures on this page (Related Work section)\n\n**Page 5:**\n- No figures on this page (Related Work continued)\n\n**Page 6:**\n- No figures on this page (Methodology section)\n\n**Page 7:**\n- No figures on this page (Main Results section continues)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization with naive finetuning (6 panels labeled a-f)\n\n**Page 9:**\n- Figure 4: Shows bootstrapping results on chess puzzles (2 panels labeled a-b)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss (2 panels labeled a-b)\n\nTotal figures from Page 3 to Page 10:\n- Figure 2 (Page 3)\n- Figure 3 (Page 8)\n- Figure 4 (Page 9)\n- Figure 5 (Page 10)\n\nAnswer: 4", "citations": [], "input_tokens": 121899, "output_tokens": 301, "cost_micros": 370212, "latency_ms": 35593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all the figures that appear on pages 3 through 10 of this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 1: An illustration of methodology (Traditional ML, Superalignment, Our Analogy)\n- Figure 2: Shows test accuracy on representative NLP task, Chess Puzzles, and ChatGPT Reward Modeling\n\n**Page 4:**\n- No figures visible on this page (contains related work section)\n\n**Page 5:**\n- No figures visible on this page (continues related work section)\n\n**Page 6:**\n- No figures visible on this page (continues related work section)\n\n**Page 7:**\n- No figures visible on this page (contains methodology section)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization results with multiple panels (a,b,c,d,e,f)\n- Figure 4: Shows bootstrapping results on chess puzzles\n\n**Page 9:**\n- No figures visible on this page (continues main results section)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss\n- Figure 6: Shows auxiliary loss improving generalization across datasets\n\nCounting the figures from pages 3-10:\n- Page 3: 2 figures (Figure 1, Figure 2)\n- Page 8: 2 figures (Figure 3, Figure 4)\n- Page 10: 2 figures (Figure 5, Figure 6)\n\nTotal: 6 figures\n\nAnswer: 6", "citations": [], "input_tokens": 45442, "output_tokens": 356, "cost_micros": 141666, "latency_ms": 8421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from Page 3 to Page 10 in this document.\n\n**Page 3:**\n- Figure 2: \"An illustration of our methodology...\" showing weak-to-strong generalization results\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- Figure 3: \"Promising weak-to-strong generalization...\" showing test accuracy panels (a,b,c,d,e,f)\n\n**Page 8:**\n- None\n\n**Page 9:**\n- Figure 4: \"Bootstrapping improves weak-to-strong generalization...\" showing panels (a,b)\n\n**Page 10:**\n- Figure 5: \"Substantially improved generalization...\" showing panels (a,b)\n- Figure 6: \"Simple auxiliary loss improves generalization...\" showing multiple test accuracy plots\n\nLet me count:\n- Page 3: 1 figure (Figure 2)\n- Page 7: 1 figure (Figure 3)\n- Page 9: 1 figure (Figure 4)\n- Page 10: 2 figures (Figure 5 and Figure 6)\n\nTotal: 1 + 1 + 1 + 2 = 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 49215, "output_tokens": 303, "cost_micros": 152190, "latency_ms": 7608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from page 3 to page 10 of this paper.\n\nLet me go through the document systematically:\n\n**Page 3:** Figure 1 (An illustration of methodology showing Traditional ML, Superalignment, and Our Analogy)\n\n**Page 4:** Figure 2 (Strong models trained with weak supervision generalize beyond their supervisor - showing 3 subplots for NLP Task, Chess, and ChatGPT Reward Modeling)\n\n**Page 5-6:** No figures visible in the main text sections shown\n\n**Page 7:** Figure 3 (mentioned in the text - showing test accuracy and performance gap recovered for NLP tasks, chess puzzles, and ChatGPT reward modeling - this appears to be on page 8 based on the layout)\n\n**Page 8:** Figure 3 appears here (6 subplots - a,b,c showing test accuracy and d,e,f showing performance gap recovered)\n\n**Page 9:** Figure 4 (Bootstrapping improves weak-to-strong generalization on chess puzzles - 2 subplots)\n\n**Page 10:** Figure 5 (Substantially improved generalization on NLP datasets with auxiliary loss - 2 subplots)\n\nCounting the figures:\n- Page 3: Figure 1\n- Page 4: Figure 2 \n- Page 8: Figure 3\n- Page 9: Figure 4\n- Page 10: Figure 5\n\nTotal figures from page 3 to page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 47584, "output_tokens": 358, "cost_micros": 148122, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count all figures from Page 3 to Page 10.\n\n**Page 3:**\n- Figure 2: Strong models trained with weak supervision generalize beyond their supervisor (bar chart)\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- None\n\n**Page 8:**\n- Figure 3: Promising weak-to-strong generalization with naive finetuning (grid of heatmaps/tables - appears as panels a, b, c, d, e, f)\n\n**Page 9:**\n- Figure 4: Bootstrapping improves weak-to-strong generalization on chess puzzles (2 panels: a and b)\n\n**Page 10:**\n- Figure 5: Substantially improved generalization on NLP datasets with a simple auxiliary loss (2 panels: a and b)\n\nTotal count:\n- Figure 2: 1\n- Figure 3: 1 (though it has 6 panels, it's one figure)\n- Figure 4: 1\n- Figure 5: 1\n\nAnswer: 4", "citations": [], "input_tokens": 89538, "output_tokens": 261, "cost_micros": 272529, "latency_ms": 9000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count the figures from Page 3 to Page 10.Now I need to identify which figures appear between Page 3 and Page 10. Let me read the relevant sections more carefully:Now let me check what's on page 11 to confirm the boundary:Based on my analysis, let me count the figures that appear from Page 3 to Page 10:\n\n- **Page 3**: No figures (only text - Related Work section starts)\n- **Page 4**: No figures (Related Work section continues) \n- **Page 5**: 1 figure (the PGR illustration figure showing weak performance, weak-to-strong performance, strong ceiling performance)\n- **Page 6**: No figures (text only)\n- **Page 7**: 1 figure (Figure 3 - showing weak-to-strong generalization results across NLP, Chess, and ChatGPT RM)\n- **Page 8**: 1 figure (Figure 4 - Bootstrapping improves weak-to-strong generalization on chess puzzles)\n- **Page 9**: 1 figure (Figure 5 - NLP with auxiliary loss)\n- **Page 10**: 1 figure (Figure 6 - Simple auxiliary loss improves generalization across most datasets)\n\nTotal figures from Page 3 to Page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55516, "error": null, "extra": {"thread_id": 2037, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ad05d8ff7ccf4712bbe93b3462e811bc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 271, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to locate Figure 3 in the document and count the number of subfigures it contains.\n\nLooking at the document, Figure 3 appears on page 8. The figure is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure contains:\n- Panel (a): NLP Tasks - test accuracy plot\n- Panel (b): Chess Puzzles - test accuracy plot \n- Panel (c): ChatGPT Reward Modeling - test accuracy plot\n- Panel (d): NLP Tasks - performance gap recovered plot\n- Panel (e): Chess Puzzles - performance gap recovered plot\n- Panel (f): ChatGPT Reward Modeling - performance gap recovered plot\n\nThe figure has 6 labeled panels/subfigures (a, b, c, d, e, f).\n\nAnswer: 6", "citations": [], "input_tokens": 121893, "output_tokens": 212, "cost_micros": 368859, "latency_ms": 54641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 on page 8. The caption reads:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe caption explicitly mentions panels (a,b,c) and (d,e,f), which indicates there are 6 subfigures total in Figure 3.\n\nLooking at the actual figure layout, I can see:\n- (a) NLP Tasks\n- (b) Chess Puzzles \n- (c) ChatGPT Reward Modeling\n- (d) NLP Tasks (PGR)\n- (e) Chess Puzzles (PGR)\n- (f) ChatGPT Reward Modeling (PGR)\n\nAnswer: 6", "citations": [], "input_tokens": 45436, "output_tokens": 322, "cost_micros": 141138, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 which is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure caption describes panels (a,b,c) and (d,e,f):\n- (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task\n- (d,e,f) Same as panels a,b,c but for performance gap recovered\n\nSo Figure 3 has 6 subfigures total: (a), (b), (c), (d), (e), and (f).\n\nAnswer: 6", "citations": [], "input_tokens": 49209, "output_tokens": 201, "cost_micros": 150642, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can see Figure 3 is described as:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe description explicitly mentions:\n- (a), (b), (c) - three subfigures showing test accuracy\n- (d), (e), (f) - three subfigures showing performance gap recovered\n\nThis gives us a total of 6 subfigures in Figure 3.\n\nAnswer: 6", "citations": [], "input_tokens": 47578, "output_tokens": 255, "cost_micros": 146559, "latency_ms": 8389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking through the document for Figure 3...\n\nFound it! Figure 3 is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure consists of:\n- (a) NLP Tasks - showing test accuracy\n- (b) Chess Puzzles - showing test accuracy \n- (c) ChatGPT Reward Modeling - showing test accuracy\n- (d) Performance Gap Recovered (%) - NLP Tasks\n- (e) Performance Gap Recovered (%) - Chess Puzzles\n- (f) Performance Gap Recovered (%) - ChatGPT Reward Modeling\n\nSo Figure 3 has 6 subfigures labeled (a) through (f).\n\nAnswer: 6", "citations": [], "input_tokens": 89532, "output_tokens": 204, "cost_micros": 271656, "latency_ms": 8046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count its subfigures.Let me search for Figure 3 in the document:Now let me search for where Figure 3 appears in the document:Good! I found Figure 3. Let me read the complete caption around line 975:Perfect! I can see from the caption that Figure 3 has subfigures labeled (a), (b), (c), (d), (e), and (f). Let me continue reading to confirm all subfigures:Perfect! I can now clearly see all the subfigures in Figure 3. Based on the caption and the content, Figure 3 contains:\n\n- (a) NLP Tasks - test accuracy\n- (b) Chess Puzzles - test accuracy \n- (c) ChatGPT Reward Modeling - test accuracy\n- (d) NLP Tasks - performance gap recovered\n- (e) Chess Puzzles - performance gap recovered\n- (f) ChatGPT Reward Modeling - performance gap recovered\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40671, "error": null, "extra": {"thread_id": 2038, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_131c7eb21d53472097ae34a0c3413951", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 191, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all sections that discuss the experiment setup in this paper about weak-to-strong generalization.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the general methodology and setup for weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the three main task settings: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information:\n - A.1: NLP TASKS - Details about data preprocessing, models, training hyperparameters, weak labels, and evaluation\n - A.2: CHESS PUZZLES - Details about data preprocessing, training hyperparameters, weak labels, and evaluation\n - A.3: CHATGPT REWARD MODELING - Details about data preprocessing, models, training hyperparameters, and weak labels\n - A.4: AUXILIARY CONFIDENCE LOSS - Details about the confidence loss method\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Describes additional experimental setups:\n - C.1: CHESS PUZZLES\n - C.2: NLP TASKS: DIFFICULTY THRESHOLDING\n - C.3: GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes additional experimental settings:\n - D.1: SELF-SUPERVISED VISION MODELS\n - D.2: LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Describes synthetic experiments:\n - E.1: SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n - E.2: DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n - E.3: MAKING IMITATION TRIVIAL\n\nAnswer: Section 3 (Methodology), Section 4.1 (Tasks), Appendix A (Further Experimental Details), Appendix A.1 (NLP Tasks), Appendix A.2 (Chess Puzzles), Appendix A.3 (ChatGP", "citations": [], "input_tokens": 121905, "output_tokens": 512, "cost_micros": 373395, "latency_ms": 49562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgp, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup in this document.\n\nLet me go through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the overall methodology and experimental setup for weak-to-strong learning.\n\n2. **Section 4.1 TASKS** - This describes the tasks used in experiments (NLP benchmarks, Chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix is dedicated to experimental details.\n\n4. **Appendix A.1 NLP TASKS** - Details about NLP task setup including data preprocessing, models, training hyperparameters, weak labels, and evaluation.\n\n5. **Appendix A.2 CHESS PUZZLES** - Details about chess puzzle setup including data preprocessing, training hyperparameters, weak labels, and evaluation.\n\n6. **Appendix A.3 CHATGPT REWARD MODELING** - Details about reward modeling setup including data preprocessing, models, training hyperparameters, weak labels.\n\n7. **Appendix A.4 AUXILIARY CONFIDENCE LOSS** - Detailed description of the auxiliary confidence loss method.\n\n8. **Appendix C EASY-TO-HARD GENERALIZATION** - Discusses experimental setup for easy-to-hard generalization experiments.\n\n9. **Appendix C.1 CHESS PUZZLES** - Setup for easy-to-hard generalization on chess.\n\n10. **Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING** - Setup for difficulty-based experiments on NLP.\n\n11. **Appendix C.3 GPT-4 PREDICTED DIFFICULTY** - Setup using GPT-4 predicted difficulty.\n\n12. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - Discusses alternative experimental settings.\n\n13. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Setup for vision model experiments.\n\n14. **Appendix D.2 LINEAR PROBING** - Setup for linear probing experiments.\n\n15. **Appendix E THE EFFECTS OF WEAK LABEL STRUCTURE** - Discusses experimental setup for studying weak label structure.\n\n16. **", "citations": [], "input_tokens": 45448, "output_tokens": 512, "cost_micros": 144024, "latency_ms": 10405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "16", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup from the document.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the overall methodology and setup for the weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the specific tasks used in experiments (NLP benchmarks, chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix section provides detailed experimental setup information, including:\n - A.1 NLP TASKS\n - A.2 CHESS PUZZLES\n - A.3 CHATGPT REWARD MODELING\n - A.4 AUXILIARY CONFIDENCE LOSS\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Contains experimental setup for easy-to-hard generalization experiments, including:\n - C.1 CHESS PUZZLES\n - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n - C.3 GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes setup for additional experiments:\n - D.1 SELF-SUPERVISED VISION MODELS\n - D.2 LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Contains experimental setup for studying weak label structures:\n - E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n - E.2 DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n - E.3 MAKING IMITATION TRIVIAL\n\nAnswer: Section 3, Section 4.1, Appendix A, Appendix A.1, Appendix A.2, Appendix A.3, Appendix A.4, Appendix C.1, Appendix C.2, Appendix C.3, Appendix D.1, Appendix D.2, Appendix E.1, Appendix E.2, Appendix E.3", "citations": [], "input_tokens": 49221, "output_tokens": 499, "cost_micros": 155148, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2105263157894737, "method": "list_set", "normalised_pred": "appendix, appendix 1, appendix 2, appendix 3, appendix 4, appendix c 1, appendix c 2, appendix c 3, appendix d 1, appendix d 2, appendix e 1, appendix e 2, appendix e 3, section 3, section 4 1", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experimental setup from the document.\n\nLet me scan through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the core experimental methodology for weak-to-strong learning, including creating weak supervisors, training strong students, and training with ground truth labels.\n\n2. **Section 4.1 TASKS** - This describes the specific tasks used in experiments: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information, including:\n - A.1 NLP TASKS (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n - A.2 CHESS PUZZLES (data preprocessing, training hyperparameters, weak labels, evaluation)\n - A.3 CHATGPT REWARD MODELING (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n - A.4 AUXILIARY CONFIDENCE LOSS (detailed description of the method)\n\n4. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - This discusses additional experimental settings:\n - D.1 SELF-SUPERVISED VISION MODELS\n - D.2 LINEAR PROBING\n\n5. **Appendix C** mentions experimental setup for easy-to-hard generalization experiments.\n\n6. **Appendix E** discusses experimental setup for synthetic experiments on weak label structure.\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix D OTHER WEAK-TO-STRONG SETTINGS, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING", "citations": [], "input_tokens": 47590, "output_tokens": 470, "cost_micros": 149820, "latency_ms": 10702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix d other weak to strong settings, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for sections that discuss experiment setup:\n\n1. **Section 3 METHODOLOGY** - Describes the general weak-to-strong learning setup\n2. **Section 4.1 TASKS** - Describes the three task types (NLP, Chess, Reward Modeling)\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - Contains detailed experimental setup information with subsections:\n - A.1 NLP TASKS\n - A.2 CHESS PUZZLES\n - A.3 CHATGPT REWARD MODELING\n - A.4 AUXILIARY CONFIDENCE LOSS\n4. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Describes ImageNet experiment setup\n5. **Appendix D.2 LINEAR PROBING** - Describes linear probing experiment setup\n6. **Appendix C EASY-TO-HARD GENERALIZATION** - Describes setup for easy-to-hard experiments with subsections:\n - C.1 CHESS PUZZLES\n - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n - C.3 GPT-4 PREDICTED DIFFICULTY\n7. **Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY** - Describes synthetic experiment setup\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix C EASY-TO-HARD GENERALIZATION, Appendix C.1 CHESS PUZZLES, Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING, Appendix C.3 GPT-4 PREDICTED DIFFICULTY, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING, Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 89544, "output_tokens": 499, "cost_micros": 276117, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix c 1 chess puzzles, appendix c 2 nlp tasks difficulty thresholding, appendix c 3 gpt 4 predicted difficulty, appendix c easy to hard generalization, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix e 1 synthetic experiments on simulation difficulty, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to identify all sections that discuss the experiment setup.Let me continue reading to see the complete document structure and identify all sections related to experiment setup.Now let me search specifically for \"experiment\" or \"setup\" to find sections discussing experimental setup:Now let me search more systematically for sections that discuss experimental setup:Based on my reading of the document, I can now identify all the sections that discuss experiment setup. Let me compile the complete list:Now I have a comprehensive view of the document. Let me identify all sections that discuss experiment setup:\n\nAnswer: 3 METHODOLOGY, 4.1 TASKS, A FURTHER EXPERIMENTAL DETAILS, A.1 NLP TASKS, A.2 CHESS PUZZLES, A.3 CHATGPT REWARD MODELING, A.4 AUXILIARY CONFIDENCE LOSS, E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69733, "error": null, "extra": {"thread_id": 2039, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bfc57bfcbbd44c6786a0cd5058f61c15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "1 nlp tasks, 2 chess puzzles, 3 chatgpt reward modeling, 3 methodology, 4 1 tasks, 4 auxiliary confidence loss, e 1 synthetic experiments on simulation difficulty, further experimental details", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the citation information for the paper by Long Ouyang published in 2022.\n\nLooking through the references section, I can find:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730\u201327744, 2022.\n\nHowever, the document does not contain information about how many times this paper has been cited. The document is the paper itself (\"Weak-to-Strong Generalization\") which cites the Ouyang et al. 2022 paper, but it does not provide citation counts for the references it lists.\n\nI cannot determine the number of citations for the Ouyang et al. 2022 paper from this document alone.\n\nAnswer: Cannot be determined from the provided document", "citations": [], "input_tokens": 121904, "output_tokens": 231, "cost_micros": 369177, "latency_ms": 39750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations of the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find references to \"Ouyang et al., 2022\" in the references section:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times this paper is cited throughout the document. Let me search for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Christiano et al., 2017; Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 45447, "output_tokens": 423, "cost_micros": 142686, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations to Long Ouyang 2022 in the references and throughout the document.\n\nLooking at the references section, I can find:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times \"Ouyang et al., 2022\" or similar citations appear in the document.\n\nSearching through the document:\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nLet me verify by searching more carefully through the text for all instances of \"Ouyang\":\n\n- Introduction section: 1 citation\n- Related Work section: 1 citation \n- Tasks section: 1 citation\n- ChatGPT reward modeling subsection: 1 citation\n- Models subsection (A.3): 1 citation\n- Alignment plan section: 1 citation\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 49220, "output_tokens": 405, "cost_micros": 153735, "latency_ms": 8905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations of Ouyang et al., 2022 or Long Ouyang, 2022.\n\nLooking through the document, I can find the following citations to \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"(Stiennon et al., 2020; Ouyang et al., 2022)\"\n6. Page 47: \"(h - tiano et al., 2017; Ouyang et al., 2022)\"\n\nLet me count all instances:\n1. Page 1 - 1 citation\n2. Page 2 - 1 citation\n3. Page 5 - 1 citation\n4. Page 7 - 1 citation\n5. Page 32 - 1 citation\n6. Page 47 - 1 citation\n\nTotal: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 47589, "output_tokens": 417, "cost_micros": 149022, "latency_ms": 7516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations for the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find the reference:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. *Advances in Neural Information Processing Systems*, 35: 27730\u201327744, 2022.\n\nNow I need to count how many times this paper (Ouyang et al., 2022) is cited in the document.\n\nSearching through the document for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nAnswer: 6", "citations": [], "input_tokens": 89543, "output_tokens": 289, "cost_micros": 272964, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about Long Ouyang's 2022 paper and its citation count.Let me search for \"Long Ouyang\" and \"2022\" in this document, and look for citation information.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25655, "error": null, "extra": {"thread_id": 2040, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d3a50787f8a940c590bfec1b692a8eb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 70, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2022", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\n- Subfigure (a) shows that for ground truth labels, **finetuning** (solid lines) performs best across all model sizes, reaching approximately 95% accuracy for the largest models.\n\n- Subfigure (b) shows weak-to-strong performance with weak labels, where **fewshot weak labels (n=5)** (the method shown with crosses/\u00d7) performs best for the largest student models.\n\n- Subfigure (c) directly compares methods and shows that **finetune aux. loss** (solid line with triangles) achieves the highest performance, reaching approximately 90% accuracy for the largest models.\n\nOverall, across the figure, **finetune aux. loss** appears to be the best method when considering weak-to-strong learning performance.\n\nAnswer: finetune aux. loss", "citations": [], "input_tokens": 121910, "output_tokens": 207, "cost_micros": 368835, "latency_ms": 39203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "finetune aux loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods:\n\n- Subfigure (a) compares zero-shot, 5-shot ground truth, and finetune ground truth\n- Subfigure (b) compares zero-shot, 5-shot ground truth, and 5-shot with weak labels \n- Subfigure (c) compares 5-shot with weak labels, naive finetuning, and finetuning with auxiliary confidence loss\n\nAcross all three subfigures, examining the performance curves for the largest strong student models (rightmost side of each plot), the method that achieves the highest test accuracy is **finetuning with the auxiliary confidence loss** (shown with triangles in subfigure c).\n\nAnswer: Finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 45453, "output_tokens": 193, "cost_micros": 139254, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods across model sizes:\n\nThe figure compares:\n- Zero-shot\n- Few-shot with ground truth (n=5)\n- Finetune with ground truth\n- Few-shot with weak labels (n=5)\n- Finetune (baseline)\n- Finetune with auxiliary loss\n\nFrom the plots, particularly subfigure (c) which directly compares weak-to-strong performance, the method that consistently achieves the highest test accuracy across strong student compute sizes is the finetuning with auxiliary confidence loss (shown with solid line and triangles).\n\nAnswer: finetune with aux. loss", "citations": [], "input_tokens": 49226, "output_tokens": 163, "cost_micros": 150123, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "finetune with aux loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which contains three subfigures (a), (b), and (c), I need to identify the six methods shown and determine which performs best.\n\nFrom the figure:\n- Subfigure (a) shows: zero-shot, 5-shot with ground truth (g.t.), and finetuning with ground truth (g.t.)\n- Subfigure (b) shows: zero-shot, 5-shot with ground truth, and 5-shot with weak labels\n- Subfigure (c) shows: 5-shot prompting (weak-to-strong), naive finetuning, and finetuning with auxiliary confidence loss\n\nThe six methods are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetuning with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary confidence loss\n\nLooking at subfigure (c), which directly compares three weak-to-strong methods, the finetuning with auxiliary confidence loss (shown with solid line and triangles) achieves the highest performance, especially for larger model sizes.\n\nAmong all methods shown across the three subfigures, finetuning with ground truth labels would naturally perform best, but among the weak-to-strong methods (which is the focus of subfigure c), the auxiliary confidence loss performs best.\n\nAnswer: finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 47595, "output_tokens": 339, "cost_micros": 147870, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\nIn subfigure (a), comparing zero-shot, 5-shot with ground truth, and finetuning with ground truth, **finetuning with ground truth** achieves the highest test accuracy.\n\nIn subfigure (b), which shows 5-shot prompting with weak labels from different weak supervisors, the best performing is **5-shot with weak labels from the largest weak supervisor (0.1)**.\n\nIn subfigure (c), which compares 5-shot weak labels, naive finetuning, and finetuning with auxiliary loss, **finetuning with auxiliary loss** achieves the highest performance.\n\nOverall, across all methods shown in Figure 9, the best performing method is:\n\nAnswer: finetuning with auxiliary loss", "citations": [], "input_tokens": 89549, "output_tokens": 194, "cost_micros": 271557, "latency_ms": 8141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and identify which method performs best among the six shown.Let me search for Figure 9 in the document:Let me read the section containing Figure 9 to see the actual figure and understand which methods are being compared:Perfect! Now I can see Figure 9 with three subfigures (a), (b), and (c). Let me read a bit more to understand the comparison better:Based on my reading of the document, Figure 9 contains three subfigures (a, b, c) that compare six different methods:\n\nFrom the figure caption and description:\n- **Panel (a)**: zero-shot, 5-shot with ground truth, finetune with ground truth\n- **Panel (b)**: 5-shot with weak labels (multiple supervisor sizes), zero-shot, 5-shot with ground truth\n- **Panel (c)**: 5-shot weak-to-strong prompting, naive finetuning (weak-to-strong), and **finetuning with the confidence loss (auxiliary loss)**\n\nThe text at line 2078-2079 states: \"However, **weak-to-strong finetuning with the confidence loss still generally outperforms** weak-to-strong few-shot prompting.\"\n\nAnd the figure caption at line 1942-1944 states: \"Few-shot weak-to-strong performance becomes competitive with or outperforms finetuning for the largest strong students, though **finetuning with the confidence loss does better**.\"\n\nThe six methods being compared across the three subfigures are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetune with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary/confidence loss\n\nAnswer: Finetuning with the confidence loss (auxiliary loss)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47672, "error": null, "extra": {"thread_id": 2041, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4655e497891e45598d32be191f19ac1d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with confidence loss auxiliary loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves.\n\nLooking at Figure 1 (the technology tree of RAG research), I can see it's organized by year (2020, 2023, 2024) and has three main branches at the top labeled:\n- \"Augmentation Stage\"\n- \"Fine-tuning\" \n- \"Pre-training\"\n\nLet me count the leaves (methods/papers) in each branch:\n\n**Pre-training branch:**\n- 2020: REALM (implied from context)\n- 2022: Retro, CoG, SANTA, TIGER\n- 2023-2024: RAVEN, Retro++, InstructRetro\n\nThis appears to have around 7-8 leaves.\n\n**Fine-tuning branch:**\nThis has many methods spread across 2023-2024, including PROMPTAGATOR, LM-Indexer, various methods - appears to have 15+ leaves.\n\n**Inference branch:**\nThis has the most methods, with numerous entries across 2023-2024 - appears to have 30+ leaves.\n\nLooking more carefully at the tree structure, the \"Pre-training\" branch (on the left side of the tree) has the fewest methods/leaves compared to the other branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 33376, "output_tokens": 305, "cost_micros": 104703, "latency_ms": 9515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 (the Technology tree of RAG research), I need to count the number of methods/papers shown in each branch:\n\n- **Pre-training branch** (bottom): Contains methods like TIGER, SANTA, Atlas, RAVEN, RETRO++, INSTRUCTRETRO, CoG, EAR, KALMV - approximately 9-10 items\n\n- **Fine-tuning branch** (middle): Contains methods like G-Retriever, RADA, CT-RAG, HyKGE, FILCO, InstructRetro, RA-DIT, Self-RAG, FLARE, and many others - this is the largest branch with approximately 25+ items\n\n- **Inference branch** (top): Contains methods like RAPTOR, UniMS-RAG, CRAG, BGM, IAG, BEQ, ARM-RAG, and several others - approximately 15-20 items\n\nThe Pre-training branch clearly has the fewest methods/leaves compared to the other two branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 40683, "output_tokens": 242, "cost_micros": 125679, "latency_ms": 8514, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the figure in the document that has a tree shape and identify which branch has the least leaves.\n\nLooking at the document, I can see Figure 1, which is described as a \"Technology tree of RAG research.\" This figure shows three main stages: Pre-training, Fine-tuning, and Inference, with various methods listed under each branch.\n\nLet me count the items in each branch:\n\n**Pre-training branch:**\n- CoG, RAVEN, Atlas, RAG, RETRO++, INSTRUCTRETRO, TIGER, SANTA (and a few more visible items)\n- This appears to have around 8-10 items\n\n**Fine-tuning branch:**\n- UniMS-RAG, Self-RAG, MK-ToD, QLM-Doc-ranking, SKR, SANTA, IRCOT, TIGER, Self-Mem, SCM4LLMs (and more)\n- This appears to have the most items, around 15-20+\n\n**Inference branch:**\n- CRAG, CT-RAG, EARCoN, CREA-ICL, PRCA, BEQ, ARM-RAG, PROMPTAGATOR, ZemiR-GQA (and many more)\n- This also has many items\n\nLooking at the figure more carefully, the **Pre-training** branch appears to have the fewest items/leaves compared to the Fine-tuning and Inference branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 30919, "output_tokens": 336, "cost_micros": 97797, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves (items).\n\nLooking at Figure 1 titled \"Technology Tree of RAG research,\" I can see it's organized into three main columns representing different stages:\n- Pre-training\n- Fine-tuning \n- Inference\n\nLet me count the items in each branch/column:\n\n**Pre-training column:** Contains items like:\n- G-Retriever, CRAG, IAG, FILCO, ARM-RAG, RADA, RAPTOR, BGM, HyKGE, DRAGON-AI, PaperQA, CREA-ICL\n- PRCA, Token-Elimination, KALMV, ITRG\n- 1-PAGER, FABULA, SKR, ToC, QLM-Doc-ranking, Recomp\n- KGP, LLM-R, ITER-RETGEN, COQ, LLM-IE, ICRALM, DSP\n- KnowledGPT, IRCOT, PGRA, PKG, SCM4LLMs, Filter-Reranker, RePLUG\n- CoG, TIGER, Retro++\n- GenRead\n- (empty row)\n- RECITE\n\n**Fine-tuning column:** Contains items like:\n- UniMS-RAG, CT-RAG, BEQ, CoN, EAR\n- RAST, Self-RAG, LM-Indexer, RA-DIT, Dual-Feedback-ToD, MK-ToD, RAG_Robust, Retrieve-and-Sample\n- InstructRetro\n- RAVEN\n- SANTA, RRR, Self-Mem, UPRISE, R-GQA, Zemi\n- SUGRE, AAR, FLARE\n- PROMPTAGATOR, RAG-e2e\n- Atlas\n- RAG\n\n**Inference column:** This appears to be the largest with many items distributed across multiple years.\n\nComparing the three branches, the **Fine-tuning** column appears to have fewer distinct method names than the other two columns, but I need to count more carefully", "citations": [], "input_tokens": 37501, "output_tokens": 512, "cost_micros": 120183, "latency_ms": 18250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "comparing three branches fine tuning column appears to have fewer distinct method names than other two columns but i need to count more carefully", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find a figure with a tree shape and identify the branch with the least leaves.Let me search for references to figures with tree shapes in the document:Let me read through the document to find figures. I'll start by looking at the actual content:The document mentions a \"technology tree\" in Figure 1. Let me look for Figure 1:Let me search for where figures are actually displayed in the document:Good! There are embedded images. Let me read around Im1.png which should be Figure 1 (the technology tree):This looks like it's the OCR text from the tree figure. Let me continue reading to see all the branches:Perfect! I found the tree-shaped figure. It's a tree diagram with three main branches:\n1. Fine-tuning (green, left branch)\n2. Pre-training (orange/gold, middle branch) \n3. Inference (blue-green, right branch)\n\nLet me continue reading the visual description to see which branch has the least leaves (papers/items):Perfect! The visual description clearly states:\n\n- **Fine-tuning branch**: Most extensive with numerous sub-branches and approaches\n- **Pre-training branch**: Narrowest with fewer approaches\n- **Inference branch**: Dense with many recent (2024) developments\n\nThe Pre-training branch is described as the \"narrowest with fewer approaches,\" meaning it has the least leaves.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62969, "error": null, "extra": {"thread_id": 2042, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bc29640067d14f5eb3f9f7d53e8522d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 357, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 on page 4 of the document, which shows \"Comparison between the three paradigms of RAG,\" I can see three main sections representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/shapes used:\n\n1. User icon (person silhouette)\n2. Query icon (document/text box)\n3. Documents icon (stacked documents)\n4. Indexing box\n5. Retrieval box\n6. Prompt box (document icon)\n7. Frozen LLM (snowflake icon with text)\n8. Output box\n9. Pre-Retrieval box (with sub-elements like \"Query Rewriting\", \"Query Expansion\", \"Query Enhancement\")\n10. Post-Retrieval box (with sub-elements like \"Re-rank\", \"Memory\", \"Fusion\")\n11. Various module boxes in the Modular RAG section (Routing, Search, Predict, Retrieve, Rewrite, RAG, Rerank, Read, Demonstrate, Fusion, Memory)\n12. Different pattern sections showing Rewrite, Retrieve, Read combinations\n\nCounting the distinct icon types (not individual instances):\n- User icon (person)\n- Query icon\n- Documents icon\n- Standard rectangular boxes (for processes)\n- Snowflake icon (for Frozen LLM)\n- Document/page icon (for Prompt)\n\nLooking more carefully at the distinct visual elements:\n1. Person/user icon\n2. Text/query box\n3. Documents stack icon\n4. Process boxes (rectangles)\n5. Snowflake (frozen LLM)\n6. Document icon\n\nAnswer: 6", "citations": [], "input_tokens": 64616, "output_tokens": 404, "cost_micros": 199908, "latency_ms": 15077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see:\n\n1. **User Query icon** - represented by a person/user symbol\n2. **Documents icon** - represented by stacked document/paper symbols\n3. **Indexing icon** - appears to show database/storage symbols\n4. **Retrieval icon** - shown with magnifying glass or search symbols\n5. **Read icon** - shown as a document being read\n6. **Predict icon** - shown with a specific symbol\n7. **Output icon** - shown at the end of processes\n8. **Prompt icon** - shown in the middle paradigm\n9. **Frozen LLM icon** - represented by a brain or model symbol\n10. **Rerank icon** - shown in Advanced RAG\n11. **Rewrite icon** - shown in Modular RAG\n12. **Demonstrate icon** - shown in Modular RAG\n13. **Routing icon** - shown in Modular RAG\n14. **Search icon** - shown in Modular RAG\n15. **Fusion icon** - shown in Modular RAG\n16. **Memory icon** - shown in Modular RAG\n\nLet me recount more carefully by looking at the actual distinct visual icons in the figure:\n\nFrom the figure, I can identify these distinct icon types:\n- User/person icon\n- Document/paper stack icon\n- Database/storage icon\n- Magnifying glass (search/retrieval)\n- Brain/model icon (for LLM)\n- Arrow/flow icons\n- Various process boxes with different labels\n\nCounting the truly distinct visual icons (not just labeled boxes):\n1. User icon\n2. Document icon\n3. Database/indexing icon\n4. Magnifying glass/search icon\n5. Brain/LLM icon\n6. Output/result icon\n\nAnswer: 6", "citations": [], "input_tokens": 33359, "output_tokens": 449, "cost_micros": 106812, "latency_ms": 10735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 titled \"Comparison between the three paradigms of RAG,\" I can see three diagrams showing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify each distinct icon type used across all three paradigms:\n\n1. **User** - person icon (shown in all three)\n2. **Query** - document/text icon (shown in all three)\n3. **Documents** - stack of documents icon (shown in all three)\n4. **Indexing** - cylindrical database icon (shown in Naive and Advanced RAG)\n5. **Retrieval** - magnifying glass/search icon (shown in all three)\n6. **Rewrite** - circular arrow icon (shown in Advanced RAG)\n7. **Rerank** - list with arrows icon (shown in Advanced RAG)\n8. **Prompt** - text/document icon (shown in all three)\n9. **Frozen LLM** - brain/processing icon (shown in all three)\n10. **Read** - book/reading icon (shown in all three)\n11. **Output** - result/output icon (shown in all three)\n12. **Modules (R)** - box with R (shown in Modular RAG)\n13. **Search** - magnifying glass (shown in Modular RAG)\n14. **Routing** - directional arrow icon (shown in Modular RAG)\n15. **Predict** - crystal ball/prediction icon (shown in Modular RAG)\n16. **Demonstrate** - pointing hand icon (shown in Modular RAG)\n17. **Memory** - storage icon (shown in Modular RAG)\n18. **Fusion** - merge icon (shown in Modular RAG)\n19. **Summary** - document summary icon (shown in Advanced RAG)\n20. **Judge** - decision diamond (shown in Modular RAG)\n21. **Generate** - generation icon (shown in Modular RAG)\n\nLet me recount more carefully by looking at the actual distinct icon shapes:\n\n1. User (person)\n2. Query (document)\n3. Documents (stack)", "citations": [], "input_tokens": 40666, "output_tokens": 512, "cost_micros": 129678, "latency_ms": 10766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three sections (Left, Middle, Right) representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/symbols used in the figure:\n\n1. \"User Query\" box (rectangular box with text)\n2. \"Indexing\" box\n3. \"Retrieval\" box\n4. \"Rerank\" box\n5. \"Rewrite\" box\n6. \"Fusion\" box\n7. \"Summary\" box\n8. \"Demonstrate\" box\n9. \"Read\" box\n10. \"Search\" box\n11. \"Retrieve\" box\n12. \"Memory\" box\n13. \"Predict\" box\n14. \"Routing\" box\n15. \"Documents\" box\n16. \"RAG\" box\n17. \"Output\" box\n18. \"Response\" box\n19. \"Judge\" box\n20. \"Generate\" box\n21. \"Transformation/Decomposition\" box\n22. \"Iterate\" box\n23. Arrow symbols (connecting the boxes)\n24. \"Max Times/Threshold\" decision point\n25. \"Max Depth (Tree)/Threshold\" decision point\n26. \"Generate Special Token/Threshold\" decision point\n27. \"N Times\" indicator\n\nLooking more carefully at the visual elements, the distinct icon types are:\n- Rectangular boxes (for processes/modules)\n- Arrows (for flow direction)\n- Diamond or decision point shapes (for conditional logic)\n\nCounting the distinct visual icon types (not the number of instances, but types):\n1. Rectangular box/module icon\n2. Arrow icon\n3. Diamond/decision point icon\n\nAnswer: 3", "citations": [], "input_tokens": 30902, "output_tokens": 415, "cost_micros": 98931, "latency_ms": 10655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 in the document to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three main diagrams:\n\n1. **Naive RAG (Left)**: Shows a flowchart with:\n - User Query\n - Documents\n - Indexing\n - Retrieval\n - Prompt\n - Frozen LLM\n - Output\n\n2. **Advanced RAG (Middle)**: Shows:\n - User Query\n - Documents\n - Indexing\n - Pre-Retrieval (with sub-components: Query Routing, Query Rewriting, Query Expansion)\n - Retrieval\n - Post-Retrieval (with sub-components: Rerank, Summary, Fusion)\n - Prompt\n - Frozen LLM\n - Output\n\n3. **Modular RAG (Right)**: Shows:\n - Multiple modules (Routing, Search, Predict, Rewrite, RAG, Rerank, Retrieve, Read, Demonstrate, Fusion, Memory)\n - Various patterns including Naive RAG, Advanced RAG, DSP, and ITER-RETGEN\n\nLooking at the visual representation, the distinct icon shapes/types I can identify are:\n1. Rectangular boxes (for most components)\n2. Rounded rectangles/boxes (for grouping/subgraphs)\n3. Arrows/lines (for connections)\n\nHowever, if we're counting distinct visual elements or node types based on their representation in the figure, the components are primarily represented as boxes/rectangles with text labels. The figure uses a consistent box/rectangle style for representing different components.\n\nBased on the mermaid diagram structure shown, all components are represented using similar rectangular node shapes, with the main distinction being in the grouping (subgraphs) and connections rather than distinct icon types.\n\nAnswer: 1", "citations": [], "input_tokens": 37484, "output_tokens": 444, "cost_micros": 119112, "latency_ms": 12312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count the distinct icons used.Let me search for \"Figure 3\" in the document:Now let me read around line 788 to find Figure 3:Let me continue reading to find Figure 3:I see Figure 2 but need to continue searching for Figure 3. Let me search more:Perfect! I found Figure 3. Let me continue reading to see the complete visual description:Perfect! I have found Figure 3 and its visual description. Let me carefully count the distinct icons mentioned in the visual description:\n\nFrom the visual description of Figure 3, I can identify the following distinct icons:\n\n1. **User/Query icon** - mentioned in Naive RAG and Advanced RAG\n2. **Documents icon** - mentioned in Naive RAG and Advanced RAG (stacked papers/documents)\n3. **Snowflake icon** - mentioned in Naive RAG and Advanced RAG (representing Frozen LLM)\n4. **Document icon** - mentioned in Naive RAG (at the Prompt stage)\n5. **Shuffle/rerank icon** - mentioned in Advanced RAG Post-Retrieval\n6. **Waveform/summary icon** - mentioned in Advanced RAG Post-Retrieval\n7. **Layers/fusion icon** - mentioned in Advanced RAG Post-Retrieval\n\nLet me recount more carefully by looking at what the visual description explicitly states:\n- User/Query icons (top of diagrams)\n- Documents icons (top of diagrams)\n- Document icon (at Prompt stage)\n- Snowflake icon (representing Frozen LLM)\n- Shuffle/rerank icon (in Post-Retrieval)\n- Waveform/summary icon (in Post-Retrieval)\n- Layers/fusion icon (in Post-Retrieval)\n\nThat gives us 7 distinct icons total.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51812, "error": null, "extra": {"thread_id": 2043, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c31c1d3df3774ac882e3f8a22f227910", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 283, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that has a retrieval granularity of \"Phrase\" in Table I.\n\nLooking at Table I (Summary of RAG methods) on page 6, I can see the columns: Method, Retrieval Source, Retrieval Data Type, Retrieval Granularity, Augmentation Stage, and Retrieval process.\n\nScanning the \"Retrieval Granularity\" column, I find one entry with \"Phrase\":\n- CoG [29] - which has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of reference [29]. Looking at the references section starting on page 17, reference [29] is:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 64633, "output_tokens": 220, "cost_micros": 197199, "latency_ms": 34000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that uses \"phrase\" as its retrieval granularity.\n\nLooking at Table I \"SUMMARY OF RAG METHODS\", I can find the retrieval granularity column. Let me search for entries with \"Phrase\" as the retrieval granularity.\n\nFrom Table I, I can see:\n- CoG [29] has:\n - Retrieval Source: Wikipedia\n - Retrieval Data Type: Text\n - Retrieval Granularity: Phrase\n - Augmentation Stage: Pre-training\n - Retrieval process: Iterative\n\nNow I need to find the full title of the CoG paper. Looking at the references section:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 33376, "output_tokens": 222, "cost_micros": 103458, "latency_ms": 7266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for methods with \"Phrase\" retrieval granularity in Table I...\n\nFrom Table I (SUMMARY OF RAG METHODS), I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nLooking at the references section for reference [29]:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 40683, "output_tokens": 133, "cost_micros": 124044, "latency_ms": 8407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with phrase-level retrieval granularity.\n\nLooking at Table I (Summary of Retrieval Source and Retrieval Granularity Used in RAG Methods), I can find methods with \"Phrase\" as their retrieval granularity.\n\nFrom the table, I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of the CoG paper from the references section.\n\nLooking at the references, reference [29] is:\n\"T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\"\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 30919, "output_tokens": 187, "cost_micros": 95562, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with retrieval granularity of \"Phrase\".\n\nLooking at Table I (Summary of RAG Methods), I can search for entries where \"Retrieval Granularity\" is \"Phrase\".\n\nScanning through Table I, I find:\n- CoG [29] has retrieval granularity: Phrase\n\nNow I need to find the full title of paper [29] from the references section.\n\nLooking at reference [29]:\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in *The Eleventh International Conference on Learning Representations*, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 37501, "output_tokens": 174, "cost_micros": 115113, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about retrieval granularity and phrase-level methods in the provided documents.Let me continue reading to find the title and look for information about retrieval granularity:Good! I found the paper title: \"Retrieval-Augmented Generation for Large Language Models: A Survey\". Now I need to search for information about retrieval granularity and specifically which method has \"phrase\" as its retrieval granularity. Let me search for tables or sections that discuss retrieval granularity:Perfect! I found a table that mentions \"Phrase\" as a retrieval granularity. Let me read that section to find the complete information:Great! I can see that \"CoG [29]\" has \"Phrase\" as its retrieval granularity. Now I need to find the full title of the CoG paper. Let me look for the reference [29]:Perfect! I found reference [29]. Let me read that section to get the full title:I can see reference [29] starts at line 4090. Let me read a bit more to get the complete title:Perfect! I found the full title of reference [29], which is the paper that proposes the method (CoG) with phrase retrieval granularity.\n\nThe full title is: **\"Copy is all you need\"**\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52827, "error": null, "extra": {"thread_id": 2044, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_27dfaf3e8bfa47e1b55fb4feceaed85a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 297, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through Table II to find datasets that have exactly three methods listed.\n\nLooking at Table II (SUMMARY OF RAG METHODS), I need to count the methods for each dataset:\n\n- WebQ (Web Questions): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - 3 methods \u2713\n- MS MARCO: [4], [40], [52] - 3 methods \u2713\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum(QM): [60], [123] - 2 methods\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB [128],MMCU_Medical [129]: [81] - 1 method\n- QuALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n- GraphQA: [84] - 1 method\n- Amazon(Toys,Sport,Beauty): [39], [40] - 2 methods\n- T-REx [141],ZsRE [142]: [27], [51] - 2 methods\n- HellaSwag: [20], [66] - 2 methods\n- CoT Reasoning: [27] - 1 method\n- CSQA: [55] - 1 method\n- StrategyQA: [14], [24], [48], [51], [55], [58] - 6 methods\n- XSum: [17] - 1 method\n- VioLens: [19] - 1 method\n- TREC: [33] - 1 method\n- SST-2: [20], [33], [38] - 3 methods \u2713\n- CodeSearchNet: [76] -", "citations": [], "input_tokens": 64631, "output_tokens": 512, "cost_micros": 201573, "latency_ms": 17125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "codesearchnet 76", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): [3], [4], [22], [27], [40], [43], [54], [62], [71], [112] [20], [44], [72] [13], [30], [34], [45], [50], [64] - more than 3\n- TriviaQA (TQA): [4], [27], [59], [62], [112] [22], [25], [43], [44], [71], [72] [20], [23], [30], [32], [45], [69], [112] - more than 3\n- SQUAD: [3], [4], [13], [30], [50], [68] - 6 methods\n- Web Questions (WebQ): [4], [40], [52] - exactly 3 methods\n- PopQA: [4], [40], [52] - exactly 3 methods\n- MS MARCO: [117] - only 1 reference\n\n**QA - Multi-hop:**\n- HotpotQA: [7], [14], [22], [27], [59], [62], [69], [71], [91] - 9 methods\n- 2WikiMultiHopQA: [14], [51], [61], [91] - 4 methods\n- MuSiQue: [120] - only 1 reference\n\n**QA - Long-form:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB: [", "citations": [], "input_tokens": 33374, "output_tokens": 512, "cost_micros": 107802, "latency_ms": 11546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "cmb", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): More than 3 methods\n- TriviaQA (TQA): More than 3 methods\n- SQUAD: More than 3 methods\n- Web Questions (WebQ): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - **exactly 3 methods**\n- MS MARCO: [4], [40], [52] - **exactly 3 methods**\n\n**QA - Multi-hop:**\n- HotpotQA: More than 3 methods\n- 2WikiMultiHopQA: [14], [24], [48], [59], [61], [91] - 6 methods\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n\n**QA - Long-form QA:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain QA:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB, MMCU_Medical: [81] - 1 method\n\n**QA - Multi-Choice QA:**\n- QUALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): [13], [27], [34], [42] - 4 methods\n- KBP: [74],", "citations": [], "input_tokens": 40681, "output_tokens": 512, "cost_micros": 129723, "latency_ms": 12062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "kbp 74", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Table II and find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n1. Natural Question (NQ) - has 10 methods listed\n2. TriviaQA (TQA) - has 5 methods listed\n3. SQuAD - has 7 methods listed\n4. Web Questions (WebQ) - has 6 methods listed\n5. PopQA - has 3 methods: [7], [25], [67] \u2713\n6. MS MARCO - has 3 methods: [4], [40], [52] \u2713\n7. HotpotQA - has 8 methods listed\n8. 2WikiMultiHopQA - has 6 methods listed\n9. MuSiQue - has 4 methods listed\n10. ELI5 - has 5 methods listed\n11. NarrativeQA (NQA) - has 4 methods listed\n12. ASQA - has 2 methods listed\n13. QMSum (QM) - has 2 methods listed\n14. Qasper - has 2 methods listed\n15. COVID-QA - has 2 methods listed\n16. CMB, MMCU Medical - has 1 method listed\n17. QuALITY - has 2 methods listed\n18. ARC - has 2 methods listed\n19. CommonsenseQA - has 2 methods listed\n20. GraphQA - has 1 method listed\n21. Wizard of Wikipedia (WoW) - has 4 methods listed\n22. KBP - has 2 methods listed\n23. DuleMon - has 1 method listed\n24. CamRest - has 2 methods listed\n25. Amazon (Toys, Sport, Beauty) - has 2 methods listed\n26. WikiEvent - has 4 methods listed\n27. RAMS - has 2 methods listed\n28. T-REx, ZsRE - has 2 methods listed\n29. HellaSwag - has 2 methods listed\n30. CoT Reasoning - has 1 method listed", "citations": [], "input_tokens": 30917, "output_tokens": 512, "cost_micros": 100431, "latency_ms": 11141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "30 cot reasoning has 1 method listed", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to find datasets in Table II that have exactly three methods listed.\n\nLet me go through Table II systematically and count the number of methods for each dataset:\n\n**QA - Single-hop:**\n- Natural Question (NQ): 12 methods\n- TriviaQA (TQA): 12 methods\n- SQuAD: 7 methods\n- Web Questions (WebQ): 6 methods\n- PopQA: 3 methods \u2713\n- MS MARCO: 3 methods \u2713\n\n**QA - Multi-hop:**\n- HotpotQA: 15 methods\n- 2WikiMultiHopQA: 6 methods\n- MuSiQue: 4 methods\n\n**QA - Long-form QA:**\n- ELI5: 5 methods\n- NarrativeQA (NQA): 4 methods\n- ASQA: 2 methods\n- QMSum (QM): 2 methods\n\n**QA - Domain QA:**\n- Qasper: 2 methods\n- COVID-QA: 2 methods\n- CMB, MMCU_Medical: 1 method\n\n**QA - Multi-Choice QA:**\n- QuALITY: 2 methods\n- ARC: 2 methods\n- CommonsenseQA: 2 methods\n\n**QA - Graph QA:**\n- GraphQA: 1 method\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): 4 methods\n- KBP: 2 methods\n- DuleMon: 1 method\n- CamRest: 2 methods\n- Amazon (Toys, Sport, Beauty): 2 methods\n\n**IE:**\n- WikiEvent: 4 methods\n- RAMS: 2 methods\n- T-REx, ZsRE: 2 methods\n\n**Reasoning:**\n- HellaSwag: 2 methods\n- CoT Reasoning: 1 method\n- CSQA: 1 method\n\n**Others:**\n- MMLU: 7 methods\n- WikiText-103: 4 methods\n-", "citations": [], "input_tokens": 37499, "output_tokens": 512, "cost_micros": 120177, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table II and identify datasets with exactly three methods.Let me search for \"TABLE II\" in the document:Now let me read the section containing TABLE II:Now I can see Table II. Let me analyze it carefully to find datasets with exactly three methods. I need to count the number of method references for each dataset. Let me go through the table systematically:\n\nLooking at the table structure, I can see:\n- PopQA [116]: [7], [25], [67] - exactly 3 methods\n- MS MARCO [117]: [4], [40], [52] - exactly 3 methods\n- PopQA has methods: [7], [25], [67] - that's 3 methods\n- MS MARCO has methods: [4], [40], [52] - that's 3 methods\n- SST-2 [156]: [20], [33], [38] - exactly 3 methods\n\nLet me verify by checking each row more carefully:\n\n1. PopQA [116]: [7], [25], [67] = 3 methods\n2. MS MARCO [117]: [4], [40], [52] = 3 methods \n3. QMSum(QM) [125]: [60], [123] = 2 methods\n4. Qasper [126]: [60], [63] = 2 methods\n5. COVID-QA [127]: [35], [46] = 2 methods\n6. QUALITY [130]: [60], [63] = 2 methods\n7. ARC [131]: [25], [67] = 2 methods\n8. CommonsenseQA [132]: [58], [66] = 2 methods\n9. GraphQA [84]: [84] = 1 method\n10. KBP [134]: [74], [135] = 2 methods\n11. DuleMon [136]: [74] = 1 method\n12. CamRest [137]: [78], [79] = 2 methods\n13. Amazon(Toys,Sport,Beauty) [138]: [39], [40] = 2 methods\n14. RAMS [140]: [36], [37] = 2 methods\n15. T-REX [141],ZsRE [142]: [27], [51] = 2 methods\n16. HellaSwag [143]: [20], [66] = 2 methods\n17. CoT Reasoning [144]: [27] = 1 method\n18. CSQA [145]: [55] = 1 method\n19. PubHealth [150]: [25], [67] = 2 methods\n20. Biography [151]: [67] = 1 method\n21. WikiASP [152]: [24] = 1 method\n22. XSum [153]: [17] = 1 method\n23. VioLens [154]: [19] = 1 method\n24. TREC [155]: [33] = 1 method\n25. SST-2 [156]: [20], [33], [38] = 3 methods\n26. CodeSearchNet [157]: [76] = 1 method\n27. NoMIRACL [56]: [56] = 1 method\n28. GSM8K [158]: [73] = 1 method\n29. JRC-Acquis [159]: [17] = 1 method\n\nAnswer: PopQA, MS MARCO, SST-2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42953, "error": null, "extra": {"thread_id": 2045, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ea697b93518746f587b9ed16f4f922df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 257, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "ms marco, popqa, sst 2", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 10 where Section V \"AUGMENTATION PROCESS IN RAG\" begins, I can see the subsections listed:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 64634, "output_tokens": 83, "cost_micros": 195147, "latency_ms": 20108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the section titled \"AUGMENTATION PROCESS IN RAG\" and identify its subsections.\n\nFrom Section V (AUGMENTATION PROCESS IN RAG), I can see the following structure:\n\nV. AUGMENTATION PROCESS IN RAG\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nThese are the three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 33377, "output_tokens": 127, "cost_micros": 102036, "latency_ms": 4500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document, I can identify the subsections by examining the structure.\n\nThe section starts on page 10 and includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval \nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 40684, "output_tokens": 95, "cost_micros": 123477, "latency_ms": 4765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for the section \"AUGMENTATION PROCESS IN RAG\" (Section V), I can identify its subsections:\n\nFrom the document structure, Section V contains:\n- A. Iterative Retrieval\n- B. Recursive Retrieval \n- C. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 30920, "output_tokens": 91, "cost_micros": 94125, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document:\n\nThe section includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 37502, "output_tokens": 76, "cost_micros": 113646, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the \"AUGMENTATION PROCESS IN RAG\" section and identify its subsections.Let me search for the \"AUGMENTATION PROCESS IN RAG\" section in the document.Now let me read the section around line 2636 to see the subsections.Let me continue reading to see all subsections under \"AUGMENTATION PROCESS IN RAG\".Perfect! I can now see the structure of the \"AUGMENTATION PROCESS IN RAG\" section. It has three subsections:\n\n1. A. Iterative Retrieval (line 2646)\n2. B. Recursive Retrieval (line 2827)\n3. C. Adaptive Retrieval (line 2858)\n\nThe next section \"VI. TASK AND EVALUATION\" starts at line 2908, confirming that these are the only three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34782, "error": null, "extra": {"thread_id": 2046, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dfd2aaa1a1dd45f1bdec0523c2899c57", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 177, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I need to search through the document for information about methods that integrate knowledge into white-box models via directive fine-tuning.\n\nLooking through the document, I found relevant information on page 10 (document page 9-10) in section III. RETRIEVAL, subsection E. Adapter:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text explicitly states that PKG (which likely stands for \"Parametric Knowledge Guiding\" based on reference [75] mentioned in the text) introduces this method.\n\nAnswer: PKG", "citations": [], "input_tokens": 64627, "output_tokens": 158, "cost_micros": 196251, "latency_ms": 20969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 33370, "output_tokens": 94, "cost_micros": 101520, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a method that integrates knowledge into white-box models via directive fine-tuning.\n\nIn Section III.E (Adapter), I can find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text clearly states that PKG introduces this method for integrating knowledge into white-box models via directive fine-tuning.\n\nAnswer: PKG", "citations": [], "input_tokens": 40677, "output_tokens": 130, "cost_micros": 123981, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nThe document discusses RA-DIT (Retrieval-Augmented Dual Instruction Tuning) in reference [27], which is described as \"retrieval-augmented dual instruction tuning.\" This method involves fine-tuning both the retriever and generator components.\n\nIn Section IV.B on LLM Fine-tuning, the document states: \"Fine-tuning of LLM can also be coordinated with fine-tuning of the retriever to align preferences. A typical approach, such as RA-DIT [27], aligns the scoring functions between Retriever and Generator using KL divergence.\"\n\nThis method specifically focuses on instruction tuning (a form of directive fine-tuning) to integrate retrieval-augmented knowledge into language models.\n\nAnswer: RA-DIT", "citations": [], "input_tokens": 30913, "output_tokens": 200, "cost_micros": 95739, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ra dit", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about integrating knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 37495, "output_tokens": 95, "cost_micros": 113910, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about which method integrates knowledge into white-box models via directive fine-tuning.Let me search for content about \"directive fine-tuning\" and \"white-box models\":Let me read more of the document to find tables or sections discussing methods:Let me search for tables that compare methods:Let me read around the tables to find information about methods:Let me search for \"Table I\" to find the table that might contain method comparisons:This table shows methods but doesn't mention white-box or directive fine-tuning. Let me search more specifically:Let me search for a combination of terms:Let me look for \"fine-tuning\" methods and check around line 2053 which mentions modular RAG integration with fine-tuning:Let me look for sections on fine-tuning methods:Found it! Let me read around line 2526:Perfect! I found the answer. The text states \"PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\" Let me verify this is PKG by reading a bit more context:Perfect! The answer is clear. The document states: \"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\"\n\nAnswer: PKG", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 72530, "error": null, "extra": {"thread_id": 2047, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9ee1ee526a9645f5ba543d1cda3a3a6d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 4 to identify which nodes RAPTOR retrieved for both questions about the Cinderella story.\n\nFrom the document, Figure 4 shows the querying process for two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nLooking at Figure 4 on page 7, I can see:\n- Nodes highlighted indicate RAPTOR's selections\n- The figure shows a tree structure with nodes numbered 0-15 at the leaf layer, nodes 16-22 at an intermediate layer, and nodes 23-26 at the root layer\n\nFor Question 1 (central theme), RAPTOR retrieved nodes that are highlighted in one color.\nFor Question 2 (happy ending), RAPTOR retrieved nodes that are highlighted in another color.\n\nThe nodes that overlap (retrieved for BOTH questions) would be those highlighted with both colors.\n\nFrom the visual representation in Figure 4, I can see that certain nodes appear to be selected for both queries. Looking at the highlighted patterns, the nodes that appear to be retrieved for both questions are nodes 18 and 19 (visible in the intermediate layer where both highlighting patterns overlap).\n\nAnswer: 18, 19", "citations": [], "input_tokens": 58460, "output_tokens": 279, "cost_micros": 179565, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "18, 19", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are highlighted (retrieved by RAPTOR) for both questions about Cinderella.\n\nThe figure shows two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nAccording to the figure caption: \"Highlighted nodes indicate RAPTOR's selections, while arrows point to DPR's leaf nodes.\"\n\nExamining the visual representation in Figure 4, I can see the tree structure with nodes numbered 0-26. For nodes to be retrieved for both questions, they must be highlighted for both question 1 and question 2.\n\nLooking at the overlap between the two retrievals shown in the figure, the nodes that appear to be selected for both questions are those at the higher levels of the tree that contain summary information relevant to both the central theme and the happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved for both questions appear to be from the upper layers of the tree.\n\nAnswer: 23, 24, 25, 26", "citations": [], "input_tokens": 22903, "output_tokens": 241, "cost_micros": 72324, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "23, 24, 25, 26", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4, I need to identify which nodes are retrieved by RAPTOR for both questions about Cinderella.\n\nThe legend indicates:\n- Nodes retrieved for Question 1 (central theme) are shown in one color\n- Nodes retrieved for Question 2 (happy ending) are shown in another color\n- Nodes retrieved for BOTH questions would be those highlighted with both colors\n\nFrom the figure, examining the tree structure and the highlighted nodes, the nodes that appear to be retrieved for both questions are the nodes that contain overlapping/comprehensive information about the story.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions are:\n\nAnswer: 16, 17, 18", "citations": [], "input_tokens": 27206, "output_tokens": 161, "cost_micros": 84033, "latency_ms": 5530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "list_set", "normalised_pred": "16, 17, 18", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes RAPTOR retrieves for both Question 1 (\"What is the central theme of the story?\") and Question 2 (\"How did Cinderella find a happy ending?\").\n\nFrom the figure description and the illustration:\n- For Question 1, RAPTOR retrieves certain highlighted nodes\n- For Question 2, RAPTOR retrieves certain highlighted nodes\n\nThe nodes that appear to be retrieved for BOTH questions (nodes that would be highlighted for both questions) are the nodes that contain information relevant to both the central theme and Cinderella's happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions appear to be nodes 9 and 10 (which are the upper-level summary nodes that contain broader thematic information relevant to both questions).\n\nAnswer: Node 9, Node 10", "citations": [], "input_tokens": 23495, "output_tokens": 203, "cost_micros": 73530, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "node 10, node 9", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are retrieved by RAPTOR for both questions.\n\nThe legend indicates:\n- Orange nodes/arrows: RAPTOR retrieved for Question 1 (\"What is the central theme of the story?\")\n- Purple nodes/arrows: RAPTOR retrieved for Question 2 (\"How did Cinderella find a happy ending?\")\n\nFrom the tree diagram, I can see:\n- Question 1 (orange): Node 25 and Node 19 are highlighted\n- Question 2 (purple): Node 17 and Node 20 are highlighted\n\nLooking for nodes that appear in both colors (retrieved for both questions), I need to check if any nodes have both orange and purple highlighting. However, from the visual representation, each highlighted node appears to be associated with only one question - the nodes are distinct between the two questions.\n\nUpon careful examination of Figure 4, there don't appear to be any nodes that are highlighted in both colors, meaning no nodes are retrieved by RAPTOR for both questions simultaneously.\n\nAnswer: None", "citations": [], "input_tokens": 26136, "output_tokens": 239, "cost_micros": 81993, "latency_ms": 7280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which nodes are retrieved by RAPTOR for both questions.Let me search for Figure 4 in the document:Now let me read the specific section containing Figure 4 from the target document:Now let me look at the actual figure content. I need to read more lines to see the figure structure:I can see the figure caption clearly states the two questions. Now I need to look at the actual tree diagram to see which nodes are highlighted for both questions. Let me check if there's more detail in the figure or if I need to examine it more carefully. Let me search for more context about the nodes:Let me look at the image content more carefully. Let me read around line 863 where the OCR text shows the nodes:Perfect! Now I can see the key information. Looking at the visual description:\n\n- **Orange/peach borders** (RAPTOR for Question 1): Nodes 15, 11, 18\n- **Purple/magenta borders** (RAPTOR for Question 2): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe nodes that appear in BOTH lists (retrieved by RAPTOR for both questions) is: **Node 11**\n\nHowever, let me also check the arrows to be thorough:Based on the visual description of Figure 4:\n\n**Question 1** (orange/peach): Nodes 15, 11, 18\n**Question 2** (purple/magenta): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe node that appears in both lists is **11**.\n\nAdditionally, looking at the arrows (which point to DPR's retrievals), nodes 3 and 11 have both orange and purple arrows, but the question asks specifically about which nodes are retrieved by RAPTOR (indicated by the border colors), not DPR (indicated by the arrows).\n\nAnswer: 11", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61625, "error": null, "extra": {"thread_id": 2048, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_99be096db80e4817854f19cccddee98b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 378, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "11", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 6 of the document, I can identify the axes of the graph.\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis is labeled \"F1\" (which represents the F1 score metric).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 58457, "output_tokens": 109, "cost_micros": 177006, "latency_ms": 33343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which is titled \"Comparison of querying methods\":\n\nThe horizontal axis is labeled \"Context Length\" (measuring from 500 to 2500).\n\nThe vertical axis is labeled \"F1\" (measuring from approximately 40 to 55).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 22900, "output_tokens": 74, "cost_micros": 69810, "latency_ms": 4109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis shows the performance metric (appears to be accuracy or F1 score percentage based on the scale showing values like 45, 80, etc.).\n\nAnswer: Context Length, Performance Score (F1/Accuracy)", "citations": [], "input_tokens": 27203, "output_tokens": 116, "cost_micros": 83349, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance score f1 accuracy", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, I need to identify the horizontal and vertical axes.\n\nFrom the figure caption and the graph itself:\n- The horizontal axis shows \"Context Length\" (measured in tokens, ranging from 500 to 2500)\n- The vertical axis shows the performance metric (which appears to be accuracy or F1 score, ranging from approximately 50 to 58)\n\nAnswer: Context Length, Performance metric (accuracy/F1 score)", "citations": [], "input_tokens": 23492, "output_tokens": 105, "cost_micros": 72051, "latency_ms": 5092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance metric accuracy f1 score", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods\" with results on 20 stories from the QASPER dataset.\n\nFrom the table in Figure 3:\n- The horizontal axis shows \"Context Length\" (measured in tokens: 500, 1000, 1250, 1500, 1750, 2000, 2500)\n- The vertical axis shows \"F1\" (performance metric)\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 26133, "output_tokens": 113, "cost_micros": 80094, "latency_ms": 4717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify its axes.Let me search for \"Figure 3\" in the document.Now let me search for the figure content with context:Let me read the section around line 786 to see the full figure details:Let me continue reading to see the complete axis information:This appears to be describing a different figure. Let me look more carefully at the figure data around line 826:Based on the figure caption and the data I see, I can see \"Context Length\" is mentioned at line 826. However, I need to find what the vertical axis represents. Let me search around the figure more carefully. The numbers like 45, 5, 7, 3, 4, 80 seem to be data values. Let me look for axis labels by reading the embedded image description or OCR text more carefully:Let me search for any text that might indicate what metric is being measured (the vertical axis). Let me look more broadly around Figure 3:I can see \"Context Length\" appears at line 826, which is likely the horizontal axis. The text mentions \"Figure 3 shows the performance\" but I need to find what specific performance metric. Let me search for what metric is being used in QASPER:Great! I found it. Let me read around line 712 to confirm this is for Figure 3:Perfect! I found the information. Looking at lines 693, 704, 711-712:\n- Line 693: \"F1\"\n- Line 704: \"Context Length\"\n- Line 711: \"X-axis shows context length\"\n- Line 712: \"Y-axis shows F1 score\"\n\nSo for Figure 3:\n- Horizontal axis (X-axis) = Context Length\n- Vertical axis (Y-axis) = F1\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 67266, "error": null, "extra": {"thread_id": 2049, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3509043a1828411e846352ac4e585d70", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 401, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines in each algorithm.\n\nLooking at Appendix F on page 18, I can see:\n\n**Algorithm 1: Tree Traversal Algorithm**\nLet me count the lines of code (excluding comments and function declarations):\n- Line 1: Scurrent \u2190 tree.layer[0]\n- Line 2: for layer in range(tree.num layers) do\n- Line 3: topk \u2190 []\n- Line 4: for node in Scurrent do\n- Line 5: score \u2190 dot product(query, node)\n- Line 6: top k.append((node, score))\n- Line 7: end for\n- Line 8: Slayer \u2190 sorted(top k)[:k].nodes\n- Line 9: Scurrent \u2190 Slayer\n- Line 10: end for\n- Line 11: return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\n\nThat's approximately 11 lines of code.\n\n**Algorithm 2: Collapsed Tree Algorithm** (page 19)\nLet me count the lines:\n- Line 1: tree \u2190 flatten(tree)\n- Line 2: top nodes \u2190 []\n- Line 3: for node in tree do\n- Line 4: top nodes.append((node, dot product(query, node))\n- Line 5: end for\n- Line 6: top nodes \u2190 sorted(top nodes)\n- Line 7: result \u2190 []\n- Line 8: total tokens \u2190 0\n- Line 9: for node in top nodes do\n- Line 10: if total tokens + node.token size < max tokens then\n- Line 11: result.append(node)\n- Line 12: end if\n- Line 13: total tokens \u2190 total tokens + node.token size\n- Line 14: end for\n- Line 15: return result\n\nThat's approximately 15 lines of code.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 58465, "output_tokens": 483, "cost_micros": 182640, "latency_ms": 36671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode section in Appendix F:\n\n**Algorithm 1: Tree Traversal Algorithm**\n- Lines: function declaration, initialization (Scurrent), for loop, topk initialization, nested for loop, score calculation, top_k.append, end for, Slayer assignment, Scurrent assignment, end for, return statement, end function\n- Total: **13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm**\n- Lines: function declaration, tree flatten, top_nodes initialization, for loop, top_nodes.append, end for, top_nodes sort, result initialization, total_tokens initialization, for loop, if statement, result.append, end if, total_tokens update, end for, return statement, end function\n- Total: **17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 22908, "output_tokens": 184, "cost_micros": 71484, "latency_ms": 5922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Appendix F:\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines 1-13 (including function definition, loops, operations, and end statements)\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines 1-17 (including function definition, loops, operations, and end statements)\n\nCounting all lines including the function declarations, loop structures, operations, and end statements, Algorithm 2 (Collapsed Tree Algorithm) has more lines.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 23500, "output_tokens": 124, "cost_micros": 72360, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Section F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines include: function declaration, variable initialization, for loop with nested for loop, operations inside loops, and return statement\n- Counting all lines: approximately 13 lines\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines include: function declaration, tree flattening, initialization, for loop for scoring, sorting, result initialization, token counter, for loop with conditional, and return statement\n- Counting all lines: approximately 15 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 26141, "output_tokens": 143, "cost_micros": 80568, "latency_ms": 6157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines for each algorithm.Let me search for the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section:Now let me search for more specific context around pseudocode:Let me read the section with the algorithms:Perfect! I can see both algorithms. Let me count the lines:\n\n**Algorithm 1: Tree Traversal Algorithm** (lines 2344-2356):\n- Line 2344: function TRAVERSETREE(tree, query, k)\n- Line 2345: Scurrent + tree.layer[0]\n- Line 2346: for layer in range(tree.num_layers) do\n- Line 2347: topk + []\n- Line 2348: for node in Scurrent do\n- Line 2349: score + dot_product(query, node)\n- Line 2350: top_k.append((node, score))\n- Line 2351: end for\n- Line 2352: Slayer + sorted(top_k)[:k].nodes\n- Line 2353: Scurrent + Slayer\n- Line 2354: end for\n- Line 2355: return So U S1 U S2 U . . . USk\n- Line 2356: end function\n\n**Total: 13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm** (lines 2375-2391):\n- Line 2375: function COLLAPSEDTREE(tree, query, k, max_tokens)\n- Line 2376: tree \u2039- flatten(tree)\n- Line 2377: top_nodes <- []\n- Line 2378: for node in tree do\n- Line 2379: top_nodes.append((node, dot_product(query, node))\n- Line 2380: end for\n- Line 2381: top_nodes \u2039- sorted(top_nodes)\n- Line 2382: result +- []\n- Line 2383: total_tokens <- 0\n- Line 2384: for node in top nodes do\n- Line 2385: if total_tokens + node.token_size < max_tokens then\n- Line 2386: result.append(node)\n- Line 2387: end if\n- Line 2388: total_tokens \u2039- total_tokens + node.token_size\n- Line 2389: end for\n- Line 2390: return result\n- Line 2391: end function\n\n**Total: 17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33921, "error": null, "extra": {"thread_id": 2050, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8432e22fe2074cf88b3f3fbd3ebd2c32", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1 on page 2, which shows the \"Formation of one tree layer\" process:\n\nThe figure shows the clustering step (labeled \"1. Clustering\") where text chunks numbered 1-5 are grouped. The diagram illustrates how nodes are clustered together, with some nodes appearing to be part of multiple clusters during the soft clustering process.\n\nIn the clustering visualization, I can see that:\n- The nodes are represented as numbered boxes (1, 2, 3, 4, 5)\n- Some nodes appear in multiple cluster groupings\n- Looking at the color coding in the diagram, nodes that belong to multiple clusters appear to be shown with a pink/red outline or highlighting\n\nExamining the \"Contents of a node\" box on the right side and the clustering diagram in the middle, nodes that appear in multiple clusters are highlighted in pink/red color.\n\nAnswer: pink, red", "citations": [], "input_tokens": 58465, "output_tokens": 221, "cost_micros": 178710, "latency_ms": 37828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink, red", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Tree construction process\" and \"Formation of one tree layer\":\n\nThe figure shows:\n- Text chunks numbered 1-5 at the bottom (leaf layer)\n- A clustering process in the middle showing how these chunks are grouped\n- The tree structure on the right\n\nIn the \"Formation of one tree layer\" section, I can see:\n- The clustering diagram shows nodes being grouped together\n- Looking at the clustering visualization, I need to identify which numbered nodes appear in multiple clusters\n\nExamining the clustering section carefully:\n- Node 1 appears to be in one cluster\n- Node 2 appears in multiple positions\n- Node 3 appears in multiple positions \n- Node 4 appears to be in one cluster\n- Node 5 appears in multiple positions\n\nLooking at the colors used in the figure:\n- The nodes that appear in multiple clusters appear to be colored in shades that look like they could be blue, green, or orange/yellow tones in the clustering diagram.\n\nHowever, upon closer examination of Figure 1, the nodes shown in the \"1. Clustering\" section use different colors. The nodes that appear in overlapping or multiple cluster regions appear to use colors like blue and green based on the grayscale representation in the figure.\n\nLooking more carefully at the specific nodes (2, 3, 5) that appear in the clustering overlap, they appear to use blue and green colors.\n\nAnswer: blue, green", "citations": [], "input_tokens": 22908, "output_tokens": 347, "cost_micros": 73929, "latency_ms": 10734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "blue, green", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify nodes that appear in multiple clusters during the clustering step.\n\nIn the \"Formation of one tree layer\" section of Figure 1, I can see the clustering process where nodes 1-5 are being grouped. \n\nExamining the clusters:\n- One cluster contains nodes 2 and 3\n- Another cluster contains nodes 4 and 5\n\nLooking at the visual representation, the nodes that appear in the clusters are shown with different colors. The nodes that would appear in multiple clusters (if any existed) would need to be identified by their color.\n\nHowever, from the figure, each node (1-5) appears to belong to only one cluster. Node 3 appears in the cluster with node 2, and node 5 appears in the cluster with node 4. None of the nodes are shown appearing in more than one cluster simultaneously.\n\nUpon careful examination of the figure, there don't appear to be any nodes shown in multiple clusters at the same time - the soft clustering concept is mentioned in the text but not visually demonstrated in this particular figure with overlapping memberships.\n\nAnswer: None", "citations": [], "input_tokens": 27211, "output_tokens": 246, "cost_micros": 85323, "latency_ms": 7733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 carefully to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Formation of one tree layer\" section:\n\nThe figure shows a clustering process where text chunks (numbered 1-5 at the bottom) are being clustered. \n\nIn the clustering visualization, I can see:\n- Text chunks numbered 1, 2, 3, 4, 5 at the bottom\n- The chunks are shown being grouped into clusters\n- After clustering (step 1), chunks 3 and 5 appear to be in multiple clusters based on the overlapping circles/groupings shown\n\nLooking at the visual representation, the nodes that appear in more than one cluster are shown in a yellow/gold color, while nodes that appear in only one cluster are shown in blue.\n\nSpecifically, nodes 3 and 5 appear in overlapping cluster regions and are colored yellow, indicating they belong to multiple clusters (soft clustering).\n\nAnswer: yellow", "citations": [], "input_tokens": 23500, "output_tokens": 224, "cost_micros": 73860, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "yellow", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to examine the tree structure diagram to identify nodes that appear in multiple clusters.\n\nFrom the diagram showing \"RAPTOR Tree\" and \"Formation of one tree layer\":\n\nThe diagram shows:\n- Text chunks labeled 1, 2, 3, 4, 5 at the bottom (leaf nodes)\n- These are clustered into groups\n- Summary nodes 6, 7, 8 are created from clusters\n- Higher level nodes 9, 10 are created from further clustering\n\nExamining the tree structure:\n- Node 6 is connected to nodes 1 and 2\n- Node 7 is connected to node 3\n- Node 8 is connected to nodes 4 and 5\n- Node 9 is connected to nodes 6 and 7\n- Node 10 is connected to node 8\n\nLooking at the clustering process description, the figure shows standard hierarchical clustering where each node belongs to one cluster at each level. The nodes are represented in the diagram without specific color coding mentioned in the text, and the diagram appears to be a standard tree structure where nodes don't appear in multiple clusters simultaneously.\n\nHowever, based on the visual representation in the document, the nodes appear to be shown in a neutral/default color (likely gray or black outlines) in the standard tree diagram format.\n\nAnswer: The figure does not show nodes in multiple clusters with distinct colors", "citations": [], "input_tokens": 26141, "output_tokens": 317, "cost_micros": 83178, "latency_ms": 9719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure does not show nodes in multiple clusters with distinct colors", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify the colors of nodes that appear in more than one cluster.Let me continue reading to see the full description of Figure 1:Based on my reading of the document, I can see the description of Figure 1. Let me look more carefully at the visual description section that describes the tree construction process and the colors of nodes. Looking at lines 426-456, I found the relevant information:\n\nIn the visual description of Figure 1:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nThe description states that in the tree traversal diagram:\n- \"Purple/lavender nodes at leaf positions\"\n- \"Pink nodes appear at internal/decision points\"\n\nAnd in the Part B (Collapsed Tree Retrieval) section, it mentions:\n- \"Mix of purple and pink boxes in linear sequence\"\n\nFrom the description of the tree formation process in lines 305-385, it shows nodes being clustered together, and the color coding explanation in lines 447-453 clearly indicates:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nSince the question asks about nodes that appear in more than one cluster, and based on the visual description which shows that pink nodes appear at internal/decision points while purple/lavender nodes are at leaf positions, the nodes that would appear in multiple clusters (as parent nodes summarizing multiple child clusters) would be the **pink** nodes.\n\nHowever, let me verify this interpretation by checking if there's more specific information about which colored nodes appear in multiple clusters in the demonstration.\n\nLooking at the figure caption (lines 302-303): \"Nodes clustered together are siblings; a parent node contains the text summary of that cluster.\"\n\nThis indicates that parent nodes (which would be pink based on the color coding) are the ones that span multiple clusters by summarizing them.\n\nAnswer: pink", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36328, "error": null, "extra": {"thread_id": 2051, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_50d7b2d924b34b7ab5e0bc22ccd0b3d6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 237, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the clustering algorithm used in this paper and what challenges it faces.\n\nFrom page 3-4, I can find the relevant information about the clustering algorithm:\n\nThe paper states: \"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nRegarding the challenge, it states: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces (Aggarwal et al., 2001).\"\n\nTo address this challenge, the paper mentions: \"To mitigate this, we employ Uniform Manifold Approximation and Projection (UMAP), a manifold learning technique for dimensionality reduction (McInnes et al., 2018).\"\n\nAnswer: Gaussian Mixture Models (GMMs), high dimensionality of vector embeddings", "citations": [], "input_tokens": 58463, "output_tokens": 208, "cost_micros": 178509, "latency_ms": 33342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "gaussian mixture models gmms, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll search through the document for information about the clustering algorithm and its challenges.\n\nLooking at Section 3 \"METHODS\", specifically the \"Clustering Algorithm\" subsection:\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nAnd regarding the challenge:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 22906, "output_tokens": 134, "cost_micros": 70728, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", the document states:\n\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nIt then discusses the challenge:\n\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 27209, "output_tokens": 120, "cost_micros": 83427, "latency_ms": 5312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", I can find the relevant information:\n\nThe clustering algorithm is based on **Gaussian Mixture Models (GMMs)**.\n\nThe challenge presented to it is stated as: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces.\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 23498, "output_tokens": 114, "cost_micros": 72204, "latency_ms": 4781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the clustering algorithm and what presents a challenge to it.\n\nFrom Section 3 \"METHODS\" under \"Clustering Algorithm\":\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nThen it mentions:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 26139, "output_tokens": 134, "cost_micros": 80427, "latency_ms": 6639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the clustering algorithm, what model it's based on, and what presents a challenge to it.Let me search for information about the clustering algorithm and its base model:Perfect! I found the information about the clustering algorithm. Let me verify by reading a bit more to ensure I have the complete answer:Based on the document, I have found the answer to the question:\n\n1. The clustering algorithm is based on **Gaussian Mixture Models (GMMs)**\n2. The challenge to it is **the high dimensionality of vector embeddings** (as stated: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\")\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34000, "error": null, "extra": {"thread_id": 2052, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6459e8a5fde745a3b9337a10119c8000", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 161, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the pseudocode for the tree traversal method in the appendix and identify step 5.\n\nLooking at Appendix F, I can see Algorithm 1 which is the Tree Traversal Algorithm.\n\nLet me examine the steps in the algorithm:\n\n```\nfunction TRAVERSETREE(tree, query, k)\n Scurrent \u2190 tree.layer[0]\n for layer in range(tree.num layers) do\n topk \u2190 []\n for node in Scurrent do\n score \u2190 dot product(query, node)\n top k.append((node, score))\n end for\n Slayer \u2190 sorted(top k)[:k].nodes\n Scurrent \u2190 Slayer\n end for\n return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\nend function\n```\n\nCounting the steps:\n1. `Scurrent \u2190 tree.layer[0]`\n2. `for layer in range(tree.num layers) do`\n3. `topk \u2190 []`\n4. `for node in Scurrent do`\n5. `score \u2190 dot product(query, node)`\n\nStep 5 is: `score \u2190 dot product(query, node)`\n\nAnswer: score \u2190 dot product(query, node)", "citations": [], "input_tokens": 58458, "output_tokens": 318, "cost_micros": 180144, "latency_ms": 33764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.15384615384615383, "method": "str_norm", "normalised_pred": "score \u2190 dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method steps in Section 3 of the document, step 5 states:\n\"5. Continue this process recursively for d layers, producing sets S1, S2, . .. , Sd.\"\n\nNow checking Algorithm 1 (Tree Traversal Algorithm) in Appendix F, the line that corresponds to this step is:\n\n```\nfor layer in range(tree.num_layers) do\n```\n\nThis is the loop that continues the process recursively through the layers.\n\nAnswer: for layer in range(tree.num_layers) do", "citations": [], "input_tokens": 22901, "output_tokens": 129, "cost_micros": 70638, "latency_ms": 5359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, in Section 3 under \"Querying\", step 5 of the tree traversal method states:\n\n\"5. Continue this process recursively for d layers, producing sets S1, S2, ... , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. Step 5 corresponds to the for loop that iterates through layers:\n\n```\nfor layer in range(tree.num_layers) do\n topk + []\n for node in Scurrent do\n score + dot_product(query, node)\n top_k.append((node, score))\n end for\n Slayer + sorted(top_k)[:k].nodes\n Scurrent + Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 27204, "output_tokens": 200, "cost_micros": 84612, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (Querying section), step 5 states:\n\n\"5. Continue this process recursively for d layers, producing sets S\u2081, S\u2082, . . . , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. The code that corresponds to step 5 is the for loop that iterates through the layers:\n\n```\nfor layer in range(tree.num_layers) do\n top\u2096 \u2190 []\n for node in Scurrent do\n score \u2190 dot_product(query, node)\n top_k.append((node, score))\n end for\n Slayer \u2190 sorted(top_k)[:k].nodes\n Scurrent \u2190 Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 23493, "output_tokens": 212, "cost_micros": 73659, "latency_ms": 7062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (page 4-5), step 5 states:\n\n\"5. Continue this process recursively for $d$ layers, producing sets $S_1, S_2, \\dots, S_d$.\"\n\nNow looking at Algorithm 1 (Tree Traversal Algorithm) in Appendix F (page 18), the pseudo code line that corresponds to this step is:\n\n```\nS_current \u2190 S_layer\n```\n\nThis line updates the current set of nodes to continue the process to the next layer.\n\nAnswer: S_current \u2190 S_layer", "citations": [], "input_tokens": 26134, "output_tokens": 141, "cost_micros": 80517, "latency_ms": 5858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "s current \u2190 s layer", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the pseudocode in the appendix related to step 5 of the tree traversal method.Let me search for \"tree traversal\" and look at the appendix section to find the pseudocode.Now let me find the actual appendix section with pseudocode. Let me search for \"step 5\" or look at the end of the document where appendices typically are.Let me continue looking for the appendix sections. Let me search further in the document.Good, I found Appendix A. Let me continue searching for Appendix F which was mentioned to contain the pseudocode for both methods.Perfect! Found Appendix F. Let me read that section:Perfect! I found Algorithm 1 for Tree Traversal. Now let me look closely at the steps. The pseudocode shows:\n\n1. Line 2345: `Scurrent + tree.layer[0]` (Initialize current set)\n2. Line 2346: `for layer in range(tree.num_layers) do` (Loop through layers)\n3. Line 2347: `topk + []` (Initialize topk list)\n4. Line 2348: `for node in Scurrent do` (Loop through nodes)\n5. Line 2349: `score + dot_product(query, node)` (Calculate score)\n\nStep 5 corresponds to line 2349 which calculates the score using dot_product.\n\nAnswer: score + dot_product(query, node)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 60780, "error": null, "extra": {"thread_id": 2053, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cbc9bd5486c544c293802a0ecf0d5bc7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 329, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "score dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 18671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify which figures in the paper contain line plots.\n\nLet me go through the figures mentioned in the document:\n\n**Figure 1**: Shows the Chameleon architecture with image tokenizer, mixed-modal auto-regressive LM, and image de-tokenizer. This is a diagram, not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation. This is an example output, not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon. This is an example output, not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs. This is an example output, not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"output norm and training loss curves\" - this includes line plots\n- Figure 5b: \"An ablation with Chameleon-7B with and without QK-Norm\" - shows training loss, which is a line plot\n- Figure 5c: \"An ablation with Chameleon-7B with and without dropout\" - shows training loss, which is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Curves for 600k steps for Chameleon-7B and Chameleon-34B\" - this is a line plot\n- Figure 6b: Shows \"Training loss curve with image generation disabled\" - this is a line plot\n- Figure 6c: Shows training loss curves with and without norm-reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories. This appears to be text/table format, not line plots.\n\n**Figure 8**: Shows task categories and examples of prompts. This is not a line plot.\n\n**Figure 9**: Contains:\n- Figure 9a: Shows \"prompt task fulfillment rates\" as a horizontal bar chart\n- Figure 9b: Shows comparison results as a horizontal bar chart\n\n**Figure 10**: Shows \"inter-annotator agreement\" as a horizontal bar chart.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 25078, "output_tokens": 479, "cost_micros": 82419, "latency_ms": 11155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram of the Chameleon architecture with image and text tokenization flow - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"Output Norm\" vs \"Step\" - this is a line plot\n- Figure 5b: Shows \"Training Loss\" vs \"Step\" with and without QK-Norm - this is a line plot\n- Figure 5c: Shows \"Training Loss\" vs \"Step\" with and without dropout - this is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Loss\" vs \"Step\" for 7B and 34B models - this is a line plot\n- Figure 6b: Shows \"Training Loss\" vs \"Step\" for 7B without image generation - this is a line plot\n- Figure 6c: Shows \"Training Loss\" vs \"Step\" with and without norm reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts - not a line plot.\n\n**Figure 9**: Contains two subfigures:\n- Figure 9a: Shows a bar chart of task fulfillment rates - not a line plot\n- Figure 9b: Shows a horizontal bar chart comparing Chameleon vs baselines - not a line plot\n\n**Figure 10**: Shows a horizontal bar chart of inter-annotator agreement - not a line plot.\n\nAnswer: Figure 5a, Figure 5b, Figure 5c, Figure 6a, Figure 6b, Figure 6c", "citations": [], "input_tokens": 34447, "output_tokens": 457, "cost_micros": 110196, "latency_ms": 10469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure 5a, figure 5b, figure 5c, figure 6a, figure 6b, figure 6c", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the document that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows the Chameleon architecture diagram with tokens - not a line plot.\n\n**Figure 2**: Sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Sample generation from Chameleon with images - not a line plot.\n\n**Figure 4**: Sample Chameleon outputs with recipe - not a line plot.\n\n**Figure 5**: This figure has three subfigures:\n- (a) Shows \"Uncontrolled growth of output norms\" with what appears to be line plots showing training progress\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - appears to be line plots\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - appears to be line plots\n\n**Figure 6**: This figure has parts showing training curves:\n- (a) \"Training loss curve with image generation disabled\" - line plot\n- (b) Shows training for Chameleon-34B - line plots\n\nThe caption mentions \"Training loss curves for Chameleon models under various settings.\"\n\n**Figure 7**: Example alignment data - not line plots.\n\n**Figure 8**: Task categories and examples of prompts - not line plots.\n\n**Figure 9**: The text mentions Figure 9a and 9b for task fulfillment and win rates - likely bar charts or similar, need to check the context. The text describes percentages but doesn't clearly indicate these are line plots.\n\n**Figure 10**: \"The inter-annotator agreement on the questions in the absolute evaluation\" - context suggests this might be a bar chart or similar visualization.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 26087, "output_tokens": 410, "cost_micros": 84411, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram with flowcharts for pre-training and generation processes - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: This figure has three parts:\n- (a) Shows \"Output norm\" over \"Step\" - this is a line plot\n- (b) Shows training loss over steps with and without QK-Norm - this is a line plot\n- (c) Shows training loss over steps with and without dropout - this is a line plot\n\n**Figure 6**: This figure has three parts:\n- (a) Shows \"Training Curves\" with loss over steps - this is a line plot\n- (b) Shows \"Training loss curve\" - this is a line plot\n- (c) Shows training loss with different settings - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts in a table - not a line plot.\n\n**Figure 9**: Shows performance comparison tables - not line plots, these are tables.\n\n**Figure 10**: Shows inter-annotator agreement in a table format - not a line plot.\n\nSo the figures containing line plots are: Figure 5 (parts a, b, c) and Figure 6 (parts a, b, c).\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 32138, "output_tokens": 382, "cost_micros": 102144, "latency_ms": 8875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify which figures contain line plots.Let me search for more specific information about each figure and read the sections containing figure descriptions:Now let me look at Figure 5 more carefully and check other figures:Let me look at the beginning of Figure 5 to see the first part:Good! I can see Figure 5 has multiple line plots. Now let me check other figures. Let me search for more figure captions and descriptions:Let me look for Figure 7 and Figure 8:Let me continue reading Figure 8 and check if there are more figures:I can see Figure 9a is a bar chart. Let me continue examining more figures. Let me search for all figures systematically by searching for specific figure numbers:Now let me look at the actual content of each figure more carefully. Let me check Figure 1:Figure 1 appears to be a diagram/schematic, not a line plot. Let me check Figure 2 and 3:Figures 2 and 3 appear to be examples of text and image generation, not line plots. Let me check Figure 4:Figure 4 is also a sample output, not a line plot. Now let me look more carefully at Figure 10 which I saw earlier has a bar chart format:Figure 10 is a bar chart showing counts for different agreement levels. Let me check if there are any other figures I might have missed by searching for more figure references:Figure 8 shows task categories and examples (text-based). Now let me systematically review what I found:\n\nBased on my reading of the document:\n\n- **Figure 1**: Diagram/schematic showing the Chameleon architecture\n- **Figure 2**: Sample text and image generation (not a line plot)\n- **Figure 3**: Sample generation (not a line plot) \n- **Figure 4**: Sample outputs (not a line plot)\n- **Figure 5**: Contains THREE line plots:\n - (a) Output norm vs Step (line plot)\n - (b) Training Loss vs Step with/without QK-Norm (line plot)\n - (c) Training Loss vs Step with/without dropout (line plot)\n- **Figure 6**: Contains THREE line plots:\n - (a) Training Loss vs Step for 7B and 34B models (line plot)\n - (b) Training Loss vs Step without image generation (line plot)\n - (c) Training Loss vs Step with/without norm reordering (line plot)\n- **Figure 7**: Example alignment data for different categories (not a line plot)\n- **Figure 8**: Task categories and examples of prompts (not a line plot)\n- **Figure 9**: Contains bar charts showing performance comparison (not line plots)\n- **Figure 10**: Bar chart showing inter-annotator agreement (not a line plot)\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 104391, "error": null, "extra": {"thread_id": 2054, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3ce67a1a47334e71b23dc25fd26d8b74", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 654, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 25075, "output_tokens": 41, "cost_micros": 75840, "latency_ms": 3905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 34444, "output_tokens": 38, "cost_micros": 103902, "latency_ms": 3641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 26084, "output_tokens": 41, "cost_micros": 78867, "latency_ms": 3483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 and its caption, I can see that the figure shows Chameleon's architecture with different colored elements representing different types of tokens.\n\nThe caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 32135, "output_tokens": 62, "cost_micros": 97335, "latency_ms": 4108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Fig 1 and determine what the green squares denote.Let me search for \"Fig 1\" in the document to locate the figure and its caption.Let me search for \"Figure 1\" instead:Good, I found Figure 1. Now let me read the section around line 354 to see the figure and find information about green squares:Perfect! I found the answer. In line 355, the figure caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 31469, "error": null, "extra": {"thread_id": 2055, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_242072fc7b9a40f1ba1d0b58497f99ea", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 150, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in chat example figures, excluding the Appendix.\n\nLet me go through the document systematically:\n\n**Figure 2** (page 3): Sample interleaved image and text generation from Chameleon\n- Shows 3 images (Keel-Billed Toucan, Puffin, Golden Pheasant)\n\n**Figure 3** (page 4): Sample generation from Chameleon\n- Shows 2 images (the STOP sign input image and the generated chameleon image)\n\n**Figure 4** (page 5): Sample Chameleon outputs\n- Shows 2 images (bananas input image and the banana bread output image)\n\n**Figure 7** (page 10): Example alignment data for different categories\n- This shows examples of different data types:\n - (a) Text - no images\n - (b) Code - no images\n - (c) Image Generation - 1 image shown\n - (d) Visual Chat - 1 image shown (flower arrangement)\n - (e) Interleaved Text/Image Generation - 2 images shown (appears to be related to Gyumri/Armenian content)\n\n**Figure 8** (page 11): Task categories and examples of prompts\n- Shows 4 small example images in the prompt examples\n\nNow let me check the Appendix section (starts at page 23):\n- The Appendix section A \"Samples\" contains additional examples, but I should exclude these.\n\nTotal count (excluding Appendix):\n- Figure 2: 3 images\n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 4 images (1 + 1 + 2)\n- Figure 8: 4 images\n\nTotal: 3 + 2 + 2 + 4 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 25074, "output_tokens": 446, "cost_micros": 81912, "latency_ms": 11907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows 3 bird images (Keel-Billed Toucan, Puffin, Golden Pheasant)\nTotal: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows 1 stop sign image in the prompt\n- Shows 1 chameleon image in the response\nTotal: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows 1 banana image in the prompt\n- Shows 1 banana bread image in the response\nTotal: 2 images\n\nThe Appendix starts at page 23 with \"A Samples\". The figures after that are in the Appendix and should be excluded.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 34443, "output_tokens": 263, "cost_micros": 107274, "latency_ms": 7530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures/images used in the chat example figures in the main body of the document (excluding the Appendix).\n\nLet me go through the figures that show chat/conversation examples:\n\n1. **Figure 1**: Shows a recipe example with bananas image and banana bread image = 2 images\n\n2. **Figure 2**: \"Sample interleaved image and text generation from Chameleon\" - This shows a response about quirky-looking birds with 3 bird images (Keel-Billed Toucan, Puffin, and Golden Pheasant) = 3 images\n\n3. **Figure 3**: \"Sample generation from Chameleon\" - Shows a stop sign image and a chameleon image = 2 images\n\n4. **Figure 4**: \"Sample Chameleon outputs\" - Shows bananas image and banana bread image = 2 images\n\n5. **Figure 7**: \"Example alignment data for different categories\" - This shows multiple examples including:\n - Poem (no image)\n - Renaissance Square (1 image)\n - Code example (no image)\n - Gyumri text (no image)\n - Flower arrangement (1 image)\n = 2 images\n\n6. **Figure 8**: \"Task categories and examples of prompts\" - Shows 4 images in the examples (pastry, pony, construction site, and one more)\n\nWait, let me recount Figure 8 more carefully. Looking at the attributions at the bottom: \"Seguin (2010); Agriflanders (2009); Tuszy\u0144ski (2015); Sokolov (2022)\" - this indicates 4 images.\n\nTotal count:\n- Figure 1: 2 images\n- Figure 2: 3 images \n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 2 images\n- Figure 8: 4 images\n\nTotal = 2 + 3 + 2 + 2 + 2 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 26083, "output_tokens": 489, "cost_micros": 85584, "latency_ms": 11483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows a conversation about quirky birds\n- Contains 3 images: Keel-Billed Toucan, Puffin, and Golden Pheasant\n- Count: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows a conversation about a stop sign and chameleon camouflage\n- Contains 2 images: stop sign in wooded area, and a reddish-brown chameleon on tree branch\n- Count: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows a conversation about baking with bananas\n- Contains 2 images: bowl of sliced bananas, and a loaf of banana bread\n- Count: 2 images\n\nThe Appendix section starts at \"Appendix\" on page 23. The example in Section A (page 24) with the Mountain Cur dog and the pothos plant are in the Appendix, so I should not count those.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 32134, "output_tokens": 325, "cost_micros": 101277, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find chat example figures and count the pictures in them, excluding any in the Appendix.Let me search for figures more specifically:Now let me look for chat examples and appendix sections. Let me read through the document systematically:Let me read the figures mentioned (Figures 2-4) which appear to be the chat example figures, and check where the Appendix starts:Now let me check Figure 4:Let me go back and read Figure 2 more carefully to count the images:Now let me look at the beginning of Figure 2 to see all images:I see there's a mislabeled image at the beginning. Let me count more carefully. Looking at Figure 2, I can see references to:\n- Im3.png (Toucan)\n- Im4.png (Puffin) \n- Im5.png (Golden Pheasant mentioned with marker)\n\nLet me check Figure 3 now:Let me look for where Figure 3 starts:I need to see if there's another image in Figure 3. Let me check after line 747:Good! So I see Im7.png is a chameleon image. Let me check if this is part of Figure 3. Looking at the page break, Im7.png appears after the section break at line 756 where \"2.1 Tokenization\" starts. Let me verify where Figure 3 ends and Figure 4 starts:Perfect! Now I can see Figure 4 ends at line 922. Let me look for more images in Figure 4. The prompt at line 822 mentions an tag. Let me check if there's an image for that:Now let me verify where the Appendix starts to ensure I don't count figures from there:Good! The Appendix starts at line 3372. Now let me systematically count the images in Figures 2, 3, and 4.\n\nLet me go back and look at the full scope of each figure more carefully:\n\n**Figure 2** (lines 471-620):\n- Starts with prompt at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Im5.png - Golden Pheasant (line 641)\n- Figure 2 caption at line 619\n\nWait, Im5.png at line 641 seems to be after the Figure 2 caption. Let me re-check:I see! There's a STOP sign figure at line 633-637, and then Im5.png (the Golden Pheasant) at line 641. The STOP sign appears to be the beginning of Figure 3's prompt. Let me trace this more carefully:So the STOP sign at line 633-637 with Im5.png description is actually part of Figure 3's input (the in the prompt at line 675 refers to it). Let me reorganize:\n\n**Figure 2** (chat example about birds):\n- Prompt starts at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Golden Pheasant text mentions at line 614, but the actual image appears to be missing from Figure 2 content\n- Figure 2 caption at line 619-620\n\nWait, I need to look more carefully at the structure. The embedded images seem to be labeled differently. Let me look for all embedded images in the main body before the Appendix:Now let me determine which images belong to Figures 2, 3, and 4 by checking the line ranges:\n\n- Figure 2 caption: line 619\n- Figure 3 caption: line 701 \n- Figure 4 caption: line 922\n- Appendix starts: line 3372\n\nImages before Appendix:\n- Image4.jpg (line 407) - before Figure 2\n- Image5.jpg (line 480) - This is the mislabeled banana bread image, but checking context it's BEFORE the birds prompt starts at line 471\n- Im3.png (line 543) - in Figure 2 range\n- Im4.png (line 579) - in Figure 2 range\n- Im5.png (line 641) - between Figure 2 (ends 620) and Figure 3 (ends 701)\n- Im6.png (line 711) - between Figure 3 (ends 701) and section 2.1 starts\n- Im7.png (line 777) - after section 2.1 starts (line 756)\n- Im8.png (line 876) - in Figure 4 range (Figure 4 caption at 922)\n- Im9.png (line 991) - after Figure 4 ends\n\nLet me read around line 480 to verify Image5.jpg:This Image5.jpg (banana bread) is clearly mislabeled/misplaced content. The prompt at line 471 asks for birds, but Image5.jpg shows banana bread. This seems to be a document processing error. However, it appears WITHIN the Figure 2 example (after the prompt, before the response text starts at line 518).\n\nLet me check where Figure 2 actually starts and what the structure is:So Figure 2 starts at line 471. Let me now carefully count all images that appear in the three chat example figures (2, 3, 4) before the Appendix:\n\nLooking at my grep results and the line ranges:\n\n**Figure 2** (lines 471-620):\n- Image5.jpg (line 480) - appears after the prompt but seems misplaced\n- Im3.png (line 543) - Toucan\n- Im4.png (line 579) - Puffin\n\nHowever, Image5.jpg shows banana bread which doesn't match the birds prompt. Let me check if there's a third bird image I'm missing. The text mentions three birds (Toucan, Puffin, Golden Pheasant) and has markers. Let me look for where the third bird image might be:\n\nLooking at my earlier reading, Im5.png (line 641) shows a bird (Golden Pheasant based on the description at lines 649-668). The caption for Figure 2 is at line 619-620, so Im5.png at line 641 is AFTER the Figure 2 caption.\n\nLet me check if Im5.png is actually part of Figure 3:I see! Im5.png (the Golden Pheasant/colorful bird) appears at line 641, but the prompt for Figure 3 starts at line 675 which references \"\" - and that is referring to the STOP sign that appeared earlier. So Im5.png is actually the third bird from Figure 2, even though it appears after the Figure 2 caption.\n\nLooking back at the structure more carefully, the figure captions seem to come before all the images are shown. Let me look at where the STOP sign figure starts:\n\nFrom line 633-637, there's a figure with \"STOP\" as OCR text. This must be the input image for Figure 3.\n\nLet me now systematically list the images:\n\n**Before Figure 2 caption (line 619):**\n- Image5.jpg (line 480) - banana bread (misplaced/error)\n- Im3.png (line 543) - Toucan \n- Im4.png (line 579) - Puffin\n\n**After Figure 2 caption but before Figure 3 caption (line 701):**\n- Im5.png (line 641) - Golden Pheasant (part of Figure 2 content)\n\n**Around Figure 3:**\n- STOP sign figure (lines 633-637, but before Im5.png structurally in the document)\n- Im6.png (line 711) - STOP sign (after Figure 3 caption)\n\nWait, I need to look at this more systematically. Let me check what the STOP sign figure at line 633 is:The figure at 633-637 has OCR text \"STOP\" but the embedded image Im5.png (line 641) has \"NO_CONTENT_HERE\" as OCR and shows a bird. This is confusing. Let me look at Im6.png which should be the STOP sign:Yes! Im6.png is the STOP sign. So the document structure has the figure tags and embedded images separated. The figure at line 633 with \"STOP\" corresponds to Im6.png (the actual embedded image file) at line 711.\n\nLet me now map this out properly by looking at the figure captions and the embedded images:\n\n**Figure 2** (caption at line 619): Chat about birds\n**Figure 3** (caption at line 701): Chat about chameleon camouflage \n**Figure 4** (caption at line 922): Chat about baking with bananas\n\nNow let me find which embedded images belong to each:So Im6.png appears RIGHT AFTER the Figure 3 caption. Now let me check Figure 4:So Im8.png (bananas) appears before the Figure 4 caption at line 922, and there's an marker at line 920 suggesting another image.\n\nNow let me look at Im7.png and Im9.png to see which figure they belong to:\n\nIm7.png is at line 777. Let me check the context:Im7.png (chameleon) is at line 777, which is AFTER section 2.1 starts at line 756 and after Figure 3 caption at line 701. But looking at the context, Im7.png shows a chameleon which matches Figure 3's theme. Let me check if this is part of the Figure 3 example by looking backwards:\n\nThe Figure 3 response at line 747 has an marker, and the text before talks about generating a chameleon image. So Im7.png is part of Figure 3's output!\n\nNow let me check Im9.png:Im9.png shows a loaf of baked good (banana bread final result). This is at line 991, which is after Figure 4 caption at line 922. The marker at line 920 in Figure 4 refers to this final dish image. So Im9.png is part of Figure 4.\n\nNow let me go back and check Im5.png more carefully. At line 641, the bird image appears after Figure 2 caption but before Figure 3 content. Looking at Figure 2's text, the Golden Pheasant is mentioned with at line 614. So Im5.png (Golden Pheasant) is part of Figure 2.\n\nLet me now do a final count:\n\n**Figure 2** (Chat about birds):\n- Im3.png - Toucan\n- Im4.png - Puffin \n- Im5.png - Golden Pheasant\nTotal: 3 images\n\n**Figure 3** (Chat about chameleon camouflage):\n- Im6.png - STOP sign (input)\n- Im7.png - Chameleon (generated output)\nTotal: 2 images\n\n**Figure 4** (Chat about baking with bananas):\n- Im8.png - Bananas (input)\n- Im9.png - Banana bread (generated output)\nTotal: 2 images\n\n**Total images in chat example figures (Figures 2-4), excluding Appendix: 3 + 2 + 2 = 7**\n\nWait, but I should double-check if Image5.jpg (the banana bread at line 480) is actually meant to be part of Figure 2 or if it's a document error. Let me look at the context again:Image5.jpg (banana bread) appears right after the birds prompt but before the response starts. This appears to be a document processing error or misplacement - it doesn't match the context at all. The response starting at line 518 talks about birds, not banana bread.\n\nGiven that this is clearly an error in the document, I should not count Image5.jpg as part of the chat example figures. The actual chat examples are:\n\n**Figure 2**: 3 bird images (Im3.png, Im4.png, Im5.png)\n**Figure 3**: 2 images (Im6.png STOP sign, Im7.png chameleon) \n**Figure 4**: 2 images (Im8.png bananas, Im9.png banana bread)\n\n**Total: 7 images**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 178546, "error": null, "extra": {"thread_id": 2056, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d55f4fd667ac403b901ea3294619b198", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1600, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 17687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find subplots that show obvious loss spikes in the document.\n\nLet me examine the figures that show training loss curves:\n\n1. **Figure 5**: Contains 3 subplots:\n - (a) Shows output norm with clear divergence/spike\n - (b) Shows training loss comparison with and without QK-Norm - one curve diverges (spikes)\n - (c) Shows training loss with and without dropout - appears stable\n\n2. **Figure 6**: Contains 3 subplots:\n - (a) Shows training loss curves for 7B and 34B models - appears stable without obvious spikes\n - (b) Shows training loss curve with image generation disabled - appears stable without obvious spikes\n - (c) Shows training loss with and without norm reordering - shows clear divergence/spike in one curve\n\nLet me count the subplots with obvious loss spikes:\n- Figure 5(a): Shows norm growth leading to divergence (1)\n- Figure 5(b): Shows loss divergence/spike (1)\n- Figure 6(c): Shows loss divergence/spike (1)\n\nTotal: 3 subplots show obvious loss spikes or divergences.\n\nAnswer: 3", "citations": [], "input_tokens": 25071, "output_tokens": 284, "cost_micros": 79473, "latency_ms": 8016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss curves with obvious spikes.\n\nLooking through the document:\n\n**Figure 5** (page 6-7):\n- (a) Shows \"Output norm\" vs Step - not a loss curve with spikes\n- (b) Shows \"Training Loss\" with and without QK-Norm - shows relatively smooth curves, one diverges but no obvious spikes\n- (c) Shows \"Training Loss\" with and without dropout - shows smooth curves\n\n**Figure 6** (page 7):\n- (a) Shows \"Training Loss\" for 7B and 34B models over 600k steps - smooth curves, no obvious spikes\n- (b) Shows \"Training Loss\" for 7B without image generation - smooth curve, no obvious spikes\n- (c) Shows \"Training Loss\" with and without norm reordering - one curve shows a clear spike/divergence around 8k-10k steps\n\nFrom Figure 6c, I can see one subplot that clearly shows an obvious loss spike (the curve without norm reordering that spikes dramatically).\n\nLooking more carefully at Figure 5b, the curve \"w/o QK-norm\" shows a sharp spike/divergence near the end.\n\nSo counting subplots with obvious loss spikes:\n- Figure 5b: 1 subplot (shows divergence/spike)\n- Figure 6c: 1 subplot (shows clear spike)\n\nAnswer: 2", "citations": [], "input_tokens": 34440, "output_tokens": 324, "cost_micros": 108180, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures in the document that show training loss curves with obvious loss spikes.\n\nLooking through the document:\n\n**Figure 5**: \"Output norm and training loss curves for Chameleon models under various settings\"\n- (a) Shows uncontrolled growth of output norms\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - this shows training loss with a divergence/spike\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - this shows training loss with divergences/spikes\n\n**Figure 6**: \"Training loss curves for Chameleon models under various settings\"\n- (a) Shows \"Training loss curve with image generation disabled does not suffer from instability issues\" - smooth curves, no spikes\n- (b) Shows \"For Chameleon-34B, using dropout does not fix divergences, both with and without reordering\" - this shows loss spikes/divergences\n\nCounting subplots with obvious loss spikes:\n- Figure 5(b): 1 subplot with loss spike\n- Figure 5(c): 1 subplot with loss spike \n- Figure 6(b): 1 subplot with loss spikes\n\nTotal: 3 subplots\n\nAnswer: 3", "citations": [], "input_tokens": 26080, "output_tokens": 301, "cost_micros": 82755, "latency_ms": 9171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss spikes in the document.\n\nLooking through the document for figures showing training loss or related metrics:\n\n**Figure 5: \"Output norm and training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows output norms with curves that spike/diverge\n- (b) Shows training loss with and without QK-Norm - one curve diverges/spikes\n- (c) Shows training loss with and without dropout - one curve appears to spike\n\n**Figure 6: \"Training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows smooth training curves for 7B and 34B models - no obvious spikes\n- (b) Shows training loss without image generation - smooth curve, no spikes\n- (c) Shows training with/without norm reordering where one diverges - this shows a spike\n\nSo counting subplots with obvious loss spikes:\n- Figure 5(a): 1 (shows divergence/spikes in output norms)\n- Figure 5(b): 1 (shows divergence in loss)\n- Figure 5(c): 1 (shows divergence in loss)\n- Figure 6(c): 1 (shows divergence)\n\nTotal: 4 subplots show obvious spikes/divergences\n\nAnswer: 4", "citations": [], "input_tokens": 32131, "output_tokens": 324, "cost_micros": 101253, "latency_ms": 9125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about subplots related to obvious loss spikes.Let me search for content related to \"loss spikes\" in the document:Let me search for \"subplot\":Let me read through the document more systematically to look for figures and their descriptions:Now let me look at Figure 5 and Figure 6 which seem to show training loss curves. Let me read those sections:Now let me look more closely at Figure 6 which appears to have subplots. Let me search for any reference to \"loss spikies\" or similar terms:Let me look at Figure 5 more carefully, as it shows \"Output norm and training loss curves for Chameleon models under various settings\":I can see Figure 5 has three subplots (a, b, c). Now let me check the figure caption more carefully and look at the actual charts. The caption says \"Figure 5 Output norm and training loss curves for Chameleon models under various settings.\" Let me read the text more carefully to understand what \"obvious loss spikies\" might refer to. Let me search for variations:Let me look at the entire document more systematically. Let me check around Figure 5 and 6 descriptions:Let me look more carefully at the whole document. Maybe \"spikies\" is a colloquial term in the figure or caption. Let me check the full text around Figures 5 and 6:So Figure 5 has subplots (a), (b), and (c). Let me check Figure 6:So Figure 6 also has subplots (a), (b), and (c). Now let me look at the actual chart data more carefully. The question asks about \"obvious loss spikies\" which likely refers to spikes in the loss curves. Let me look at the loss curve data more closely. Let me search the document for any terms that might be misspelled or colloquial:Based on my reading of the document, I can see that:\n\n**Figure 5** has three subplots:\n- (a) Output norms plot showing growth over training steps\n- (b) Training loss with and without QK-Norm\n- (c) Training loss with and without dropout\n\n**Figure 6** has three subplots:\n- (a) Training curves for 600k steps for Chameleon-7B and Chameleon-34B\n- (b) Training loss curve with image generation disabled\n- (c) For Chameleon-34B, using dropout comparison with and without norm-reordering\n\nLooking at the descriptions and the actual chart data in the text, the question asks about \"obvious loss spikies\" (spikes). From examining the text descriptions:\n\n- Figure 5(a) shows \"Uncontrolled growth of output norms\" - this is about norms, not loss spikes\n- Figure 5(b) shows training loss curves with the one \"w/o QK-norm\" showing a divergence\n- Figure 5(c) shows training loss curves with and without dropout - the \"w/o dropout\" curve appears to have issues\n- Figure 6(a) shows smooth training curves for both 7B and 34B models\n- Figure 6(b) shows a smooth training loss curve without image generation\n- Figure 6(c) shows curves where \"w/o norm reordering\" appears to diverge/spike\n\nLooking at the subplot captions that mention instability or divergences, which would correspond to loss spikes:\n- Figure 5(b): \"w/o QK-norm\" diverges\n- Figure 5(c): \"w/o dropout\" has spikes\n- Figure 6(c): \"w/o norm reordering\" has divergences/spikes\n\nThat would be 3 subplots showing obvious loss spikes.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 88405, "error": null, "extra": {"thread_id": 2057, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d21eabfb0c2b44d69030354b180154e2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 635, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 15734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a \"Summary of core architecture and optimization decisions made in Chameleon in contrast to LLaMa-1 and LLaMa-2\", I can see that:\n\n- LLaMa-1 7B has an \"\u00d7\" (cross mark) in the Qknorm column\n- LLaMa-1 33B has an \"\u00d7\" (cross mark) in the Qknorm column\n\nThis indicates that LLaMa-1 does NOT apply QKnorm.\n\nAnswer: no", "citations": [], "input_tokens": 25087, "output_tokens": 130, "cost_micros": 77211, "latency_ms": 5343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which compares core architecture and optimization decisions across models:\n\nFor LLaMa-1 models (both 7B and 33B), the Qknorm column shows \"\u00d7\" (which indicates no).\n\nAnswer: no", "citations": [], "input_tokens": 34456, "output_tokens": 63, "cost_micros": 104313, "latency_ms": 4297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which summarizes the core architecture and optimization decisions:\n\nFor LLaMa-1 models (both 7B and 33B), the \"Qknorm\" column shows \"\u00d7\", which indicates that QK-Norm was not applied.\n\nAnswer: No", "citations": [], "input_tokens": 26096, "output_tokens": 71, "cost_micros": 79353, "latency_ms": 4531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a comparison of core architecture and optimization decisions:\n\nFor LLaMa-1 (both 7B and 33B models), the \"Qknorm\" column shows \"\u00d7\", which indicates that query-key normalization (QK-Norm) was **not** applied.\n\nAnswer: No", "citations": [], "input_tokens": 32147, "output_tokens": 81, "cost_micros": 97656, "latency_ms": 4671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about whether llama-1 applies qknorm.Let me search for \"qknorm\" and \"llama\" in the document to find relevant information.Let me read the table area that contains information about qknorm and llama-1:Perfect! I found the table that shows whether llama-1 applies qknorm. In the table, for both LLaMa-1 models (7B and 33B), the Qknorm column shows \"\u00d7 \u2612\" which indicates \"no\".\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 30905, "error": null, "extra": {"thread_id": 2058, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2c3221de2294418da7d44837e6c67391", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 146, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm\", I can see the training loss curves.\n\nThe curve labeled \"w/o QK-norm\" (without QK-norm) shows a divergence/spike in the loss after approximately 20% of training progress, which appears to occur around step 20,000-25,000 based on the x-axis scale shown in the figure.\n\nAnswer: Around step 20,000-25,000", "citations": [], "input_tokens": 25078, "output_tokens": 122, "cost_micros": 77064, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around step 20 000 25 000", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for both conditions.\n\nThe curve labeled \"w/o QK-norm\" (without QK-Norm) shows the training progressing normally until it experiences a sudden spike and divergence.\n\nAnswer: Around 125k steps", "citations": [], "input_tokens": 34447, "output_tokens": 89, "cost_micros": 104676, "latency_ms": 5452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 125k steps", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves comparing the two conditions.\n\nThe figure shows that the model without QK-Norm diverges (loss spikes) after approximately 20% of training progress.\n\nAnswer: After approximately 20% of training", "citations": [], "input_tokens": 26087, "output_tokens": 87, "cost_micros": 79566, "latency_ms": 5046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "after approximately 20 of training", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for models with and without QK-norm.\n\nThe graph shows that the model without QK-norm or dropout (red line) diverges after approximately 20k-30k steps, where the loss spikes dramatically upward.\n\nAnswer: Around 20k-30k steps", "citations": [], "input_tokens": 32138, "output_tokens": 100, "cost_micros": 97914, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 20k 30k steps", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about loss spikes when without QK-norm.Let me search for \"QK-norm\" in the document to find the relevant information.Now let me read the specific sections that mention loss spikes and QK-norm. Let me look at the chart/figure around lines 1028-1076 which seems to show training loss curves.I can see a figure showing training loss with and without QK-norm. The x-axis shows steps from 0k to 175k (approximately). Let me look more carefully at the surrounding text to understand where the spike occurs. Let me check the text before and after this figure.Let me read more to find the caption or description of this figure:Good! Now I can see that figure (b) shows training loss with and without QK-Norm. Let me look back at the chart more carefully to understand where the spike happens. Let me check the text around lines 938-1000 to see if there's more description:Now I need to look at the actual description of figure 5b more carefully. Let me read around the text that discusses the training loss spike:Perfect! I found the answer. The text clearly states:\n\n\"In Figure 5b, we show training loss curves for Chameleon-7B with and without QK-Norm, and the latter diverges after approximately 20% of a training epoch.\"\n\nLet me look at the figure again to verify the step count. Looking at the figure data from earlier, the x-axis goes from 0k to approximately 175k steps. The text mentions \"after approximately 20% of a training epoch\" for the divergence/spike.\n\nHowever, let me look more carefully at what specific step number might correspond to this. Let me check if there's more specific information:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 73108, "error": null, "extra": {"thread_id": 2059, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c22c13a6350947eda6562103bdc5468c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 343, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "however let me look more carefully at what specific step number might correspond to this let me check if there s more specific information", "normalised_gold": "150k"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I need to find who verified and signed the complaint by looking at the verification section of the document.\n\nLooking at page 16 of the document, I can see the \"VERIFICATION\" section which states:\n\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury of the laws of the United States of America that the facts contained therein are true to the best of my knowledge and belief, based upon knowledge possessed by me and/or on information received from other law enforcement agents.\"\n\nThis is followed by a signature and \"Special Agent Marc Silski, Federal Bureau of Investigation\" with the date \"September 6, 2018\".\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 25011, "output_tokens": 176, "cost_micros": 77673, "latency_ms": 14359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (PageID.16 Page 16 of 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5579, "output_tokens": 52, "cost_micros": 17517, "latency_ms": 3594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "According to the verification section at the end of the document, the officer who verified and signed the complaint was:\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 6098, "output_tokens": 34, "cost_micros": 18804, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16):\n\nThe verification states:\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury...\"\n\nIt is signed by:\n\"Special Agent Marc Silski\nFederal Bureau of Investigation\nDated: September 6, 2018\"\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 5228, "output_tokens": 109, "cost_micros": 17319, "latency_ms": 3780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5778, "output_tokens": 43, "cost_micros": 17979, "latency_ms": 2312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find which officer verified and signed the complaint.Based on the document, I found the verification section on page 16. The officer who verified and signed the complaint is:\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23546, "error": null, "extra": {"thread_id": 2060, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890676fb4dc0465f94bad1a9648bd51a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 82, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I need to find all the checks issued to Mont Blanc company mentioned in the document.\n\nLooking through the document:\n\nOn page 11 (PageID.11), paragraph e states:\n\"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nOn page 11 (PageID.11), paragraph f states:\n\"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nThese are the two checks issued to Mont Blanc company.\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 25031, "output_tokens": 170, "cost_micros": 77643, "latency_ms": 10219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5599, "output_tokens": 129, "cost_micros": 18732, "latency_ms": 3389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32, subsections e and f:\n\n1. Check number 150301 issued in September 2012: $35,000\n2. Check number 151545 issued in February 2013: $40,684\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 6118, "output_tokens": 79, "cost_micros": 19539, "latency_ms": 3640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for checks issued to Mont Blanc company:\n\nFrom paragraph 32.e: \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32.f: \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5248, "output_tokens": 128, "cost_micros": 17664, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5798, "output_tokens": 127, "cost_micros": 19299, "latency_ms": 3592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about checks issued to Mont Blanc company.Let me continue reading the document to find information about checks issued to Mont Blanc company.Let me continue reading to find information about checks issued to Mont Blanc.Perfect! I found the information about checks issued to Mont Blanc. Let me verify by reading a bit more to ensure I have all the check amounts.\n\nBased on the document, I found the following information about checks issued to Mont Blanc:\n\n1. Check number 150301 - $35,000 (issued in September 2012)\n2. Check number 151545 - $40,684 (issued in February 2013)\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32530, "error": null, "extra": {"thread_id": 2061, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5f1b7e2037a34d839cda54ca371fcea3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 134, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the CA Bar number in the document.\n\nLooking at page 15, I can see the signature page where it states:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 25014, "output_tokens": 103, "cost_micros": 76587, "latency_ms": 10625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 15 (PageID.15), I can find the CA Bar number listed for Assistant United States Attorney Adriana Dydell.\n\nAnswer: 239516", "citations": [], "input_tokens": 5582, "output_tokens": 47, "cost_micros": 17451, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, in the signature block for the Assistant United States Attorney, I can see:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 6101, "output_tokens": 108, "cost_micros": 19923, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, the signature block shows:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nDated: September, 2018\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 5231, "output_tokens": 107, "cost_micros": 17298, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I can find the CA Bar number at the bottom of page 15, where it states:\n\n\"Dated: September 13, 2018 (CA. Bar No. 239516)\"\n\nThis appears next to the signature block for Adriana Dydell, Assistant United States Attorney.\n\nAnswer: 239516", "citations": [], "input_tokens": 5781, "output_tokens": 78, "cost_micros": 18513, "latency_ms": 3358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find the CA Bar number.Answer: 239516", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35797, "error": null, "extra": {"thread_id": 2062, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67c2db9144b348b6ba61d0ad2f21abaf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 115, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll examine each page to identify where information has been blacked out (redacted).\n\nPage 1: No redactions visible\nPage 2: No redactions visible\nPage 3: No redactions visible\nPage 4: No redactions visible\nPage 5: No redactions visible\nPage 6: No redactions visible\nPage 7: No redactions visible\nPage 8: No redactions visible\nPage 9: No redactions visible\nPage 10: No redactions visible\nPage 11: No redactions visible\nPage 12: No redactions visible\nPage 13: Contains multiple black redaction boxes blocking out names and information (in paragraph l and m)\nPage 14: Contains black redaction boxes blocking out information (in paragraph n)\nPage 15: No redactions visible\nPage 16: No redactions visible\n\nAnswer: 13, 14", "citations": [], "input_tokens": 25034, "output_tokens": 196, "cost_micros": 78042, "latency_ms": 11015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages have information that has been blacked out or redacted.\n\nLooking through the document:\n\n- Page 1 (PageID.1): No visible redactions\n- Page 2 (PageID.2): No visible redactions\n- Page 3 (PageID.3): No visible redactions\n- Page 4 (PageID.4): No visible redactions\n- Page 5 (PageID.5): No visible redactions\n- Page 6 (PageID.6): No visible redactions\n- Page 7 (PageID.7): No visible redactions\n- Page 8 (PageID.8): No visible redactions\n- Page 9 (PageID.9): No visible redactions\n- Page 10 (PageID.10): No visible redactions\n- Page 11 (PageID.11): No visible redactions\n- Page 12 (PageID.12): No visible redactions\n- Page 13 (PageID.13): In paragraph 1, there is redacted text after \"the last names of Iacobelli and\" and in paragraph m, there are multiple redactions of names\n- Page 14 (PageID.14): In paragraph n, there are redacted names at the beginning and within the text\n- Page 15 (PageID.15): No visible redactions\n- Page 16 (PageID.16): No visible redactions\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5602, "output_tokens": 339, "cost_micros": 21891, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for redacted/blacked out information:\n\nOn page 14 (PageID.14), paragraph m, there are multiple instances of redacted information indicated by blank spaces where names should be:\n- \"was interviewed by agents.\"\n- \"was a former co-worker\"\n- \"During the interview,\"\n- \"informed agents that Iacobelli bragged to her about a Mont Blanc pen that he carried in his shirt pocket costing $25,000. Iacobelli also informed that\"\n\nOn page 14 (PageID.14), paragraph n, there is also redacted information:\n- \"On July 31, 2017, agents contacted who stated that\"\n- \"he was in possession of the Lincoln commemorative Mont Blanc pen. added that he ordered the pen, but that Iacobelli paid for both and\"\n\nPage 13 (PageID.13), paragraph l mentions \"having the last names of Iacobelli and\" followed by what appears to be a redaction.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 6121, "output_tokens": 238, "cost_micros": 21933, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully review the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Page 2: No redactions visible\n- Page 3: No redactions visible\n- Page 4: No redactions visible\n- Page 5: No redactions visible\n- Page 6: No redactions visible\n- Page 7: No redactions visible\n- Page 8: No redactions visible\n- Page 9: No redactions visible\n- Page 10: No redactions visible\n- Page 11: No redactions visible\n- Page 12: No redactions visible\n- Page 13: Contains redacted/blacked out information (names are replaced with blank spaces/lines in sections l and m)\n- Page 14: Contains redacted/blacked out information (names are replaced with blank spaces/lines in section n)\n- Page 15: No redactions visible\n- Page 16: No redactions visible\n\nThe pages with blacked out information are pages 13 and 14, where personal names have been redacted.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5251, "output_tokens": 248, "cost_micros": 19473, "latency_ms": 5733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Pages 1-12: No redactions visible\n- Page 13 (PageID.13): Contains multiple redactions marked with \"\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\" or similar black bars in paragraph m and n. Specifically:\n - \"former co-worker \u2588\u2588\u2588\u2588\u2588\u2588\" \n - Multiple instances of \"\u2588\u2588\u2588\u2588\u2588\u2588\" referring to a person's name\n - \"podiatrist named \u2588\u2588\u2588\u2588\u2588\u2588\"\n - \"[\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588]\" in paragraph n\n - \"[\u2588\u2588\u2588\u2588]\" multiple times in paragraph n\n\n- Page 14 (PageID.14): No redactions visible\n- Pages 15-16: No redactions visible\n\nThe redactions appear only on page 13 of the document (which corresponds to PageID.13, Page 13 of 16).\n\nAnswer: 13", "citations": [], "input_tokens": 5801, "output_tokens": 206, "cost_micros": 20493, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "13", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify pages with blacked out information.Based on my review of the document, I can identify pages with redacted (blacked out) information:\n\n- **Page 13**: Contains multiple black redaction bars in paragraphs l and m, obscuring names and identifying information. The visual description explicitly notes \"Multiple horizontal black bars throughout the document\" and \"Redacted sections appear in paragraphs l and m.\"\n\n- **Page 14**: Contains three black redaction blocks in paragraph n, obscuring names/identifying information. The visual description notes \"Contains redacted sections shown as solid black rectangular blocks\" and \"Multiple solid black rectangular blocks throughout paragraph n.\"\n\nAnswer: 13, 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37265, "error": null, "extra": {"thread_id": 2063, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5190eed28c9d49e68875e654ed7fc489", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 163, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of this document.\n\nLooking at page 15 (PageID.15):\nI can see a handwritten date that appears to be \"September 12, 2018\" where \"12\" is written manually.\n\nLooking at page 16 (PageID.16):\nI can see a handwritten date that appears to be \"September 6, 2018\" where \"6\" is written manually.\n\nThe manually written numbers are:\n- Page 15: 12\n- Page 16: 6\n\nSum: 12 + 6 = 18\n\nAnswer: 18", "citations": [], "input_tokens": 25026, "output_tokens": 148, "cost_micros": 77298, "latency_ms": 8937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date shows \"September 3 2018\" - there appears to be a handwritten \"3\" in the date field\n\nLooking at page 16 (PageID.16):\n- The date shows \"September 6 2018\" - there appears to be a handwritten \"6\" in the date field\n\nThe sum of the manually written numbers is: 3 + 6 = 9\n\nAnswer: 9", "citations": [], "input_tokens": 5594, "output_tokens": 130, "cost_micros": 18732, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nThe date shows \"September 13 2018\" where \"13\" appears to be handwritten.\n\nLooking at page 16 (PageID.16):\nThe date shows \"September 6, 2018\" where \"6\" appears to be handwritten.\n\nSum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 6113, "output_tokens": 112, "cost_micros": 20019, "latency_ms": 3219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine pages 15 and 16 of the document to find manually written numbers.\n\nLooking at page 15:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.15 Page 15 of 16\"\n- The page contains typed text including \"Respectfully submitted,\" signature block for Matthew Schneider and Adriana Dydell\n- At the bottom it shows \"Dated: September, 2018\"\n- The number \"15\" appears at the bottom of the page\n\nLooking at page 16:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.16 Page 16 of 16\"\n- Contains a verification section signed by Marc Silski\n- At the bottom it shows \"Dated: September 6, 2018\"\n- The number \"16\" appears at the bottom of the page\n\nThe manually written/handwritten numbers visible on these pages are:\n- Page 15: The page number \"15\" at the bottom\n- Page 16: The page number \"16\" at the bottom\n\nSum: 15 + 16 = 31\n\nAnswer: 31", "citations": [], "input_tokens": 5243, "output_tokens": 307, "cost_micros": 20334, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "31", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date \"September 13, 2018\" appears to have \"13\" that could be handwritten, but examining the format, it appears to be typed text, not manually written.\n\nLooking at page 16 (PageID.16):\n- The date \"September 6, 2018\" appears, where \"6\" could potentially be handwritten.\n- There is a signature line with \"Special Agent Marc Silski\" and a signature.\n\nHowever, upon closer examination of the document format, these appear to be typed dates in the standard court document format. The document shows underlined spaces where information was filled in, but the actual numbers \"13\" on page 15 and \"6\" on page 16 appear in the standard typed format of the document.\n\nLooking more carefully, the date on page 15 shows \"September 13, 2018\" where \"13\" appears to be typed/filled in on an underlined space, and on page 16 \"September 6, 2018\" where \"6\" appears typed.\n\nIf these are considered the manually written/filled-in numbers:\n- Page 15: 13\n- Page 16: 6\n- Sum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 5793, "output_tokens": 316, "cost_micros": 22119, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I'll help you find the manually written numbers on pages 15 and 16 of the document. Let me first read the document that was mentioned in the context.Let me continue reading to find pages 15 and 16. I need to search through the document to locate these specific pages.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27906, "error": null, "extra": {"thread_id": 2064, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3dc2a747e29a4ced83b1f3d763f972af", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 79, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to analyze the document to identify all the colors present in it.\n\nLooking at the document pages provided:\n- The text appears in black\n- The background is white\n- There are blue hyperlinks/case numbers at the top of each page\n- There are black redaction marks/blocks in some areas (pages 13-14)\n\nThe colors present in this document are:\n- Black (text and redactions)\n- Blue (case numbers and headers)\n- White (background)\n\nAnswer: black, blue, white", "citations": [], "input_tokens": 25038, "output_tokens": 116, "cost_micros": 76854, "latency_ms": 10546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "black, blue, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this legal document.\n\nScanning through the document for color references:\n\n1. Page 11, paragraph d: \"red 2013 Ferrari 458 Spider convertible\"\n2. Page 12, paragraph j: mentions \"gold\" (portrait in gold) and \"sapphire\" (which is blue)\n\nLet me verify these are the only color mentions:\n- \"red\" - explicitly mentioned for the Ferrari\n- \"gold\" - mentioned for Lincoln's portrait on the pen clip\n- The sapphire mentioned would typically be blue, but the document says \"sapphire\" not \"blue\"\n\nLooking more carefully at the text:\n- \"red 2013 Ferrari\" (paragraph 32d)\n- \"gold\" (paragraph 32j - \"Lincoln's portrait in gold\")\n- \"sapphire\" (paragraph 32j - \"has a sapphire\")\n\nThe colors explicitly stated are:\n- gold\n- red\n\nAlphabetically ordered:\n\nAnswer: gold, red", "citations": [], "input_tokens": 5606, "output_tokens": 228, "cost_micros": 20238, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "gold, red", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine the document to identify all colors mentioned or visible in it.\n\nLooking through the document:\n\n1. The document itself appears to be a black and white legal document (black text on white background)\n2. In paragraph 32(d), a \"red 2013 Ferrari 458 Spider convertible\" is mentioned\n3. In paragraph 32(j), \"gold\" is mentioned (describing Lincoln's portrait in gold on the pen clip)\n4. In paragraph 32(j), \"sapphire\" is mentioned (the pen's clip has a sapphire)\n\nColors explicitly mentioned in the text:\n- Gold\n- Red\n- Sapphire (which is blue)\n\nColors of the document format itself:\n- Black (text)\n- White (background)\n\nListing all colors alphabetically:\n- Black\n- Blue (sapphire)\n- Gold\n- Red\n- White\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 6125, "output_tokens": 214, "cost_micros": 21585, "latency_ms": 5405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this document.\n\nLet me search through the document for any color references:\n\n1. In paragraph 32.a, there is mention of \"a red 2013 Ferrari 458 Spider convertible\"\n2. In paragraph 32.j, there is mention of \"gold\" (describing Lincoln's portrait on the pen clip)\n3. The document is a legal court document that appears to be in black text on white paper (standard document format)\n\nThe colors explicitly mentioned in the text are:\n- Gold (mentioned in the description of the pen)\n- Red (mentioned in the description of the Ferrari)\n\nSince this is a standard black and white legal document with black text, and the question asks for colors in the document, I should include the standard document colors as well as the colors mentioned in the content.\n\nColors found:\n- Black (text)\n- Gold (pen description)\n- Red (Ferrari description)\n- White (paper/background)\n\nIn alphabetical order: black, gold, red, white\n\nAnswer: black, gold, red, white", "citations": [], "input_tokens": 5255, "output_tokens": 238, "cost_micros": 19335, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5714285714285715, "method": "list_set", "normalised_pred": "black, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in the document.\n\nGoing through the document carefully:\n\n1. Page 10, paragraph d: \"red 2013 Ferrari 458 Spider convertible\" - mentions **red**\n2. Page 12, paragraph j: \"sapphire\" (referring to a sapphire on the pen clip) - **blue** (sapphire is blue)\n3. Page 12, paragraph j: \"gold\" (portrait in gold on the pen's clip) - mentions **gold**\n\nLooking at the document format itself, this appears to be a standard black text on white background legal document, so:\n- **black** (text)\n- **white** (background)\n\nAlphabetically ordered:\n- black\n- blue\n- gold\n- red\n- white\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 5805, "output_tokens": 189, "cost_micros": 20250, "latency_ms": 6407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to identify all the colors present.Based on my careful reading of the document, I can identify the following colors mentioned or described:\n\nFrom the visual descriptions and content:\n- **Black** - mentioned multiple times in visual descriptions (\"black text\", \"black bars\", \"black rectangular blocks\" for redactions)\n- **Blue** - mentioned in the visual description (\"Blue text displaying case number\")\n- **Gold** - mentioned in the pen description (\"Lincoln's portrait in gold\")\n- **Red** - mentioned for the Ferrari (\"red 2013 Ferrari 458 Spider\")\n- **White** - mentioned in visual descriptions (\"white background\")\n\nThere's also a **sapphire** mentioned on the pen clip, which is typically blue, but since \"blue\" is already listed separately and sapphire is a gemstone rather than a color descriptor in this context, I'll include it as a separate item if we're being comprehensive about color-related terms.\n\nHowever, looking at the actual color words used as descriptors in the document:\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37108, "error": null, "extra": {"thread_id": 2065, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8c1d9833eaf14e8eae921556f7ecfecf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 186, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL, Assistant United States Attorney\"\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski, Federal Bureau of Investigation\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 25021, "output_tokens": 122, "cost_micros": 76893, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL\" (Assistant United States Attorney).\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski\" (Federal Bureau of Investigation).\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5589, "output_tokens": 124, "cost_micros": 18627, "latency_ms": 3781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15:\n- There is one signature above the typed name \"ADRIANA DYDELL\"\n\nLooking at page 16:\n- There is one signature above the typed name \"Special Agent Marc Silski\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 6108, "output_tokens": 100, "cost_micros": 19824, "latency_ms": 3750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures that appear on pages 15 and 16 of the document.\n\nLooking at page 15:\n- This page shows the document dated September, 2018\n- It has the names \"MATTHEW SCHNEIDER\" and \"ADRIANA DYDELL\" typed, but these appear to be typed names as part of the document format, not actual signatures\n- I do not see any actual handwritten or image signatures on page 15\n\nLooking at page 16:\n- This is the VERIFICATION page\n- It shows \"Special Agent Marc Silski\" and \"Federal Bureau of Investigation\" with a date of \"September 6, 2018\"\n- This also appears to be typed text rather than an actual signature image\n\nThe question asks for signatures \"appeared\" on these pages. Looking carefully at both pages, I can see typed names but no visible handwritten signatures or signature images in the document.\n\nHowever, these are signature blocks where signatures would typically appear. The document shows:\n- Page 15: 1 signature block (for Adriana Dydell)\n- Page 16: 1 signature block (for Marc Silski)\n\nTotal signature blocks: 2\n\nSince the question asks for the count in \"float format\" but then requests \"a single integer only,\" and given that there are 2 signature blocks present on these pages combined:\n\nAnswer: 2", "citations": [], "input_tokens": 5238, "output_tokens": 308, "cost_micros": 20334, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\n**Page 15 (PageID.15):**\nLooking at page 15, I can see:\n1. A signature above \"ADRIANA DYDELL\" - there is an underlined signature line with \"Adriana Dydell\" written above it\n\nSo there is 1 signature on page 15.\n\n**Page 16 (PageID.16):**\nLooking at page 16, I can see:\n1. A signature notation \"[Signature of Marc Silski]\" above \"Special Agent Marc Silski\"\n\nSo there is 1 signature on page 16.\n\n**Total signatures on pages 15 and 16:** 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5788, "output_tokens": 185, "cost_micros": 20139, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find the signatures on pages 15 and 16.Let me search for content related to pages 15 and 16 by reading more of the document:Let me read the entire document to find pages 15 and 16:Great! I found the relevant sections. Let me look more carefully at the content around pages 15 and 16. I can see:\n\n- Line 616: ``\n- Line 637: ``\n\nNow let me examine the signatures on these pages. From what I can see:\n\n**Page 15** (lines 590-618): Shows signatures for:\n1. Matthew Schneider (United States Attorney) \n2. Adriana Dydell (Assistant United States Attorney)\n\n**Page 16** (lines 619-638): Shows signature for:\n1. Marc Silski (Special Agent, Federal Bureau of Investigation)\n\nLet me read the section more carefully to confirm:Perfect! Now I can clearly see the signatures on both pages:\n\n**Page 15** (lines 596-616):\n- Shows \"Respectfully submitted,\" followed by:\n 1. MATTHEW SCHNEIDER (United States Attorney)\n 2. \"Adriana@iol\" (appears to be a signature)\n 3. ADRIANA DYDELL (Assistant United States Attorney)\n- The \"Adriana@iol\" on line 603 appears to be a signature representation\n- Dated: September 13, 2018\n\n**Page 16** (lines 619-637):\n- Shows VERIFICATION section with:\n 1. \"Josh,\" on line 631 (appears to be a signature)\n 2. Special Agent Marc Silski (Federal Bureau of Investigation)\n- Dated: September 6, 2018\n\nCounting the signatures:\n- Page 15: 1 signature (the \"Adriana@iol\" signature for Adriana Dydell)\n- Page 16: 1 signature (the \"Josh,\" signature for Marc Silski)\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42092, "error": null, "extra": {"thread_id": 2066, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ddcf7e386683499793cc579d23850dce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 267, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "According to the document, the telephone number for The Limes Residential Home is:\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 37738, "output_tokens": 30, "cost_micros": 113664, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10257, "output_tokens": 37, "cost_micros": 31326, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nThe address and contact details are:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 12045, "output_tokens": 79, "cost_micros": 37320, "latency_ms": 3766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nThe address block shows:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10599, "output_tokens": 73, "cost_micros": 32892, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10834, "output_tokens": 33, "cost_micros": 32997, "latency_ms": 3625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the telephone number for The Limes Residential Home.Let me search for \"The Limes Residential Home\" in the document:Let me search with more context to see the actual content:I can see from the search results that the telephone number appears on line 168. Let me read that section:Perfect! I found the telephone number for The Limes Residential Home on line 174.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34734, "error": null, "extra": {"thread_id": 2067, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_de5d9004414b47159eff08bc68030dbf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 151, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 3 which contains the section \"The five questions we ask about services and what we found\", I can identify the primary questions asked in this inspection report.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 37742, "output_tokens": 73, "cost_micros": 114321, "latency_ms": 7515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can identify the five primary questions that were asked about the services in this inspection report.\n\nThese questions are clearly listed in the \"Summary of findings\" section under \"The five questions we ask about services and what we found\":\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10261, "output_tokens": 84, "cost_micros": 32043, "latency_ms": 3844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically the \"Summary of findings\" section which contains \"The five questions we ask about services and what we found\", I can identify the primary questions asked about the services.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 12049, "output_tokens": 72, "cost_micros": 37227, "latency_ms": 4234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can see the \"five questions we ask about services\" section which lists the primary questions asked during the inspection.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10603, "output_tokens": 61, "cost_micros": 32724, "latency_ms": 3125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document, I can identify the five primary questions that were consistently asked about the services throughout the inspection report.\n\nThese questions appear in the \"Summary of findings\" section and structure the entire report, with each having its own detailed findings section:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10838, "output_tokens": 126, "cost_micros": 34404, "latency_ms": 4328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find the primary questions asked about the services.Looking at the document, I can see this is a Care Quality Commission inspection report. The report explicitly states \"The five questions we ask about services\" in a table starting around line 314. Let me identify these questions from the document structure:\n\nFrom the document, the five primary questions asked about the services are clearly listed:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27546, "error": null, "extra": {"thread_id": 2068, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bdf643e270448d6aadaffef3d9744b8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 108, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLet me examine each page:\n\nPage 1: I can see the CQC logo (the circular Q logo with \"Care Quality Commission\" text) at the top of the page.\n\nPage 2: No CQC logo visible on this page.\n\nPage 3: No CQC logo visible on this page.\n\nPage 4: No CQC logo visible on this page.\n\nPage 5: I can see the CQC logo at the top of this page.\n\nPages 6-17: Examining the remaining pages, I do not see the CQC logo appearing on any of these pages.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 37764, "output_tokens": 169, "cost_micros": 115827, "latency_ms": 7890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of certain pages, which typically indicates where a logo would be placed in the actual PDF document.\n\nLet me go through each page:\n\nPage 1: \"Care Quality Commission\" appears at the top - \"The Limes Residential Care Home Limited The Limes Residential Home\"\n\nPage 2: \"Summary of findings\" section continues\n\nPage 3: \"Summary of findings\" continues\n\nPage 4: \"Summary of findings\" continues\n\nPage 5: \"Care Quality Commission\" appears - \"The Limes Residential Home Detailed findings\"\n\nPage 6: Continues with \"Is the service safe?\"\n\nPage 7: Continues with \"Is the service safe?\"\n\nPage 8: \"Inadequate\" and \"Is the service effective?\"\n\nPage 9: Continues with \"Is the service effective?\"\n\nPage 10: \"Requires improvement\" and \"Is the service caring?\"\n\nPage 11: Continues with \"Is the service caring?\"\n\nPage 12: \"Requires improvement\" and \"Is the service responsive?\"\n\nPage 13: \"Requires improvement\" and \"Is the service well-led?\"\n\nPage 14: Continues with \"Is the service well-led?\"\n\nPage 15: \"Action we have told the provider to take\"\n\nPage 16: Continues with \"Action we have told the provider to take\"\n\nPage 17: \"Enforcement actions\"\n\nBased on the document structure, \"Care Quality Commission\" appears as a header on pages 1 and 5, which is where the CQC logo would typically be located.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10283, "output_tokens": 395, "cost_micros": 36774, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the Care Quality Commission (CQC) logo appears in this document.\n\nLooking through the document:\n\n1. Page 1 (cover page) - Yes, CQC logo appears at the top\n2. Page 2 - No visible logo\n3. Page 3 - No visible logo\n4. Page 4 - No visible logo\n5. Page 5 - Yes, CQC logo appears at the top (start of \"Detailed findings\" section)\n6. Page 6 - No visible logo\n7. Page 7 - No visible logo\n8. Page 8 - No visible logo\n9. Page 9 - No visible logo\n10. Page 10 - No visible logo\n11. Page 11 - No visible logo\n12. Page 12 - No visible logo\n13. Page 13 - No visible logo\n14. Page 14 - No visible logo\n15. Page 15 - No visible logo\n16. Page 16 - No visible logo\n17. Page 17 - No visible logo\n\nThe CQC logo appears on:\n- Page 1 (the title page/cover)\n- Page 5 (at the start of the detailed findings section)\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 12071, "output_tokens": 294, "cost_micros": 40623, "latency_ms": 5390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of several pages. The logo/header \"Care Quality Commission\" appears on:\n\n- Page 1 (the title page with \"The Limes Residential Care Home Limited\")\n- Page 5 (at the top before \"Detailed findings\")\n- Page 15 (at the top before \"Action we have told the provider to take\")\n\nLet me verify by checking each page systematically:\n\nPage 1: \"Care Quality Commission\" header present\nPage 2-4: Summary of findings pages - no clear CQC logo/header visible in the page headers\nPage 5: \"Care Quality Commission\" header present before \"Detailed findings\"\nPages 6-14: Detailed findings sections - these appear to be continuation pages\nPage 15: \"Care Quality Commission\" header present before enforcement actions section\nPages 16-17: Continuation of enforcement actions\n\nBased on the document structure, the CQC logo/header appears on:\n\nAnswer: Page 1, Page 5, Page 15", "citations": [], "input_tokens": 10625, "output_tokens": 262, "cost_micros": 35805, "latency_ms": 7218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "page 1, page 15, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document systematically:\n\nPage 1: \"Care Quality Commission\" appears at the top as a header\nPage 2: \"Summary of findings\" - no logo visible in header\nPage 3: \"Summary of findings\" continues - no logo visible\nPage 4: \"Summary of findings\" continues - no logo visible\nPage 5: \"CareQuality Commission\" appears at the top (note: slightly different formatting)\nPage 6-17: These pages show section headings but I need to check for the CQC logo/branding\n\nLooking more carefully at the document structure, the CQC logo/branding appears to be present on:\n- Page 1 (top of document with \"Care Quality Commission\")\n- Page 5 (shows \"CareQuality Commission\" at top of \"Detailed findings\" section)\n\nThese appear to be the only pages with the CQC branding/logo clearly marked at the top.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10860, "output_tokens": 243, "cost_micros": 36225, "latency_ms": 6234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify all pages where the CQC logo appears.Let me search for \"CQC\" or \"logo\" throughout the document to find all pages where the logo appears:Now let me search for mentions of \"logo\" or \"Care Quality Commission\" to find where the logo appears:Let me search more specifically for image and figure tags to identify all instances of the logo:Let me read through the entire document more systematically to find all logo occurrences. Let me check what page the first logo is on and look for patterns:The first logo appears before \"PageFooter=1\", so it's on Page 1. Now let me search the entire document systematically for all instances of the CQC logo. Let me search for figures or images:Good! Now I found 7 figure tags. Let me read the context around each one to identify which ones are the CQC logo:Now I've identified that several figures contain only page headers. Let me verify which ones actually contain the CQC logo by checking the content. Based on my readings:\n\n1. Line 121 (before PageFooter 1) - Contains \"Care Quality Commission\" logo with visual description - **Page 1**\n2. Line 409 (after PageBreak on line 406, before PageFooter 4 on line 405) - Contains \"Care Quality Commission\" - **Page 5** (since PageFooter 4 appears at line 405-406, this is after the page break, so it's on page 5)\n\nLet me verify by checking the page breaks more carefully:Perfect! So after PageFooter 4 and PageBreak, we get Page 5, which has the CQC logo. Now let me check if there are any other CQC logos. The other figures at lines 868, 981, 1036, 1134, and 1247 only contain PageHeaders, not the logo.\n\nLet me verify this is correct by checking one more time if there are any other embedded images:Let me search for any other embedded images:Good! Only one embedded image (I1.jpg) exists in the document. Now let me confirm the two locations of \"Care Quality Commission\" text in figures:So the CQC logo on Page 5 appears between PageFooter 4 (line 405) and PageFooter 5 (line 456). That confirms it's on Page 5.\n\nNow let me verify there's only one actual logo image (I1.jpg) on Page 1, and the Page 5 figure is just text without an image:I notice that on Page 5 (line 409-414), there's a figure with \"Care Quality Commission\" text but NO embedded image notation. Let me check if there's an image reference after this figure:No embedded image noted for Page 5's figure. So the Page 5 figure has \"Care Quality Commission\" text but the question asks specifically about the \"logo\" which includes the visual/graphical element. Let me re-read the Page 1 section to see if it explicitly identifies this as a logo:\n\nLooking back at lines 130-147, Page 1 clearly shows:\n- \"**Embedded image:** `I1.jpg`\"\n- \"**Visual description:**\"\n- \"Organizational logo combining a graphical symbol with text\"\n\nThis definitively identifies Page 1 as having the CQC logo (the actual image/visual logo).\n\nOn Page 5 (lines 409-414), there's only the text \"Care Quality Commission\" in a figure, but no embedded image or logo graphic.\n\nHowever, let me check if both figures might represent the same logo - the system might have extracted it once with full details (Page 1) and then just shown the text on Page 5. In typical inspection reports, logos often appear on multiple pages. Let me search more broadly to understand the document structure better. Let me see if there are image references I might have missed:Only one \"Visual description\" exists - on Page 1. The interpretation here is that:\n- Page 1 has the actual CQC logo image (I1.jpg with full visual description)\n- Page 5 has a figure with just \"Care Quality Commission\" text\n\nGiven that the question asks for \"the logo of CQC\" (which implies the graphical/visual logo, not just text), and given that only Page 1 has an embedded image with the visual logo description, the answer should be Page 1 only.\n\nHowever, both figures contain \"Care Quality Commission\" and in PDF documents, logos can appear as repeated elements. The figure on Page 5 likely also represents the logo (just extracted differently by the parser). Let me verify by checking if standard CQC inspection reports have logos on multiple pages.\n\nBased on the document structure, I have:\n1. **Page 1** - Clear CQC logo with embedded image I1.jpg and full visual description\n2. **Page 5** - Figure with \"Care Quality Commission\" text (likely the same logo, but extraction didn't capture the image separately)\n\nGiven the ambiguity, I should report both pages where the CQC element appears in a figure tag, as both likely represent the logo placement in the original PDF.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 135453, "error": null, "extra": {"thread_id": 2069, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a8af9e5bd4c48cebf2e6cbec000700b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1002, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this CQC inspection report.\n\nLet me go through the document systematically:\n\n**From \"Action we have told the provider to take\" section (pages 15-16):**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From \"Enforcement actions\" section (page 17):**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nCounting the unique regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 HSCA (RA) (Staffing)\n- Regulation 18 CQC (Registration) (Notification)\n\nThat's 8 regulations total.\n\nAnswer: 8", "citations": [], "input_tokens": 37740, "output_tokens": 419, "cost_micros": 119505, "latency_ms": 10562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches of HSCA (Regulated Activities) Regulations 2014:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - unlawful restraint and failure to respond to allegations/safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (mentioned twice - failure to assess risks, and failure to manage medicines safely)\n\n3. **Regulation 18** - Staffing (insufficient staffing levels)\n\n4. **Regulation 18** - Staffing (again, for induction, supervision and training issues)\n\n5. **Regulation 11** - Need for consent (MCA 2005 implementation)\n\n6. **Regulation 10** - Dignity and respect\n\n7. **Regulation 9** - Person-centred care\n\n8. **Regulation 17** - Good governance (mentioned twice - failure to assess/monitor quality, and failure to maintain accurate records)\n\n**Breach of HSCA (Registration) Regulations 2009:**\n\n9. **Regulation 18** - Notification of other incidents (CQC Registration Regulations 2009)\n\nNow let me count the distinct regulation numbers that were breached:\n- Regulation 13 (RA Regs 2014)\n- Regulation 12 (RA Regs 2014)\n- Regulation 18 (RA Regs 2014)\n- Regulation 11 (RA Regs 2014)\n- Regulation 10 (RA Regs 2014)\n- Regulation 9 (RA Regs 2014)\n- Regulation 17 (RA Regs 2014)\n- Regulation 18 (Registration Regs 2009)\n\nThat's 7 different regulations from the RA Regulations 2014 and 1 from the Registration Regulations 2009, making 8 total distinct regulations breached.\n\nAnswer: 8", "citations": [], "input_tokens": 10259, "output_tokens": 499, "cost_micros": 38262, "latency_ms": 8109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breached regulations mentioned in this inspection report.\n\nLet me go through the document systematically:\n\n**From \"Is the service safe?\" section:**\n1. Regulation 13 - Safeguarding (failure to respond to allegations and investigate safeguarding incidents)\n2. Regulation 12 - Safe care and treatment (failure to assess and mitigate risks)\n3. Regulation 18 - Staffing (insufficient staffing levels)\n4. Regulation 12 - Safe care and treatment (medicines not managed safely) - already counted above\n\n**From \"Is the service effective?\" section:**\n5. Regulation 18 - Staffing (induction, supervision and training) - already counted above\n6. Regulation 13 - Safeguarding (unlawful restraint) - already counted above\n7. Regulation 11 - Need for consent (MCA 2005 not implemented)\n\n**From \"Is the service caring?\" section:**\n8. Regulation 10 - Dignity and respect\n\n**From \"Is the service responsive?\" section:**\n9. Regulation 9 - Person-centred care (care records not reflecting current needs)\n\n**From \"Is the service well-led?\" section:**\n10. Regulation 17 - Good governance (failure to assess, monitor and improve quality)\n11. Regulation 17 - Good governance (failure to maintain accurate records) - already counted above\n12. Regulation 18 CQC (Registration) Regulations 2009 - Notification of incidents\n\nLet me count the unique HSCA 2008 regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 (Staffing)\n\nThat's 7 regulations under HSCA 2008 (Regulated Activities) Regulations 2014.\n\nPlus 1 regulation under CQC (Registration) Regulations 2009 (Regulation 18).\n\nThe question asks specifically about \"regulations of the", "citations": [], "input_tokens": 12047, "output_tokens": 512, "cost_micros": 43821, "latency_ms": 8780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this report.\n\nLet me go through the document systematically:\n\n**From the \"Action we have told the provider to take\" section:**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From the \"Enforcement actions\" section:**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nLet me verify by checking the main body of the report for all breach mentions:\n- Regulation 13 (safeguarding) - breach mentioned\n- Regulation 12 (safe care and treatment, including medicines and risk assessment) - breach mentioned\n- Regulation 18 (staffing) - breach mentioned\n- Regulation 11 (Mental Capacity Act) - breach mentioned\n- Regulation 10 (dignity and respect) - breach mentioned\n- Regulation 9 (person-centred care) - breach mentioned\n- Regulation 17 (good governance - two separate breaches mentioned for different aspects) - breach mentioned\n- Regulation 18 CQC (Registration) Regulations 2009 (notifications) - breach mentioned\n\nTotal unique regulations breached: 8\n\nAnswer: 8", "citations": [], "input_tokens": 10601, "output_tokens": 448, "cost_micros": 38523, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA 2008) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches found in the main body of the report:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (failure to respond to allegations and report safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (failure to assess, record and mitigate risks to people's health and safety)\n\n3. **Regulation 18** - Staffing (failure to ensure sufficient staffing levels)\n\n4. **Regulation 12** - Safe care and treatment (failure to manage medicines safely) - *same regulation as #2*\n\n5. **Regulation 18** - Staffing (failure to ensure staff received appropriate induction, supervision and training) - *same regulation as #3*\n\n6. **Regulation 13** - Safeguarding (failure to ensure people were not unlawfully restrained) - *same regulation as #1*\n\n7. **Regulation 11** - Need for consent (failure to ensure MCA 2005 code of practice was implemented)\n\n8. **Regulation 10** - Dignity and respect (failure to treat people with respect and dignity at all times)\n\n9. **Regulation 9** - Person-centred care (failure to ensure care records reflected current needs and preferences)\n\n10. **Regulation 17** - Good governance (failure to effectively assess, monitor and improve quality and safety of services)\n\n11. **Regulation 17** - Good governance (failure to maintain accurate and complete records) - *same regulation as #10*\n\n12. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - This is a different Act (Registration Regulations, not Regulated Activities Regulations)\n\n**Counting unique HSCA 2008 (Regulated Activities) Regulations 2014 breaches:**\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n-", "citations": [], "input_tokens": 10836, "output_tokens": 512, "cost_micros": 40188, "latency_ms": 9764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the provided document to find information about HSCA 2008 regulations breaches.Let me continue reading to find any summary or conclusion section that might provide the total count.Based on my careful reading of the document, I need to count all the breaches of the Health and Social Care Act 2008 (Regulated Activities) Regulations 2014 (HSCA 2008).\n\nThe breaches mentioned in the report are:\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - for failing to respond to allegations and for unlawful restraint)\n2. **Regulation 12** - Safe care and treatment (mentioned twice - for failing to assess/mitigate risks and for failing to manage medicines safely)\n3. **Regulation 18** - Staffing (mentioned twice - for insufficient staffing levels and for inadequate training/supervision)\n4. **Regulation 11** - Need for consent (MCA 2005 implementation)\n5. **Regulation 10** - Dignity and respect\n6. **Regulation 9** - Person-centred care\n7. **Regulation 17** - Good governance (mentioned twice - for failing to assess/monitor quality and for failing to maintain accurate records)\n\nAdditionally, there is one breach of:\n8. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - Notification of other incidents\n\nCounting the distinct regulation numbers breached under HSCA (RA) Regulations 2014:\n- Regulation 13\n- Regulation 12\n- Regulation 18\n- Regulation 11\n- Regulation 10\n- Regulation 9\n- Regulation 17\n\nThat's 7 different regulations of the HSCA (RA) Regulations 2014 that were breached.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38483, "error": null, "extra": {"thread_id": 2070, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ce6489cb9fc4be0aad3905ed014d904", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 217, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "7"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from 3M's cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018 (page 60):\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount for FY2018 is shown as purchases of property, plant and equipment.\n\nAnswer: 1577", "citations": [], "input_tokens": 390262, "output_tokens": 116, "cost_micros": 2344182, "latency_ms": 44468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the years ended December 31, I can find the capital expenditures under \"Cash Flows from Investing Activities.\"\n\nFrom the document:\n\n**Cash Flows from Investing Activities:**\nYears ended December 31\n(Millions) 2018 2017 2016\n\nPurchases of property, plant and equipment (PP&E) $(1,577) $(1,373) $(1,420)\n\nThe capital expenditure for FY2018 is shown as purchases of property, plant and equipment (PP&E) which equals $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 140538, "output_tokens": 171, "cost_micros": 424179, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount is shown as a negative number in the cash flow statement because it represents a cash outflow.\n\nThe capital expenditure amount for FY2018 is $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 207953, "output_tokens": 130, "cost_micros": 1250643, "latency_ms": 8718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for FY2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 163241, "output_tokens": 77, "cost_micros": 490878, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for fiscal year 2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 177504, "output_tokens": 78, "cost_micros": 533682, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the capital expenditure amount from the cash flow statement.Let me search for the cash flow statement in this document.Great! I found the Consolidated Statement of Cash Flows. Let me read that specific chunk to find the capital expenditure line.Let me search for variations of capital expenditure terminology:Perfect! I found it. The \"Purchases of property, plant and equipment (PP&E)\" line shows the capital expenditure amounts. For 2018, it shows $(1,577) million. Let me read the actual chunk to confirm this is from the cash flow statement.Let me check a bit earlier to confirm this is the Consolidated Statement of Cash Flows:Let me check the title of this statement:Perfect! I've confirmed this is the \"3M Company and Subsidiaries Consolidated Statement of Cash Flows\" for years ended December 31, with columns for 2018, 2017, and 2016.\n\nIn the \"Cash Flows from Investing Activities\" section, the line item \"Purchases of property, plant and equipment (PP&E)\" shows:\n- 2018: (1,577)\n- 2017: (1,373)\n- 2016: (1,420)\n\nThe amounts are in millions of USD, and the parentheses indicate these are cash outflows. The FY2018 capital expenditure amount is 1,577 million USD.\n\nAnswer: 1577.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53671, "error": null, "extra": {"thread_id": 2071, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b68bd46e8dab41c29dc9086112158cbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 310, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for 3M's business segments in FY2018...\n\nFrom the document, specifically on pages discussing business segments (e.g., page 4, page 33-38), I can identify the following business segments:\n\n1. Industrial Business\n2. Safety and Graphics Business\n3. Health Care Business\n4. Electronics and Energy Business\n5. Consumer Business\n\nThese five segments are consistently mentioned throughout the document as 3M's reportable segments for fiscal year 2018.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 390260, "output_tokens": 126, "cost_micros": 2344395, "latency_ms": 49344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to identify 3M's business segments for FY2018.\n\nFrom the document, I can find the business segment information in multiple places. The clearest statement is in the Overview section and repeated throughout:\n\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nThis is also confirmed in the Business Segment Products table which lists the major products for each segment:\n- Industrial\n- Safety and Graphics\n- Health Care\n- Electronics and Energy\n- Consumer\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 140536, "output_tokens": 140, "cost_micros": 423708, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information clearly stated in multiple places:\n\nIn the \"BUSINESS SEGMENTS\" section (page 4), it states:\n\"3M manages its operations in five business segments. The reportable segments are Industrial, Safety and Graphics, Health Care, Electronics and Energy, and Consumer.\"\n\nThis is also confirmed in the \"OVERVIEW\" section (page 15):\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnd in the \"PERFORMANCE BY BUSINESS SEGMENT\" section (page 32):\n\"3M manages its operations in five business segments. The reportable segments are Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 207951, "output_tokens": 206, "cost_micros": 1252341, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018:\n\nFrom the document, I can find the business segments clearly stated in multiple locations, including:\n\n1. In the Business Segments section (page 4):\n\"3M manages its operations in five business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\n2. In the Table of Contents and throughout the financial statements, these five segments are consistently referenced.\n\n3. In Note 18 (Business Segments), the same five segments are listed with their products and financial information.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 163239, "output_tokens": 147, "cost_micros": 491922, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information in multiple sections, including:\n\n1. In the Executive Summary/Overview section\n2. In Note 18 - Business Segments\n3. In the Business Segments section of Item 1\n\nThe document clearly states that 3M manages its operations in **five business segments**:\n\n1. **Industrial** - serves automotive OEM, automotive aftermarket, electronics, appliance, paper and printing, packaging, food and beverage, and construction markets\n2. **Safety and Graphics** - serves markets for personal protection products, commercial solutions, transportation safety, and roofing granules\n3. **Health Care** - serves medical clinics and hospitals, pharmaceuticals, dental and orthodontic practitioners, health information systems, and food manufacturing and testing\n4. **Electronics and Energy** - serves electronics and energy markets, including display materials, electrical products, and power generation\n5. **Consumer** - serves consumer retail, office retail, home improvement, drug and pharmacy retail markets\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 177502, "output_tokens": 255, "cost_micros": 536331, "latency_ms": 14344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about their business segments.Perfect! I found the information. Let me read the specific section that clearly states the five business segments.Answer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32219, "error": null, "extra": {"thread_id": 2072, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5721bf743c374f36a4a98f84b6cf13eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 119, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nLooking through the document, I can find information about derivative instruments in Note 14 (Derivatives) and the tables showing \"Location and Fair Value Amount of Derivative Instruments.\"\n\nFrom page 104-105, I can see tables showing the fair value of derivative instruments as of December 31, 2018:\n\n**Assets (December 31, 2018):**\n- Foreign currency forward/option contracts: $127 million\n- Interest rate swap contracts: $19 million\n- Total derivative assets: $146 million\n\n**Liabilities (December 31, 2018):**\n- Foreign currency forward/option contracts: $22 million\n- Interest rate swap contracts: $31 million\n- Total derivative liabilities: $53 million\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 390258, "output_tokens": 225, "cost_micros": 2346610, "latency_ms": 34921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments as of December 31, 2018.\n\nFrom the section \"Location and Fair Value Amount of Derivative Instruments\" on page 104, I can see a table showing derivative instruments as of December 31, 2018:\n\n**Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative instruments (Assets): $146 million\n\n**Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative instruments (Liabilities): $53 million\n\nNet fair value = Total Assets - Total Liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 140534, "output_tokens": 198, "cost_micros": 424572, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom Note 14 \"Derivatives\" and Note 15 \"Fair Value Measurements\", I can find the fair value of derivative instruments.\n\nIn the section \"Location and Fair Value Amount of Derivative Instruments\" under Note 14, there is a table showing:\n\n**December 31, 2018:**\n\nAssets:\n- Foreign currency forward/option contracts (current): $74 million\n- Foreign currency forward/option contracts (non-current): $39 million\n- Interest rate swap contracts (current): $0 million\n- Interest rate swap contracts (non-current): $19 million\n- Foreign currency forward/option contracts (not designated): $14 million\n**Total derivative assets: $146 million**\n\nLiabilities:\n- Foreign currency forward/option contracts (current): $12 million\n- Foreign currency forward/option contracts (non-current): $4 million\n- Interest rate swap contracts (current): $14 million\n- Interest rate swap contracts (non-current): $17 million\n- Foreign currency forward/option contracts (not designated): $6 million\n**Total derivative liabilities: $53 million**\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 207949, "output_tokens": 317, "cost_micros": 1254826, "latency_ms": 11608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom the \"Location and Fair Value Amount of Derivative Instruments\" table for December 31, 2018:\n\n**Derivative Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative assets: $146 million\n\n**Derivative Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative liabilities: $53 million\n\n**Net Fair Value = Total Assets - Total Liabilities**\nNet Fair Value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 163237, "output_tokens": 185, "cost_micros": 492486, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the fair value of derivative instruments from the document:\n\nFrom the table \"Location and Fair Value Amount of Derivative Instruments\" on page 104:\n\n**December 31, 2018:**\n- Total derivative assets (Fair Value Amount): $146 million\n- Total derivative liabilities (Fair Value Amount): $53 million\n\nNet fair value = Assets - Liabilities = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 177500, "output_tokens": 105, "cost_micros": 534075, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about the net fair value of derivative instruments.Let me search for information about derivative instruments in the document.Let me search more specifically for derivative instruments and their fair values in a table format.Now let me read the section on derivatives to find the fair value information, particularly around line 15104 where it mentions \"Location and Fair Value Amount of Derivative Instruments\":Perfect! I found the table showing the fair value of derivative instruments for 3M in FY2018. Looking at the table on lines 15230-15235:\n\nFor December 31, 2018:\n- Total derivative instruments (Assets): $146 million\n- Total derivative instruments (Liabilities): $53 million\n\nThe net fair value would be: Assets - Liabilities = $146 - $53 = $93 million\n\nAnswer: 93.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50969, "error": null, "extra": {"thread_id": 2073, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890847b672154ab2beed6f213e7b03d2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 220, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for advertising and merchandising costs:\n\nFrom the document (page 65):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nTo calculate the growth rate for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635... \u00d7 100\nGrowth rate = -3.649635...\nRounded to two decimal places = -3.65\n\nAnswer: -3.65", "citations": [], "input_tokens": 390267, "output_tokens": 193, "cost_micros": 2345944, "latency_ms": 37280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 2018 and 2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- 2018: $396 million\n- 2017: $411 million\n\nGrowth rate = (2018 value - 2017 value) / 2017 value \u00d7 100\nGrowth rate = ($396 - $411) / $411 \u00d7 100\nGrowth rate = -$15 / $411 \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 140543, "output_tokens": 230, "cost_micros": 425079, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- FY2018: $396 million\n- FY2017: $411 million\n\nGrowth rate = (FY2018 - FY2017) / FY2017 \u00d7 100\nGrowth rate = (396 - 411) / 411 \u00d7 100\nGrowth rate = -15 / 411 \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 207958, "output_tokens": 224, "cost_micros": 1252788, "latency_ms": 9453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, under \"Advertising and merchandising\":\n- 2018: $396 million\n- 2017: $411 million\n- 2016: $385 million\n\nThe growth rate formula is: ((2018 - 2017) / 2017) \u00d7 100\n\nGrowth rate = ((396 - 411) / 411) \u00d7 100\nGrowth rate = (-15 / 411) \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 163246, "output_tokens": 178, "cost_micros": 492408, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the advertising and merchandising costs:\n\nFrom the document (page 64):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nGrowth rate calculation for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 177509, "output_tokens": 176, "cost_micros": 535167, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the advertising and merchandising costs for FY2018 and the prior year to calculate the growth rate.Let me search for \"advertising\" in the document to find the relevant information quickly.Great! I found a reference to advertising and merchandising costs. Let me read the chunk that contains this information.Perfect! I can see that advertising and merchandising costs were $396 million in 2018 and $411 million in 2017. Let me read more context to ensure I have the complete information.Perfect! I now have the complete information:\n- 2018: $396 million\n- 2017: $411 million\n\nNow I'll calculate the growth rate:\nGrowth rate = [(2018 value - 2017 value) / 2017 value] \u00d7 100\nGrowth rate = [(396 - 411) / 411] \u00d7 100\nGrowth rate = [-15 / 411] \u00d7 100\nGrowth rate = -0.036496... \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36500, "error": null, "extra": {"thread_id": 2074, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3f1bac0372cd48f5b95293aa87160368", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 175, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl deleted file mode 100644 index 1b7494211c..0000000000 --- a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl +++ /dev/null @@ -1,1026 +0,0 @@ -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:15Z", "latency_ms": 20985, "error": null, "raw_text_chars": 696}]}, "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many adults rated Trump's government ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nLooking through the document, I find relevant information on page 4 (document page 6):\n\nThe document states: \"About four-in-ten Americans rate the ethical standards of Trump administration officials as excellent (9%) or good (30%), while 58% say they are not good (21%) or poor (36%).\"\n\nThe survey was conducted among 1,503 adults (as stated in the methodology section on page 14).\n\nTo find the number of adults who rated the ethical standards as \"poor\":\n36% of 1,503 = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 33894, "output_tokens": 191, "cost_micros": 104547, "latency_ms": 20968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults\n- 36% rated the ethical standards as \"poor\" (from the table showing \"% who say that the ethical standards of top Trump administration officials are...\")\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 6925, "output_tokens": 125, "cost_micros": 22650, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults (from the Methodology section on page 14)\n- According to the chart on page 2, 36% of the total rated the ethical standards as \"poor\"\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9417, "output_tokens": 128, "cost_micros": 30171, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document, I can find:\n1. The total sample size: 1,503 adults (from the Methodology section)\n2. The percentage who rated ethical standards as \"poor\": 36% (from the table \"Most rate top Trump administration officials' ethics as 'not good' or 'poor'\")\n\nTo calculate the number of adults:\n1,503 \u00d7 0.36 = 541.08\n\nSince we need an integer and this represents people, I should round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 7774, "output_tokens": 158, "cost_micros": 25692, "latency_ms": 6405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump's government ethical standards as poor.\n\nFrom the document:\n- The survey was conducted April 25-May 1, 2018 among 1,503 adults (from the Methodology section on page 14)\n- 36% rated the ethical standards of top Trump administration officials as \"poor\" (from the table on page 2)\n\nCalculation:\n1,503 adults \u00d7 36% = 1,503 \u00d7 0.36 = 541.08\n\nSince we need to report the number of adults, I'll round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9517, "output_tokens": 145, "cost_micros": 30726, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question about the survey conducted April 25 - May 1, 2018.Let me search for the specific data about ethical standards ratings. I can see the document mentions ethical standards. Let me look for the detailed breakdown.I can see from the grep results that there's a table showing ethical standards ratings. Let me read the specific section more carefully:I can see a table showing percentages for ethical standards of Trump administration officials. However, I need to understand the table structure better. The table shows:\n- Total: 58, 36, 9, 39\n- These appear to be percentages for different categories\n\nLet me look at the context more carefully to understand what \"58\" represents:Now I understand the table structure. Looking at the figure:\n- The categories are listed as: Poor, Not good, Good, Excellent\n- For \"Total\" (all adults): The values appear to be 58, 36, 9, 39\n\nHowever, the text says \"58% rate them as not good or poor\" which suggests 58% is the combined total of \"not good\" and \"poor\". Let me look more carefully at how to read this table. Looking at lines 248-249: \"39% say the administration's ethical standards are excellent or good, while 58% rate them as not good or poor.\"\n\nThis indicates:\n- 39% = Excellent + Good (combined)\n- 58% = Not good + Poor (combined)\n\nLooking at the table values for Total: 58, 36, 9, 39\n\nThe layout suggests:\n- Row 1 (Poor): 58\n- Row 2 (Not good): 36 \n- Row 3 (Good): 9\n- Row 4 (Excellent): 39\n\nBut this doesn't make sense because 58 + 36 would be 94%, not matching the text's 58% for \"not good or poor\" combined.\n\nLet me reconsider the table structure. Perhaps it's arranged differently - the numbers might be in columns rather than associating each number with the label above it. Let me look for more context about the survey sample size:The survey was conducted among 1,503 adults. Now, looking back at the text on line 248: \"58% rate them as not good or poor\"\n\nIf 58% of 1,503 adults rated Trump's ethical standards as \"not good or poor\", then:\n58% \u00d7 1,503 = 0.58 \u00d7 1,503 = 871.74 \u2248 872 adults\n\nBut the question specifically asks for those who rated it as \"poor\", not \"not good or poor\" combined.\n\nLet me re-examine the table structure. The table shows categories and then rows for Total, Rep/Lean Rep, and Dem/Lean Dem. Looking at the numbers again:\n\nFor the \"Total\" row, I see: 58, 36, 9, 39\n\nGiven that the text says 58% rated as \"not good or poor\" combined, and the categories listed are Poor, Not good, Good, Excellent - perhaps the table is showing these in a different order or the numbers represent something else.\n\nActually, looking at line 289-295 more carefully, let me reconsider: perhaps the numbers don't align sequentially with the labels above. The figure might have a more complex layout. Let me look at the Republican row: 22, 7, 19, 75. And Democrat row: 86, 61, 2, 12.\n\nFor Republicans: The text (line 257) says \"75% giving the administration high marks\" - this matches the \"75\" in the data.\nFor Democrats: The text (line 258) says \"86% of Democrats rating its ethical standards negatively\" - this matches the \"86\" in the data.\n\nSo for the Total row showing 58, 36, 9, 39:\n- 58 + 36 = 94 (doesn't match the 58% mentioned)\n- 36 + 58 = 94 \n- 9 + 39 = 48 (doesn't match the 39% for excellent/good)\n\nWait - perhaps I'm misreading the structure. Let me think about this differently. The text says:\n- 39% say excellent or good\n- 58% say not good or poor\n\nThese add to 97%, with 3% presumably \"don't know\" (which the note says is not shown).\n\nLooking at the table structure, maybe it's:\n- Poor: 36\n- Not good: 22 (so 36 + 22 = 58% for \"poor or not good\")\n- Good: ? \n- Excellent: ?\n\nActually, rereading the visual structure between lines 273-315, the checkboxes (\u2610) appear before each category label, and then the numbers follow. Let me parse this more carefully as a table where columns might represent the categories.\n\nLooking at the pattern, it seems like the table might have columns for each rating category. If the first number in each row represents \"Poor\":\n- Total: 58 (but this would be just \"Poor\", not \"Poor + Not good\")\n\nBut the text clearly states 58% is the combined \"not good or poor\". So if:\n- Poor alone = X%\n- Not good alone = Y% \n- X + Y = 58%\n\nAnd looking at potential individual values, perhaps:\n- Poor = 36%\n- Not good = 22%\n- 36 + 22 = 58% \u2713\n\nThis would make sense! So in the Total row (58, 36, 9, 39), these might be arranged as:\n- Combined negative: 58%\n- Poor: 36%\n- Good: 9%\n- Combined positive: 39% (should be Good 9% + Excellent, but listed as 39%)\n\nWait, that still doesn't work cleanly. Let me look for another table or clarification in the document:Perfect! I found a more detailed table starting at line 875. This table clearly shows the structure with labels on lines 880-885:\n- Poor\n- Not good\n- Good\n- Excellent\n\nAnd then for \"Total\" (lines 890-898):\n- 58\n- 36\n- 9\n- 39\n\nBut wait - this arrangement still seems odd. Let me look at the actual layout more carefully. Lines 890-898 show:\n```\nTotal\n58\n36\n9\n39\n```\n\nLooking at the structure and comparing with the text, I think the table columns might be:\n- Column 1: Row label (Total, Rep/Lean Rep, etc.)\n- Column 2: Combined \"Not good or Poor\" (58 for Total)\n- Column 3: Poor specifically (36 for Total)\n- Column 4: Good specifically (9 for Total)\n- Column 5: Combined \"Good or Excellent\" (39 for Total)\n\nThis would make sense because:\n- 58% = \"not good or poor\" combined (matches the text on line 248)\n- 36% = \"poor\" alone\n- 9% = \"good\" alone\n- 39% = \"good or excellent\" combined (matches the text on line 247)\n\nSo the answer to \"how many adults rated Trump's government ethical standards as poor\" would be:\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08 \u2248 541 adults\n\nActually, let me double-check by reading the column headers more carefully in this second table:Looking at the table header (lines 880-885), the categories are listed as:\n- Poor\n- Not good\n- Good \n- Excellent\n\nAnd for Total (lines 892-898): 58, 36, 9, 39\n\nGiven the table structure and that these are percentages, I now believe the columns represent:\n1. First column after \"Total\" = one rating category\n2. Second column = another rating category\n3. Third column = another rating category\n4. Fourth column = another rating category\n\nBut there are only 4 numbers (58, 36, 9, 39) for 4 categories (Poor, Not good, Good, Excellent).\n\nSo the mapping should be:\n- Poor: 58%? or 36%?\n- Not good: 36%? or 9%?\n- Good: 9%? or 39%?\n- Excellent: 39%? or something else?\n\nLet me check against the text constraints:\n- Text says \"39% say excellent or good\" (line 247-248) \u2192 Good + Excellent = 39%\n- Text says \"58% rate them as not good or poor\" (line 248-249) \u2192 Not good + Poor = 58%\n\nIf the four values are 58, 36, 9, 39:\n- 58 + 36 + 9 + 39 = 142% (impossible for percentages)\n\nSo these must not all be individual category percentages. Some must be combined values. Given the text:\n- 58% = Not good + Poor (combined)\n- 39% = Good + Excellent (combined)\n- 9% = one individual category\n- 36% = one individual category\n\nSince 58% + 39% = 97% (with 3% don't know), and we have two individual values (36 and 9), these must be subcategories within the combined values:\n- 36% = Poor (part of the 58% \"not good or poor\")\n- Therefore Not good = 58% - 36% = 22%\n- 9% = Good (part of the 39% \"good or excellent\") \n- Therefore Excellent = 39% - 9% = 30%\n\nThis makes sense! So the table is showing:\n- Column 1: Not good or Poor combined (58%)\n- Column 2: Poor alone (36%)\n- Column 3: Good alone (9%)\n- Column 4: Good or Excellent combined (39%)\n\nTherefore, 36% of adults rated Trump's ethical standards as \"poor\".\n\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest integer: 541 adults\n\nAnswer: 541", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94875, "error": null, "extra": {"thread_id": 1904, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7e2e86436614d5c96d6bd9dd3a561c5", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 977, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:16Z", "latency_ms": 20453, "error": null, "raw_text_chars": 544}]}, "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at page 5 of the document, which shows \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the data for \"very confident\" and \"not at all confident\" percentages across different domains.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 30%\n\nFor \"Not at all confident\" (highest percentage):\n- Make wise decisions about immigration policy: 55%\n\nAnswer: Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 33942, "output_tokens": 117, "cost_micros": 103581, "latency_ms": 20438, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the domains where adults are \"very confident\" and \"not at all confident\" in Donald Trump, and identify which has the highest percentage for each category.\n\nLooking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the breakdown of confidence levels (Very, Somewhat, Not too, Not at all) for various policy areas.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 19%\n- Make good appointments to the federal courts: 21%\n- Manage the executive branch effectively: 21%\n- Make wise decisions about immigration policy: 16%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 30%\n\nThe highest \"Very confident\" is \"Work effectively with Congress\" at 30%.\n\nFor \"Not at all confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 26%\n- Make good decisions about economic policy: 17%\n- Use military force wisely: 32%\n- Make good appointments to the federal courts: 32%\n- Manage the executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nThe highest \"Not at all confident\" is \"Make wise decisions about immigration policy\" at 39%.\n\nIn alphabetical order:\n1. Make wise decisions about immigration policy\n2. Work effectively with Congress\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 6973, "output_tokens": 374, "cost_micros": 26529, "latency_ms": 9500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to find:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table data:\n\n**Very confident (highest):**\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (HIGHEST)\n\n**Not at all confident (highest):**\n- Make wise decisions about immigration policy: 39% (HIGHEST)\n- Handle an international crisis: 35%\n- Manage the executive branch effectively: 34%\n\nThe two domains are:\n1. \"Make good decisions about economic policy\" (highest very confident at 31%)\n2. \"Make wise decisions about immigration policy\" (highest not at all confident at 39%)\n\nIn alphabetical order:\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9465, "output_tokens": 216, "cost_micros": 31635, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table \"Public's confidence in Trump to handle several policy areas mixed\" on page 4 of the document, I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table:\n\n**Very confident percentages:**\n- Negotiate favorable trade agreements: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 20%\n- Make good appointments to federal courts: 21%\n- Manage executive branch effectively: 24%\n- Make wise decisions about immigration policy: 17%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 21%\n\nHighest \"Very\" confident: **Negotiate favorable trade agreements** and **Manage the executive branch effectively** (both at 24%)\n\n**Not at all confident percentages:**\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: 29%\n- Use military force wisely: 32%\n- Make good appointments to federal courts: 32%\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nHighest \"Not at all\" confident: **Make wise decisions about immigration policy** (39%)\n\nIn alphabetical order:\n1. Manage the executive branch effectively\n2. Make wise decisions about immigration policy\n3. Negotiate favorable trade agreements with other countries\n\nAnswer: Manage the executive branch effectively, Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 7822, "output_tokens": 372, "cost_micros": 29046, "latency_ms": 8530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, manage executive branch effectively, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 3 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table, examining the \"Very\" column:\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (highest)\n- Use military force wisely: 26%\n- Make good appointments to federal courts: 25%\n- Manage executive branch effectively: 21%\n- Make wise decisions about immigration policy: 26%\n- Handle an international crisis: 25%\n- Work effectively with Congress: 13%\n\nFrom the \"Not at all\" column:\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: (not shown separately)\n- Use military force wisely: 32%\n- Make good appointments to federal courts: (not shown separately)\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39% (highest)\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9565, "output_tokens": 285, "cost_micros": 32970, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the survey data from April 25 - May 1, 2018.Let me continue reading to find the specific data about confidence levels in different domains.I need to find the specific data showing \"very confident\" and \"not at all confident\" percentages for different domains. Let me search for that data.Perfect! I found the table with confidence levels. Now I need to identify which columns represent \"Very confident\" and \"Not at all confident\". Let me re-read this section more carefully.Now I can see the table structure clearly. The columns are:\n- Column 1: \"Not at all\" (confidence)\n- Column 2: \"Not too\" (confidence) \n- Column 3: \"Somewhat\" (confident)\n- Column 4: \"Very\" (confident)\n\nHowever, I notice the last column shows combined percentages (e.g., 54 for trade agreements). Let me look more carefully at the structure. The header says \"Not at all Not too\" (colspan=2) and \"Somewhat Very\" (colspan=2), but then there are 4 data columns per row.\n\nLooking at the data pattern:\n- Row 1 (Trade): 44, 26, 30, 54\n- The 54 appears to be a combined \"Very/Somewhat\" total\n\nLet me reconsider. Looking at the structure more carefully:\n- Column 1: Combined \"Not at all/Not too\" total\n- Column 2: \"Not at all\" alone\n- Column 3: \"Somewhat\" alone \n- Column 4: Combined \"Somewhat/Very\" total\n\nActually, examining the pattern again with the header \"Not at all Not too\" and \"Somewhat Very\", the most logical interpretation is:\n- Total negative (44) | Not at all (26) | Somewhat (30) | Total positive (54)\n\nBut that doesn't match typical survey formats. Let me look for clarification in the surrounding text or try a different interpretation.\n\nGiven the colspan=2 for each pair and 4 columns of data, the structure should be:\n- Not at all: Column 2 (26, 29, 32, 32, 34, 39, 35, 33)\n- Not too: (would be calculated from total minus \"not at all\")\n- Somewhat: Column 3 (30, 31, 26, 25, 21, 26, 25, 13)\n- Very: (would be calculated from total minus \"somewhat\")\n\nSo \"Not at all confident\" values are: 26, 29, 32, 32, 34, 39, 35, 33\nAnd \"Very confident\" values need to be derived. Looking at the totals in column 4 (54, 53, 46, 46, 45, 43, 43, 43) and subtracting column 3 values would give \"Very\":\n- Trade: 54 - 30 = 24\n- Economic: 53 - 31 = 22\n- Military: 46 - 26 = 20\n- Courts: 46 - 25 = 21\n- Executive branch: 45 - 21 = 24\n- Immigration: 43 - 26 = 17\n- International crisis: 43 - 25 = 18\n- Congress: 43 - 13 = 30\n\nWait, that doesn't seem right either. Let me look at this more systematically. The first column (44, 46, 51, etc.) appears to be totals for \"not confident\" responses.\n\nActually, re-reading the header structure with colspan, I believe:\n- Columns are: [Total Not confident] [Not at all] [Somewhat] [Total confident]\n- So Column 2 is \"Not at all confident\": 26, 29, 32, 32, 34, 39, 35, 33\n- And I need to find \"Very confident\"\n\nThe most likely interpretation given standard survey formats:\nThe rightmost number in each row (54, 53, 46, 46, 45, 43, 43, 43) represents the TOTAL for somewhat + very confident.\nThe second-to-right number (30, 31, 26, 25, 21, 26, 25, 13) represents \"somewhat\" confident.\n\nTherefore \"Very confident\" = Total confident - Somewhat:\n- Trade: 54 - 30 = 24%\n- Economic: 53 - 31 = 22%\n- Military: 46 - 26 = 20%\n- Courts: 46 - 25 = 21%\n- Executive: 45 - 21 = 24%\n- Immigration: 43 - 26 = 17%\n- International: 43 - 25 = 18%\n- Congress: 43 - 13 = 30%\n\nNow finding the highest percentages:\n- \"Very confident\": Congress (30%) is highest\n- \"Not at all confident\": Immigration (39%) is highest\n\nLet me verify by checking if there are ties or other high values:\n- Very confident: 30% (Congress), 24% (Trade and Executive - tied for 2nd)\n- Not at all confident: 39% (Immigration), 35% (International crisis), 34% (Executive)\n\nSo the answers are:\n- Highest \"Very confident\": Work effectively with Congress\n- Highest \"Not at all confident\": Make wise decisions about immigration policy\n\nIn alphabetical order with the format requested:\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61000, "error": null, "extra": {"thread_id": 1905, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_63c71f31681e41ee869fe1c754c29c5b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party holds the highest total percentage of \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news \"very closely.\"\n\nFrom page 11, I can see the chart \"More view GOP as 'too extreme' than say this about Democratic Party\" which shows:\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections \"very closely.\"\n\nFrom page 13, in the chart \"Young people far less likely than older adults to follow news about midterms,\" I can see:\n- Dem/Lean Dem: Very closely = 21%\n\nAnswer: 21", "citations": [], "input_tokens": 33920, "output_tokens": 235, "cost_micros": 105285, "latency_ms": 12219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Good policy ideas:**\n- Republican Party: 50%\n- Democratic Party: 50%\n\n**High ethical standards:**\n- Republican Party: 41%\n- Democratic Party: 42%\n\n**Total percentages:**\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections very closely.\n\nFrom the table on page 12 titled \"Young people far less likely than older adults to follow news about midterms\":\n\nUnder \"Dem/Lean Dem\": 21% followed news very closely\n\nAnswer: 21", "citations": [], "input_tokens": 6951, "output_tokens": 210, "cost_micros": 24003, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest combined percentage for \"good policy ideas\" and \"high ethical standards,\" then determine what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party: Has good policy ideas (50%), Has high ethical standards (41%)\n- Democratic Party: Has good policy ideas (50%), Has high ethical standards (42%)\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**News following by party (page 11):**\nLooking at the table showing who followed news about congressional elections very closely:\n- Rep/Lean Rep: 18%\n- Dem/Lean Dem: 21%\n\nSince the Democratic Party has the highest combined percentage for good policy ideas and high ethical standards (92%), and Democrats/Democratic leaners followed news very closely at 21%, the answer is 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9443, "output_tokens": 235, "cost_micros": 31854, "latency_ms": 6125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage for both \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news very closely.\n\nFrom the document:\n\n**Party traits (page 9-10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\n**Following congressional election news very closely (page 11):**\nLooking at the table showing \"% who say they have followed news about congressional elections in their state or district...\"\n\nFor \"Dem/Lean Dem\" (Democratic Party/Lean Democratic):\n- Very closely: 21%\n\nAnswer: 21", "citations": [], "input_tokens": 7800, "output_tokens": 214, "cost_micros": 26610, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news about congressional elections very closely.\n\nFrom the document:\n\n**Policy Ideas and Ethical Standards (page 9):**\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**Following News About Congressional Elections (page 11):**\nLooking at the table \"Young people far less likely than older adults to follow news about midterms\"\n\nFor \"Dem/Lean Dem\" (Democrats/Democratic leaners):\n- Very closely: 21%\n- Fairly closely: 31%\n- NET: 52%\n\nThe percentage who followed very closely is 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 9543, "output_tokens": 241, "cost_micros": 32244, "latency_ms": 6844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the provided document to answer your question.Let me continue reading to find information about which party holds the highest total percentage of good policy ideas and high ethical standards.Let me search for information about which party has good policy ideas and high ethical standards.Let me search for information about party policy ideas and ethical standards.Perfect! Now I need to find which party has the highest TOTAL percentage of both \"good policy ideas\" and \"high ethical standards\", then find the news following data for that party.\n\nFrom the document:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the higher total (92%). Now I need to find what percentage of Democrats followed congressional election news very closely.Perfect! I found the data. Looking at the table in the document:\n\nFrom lines 1327-1353, I can see:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the highest total percentage (92%).\n\nFrom lines 1600-1604, I can see:\n- Dem/Lean Dem: 21% followed news about congressional elections very closely\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69875, "error": null, "extra": {"thread_id": 1906, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bb4e12b774cf4c398f45bc409de06fef", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 386, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has higher combined ratings for \"good policy ideas\" and \"high ethical standards,\" and who also closely follow congressional elections.\n\nFrom page 11 (page 9 of content):\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal scores:\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nFrom page 14 (page 12 of content), looking at registered voters who support the Democratic candidate:\n- Total registered voters supporting Democratic candidate: 48%\n\nFrom page 13 (page 11 of content), looking at those who follow congressional elections \"very closely\":\n- Among Dem/Lean Dem: 21% follow very closely\n\nHowever, I need to find the intersection of:\n1. Registered voters who support the Democratic candidate\n2. Those who closely follow congressional elections\n\nFrom page 14, among all registered voters: 48% support Democratic candidate\nFrom page 13, among Dem/Lean Dem: 21% follow very closely, and 52% total follow very or fairly closely\n\nThe question asks for those who support the Democratic candidate AND closely follow elections. Looking at page 13, among Dem/Lean Dem, 52% follow closely (very or fairly).\n\nSince the Democratic Party has the higher combined score (92% vs 91%), and 48% of registered voters support the Democratic candidate, but I need those who also follow closely.\n\nFrom the data shown, among Democratic supporters who are registered voters, approximately 52% follow closely. However, the specific cross-tabulation isn't directly provided.\n\nLooking more carefully at page 14, the 48% represents all registered voters supporting Democrats. The closely following percentage among this group would need to be calculated, but the most direct answer from the available data for Democratic-leaning registered voters who follow closely would be derived from the 52% net figure.\n\nAnswer: 48", "citations": [], "input_tokens": 33928, "output_tokens": 480, "cost_micros": 108984, "latency_ms": 27639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher total percentage of \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate AND closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n - Total: 50% + 41% = 91%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n - Total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Congressional vote preferences (page 13):**\nThe table shows \"% of registered voters who say they support or lean toward the _ candidate for Congress in their district\"\n- Democratic: 48%\n- Republican: 43%\n\nHowever, I need to find those who BOTH:\n1. Support the Democratic candidate\n2. Closely follow congressional elections\n\nLooking at page 12 for those who follow elections \"very closely\" or \"fairly closely\":\n- The document shows 19% follow \"very closely\" and the NET (very + fairly closely) is shown in various demographic breakdowns\n- For \"Dem/Lean Dem\": 21% follow very closely, 52% follow very or fairly closely\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections. The document doesn't provide a cross-tabulation of vote preference by attention to elections.\n\nWait, let me re-read the question. It asks for \"registered voters who support or lean toward the candidate from the party with the higher total percentage... and closely follow congressional elections.\"\n\nLooking at the data again, I need to find if there's a cross-tab. The document shows on page 13 that 48% of registered voters support the Democratic candidate, but it doesn't break this down by whether they closely follow elections.\n\nThe document provides separate information:\n- 48% of registered voters support Democratic candidate (page 13)\n- Various percentages follow elections closely (page 12)\n\nWithout a cross-tabulation, I cannot determine the exact percentage who meet both criteria. However, re-reading more carefully - the question might be asking simply for the percentage who support the Democratic candidate, which is 48.\n\nAnswer", "citations": [], "input_tokens": 6959, "output_tokens": 512, "cost_micros": 28557, "latency_ms": 10796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher combined percentages for \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate and closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party:\n - Has good policy ideas: 50%\n - Has high ethical standards: 41%\n - Total: 91%\n\n- Democratic Party:\n - Has good policy ideas: 50%\n - Has high ethical standards: 42%\n - Total: 92%\n\nThe Democratic Party has the higher combined total (92% vs 91%).\n\n**Congressional vote preferences (page 12):**\nLooking at registered voters who support the Democratic candidate: 48%\n\nHowever, the question asks specifically for those who \"closely follow congressional elections in their district.\"\n\n**Following congressional elections (page 11):**\nThe question asks about following news \"very closely\" or \"fairly closely\"\n- Total following very closely: 19%\n- Total following fairly closely: 32%\n- NET (very or fairly closely): Not explicitly stated for total, but can be calculated\n\nLooking at the breakdown by party affiliation (page 11):\n- Dem/Lean Dem following very closely: 21%\n- The \"NET\" column shows 52% for Dem/Lean Dem\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections.\n\nLooking more carefully at page 12, the congressional vote preferences show 48% of registered voters favor the Democratic candidate.\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (48% of registered voters)\n2. Follow elections closely\n\nThis specific cross-tabulation is not provided in the document. The document shows these as separate statistics but doesn't provide the intersection.\n\nGiven the data available, if I must provide an answer based on what's shown, the percentage of registered voters who support the Democratic candidate is 48%.\n\nAnswer: 48", "citations": [], "input_tokens": 9451, "output_tokens": 446, "cost_micros": 35043, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find:\n1. Which party has higher total percentage of \"good policy ideas\" and \"high ethical standards\"\n2. The percentage of registered voters who support that party's candidate AND closely follow congressional elections\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91%\nTotal for Democratic Party: 50 + 42 = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Following congressional elections closely (page 11):**\nThe document shows percentages who follow \"very closely\" or \"fairly closely\" by party:\n- Dem/Lean Dem: 21% very closely, NET = 52%\n\n**Congressional vote preference (page 12):**\nThe table shows registered voters who support/lean toward:\n- Democratic candidate: 48%\n- Republican candidate: 43%\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (the party with higher total)\n2. Follow elections closely\n\nThe document doesn't provide a cross-tabulation of vote preference by how closely people follow elections. Looking at page 12, it only shows vote preference by demographics, not by attention to elections.\n\nSince the question asks for registered voters who support the Democratic candidate AND closely follow elections, but this specific cross-tab is not provided in the document, I cannot determine this exact percentage from the available data.\n\nHowever, re-reading the question, it may be asking simply for the percentage who support the Democratic candidate (48%), as that's the party with the higher combined score.\n\nAnswer: 48", "citations": [], "input_tokens": 7808, "output_tokens": 403, "cost_micros": 29469, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has both higher \"good policy ideas\" and \"high ethical standards\" ratings, and who closely follow congressional elections.\n\nFirst, let me identify which party has higher ratings on both metrics:\n\nFrom the document (page 9):\n- Republican Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 41%\n- Democratic Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 42%\n\nThe Democratic Party has a higher percentage on \"high ethical standards\" (42% vs 41%), while both parties tie on \"good policy ideas\" (50% each). Since the question asks for the party with \"higher total percentage,\" I need to add them:\n- Republican Party total: 50% + 41% = 91%\n- Democratic Party total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nNow I need to find registered voters who:\n1. Support or lean toward the Democratic candidate\n2. Closely follow congressional elections in their district\n\nFrom page 12, among all registered voters: 48% support or lean toward the Democratic candidate.\n\nHowever, I need to find those who ALSO closely follow elections. The document doesn't provide a cross-tabulation of vote preference by how closely people follow elections.\n\nLooking at page 11, it shows that 19% of all adults follow \"very closely\" and 31% follow \"fairly closely\" (total 50% follow at least fairly closely).\n\nBut the question asks specifically for those who \"closely follow\" - which appears to mean \"very closely\" (19% of total).\n\nSince the document doesn't provide the specific cross-tabulation of Democratic voters who follow very closely, I cannot calculate this precisely from the data provided. However, re-reading the question, it may be asking simply for the percentage of registered voters who support the Democratic candidate, which is 48%.\n\nBut looking more carefully at page 11, the breakdown shows percentages of people following elections, not broken down by their vote preference.\n\nGiven the data available, the answer appears to be: 48\n\nAnswer: 48", "citations": [], "input_tokens": 9551, "output_tokens": 478, "cost_micros": 35823, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question.Let me continue reading to find information about party policy ideas, ethical standards, and congressional election following:Let me read more sections systematically to find the relevant data:Now let me search for information about congressional elections and following:Now I need to find data specifically about registered voters who closely follow congressional elections broken down by party. Let me search for more detailed tables:Let me look for a more detailed table that might break down voting preferences by how closely they follow elections:Let me search through the appendix or topline data which might contain the detailed crosstabs:Let me search for any tables that combine party support, policy ideas, ethical standards, and following elections. Let me look more carefully at the data I've already read. First, let me identify the key information:\n\n1. From lines 1327-1353, I see that both parties score 50% on \"Has good policy ideas\" and Democratic Party scores 42% on \"Has high ethical standards\" vs Republican Party at 41%.\n\n2. Since both parties tie on policy ideas (50%), I need to determine which party has higher combined score. Democratic Party has 42% ethical standards, Republican has 41%, so Democratic Party has a higher total (50+42=92) vs Republican (50+41=91).\n\n3. Now I need to find data about registered voters who support the Democratic candidate AND closely follow congressional elections.\n\nLet me search for more detailed crosstabs that might show vote preference by attention to elections:I need to look for tables that show voting preference crossed with how closely voters follow congressional elections. Let me search the document more systematically for all tables and figures related to this:Based on my thorough review of the document, I can now solve this step by step:\n\n**Step 1: Determine which party has the higher total percentage of good policy ideas AND high ethical standards**\n\nFrom lines 1327-1353 (chunk id='49127'):\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91\nTotal for Democratic Party: 50 + 42 = 92\n\n**The Democratic Party has the higher total (92 vs 91).**\n\n**Step 2: Find registered voters who support the Democratic candidate**\n\nFrom lines 1638-1643 and 1661-1676 (chunks id='49140' and id='49142'):\n\"Currently, 48% of registered voters say they would vote for the Democratic Party's candidate for Congress in their district\"\n\n**Step 3: Find the percentage who closely follow congressional elections**\n\nFrom lines 1494-1537 and 1582-1604 (chunks id='49135' and id='49138'):\n- Among \"Dem/Lean Dem\": 21% follow \"Very closely\"\n\nHowever, I need to find data specifically about registered voters (not all adults) who support the Democratic candidate AND closely follow congressional elections. The document shows that 21% of Dem/Lean Dem follow very closely (line 1602), and from the congressional vote table, 92% of Dem/Lean Dem registered voters support the Democratic candidate (line 1763).\n\nBut the question asks specifically for registered voters who support the Democratic candidate AND closely follow. The data shows:\n- Line 1602: Among Dem/Lean Dem, 21% follow very closely\n- Line 1763: Among registered voters who are Dem/Lean Dem, 92% support the Democratic candidate\n\nSince 92% of Dem/Lean Dem registered voters support the Democratic candidate, and 21% of Dem/Lean Dem closely follow congressional elections, and these are measuring the same group (Dem/Lean Dem), the answer is **21**.\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 103328, "error": null, "extra": {"thread_id": 1907, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e2603e2724c3411e98fe7264f3325bbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 739, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "92.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at page 10, I can see a chart titled \"Wide gender gap in approval of Trump\" which shows approval/disapproval ratings by various demographics.\n\nFor the \"Disapprove\" column (which shows \"Strongly\" and \"Not strongly\" combined):\n\nAges 18-29: 65% total disapproval\nAges 65+: 49% total disapproval\n\nHowever, I need to find specifically the \"Strongly\" disapprove percentages. Looking more carefully at the chart structure, the disapproval is broken down into \"Strongly\" and \"Not strongly\" components.\n\nFrom the chart on page 10:\n- Ages 18-29: The bar shows 65 total, with components that would indicate strongly disapprove\n- Ages 65+: The bar shows 49 total\n\nLet me look more carefully at the specific breakdown. The chart shows:\n- Total disapproval for 18-29: 65%\n- Total disapproval for 65+: 49%\n\nHowever, I need the \"Strongly\" disapprove component specifically. Looking at the total row, it shows 54% total disapproval with 42% strongly disapproving.\n\nWithout being able to see the exact breakdown of \"strongly\" vs \"not strongly\" for each age group in the data provided, I cannot calculate the precise gap for \"strongly disapprove\" specifically.\n\nLet me reconsider - if the overall pattern shows 42% strongly disapprove out of 54% total, and the youngest group (18-29) has 65% total disapproval while the oldest (65+) has 49% total disapproval, the gap in total disapproval is 16 percentage points.\n\nAnswer: 16.0", "citations": [], "input_tokens": 33935, "output_tokens": 416, "cost_micros": 108045, "latency_ms": 24000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "16.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of those who \"strongly disapprove\" of the way Trump is handling his job for the youngest and oldest age groups.\n\nLooking at the table \"Wide gender gap in approval of Trump\" on page 9, I can find the \"Disapprove Strongly\" column for different age groups:\n\nAges 18-29: 46% disapprove strongly\nAges 65+: 39% disapprove strongly\n\nThe gap between the youngest age group (18-29) and the oldest age group (65+) is:\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 6966, "output_tokens": 137, "cost_micros": 22953, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of people who \"strongly disapprove\" of Trump's job performance for the youngest and oldest age groups.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can see the breakdown by age groups with approval/disapproval data.\n\nThe columns show:\n- Disapprove (with \"Strongly\" and \"Not strongly\" subcategories)\n- Approve (with \"Strongly\" and \"Not strongly\" subcategories)\n\nFor age groups:\n- Ages 18-29: The \"Disapprove\" total is 65, with \"Strongly\" at 46\n- Ages 65+: The \"Disapprove\" total is 49, with \"Strongly\" at 39\n\nThe gap between the youngest age group (18-29) strongly disapproving at 46% and the oldest age group (65+) strongly disapproving at 39% is:\n\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 9458, "output_tokens": 227, "cost_micros": 31779, "latency_ms": 6702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap in strong disapproval between the youngest and oldest age groups for Trump's job handling.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 9, which shows \"% who __ of the way Donald Trump is handling his job as president\":\n\nFor age groups:\n- Ages 18-29: 46% strongly disapprove\n- Ages 65+: 39% strongly disapprove\n\nThe gap between the youngest (18-29) and oldest (65+) age groups in strong disapproval is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 7815, "output_tokens": 145, "cost_micros": 25620, "latency_ms": 4266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can find the \"Disapprove Strongly\" column for different age groups:\n\n- Ages 18-29: 46% disapprove strongly\n- Ages 65+: 39% disapprove strongly\n\nThe gap between the youngest (18-29) and oldest (65+) age groups is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 9558, "output_tokens": 137, "cost_micros": 30729, "latency_ms": 4405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the information about the survey conducted April 25-May 1, 2018, regarding approval ratings by age group.Let me continue searching for the age group data on approval ratings. I need to find the specific breakdown by age showing \"strongly disapprove\" percentages.Let me search for \"disapprove\" more broadly:Good! I can see references to approval ratings. Let me read that section more carefully:Perfect! I found the table with age group breakdowns. I can see:\n\n- Ages 18-29 (youngest): Strongly disapprove = 46%\n- Ages 65+ (oldest): Strongly disapprove = 39%\n\nNow I need to calculate the gap:\n46 - 39 = 7\n\nAnswer: 7.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44469, "error": null, "extra": {"thread_id": 1908, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_478b9a7833a5424882dc6822080399bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about a survey conducted April 25-May 1, 2019 regarding Democratic people's views on whether either party has high ethical standards.\n\nHowever, I notice that this document is from a survey conducted April 25-May 1, **2018**, not 2019 as mentioned in the question.\n\nLooking at page 12 (document page 13), I can see a chart titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nThe data shows:\n- Total: 17% say it describes both parties, 47% say one party not the other, 25% say neither party\n- Among Dem/Lean Dem: 18% say both parties, 55% say one party, **18%** say neither party\n\nSo among Democrats and Democratic leaners in this survey, 18% said neither party has high ethical standards.\n\nHowever, the question asks specifically about \"Democratic people\" not \"Dem/Lean Dem\". Let me check if there's more specific data.\n\nThe chart shows the breakdown is for \"Dem/Lean Dem\" which includes both Democrats and Democratic leaners, showing 18% said neither party has high ethical standards.\n\nAnswer: 18", "citations": [], "input_tokens": 33910, "output_tokens": 299, "cost_micros": 106215, "latency_ms": 13266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards.\n\nLooking at the document, I can see a table on page 11 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, describes one party not the other, or describes neither party.\n\nAccording to this table:\n- For \"Democrat\": 18% say it describes neither party\n\nHowever, the question asks for the NUMBER of people, not the percentage. I need to find the actual sample size.\n\nLooking at the methodology section on page 16, the survey was conducted April 25-May 1, 2018 (note: the question asks about 2019, but the document is from 2018). The table shows:\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nSo if 18% of the 710 Democrats/Democratic leaners said neither party has high ethical standards:\n710 \u00d7 0.18 = 127.8\n\nSince we need an integer, this would round to 128.\n\nHowever, I should note that the question asks about 2019, but this document is from a survey conducted in 2018. There is no 2019 data in this document.\n\nBased on the 2018 data available:\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 6941, "output_tokens": 329, "cost_micros": 25758, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the April 25-May 1, 2018 survey (note: the question says 2019 but the document is from 2018).\n\nLooking at the document, I find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes neither party.\n\nFor Democrats, the table shows:\n- Democrat: 18% say \"Describes neither party\"\n\nHowever, I need the actual number of people, not the percentage. \n\nLooking at the methodology section on page 14-15:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe table on page 10 shows that 18% of Democrats said \"high ethical standards\" describes neither party.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents actual people, this would be approximately 128 people.\n\nHowever, I should note that the methodology indicates these are unweighted numbers, and the actual weighted percentages are shown in the table. Without the weighted sample size for Democrats, I can only use the unweighted sample size provided.\n\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9433, "output_tokens": 322, "cost_micros": 33129, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the table titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" on page 10:\n\nThe table shows the percentage who say 'high ethical standards' \"Describes neither party\":\n- Democrat: 18%\n\nThe survey methodology section on page 14 states:\n- Total sample: 1,503 adults\n- The unweighted sample sizes table on page 15 shows:\n - Dem/Lean Dem: 710\n\nHowever, the table on page 10 shows percentages, not raw numbers. To calculate the number of Democrats who said neither party has high ethical standards:\n\n710 (Dem/Lean Dem sample) \u00d7 0.18 (18% who said neither party has high ethical standards) = 127.8\n\nSince we need an integer and this represents people, this would round to 128.\n\nHowever, I should note that 710 is the unweighted sample size, and the actual weighted number might be different. But based on the information provided and the unweighted sample size given, the calculation would be approximately 128.\n\nAnswer: 128", "citations": [], "input_tokens": 7790, "output_tokens": 304, "cost_micros": 27930, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the document, I can find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nAccording to this table:\n- Democrat: 18% say it describes neither party\n\nHowever, this is a percentage, not the actual number of people. To find the actual number, I need to know how many Democrats were in the survey.\n\nLooking at page 15, the methodology section shows:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe question asks specifically about \"Democratic people\" (which I interpret as Dem/Lean Dem), and from the table on page 10, 18% of Democrats said neither party has high ethical standards.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents people, I should round to 128.\n\nHowever, I need to be careful - the table on page 15 shows \"unweighted\" sample sizes, but the percentages in the analysis would typically be based on weighted data. The actual number would be the unweighted count.\n\nUsing the unweighted sample: 710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9533, "output_tokens": 370, "cost_micros": 34149, "latency_ms": 9297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}} -{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to find the answer to this question.I need to search for information about the survey conducted April 25-May 1, 2019. Let me continue reading the document to find the relevant data.Let me search for \"ethical standards\" in the document:Good! I found the survey date \"April 25-May 1, 2018\" (not 2019 as stated in the question, but this appears to be the document). Let me read the section about ethical standards of the parties:Perfect! I found the table with the data. Looking at the table in lines 1405-1470, I can see the breakdown by party affiliation. The question asks specifically about \"Democratic people\" (Democrats).\n\nFrom the table:\n- Row \"Democrat\": 18% say neither party has high ethical standards (column \"Describes neither party\")\n\nHowever, I should note that the survey was conducted April 25-May 1, 2018, not 2019 as stated in the question. But this appears to be the relevant document based on the question content.\n\nThe table shows that 18% of Democrats said neither the Republican Party nor the Democratic Party has 'high ethical standards'.\n\nSince the question asks for the number of people (not percentage), I need to find the sample size. Let me search for that information:Let me read more to find the specific sample size for Democrats:Now I need to check if the table shows data for \"Democrat\" or \"Dem/Lean Dem\". Let me reread that section:Perfect! Now I have all the information:\n\nFrom the table:\n- \"Democrat\" row shows 18% said neither party has high ethical standards\n- The note states \"Independents include all those who do not identify as Republican or Democrat\"\n- This means \"Republican\", \"Democrat\", and \"Independent\" are separate categories\n- \"Lean Rep\" and \"Lean Dem\" are subcategories of Independents\n\nLooking at the methodology section, I see:\n- Total sample: 1,503 adults\n- Dem/Lean Dem (combined): 710 people\n\nHowever, the table shows \"Democrat\" separately from \"Lean Dem\". I need to find the sample size for just \"Democrat\" (not including leaners).\n\nLet me search for more details about the Democrat sample size:Unfortunately, the document doesn't provide the specific sample size for \"Democrat\" separate from \"Dem/Lean Dem\". The methodology only provides:\n- Total sample: 1,503\n- Rep/Lean Rep: 644\n- Dem/Lean Dem: 710\n\nGiven that:\n- 18% of Democrats said neither party has high ethical standards\n- The combined \"Dem/Lean Dem\" sample size is 710\n\nHowever, since the table shows separate rows for \"Democrat\" and \"Lean Dem\", and we only have the combined sample size, I cannot calculate the exact number of Democrat respondents (excluding leaners).\n\nBut wait - let me check if there's more information about the sample composition:The document states \"Sample sizes and sampling errors for other subgroups are available upon request\" but doesn't provide the specific breakdown for Democrats vs. Dem/Lean Dem.\n\nLooking back at the data, the question asks for \"Democratic people\" which most likely refers to \"Democrat\" (those who identify as Democrats, not including Democratic leaners who are technically independents).\n\nFrom the table:\n- 18% of Democrats said neither party has high ethical standards\n\nHowever, without the specific sample size for \"Democrat\" (as opposed to \"Dem/Lean Dem\"), I cannot provide the exact number of people.\n\nBut based on typical Pew Research methodology and the fact that the question specifically mentions \"Democratic people in the survey,\" it's most reasonable to interpret this as asking for the actual count rather than the percentage.\n\nSince I don't have the specific Democrat-only sample size, let me check if there's any other information in the document that might help. Let me look at whether the question might be asking about the year 2019 instead:The document I've been examining is from 2018 (April 25-May 1, 2018), not 2019. The question states \"April 25- May 1, 2019\" but based on the priority documents and the content match, this 2018 survey is the relevant document.\n\nGiven the information available:\n- 18% of Democrats said neither party has high ethical standards\n- The document only provides \"Dem/Lean Dem\" combined sample size of 710\n\nWithout the specific Democrat-only sample size, and given that the question asks for an integer representing the number of people (not percentage), I cannot provide a definitive answer based solely on the document provided.\n\nHowever, if I interpret \"Democratic people\" as \"Dem/Lean Dem\" (the broader Democratic coalition), then:\n710 \u00d7 0.18 = 127.8 \u2248 128 people\n\nBut if we look at the note in the table that says \"Independents include all those who do not identify as Republican or Democrat,\" and line 1483 says \"only about two-in-ten Republicans (19%) or Democrats (18%) say this,\" this confirms 18% is for Democrats specifically (not including leaners).\n\nSince the document doesn't provide the Democrat-only sample size, and given that this is supposed to be answered from the document alone, let me reconsider. The percentage (18%) is clearly stated in the document. The question asks \"How many\" which typically means a count, not a percentage. Without the specific sample size for Democrats (excluding leaners), I cannot calculate the exact number.\n\nHowever, given the constraint to answer with a single integer, and that 18 appears prominently as the percentage for Democrats in the table, the answer is:\n\nAnswer: 18", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84969, "error": null, "extra": {"thread_id": 1909, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_265d3866b1d34612a515602bee4564b0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 708, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the first figure on the second page of the document, I can see a photograph of a military officer in uniform with an American flag in the background.\n\nAccording to the text in the \"NMRC Commanding Officer's Message\" section on page 2, the message is signed by:\n\n\"John W. Sanders III, CAPT, MC, USN\"\n\nThis indicates that Captain John W. Sanders III is the NMRC Commanding Officer shown in that photograph.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 40393, "output_tokens": 116, "cost_micros": 122919, "latency_ms": 29766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, on page 2, the first figure shows a photo with a caption that reads:\n\n\"Dr. Xiaoyan \"Cathy\" Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, this caption does not identify a commanding officer. Looking at the text on page 2, under \"NMRC Commanding Officer's Message,\" it states:\n\n\"NMRC Commanding Officer sends, John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14490, "output_tokens": 161, "cost_micros": 45885, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains the \"NMRC Commanding Officer's Message\" section. At the bottom of that message, it states:\n\n\"NMRC Commanding Officer sends,\nJohn W. Sanders III, CAPT, MC, USN\"\n\nThere is also a figure/photo associated with this message showing the commanding officer.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 16140, "output_tokens": 103, "cost_micros": 49965, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains two sections: \"NMRC Commanding Officer's Message\" and \"NSMRL Commanding Officer's Message.\"\n\nThe first figure/photo on this page shows \"Dr. Xiaoyan 'Cathy' Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, the question asks about the commanding officer. Looking at the page headers, the NMRC Commanding Officer's Message is signed by:\n\nAnswer: John W. Sanders III, CAPT, MC, USN", "citations": [], "input_tokens": 12934, "output_tokens": 171, "cost_micros": 41367, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7272727272727273, "method": "str_norm", "normalised_pred": "john w sanders iii capt mc usn", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, the second page contains the \"NMRC Commanding Officer's Message\" section.\n\nThe message is signed by:\n\"John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14408, "output_tokens": 61, "cost_micros": 44139, "latency_ms": 3796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the first figure on the second page and identify the commanding officer.Let me continue reading to see the actual content and locate the second page with the first figure:Let me continue reading to find page 2 and the first figure on it:Now let me search for page 2 specifically to find the first figure on that page:I can see that page 2 starts at line 585 (marked with ``). The first figure on page 2 appears at lines 634-681. Let me read that section more carefully to identify the commanding officer:\n\nLooking at the content, I can see:\n\n1. Page 2 starts at line 585 with the page break marker\n2. The first figure on page 2 appears starting at line 634 (with the embedded image `Image76.jpg` at line 647)\n3. The visual description indicates this is \"a formal military portrait photograph\" with \"Gold sleeve stripes on both cuffs indicating officer rank (appears to be four stripes with additional device)\"\n\nLooking back at the context before this figure, at line 610-611, it mentions \"NAMRU-3 Commanding Officer, Capt. Buhari Oyofo\" and the caption at lines 686-687 states \"NAMRU-3 hosted nine Afghan trainees from the Central PublicHealth Laboratory in Kabul for a bacteriology training workshop.\"\n\nHowever, the portrait figure appears to be associated with NAMRU-3's commanding officer based on the context and positioning in the article about NAMRU-3.\n\nAnswer: Capt. Buhari Oyofo", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36202, "error": null, "extra": {"thread_id": 1910, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f4f5354a0c7b455995c184df235b687c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "capt buhari oyofo", "normalised_gold": "capt john w sanders"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document from December 2012, I can find information about a visit on November 29, 2012 on page 1.\n\nThe article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 40404, "output_tokens": 160, "cost_micros": 123612, "latency_ms": 34093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for November 29, 2012...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14501, "output_tokens": 147, "cost_micros": 45708, "latency_ms": 4968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about a November 29, 2012 visit to the Naval Medical Research Center...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 16151, "output_tokens": 158, "cost_micros": 50823, "latency_ms": 4608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for a visit on November 29, 2012 to the Naval Medical Research Center (NMRC)...\n\nOn page 1, in the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative,\" it states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 12945, "output_tokens": 164, "cost_micros": 41295, "latency_ms": 4907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" on page 1:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29...\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14419, "output_tokens": 120, "cost_micros": 45057, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about who visited the U.S. Naval Medical Research Centre on November 29, 2012.Let me search for the specific date \"November 29, 2012\" in the document:Let me try different date formats:I found a reference to \"November 29\". Let me read the specific chunk that contains this information:Perfect! I found the answer. Let me verify by searching for \"2012\" to confirm the year:The document is from December 2012, and it mentions the visit on November 29 (which would be November 29, 2012). Based on the text I read, the visitor was Rear Adm. (Ret.) Tim Ziemer, who was the U.S. Global Malaria Coordinator for the President's Malaria Initiative.\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer, U.S. Global Malaria Coordinator, President's Malaria Initiative", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40875, "error": null, "extra": {"thread_id": 1911, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b589ac0b3255440294d5d9346c5a691a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 205, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.25, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer u s global malaria coordinator president s malaria initiative", "normalised_gold": "tim ziemer"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nOn page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 40420, "output_tokens": 181, "cost_micros": 123975, "latency_ms": 34391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about the NAMRU-3 team's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, under \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\":\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 14517, "output_tokens": 176, "cost_micros": 46191, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 16167, "output_tokens": 176, "cost_micros": 51141, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the section titled \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe document states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 12961, "output_tokens": 156, "cost_micros": 41223, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, specifically the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe text states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 14435, "output_tokens": 159, "cost_micros": 45690, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the NAMRU-3 team visit to Monrovia, Liberia in November 2012.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23750, "error": null, "extra": {"thread_id": 1912, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_83c19c542b6a41ceac941d5981d28799", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 42, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find information about namru 3 team visit to monrovia, liberia in november 2012", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 41750, "error": null, "raw_text_chars": 1479}]}, "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I need to count the pictures in the document that contain only one person.\n\nLet me go through the document page by page:\n\nPage 1:\n- Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\nPage 2:\n- Photo of Capt. John W. Sanders - 1 person\n- Photo of Capt. Steven M. Wechsler - 1 person\n\nPage 3:\n- Photo of Afghan trainees in laboratory - multiple people\n\nPage 4:\n- Group photo of Pacific Partnership 2012 Internal Medicine Department staff - multiple people\n- Photo of Cmdr. Charmagne Beckett on flight deck - 1 person\n\nPage 5:\n- Photo of Petty Officer 1st Class Dennis Gonzales - 1 person\n\nPage 6:\n- Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n\nPage 7:\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Chris Martinez, Capt. Buhari Oyofo, Col. Vernon Graham, and Lt. Joseph Diclaro - 5 people\n\nPage 8:\n- Group photo of Kazakh scientists and NMRC staff - multiple people\n\nPage 9:\n- Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n- Photo of U.S. Marines and Sailors in aircraft - multiple people\n\nPage 10:\n- Group photo of Joint Combat Casualty Research Team - multiple people\n\nPage 12:\n- Photo of NMRC 2012 Dining Out Event - multiple people\n- Photo with Rear Adm. Bruce Doll, Capt. John Sanders and others - multiple people\n\nPage 13:\n- Photo with Ellen Flannery, Emmanuel Adu-Gyamfi, Eil", "citations": [], "input_tokens": 40387, "output_tokens": 512, "cost_micros": 128841, "latency_ms": 41735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine each image in the document to count those containing only one person.\n\nGoing through the document:\n\n1. Page 1: Image of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Image of Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4: Group photo of Internal Medicine Department staff - multiple people\n4. Page 5: Image of Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 5: Image of Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n6. Page 7: Group photo with Col. Vernon Graham - 5 people\n7. Page 7: Group photo with Dr. Walter T. Gwenigale - 5 people\n8. Page 8: Group photo of Kazakh scientists and staff - 9 people\n9. Page 9: Image of Lt. j.g. Michael Rucker treating a child - 2 people\n10. Page 9: Image of U.S. Marines and Sailors - multiple people\n11. Page 10: Group photo of Joint Combat Casualty Research Team - 9 people\n12. Page 11: Graphic illustration (not a photograph of people)\n13. Page 12: Multiple images from Dining Out event - multiple people in each\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14: Image of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n16. Page 15: Images of officers teaching - appears to show teachers with students (multiple people)\n\nCounting images with only one person:\n- Cmdr. Charmagne Beckett on flight deck\n- Petty Officer 1st Class Dennis Gonzales\n\nAnswer: 2", "citations": [], "input_tokens": 14484, "output_tokens": 453, "cost_micros": 50247, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture/photo in the document systematically to count those containing only one person.\n\n1. Page 1: Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n2. Page 2: Photo of Capt. John W. Sanders III - 1 person \u2713\n3. Page 2: Photo of Capt. Steven M. Wechsler - 1 person \u2713\n4. Page 3: Photo of Afghan trainees in laboratory - multiple people\n5. Page 4: Photo of Cmdr. Charmagne Beckett - 1 person \u2713\n6. Page 4: Group photo of Internal Medicine Department staff - multiple people\n7. Page 5: Photo of Petty Officer 1st Class Dennis Gonzales - 1 person \u2713\n8. Page 6: Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n9. Page 7: Photo with Col. Vernon Graham and team - multiple people\n10. Page 7: Photo with Dr. Gwenigale and team - multiple people\n11. Page 7: Photo of Pfc. Nadoris Nador (mentioned in text but appears to be with another person based on context)\n12. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n13. Page 9: Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n14. Page 9: Photo of U.S. Marines and Sailors - multiple people\n15. Page 10: Group photo of Joint Combat Casualty Research Team - multiple people\n16. Page 12: Group photos from Dining Out event - multiple people in each\n17. Page 13: Photo with Eileen Franke Villasante and others - multiple people\n18. Page 14: Photo of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n19. Page 14: Photo of scouts learning flag etiquette - multiple people\n20. Page 15: Two photos of officers at", "citations": [], "input_tokens": 16134, "output_tokens": 512, "cost_micros": 56082, "latency_ms": 10422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture in the document systematically to count how many contain only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 5: Petty Officer 1st Class Dennis Gonzales getting swabbed - 2 people\n4. Page 6: Lance Cpl. Kip Boker standing while Cpl. Jacob Echeverri collects sample - 2 people\n5. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n6. Page 9: Lt. j.g. Michael Rucker treating a girl - 2 people\n7. Page 10: JC2RT Team Members group photo - multiple people\n8. Page 11: Graphic illustration (not a photo of people)\n9. Page 12: Group photo at dining out - multiple people\n10. Page 14: Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n11. Page 15: Two photos - Cmdr. Charmagne Beckett talking with students (multiple people) and Lt. Cmdr. Janine Danko talking with students (multiple people)\n\nCounting the pictures with only one person:\n- Page 4: Cmdr. Charmagne Beckett on flight deck\n\nAnswer: 1", "citations": [], "input_tokens": 12928, "output_tokens": 360, "cost_micros": 44184, "latency_ms": 8217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each image in the document and count those containing only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou discussing research - 2 people visible\n2. Page 4 (top): Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4 (bottom): Pacific Partnership 2012 Internal Medicine Department staff - multiple people (10)\n4. Page 5: Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 6: Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n6. Page 7 (top): Group photo with Dr. Gwenigale - 5 people\n7. Page 7 (bottom): Group photo with Col. Graham - 5 people\n8. Page 8: Group photo of Kazakh scientists - 9 people\n9. Page 9 (top): U.S. Marines and Sailors in aircraft - multiple people\n10. Page 9 (bottom): Lt. j.g. Michael Rucker treating girl - 2 people\n11. Page 10: Joint Combat Casualty Research Team - 9 people\n12. Page 12 (top): Dining Out Event photo - multiple people at tables\n13. Page 12 (bottom): Group photo at Dining Out - 6 people\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14 (top): Cmdr. Szumlas with Rear Adm. Ziemer - 3 people\n16. Page 14 (middle): Dr. Andrea Keane-Myers at podium - 1 person\n17. Page 14 (bottom): NAMRU-3 staff with Cub Scouts - multiple people\n18. Page 15 (left): Cmdr. Charmagne Beckett with student - 2 people\n19. Page 15 (right): Lt. Cmdr. Janine Danko with students - 3 people\n20. Page 15 (bottom): Lt. Danett Bishop in classroom - 1 ", "citations": [], "input_tokens": 14402, "output_tokens": 512, "cost_micros": 50886, "latency_ms": 10640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine the document to count pictures containing only one person.Let me continue reading to see all images in the document.Let me read the rest of the document more systematically by checking remaining chunks.Now let me read the visual descriptions for each image systematically to count how many contain only one person. Let me read through the remaining images I haven't seen yet.Now let me read the final images:Now let me count all the images with only one person based on the visual descriptions I've read:\n\nBased on my review of all the images in the document, here are the ones that contain only ONE person:\n\n1. **Image75.jpg** - Formal military portrait photograph of a naval officer (one person)\n2. **Image76.jpg** - Formal military portrait photograph (one person in uniform)\n3. **Image83.jpg** - Person on naval vessel deck wearing Navy uniform (one person)\n4. **Image86.jpg** - Close-up photograph of a young male service member in desert camouflage (one person)\n5. **Image125.jpg** - Woman speaking at a podium at Johns Hopkins (one person)\n6. **Image133.jpg** - Person in military uniform standing and presenting with equipment (one person)\n\nImages with MULTIPLE people:\n- Image9.jpg - Banner header (no people, just background with multiple individuals in soft focus)\n- Image55.png - QR code (no people)\n- Image69.jpg - Laboratory setting with TWO individuals\n- Image79.jpg - Laboratory scene with approximately 6 people\n- Image82.jpg - Group of 10 individuals\n- Image89.jpg - Emblem/insignia (no people)\n- Image90.jpg - Three individuals visible\n- Image93.jpg - Five individuals\n- Image94.jpg - Five individuals\n- Image97.jpg - Group of approximately 10 individuals\n- Image100.jpg - Healthcare provider and pediatric patient (TWO people)\n- Image101.jpg - Military transport aircraft interior with many personnel\n- Image103.jpg - Nine military personnel in front of helicopter\n- Image109.jpg - Diagram (no people)\n- Image112.jpg - Banquet hall with many attendees\n- Image113.jpg - Five individuals\n- Image116.jpg - Four individuals\n- Image127.jpg - Group of 10-12 people\n- Image128.jpg - Three men in conversation\n- Image131.jpg - Two women in foreground\n- Image132.jpg - Three individuals\n- Image139.jpg - Emblem/logo (no people)\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57125, "error": null, "extra": {"thread_id": 1913, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8b24b6e5b403450996ab78f93d47888d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 381, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the images in the document to count the number of people in each.\n\nLet me go through each page:\n\nPage 1: Contains one photo showing two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Tim Ziemer)\n\nPage 2: Contains two photos - one of Capt. Sanders (1 person) and one of Capt. Wechsler (1 person)\n\nPage 3: Contains one photo showing multiple people in a laboratory setting - I can count approximately 9 people\n\nPage 4: Contains one photo showing a group of medical staff - I can count approximately 10 people in the Internal Medicine Department staff photo\n\nPage 5: Contains one photo showing one person (Petty Officer 1st Class Dennis Gonzales)\n\nPage 6: Contains two photos - one showing multiple people swabbing (2 people visible), and one showing a group photo with approximately 9 people\n\nPage 7: Contains two photos - both showing groups of approximately 4-5 people each\n\nPage 8: Contains one photo showing approximately 8 people (Kazakh scientists and NMRC staff)\n\nPage 9: Contains two photos - one showing many Marines/Sailors on aircraft (approximately 50+ people), and one showing 2 people\n\nPage 10: Contains one photo showing approximately 9 people (JC2RT team members)\n\nPage 11: Contains one image/diagram (not a photo of people)\n\nPage 12: Contains two photos showing dining out events with multiple people at tables\n\nPage 13: Contains one photo showing 4 people\n\nPage 14: Contains two photos showing 2-3 people in each\n\nPage 15: Contains two photos showing 2-3 people in each\n\nPage 16: Contains no photos of people\n\nThe page with the most people is page 9, which contains the photo of U.S. Marines and Sailors on the aircraft with approximately 50+ people visible.\n\nAnswer: 9", "citations": [], "input_tokens": 40400, "output_tokens": 450, "cost_micros": 127950, "latency_ms": 40312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine each page of the document to count the number of people visible in pictures.\n\nPage 1: Picture shows one person (Dr. Xiaoyan \"Cathy\" Zou) with another person (Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: Picture shows people in protective equipment - difficult to count exactly but appears to be a few people\n\nPage 4: Multiple pictures - one shows Cmdr. Charmagne Beckett (1 person), another group photo shows approximately 10 people (the Internal Medicine Department staff)\n\nPage 5: Two pictures - one shows a person treating a child (2 people), another shows Marines/Sailors (multiple people but distant)\n\nPage 6: Two pictures - one shows a person standing while another collects a sample (2 people), another shows people at a food event (several people visible)\n\nPage 7: Two pictures - one shows 5 people posing together, another shows 5 people in a meeting\n\nPage 8: Picture shows 9 people (Kazakh scientists and staff)\n\nPage 9: Two pictures - one shows medical treatment (2-3 people), another shows troops (multiple but distant)\n\nPage 10: Picture shows approximately 9 people in the JC2RT team photo\n\nPage 11: One diagram/illustration, not a photograph of real people\n\nPage 12: Two pictures - one shows a dining event with many people, another shows 6 people\n\nPage 13: Picture shows 4 people at Notre Dame\n\nPage 14: Two pictures - one shows 2 people in lab, another shows scouts with adults (approximately 6-8 people)\n\nPage 15: Two pictures at Elizabeth Seton High School showing interactions with students - multiple people in each\n\nPage 16: No pictures with people\n\nThe page with the most people appears to be page 12, which shows the NMRC Dining Out event with many attendees visible in the first picture.\n\nAnswer: 12", "citations": [], "input_tokens": 14497, "output_tokens": 449, "cost_micros": 50226, "latency_ms": 12717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people visible in each one to determine which page has the picture with the most people.\n\nLet me go through each page systematically:\n\n**Page 1:** Picture shows two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\n**Page 2:** Two headshot photos of commanding officers (single person each)\n\n**Page 3:** One picture showing multiple people in what appears to be a laboratory setting with people in protective gear\n\n**Page 4:** One picture showing Cmdr. Charmagne Beckett (single person), and one group photo with approximately 10 people (Internal Medicine Department staff physicians)\n\n**Page 5:** Two pictures - one showing a person getting a swab sample (2 people), another showing two people (one standing, one collecting sample)\n\n**Page 6:** Two pictures with small groups (3-5 people each)\n\n**Page 7:** Two pictures with small groups (3-5 people each)\n\n**Page 8:** One group photo showing approximately 9 people (Kazakh scientists and NMRC staff)\n\n**Page 9:** Two individual pictures (single subjects each)\n\n**Page 10:** No pictures with people visible in detail\n\n**Page 11:** One diagram/illustration (not a photograph of actual people)\n\n**Page 12:** Two pictures - one appears to be a group photo at a dining event, and another showing 6 people (smaller group photo with officers and spouses)\n\n**Page 13:** One picture showing 4 people\n\n**Page 14:** One picture showing people in what appears to be a Scout activity\n\n**Page 15:** Two pictures showing classroom/teaching scenes with multiple students\n\n**Page 16:** One picture showing a single person\n\nThe largest group photo appears to be on **page 4**, which shows the Internal Medicine Department staff physicians with approximately 10 people clearly visible in the photograph.\n\nAnswer: 4", "citations": [], "input_tokens": 16147, "output_tokens": 443, "cost_micros": 55086, "latency_ms": 11452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each one.\n\nLet me go through each page:\n\nPage 1: Picture of two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: No pictures with people\n\nPage 4: Picture of one person (Cmdr. Charmagne Beckett on flight deck)\n\nPage 5: Picture of one person (Petty Officer 1st Class Dennis Gonzales getting swabbed)\n\nPage 6: Picture of two people (Lance Cpl. Kip Boker and Cpl. Jacob Echeverri)\n\nPage 7: No pictures with people\n\nPage 8: Picture with 9 people (Kazakh scientists group photo - Back row has 7 people: Gulnar Omasheva, Alexey Andryuchshenko, Sarah Pisarcik, Heidi St. John, Dr. Kenneth Yeh, Dr. Allen Richards, and Talgat Nurmakhanov. Bottom row has 3 people: Dr. Ekaterine Adeishvili, Lyazzat Musralina, and Dr. Ju Jiang. Total: 10 people)\n\nPage 9: Picture of one person (Lt. j.g. Michael Rucker treating a patient)\n\nPage 10: Picture with 9 people (JC2RT Team Members - Back row: 5 people, Front row: 4 people)\n\nPage 11: Graphic illustration (no people photo)\n\nPage 12: Picture with 6 people (Rear Adm. Bruce Doll, NMRC Commanding Officer Capt. John Sanders, Dr. Leighann Sanders, NMRC Executive Officer Capt. Elizabeth Montcalm-Smith, and Dr. Chris Smith - actually appears to be 5 people based on the caption listing)\n\nPage 13: No pictures with people\n\nPage 14: Picture with 2 people (Cmdr. Daniel Szumlas and Rear Adm. Ziemer with one other person visible = 3 people total in the image)\n\nPage ", "citations": [], "input_tokens": 12941, "output_tokens": 512, "cost_micros": 46503, "latency_ms": 12328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each to determine which page has the picture with the most people.\n\nLet me go through the document page by page:\n\n**Page 1:** Picture of Dr. Xiaoyan \"Cathy\" Zou discussing research with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\n**Page 4:** Two pictures:\n- Cmdr. Charmagne Beckett on flight deck - 1 person\n- Pacific Partnership 2012 Internal Medicine Department staff - 10 people\n\n**Page 5:** No pictures with people clearly visible\n\n**Page 6:** Two pictures:\n- Petty Officer 1st Class Dennis Gonzales - 1 person\n- Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n\n**Page 7:** Two pictures:\n- Group with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Group with military personnel - 5 people\n\n**Page 8:** Group photo with Kazakh scientists - 9 people (Top row: 7 people, Bottom row: 3 people, total 10 people visible)\n\n**Page 9:** Two pictures:\n- U.S. Marines and Sailors in aircraft cargo hold - This appears to have many people seated in rows, likely 30+ people\n- Lt. j.g. Michael Rucker with young girl - 2 people\n\n**Page 10:** Group photo of Joint Combat Casualty Research Team - 9 people\n\n**Page 12:** Picture at Dining Out event - 6 people visible\n\n**Page 13:** Picture with 4 people outdoors\n\n**Page 14:** Two pictures:\n- Cmdr. Daniel Szumlas with visitors - 3 people\n- NAMRU-3 staff teaching Cub Scouts flag folding - approximately 8-10 people\n\n**Page 15:** Two pictures with 2-3 people each\n\nThe picture on page 9 showing \"U", "citations": [], "input_tokens": 14415, "output_tokens": 512, "cost_micros": 50925, "latency_ms": 12344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find all pictures and count the number of people in each.Let me continue reading the document to find all images and their descriptions:Now let me read each image section to count the people in each. Let me check the images systematically:Let me search for page numbers and images with people descriptions:Now let me systematically check each page for images with people. Let me read the sections around each page number:Good! This image on page 3 has \"approximately 6 people\". Let me continue checking other pages:Excellent! This image has \"10 individuals\". Let me continue checking:This image has 1 person. Let me check page 5:This image has 1 person. Let me check more pages:This is an emblem with 0 people. Let me continue:This image has \"Three individuals visible in foreground and midground\" plus \"Additional blurred figures visible in far background\". Let me read more:This image has \"Five individuals\". Let me continue checking more pages:This has 5 people. Let me check page 8:Good! This image has \"approximately 10 individuals\". Let me check page 9:This image has 2 people (healthcare provider and patient). Let me check page 9 more:This aircraft image has many people, but let me get a more specific count. Let me continue checking other pages:Excellent! This image has \"nine military personnel\". Let me check page 11:This dining hall image has many people seated. Let me count more carefully by reading the description again. It mentions \"Audience members\" and \"Three individuals in naval/formal uniforms standing at front\" plus \"Head table visible in background with seated guests\". This is a large gathering but not specified exactly.\n\nLet me check more pages systematically:This has 5 people. Let me check page 13:This has 1 person at the podium. Let me check page 14:Page 14 has an image with \"approximately 10-12 people\" (Image127.jpg) and another with 3 people (Image128.jpg). Let me continue:This has \"Two women standing in foreground center\" plus \"Multiple people in burgundy/maroon uniforms scattered throughout background\". Let me check page 15:This image has \"Three individuals posing together\" plus \"Additional person visible in background on right side\" = 4 people total. Let me check page 16:Page 16 has an image with 1 person (Image133.jpg) and an emblem (Image139.jpg) with 0 people.\n\nNow let me summarize what I found. Let me verify the counts more carefully by re-reading specific key images:\n\nBased on my review:\n- Page 1: Header image with \"Multiple individuals visible in soft focus\"\n- Page 2: Individual portraits (1 person each)\n- Page 3: Laboratory scene with \"approximately 6 people\" (Image79.jpg) AND group photo with \"10 individuals\" (Image82.jpg)\n- Page 4: 1 person\n- Page 5: 1 person\n- Page 6: 5 people (two images with 3 and 5 people)\n- Page 7: 5 people\n- Page 8: \"approximately 10 individuals\" (Image97.jpg)\n- Page 9: Aircraft with many people + medical scene with 2 people\n- Page 10: Helicopter group with \"nine military personnel\" (Image103.jpg)\n- Page 11: Diagram (0 people) and banquet hall with many people (Image112.jpg - but not exactly counted)\n- Page 12: 5 people\n- Page 13: 4 people and 1 person\n- Page 14: \"approximately 10-12 people\" and 3 people\n- Page 15: Multiple people but not clearly counted\n- Page 16: 1 person and emblem\n\nThe images with the most people appear to be:\n- Page 3: Image82.jpg with \"10 individuals\"\n- Page 8: Image97.jpg with \"approximately 10 individuals\" \n- Page 10: Image103.jpg with \"nine military personnel\"\n- Page 14: Image127.jpg with \"approximately 10-12 people\"\n- Page 11: Image112.jpg (banquet hall with many people)\n\nLet me check the banquet hall image more carefully:\n\nThe banquet hall description mentions \"Audience members\" seated at tables and \"Three individuals in naval/formal uniforms standing at front\" and \"Head table visible in background with seated guests\" but doesn't give an exact count. Given it's a formal dining out event, there are likely many more than 12 people, but without an exact count stated, I should go with the clearly stated counts.\n\nThe highest clearly stated count is **\"approximately 10-12 people\"** on **page 14**.\n\nAnswer: 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 167000, "error": null, "extra": {"thread_id": 1914, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a7ec370e12904bd3b5569447e3077906", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1160, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "9"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically page 2, I can find information about where Gestalt psychology was conceived.\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 24360, "output_tokens": 56, "cost_micros": 73920, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I can find information about Gestalt psychology in the section titled \"Gestalt Principles of Visual Perception.\"\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 5194, "output_tokens": 62, "cost_micros": 16512, "latency_ms": 3421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 6446, "output_tokens": 32, "cost_micros": 19818, "latency_ms": 2437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 4354, "output_tokens": 32, "cost_micros": 13542, "latency_ms": 3719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 9744, "output_tokens": 32, "cost_micros": 29712, "latency_ms": 3890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the relevant document to find information about where Gestalt psychology was conceived.Let me search for \"Gestalt\" to find the relevant section:Perfect! I found the answer. The document clearly states where Gestalt psychology was conceived.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28250, "error": null, "extra": {"thread_id": 1915, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a7b4cbe305b48eca21d964a816bfb01", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 102, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at page 14, I can see a chart titled \"Time spent on weekends\" comparing 2005 and 2010. The chart shows two pie charts/clock-like circular diagrams.\n\nFor the 2010 chart (the larger circle on the right), I can see a segment labeled \"with family and friends\" which shows 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 24371, "output_tokens": 109, "cost_micros": 74748, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a chart titled \"Time spent on weekends\" that compares 2005 and 2010 data.\n\nIn this chart, for 2010, I can see:\n- Shopping 10%\n- Fitness 6%\n- With family and friends 35%\n- Eating out 17%\n- Hobbies 4%\n- Net surfing 10%\n- Travelling 6%\n- Reading 4%\n- Watching films 22%\n\nWait, I see \"With family and friends\" appears to be listed as 35% for 2010.\n\nHowever, looking more carefully at the layout, the percentages seem to be:\n- With family and friends 21% (this appears to be for 2005)\n- With family and friends 35% (this appears to be for 2010)\n\nThe chart shows that in 2010, 35% of time on weekends was spent with family and friends.\n\nAnswer: 35", "citations": [], "input_tokens": 5205, "output_tokens": 251, "cost_micros": 19380, "latency_ms": 5953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the chart on page 14 titled \"Time spent on weekends\", I can see two pie charts comparing 2005 and 2010.\n\nIn the 2010 chart (on the right), the segment labeled \"With family and friends\" shows 35%.\n\nAnswer: 35", "citations": [], "input_tokens": 6457, "output_tokens": 67, "cost_micros": 20376, "latency_ms": 3467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking through the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nIn this table, the row \"With family and friends\" shows:\n- 2005: 21%\n- 2010: 17%\n\nAnswer: 17", "citations": [], "input_tokens": 4365, "output_tokens": 97, "cost_micros": 14550, "latency_ms": 3280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nThe table shows:\n| Activity | 2005 | 2010 |\n| ----------------------- | ---- | ---- |\n| With family and friends | 35 | 21 |\n\nAccording to this table, time spent with family and friends in 2010 was 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9755, "output_tokens": 132, "cost_micros": 31245, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the chart on page 14 and extract the information about time spent with family and friends in 2010.Let me search for page 14 content in the document:Let me search for content related to \"family and friends\" and \"2010\":Great! I found the relevant information. Let me read that specific chunk to get the exact details:Perfect! I can see from the OCR text that the chart shows time spent on weekends in 2005 and 2010. Looking at the data:\n\nFor 2010:\n- \"With family and friends 21%\" appears on line 2410\n- \"With family and friends 17%\" appears on line 2412-2413\n\nWait, let me read this more carefully. The structure shows:\n- Line 2403: \"2005\"\n- Line 2404: \"2010\"\n- Line 2410: \"With family and friends 21%\"\n- Line 2412-2413: \"With family and friends 17%\"\n\nIt appears that 21% is associated with 2005, and 17% is associated with 2010 based on the layout. Let me verify by reading more context:Looking at the OCR text structure, it shows two columns (2005 and 2010). The pattern suggests:\n- Shopping: 10% (2005) and 10% (2010)\n- Fitness: 5% (2005) and 6% (2010)\n- With family and friends: 21% (2005) and 17% (2010)\n\nSo for 2010, the time spent with family and friends is 17%.\n\nLet me verify this is from page 14 by looking for page markers:Let me search for \"14\" near this chart content:Let me look further back in the document to find page numbering:Let me look for page markers earlier in the document structure:Let me check the document structure at the beginning to understand how pages are marked:I can see there are PageBreak markers. Let me count the PageBreaks to find page 14. Let me search more systematically:I can see the PageBreaks. The one at line 2340 appears to be just before the \"Time spent on weekends\" chart. Let me count the PageBreaks:\n\n1. Line 135\n2. Line 153\n3. Line 360\n4. Line 564\n5. Line 830\n6. Line 1381\n7. Line 1630\n8. Line 1676\n9. Line 1882\n10. Line 1951\n11. Line 2012\n12. Line 2143\n13. Line 2340\n14. Line 2470\n\nThe PageBreak at line 2340 is the 13th PageBreak. If we count pages starting from page 1, then after the 13th PageBreak we would be on page 14. The chart \"Time spent on weekends\" starts at line 2344, which is right after the 13th PageBreak at line 2340, so this chart is indeed on page 14.\n\nBased on the OCR text I found earlier, the chart shows:\n- 2005 column and 2010 column\n- \"With family and friends 21%\" (for 2005)\n- \"With family and friends 17%\" (for 2010)\n\nAnswer: 17", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79187, "error": null, "extra": {"thread_id": 1916, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6038a83cdf13408480954ef7afd6b6b7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 590, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the chart that tracks the West Nile Virus and look for the legend showing what the red color represents for distance from the Mississippi River.\n\nLooking at page 10, I can see a chart titled \"Tracking the West Nile Virus\" which shows a 3D map of what appears to be the United States with different colored regions.\n\nThe legend on the right side shows \"Approximate Distance from the Mississippi River (miles)\" with the following color-coded ranges:\n- Red: 0 - 375\n- Orange: 375 - 750\n- Yellow: 750 - 1125\n- Light yellow/cream: 1125 - 1500\n\nThe red color represents the closest distance range to the Mississippi River.\n\nAnswer: 0 - 375 miles", "citations": [], "input_tokens": 24377, "output_tokens": 178, "cost_micros": 75801, "latency_ms": 9609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart tracking the West Nile Virus and identify what the red color represents.\n\nI can see the \"Tracking the West Nile Virus\" chart which shows:\n\n**Approximate Distance from the Mississippi River (miles)**\n\nThe legend shows four distance ranges with different colors:\n- 0-375\n- 375 - 750\n- 750 -1125\n- 1125 - 1500\n\nBased on the typical color coding in such charts where red usually represents the most extreme or furthest category, the red color would represent the range **1125 - 1500** miles.\n\nNote: The question mentions \"in Europe\" but the chart shows distances from the Mississippi River, which is in North America, not Europe. The chart appears to be tracking West Nile Virus in relation to the Mississippi River region.\n\nAnswer: 1125 - 1500 miles", "citations": [], "input_tokens": 5211, "output_tokens": 207, "cost_micros": 18738, "latency_ms": 6687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "1125 1500 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to look at the \"Tracking the West Nile Virus\" chart in the document.\n\nLooking at the chart, I can see a legend that shows \"Approximate Distance from the Mississippi River (miles)\" with different ranges:\n\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nThe legend shows different shading/colors corresponding to these ranges. Based on the visual representation in the legend, the red color corresponds to the range 0-375 miles.\n\nHowever, I should note that the question asks about tracking West Nile Virus \"in Europe,\" but the chart actually shows tracking relative to the Mississippi River, which is in the United States, not Europe.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 6463, "output_tokens": 170, "cost_micros": 21939, "latency_ms": 6297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the \"Tracking the West Nile Virus\" chart in the document, I can see a legend that shows height-based ranges for approximate distance from the Mississippi River in miles.\n\nThe legend shows:\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nHowever, the document does not specify which color (including red) corresponds to which distance range in this chart. Additionally, the chart tracks West Nile Virus in relation to the Mississippi River, which is in the United States, not Europe as stated in the question.\n\nAnswer: The color coding for distance ranges is not specified in the document, and the chart is about the U.S., not Europe.", "citations": [], "input_tokens": 4371, "output_tokens": 160, "cost_micros": 15513, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color coding for distance ranges is not specified in document and chart is about u s not europe", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart about West Nile Virus and check what the red color represents.\n\nOn the page titled \"Tracking the West Nile Virus,\" I can see a 3D map of the United States (not Europe) with a legend showing:\n\n\"Approximate Distance from the Mississippi River (miles)\n[red square] 0 - 375\"\n\nHowever, I should note that this chart tracks West Nile Virus in the United States, not Europe. There is no chart about West Nile Virus in Europe in this document.\n\nAnswer: The chart tracks the United States, not Europe; red represents 0-375 miles from the Mississippi River.", "citations": [], "input_tokens": 9761, "output_tokens": 157, "cost_micros": 31638, "latency_ms": 4453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.35294117647058826, "method": "str_norm", "normalised_pred": "chart tracks united states not europe red represents 0 375 miles from mississippi river", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the chart about West Nile Virus in Europe and identify what the red color represents for distance from the Mississippi River.Let me search for content related to West Nile Virus and charts showing distance from the Mississippi River.I found a reference to a red zone around the Mississippi River. Let me read that section more carefully:Let me look at the beginning of this section to find the title and legend information:Perfect! I found the chart. Now let me check which color corresponds to which distance range. The legend shows:\n\n- 0-375 miles\n- 375-750 miles\n- 750-1125 miles\n- 1125-1500 miles\n\nAnd the description mentions that the red zone is the highest concentration around the Mississippi River area. Let me verify by checking if there's more specific information about the color coding:Based on the document, I can see that:\n- The chart shows \"Tracking the West Nile Virus\" (not in Europe as the question states, but in the US)\n- The legend shows distance ranges from the Mississippi River: 0-375, 375-750, 750-1125, 1125-1500 miles\n- The red zone represents the highest concentration, which is in the Central US region, particularly around the Mississippi River area\n- The color gradient goes: Red \u2192 Orange \u2192 Yellow \u2192 Pale Yellow (indicating decreasing distance/intensity)\n\nSince red is the highest concentration around the Mississippi River area, red represents the closest distance range.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54702, "error": null, "extra": {"thread_id": 1917, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_78ac91a640ab4a709acb37a25bd11e6b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at page 11 of the document, the Law of Good Gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nThe document further explains that this law means individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 24362, "output_tokens": 109, "cost_micros": 74721, "latency_ms": 10577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for the definition of the law of good Gestalt...\n\nThe document states: \"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. This law implies that as individuals perceive the world, they eliminate complexity and unfamiliarity so they can observe a reality in its most simplistic form.\"\n\nAnswer: Elements are grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 5196, "output_tokens": 104, "cost_micros": 17148, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7878787878787878, "method": "str_norm", "normalised_pred": "elements are grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good Gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The document also explains that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 6448, "output_tokens": 102, "cost_micros": 20874, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The law implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form. It focuses on the idea of conciseness and implies a global regularity that is mentally prioritized over spatial relations.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 4356, "output_tokens": 111, "cost_micros": 14733, "latency_ms": 3484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document's section on \"Gestalt Principles of Visual Perception\" and specifically the \"Law of good Gestalt\":\n\nThe document defines it as: **Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.**\n\nThe document further explains this implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 9746, "output_tokens": 125, "cost_micros": 31113, "latency_ms": 4062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the definition of the law of good gestalt.Let me search for \"gestalt\" in this document to find the relevant section.Let me read the document content to find mentions of \"gestalt\":Now let me read the specific section around line 1965 where \"good gestalt\" is mentioned:Perfect! I found the definition. Let me read a bit more context to ensure I have the complete definition:Based on the document, the law of good gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36484, "error": null, "extra": {"thread_id": 1918, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d9af7588088a4b3184b7d88eec528363", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 192, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the chart on page 14 that shows \"Time spent on weekends\" comparing 2005 and 2010.\n\nLooking at the two pie charts:\n\n2005 (smaller chart):\n- With family and friends: 35%\n- Watching films: 20%\n- Reading: 10%\n- Travelling: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n- Eating out: 10%\n- Fitness: 5%\n- Shopping: 10%\n\n2010 (larger chart):\n- With family and friends: 21%\n- Watching films: 22%\n- Eating out: 17%\n- Shopping: 10%\n- Fitness: 6%\n- Hobbies: 4%\n- Net surfing: 4%\n- Travelling: 6%\n\nCalculating the changes:\n- With family and friends: 21% - 35% = -14% (decrease)\n- Watching films: 22% - 20% = +2% (increase)\n- Reading: appears to be gone or very small\n- Eating out: 17% - 10% = +7% (increase)\n- Shopping: 10% - 10% = 0%\n- Fitness: 6% - 5% = +1% (increase)\n- Hobbies: 4% - 2% = +2% (increase)\n- Net surfing: 4% - 3% = +1% (increase)\n- Travelling: 6% - 5% = +1% (increase)\n\nThe largest increase is \"Eating out\" with a 7 percentage point increase.\n\nAnswer: Eating out", "citations": [], "input_tokens": 24374, "output_tokens": 401, "cost_micros": 79137, "latency_ms": 13093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the table showing time spent on weekends comparing 2005 and 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" with data for 2005 and 2010:\n\n2005 \u2192 2010:\n- Shopping: 10% \u2192 10% (0% change)\n- Fitness: 5% \u2192 6% (1% increase)\n- With family and friends: 21% \u2192 35% (14% increase)\n- Eating out: 10% \u2192 17% (7% increase)\n- Hobbies: 2% \u2192 4% (2% increase)\n- Net surfing: 3% \u2192 6% (3% increase)\n- Travelling: 5% \u2192 10% (5% increase)\n- Reading: 10% \u2192 4% (6% decrease)\n- Watching films: 20% \u2192 22% (2% increase)\n\nThe largest increase is \"With family and friends\" which went from 21% to 35%, an increase of 14 percentage points.\n\nAnswer: With family and friends", "citations": [], "input_tokens": 5208, "output_tokens": 260, "cost_micros": 19524, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" chart comparing 2005 and 2010:\n\n2005:\n- With family and friends: 21%\n- Watching films: 20%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 10%\n- Travelling: 5%\n- Fitness: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n\n2010:\n- With family and friends: 35%\n- Watching films: 22%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 6%\n- Travelling: 4%\n- Fitness: 6%\n- Net surfing: 4%\n- Hobbies: 4%\n\nCalculating increases:\n- With family and friends: 35% - 21% = +14%\n- Watching films: 22% - 20% = +2%\n- Hobbies: 4% - 2% = +2%\n- Fitness: 6% - 5% = +1%\n- Net surfing: 4% - 3% = +1%\n\nAnswer: With family and friends", "citations": [], "input_tokens": 6460, "output_tokens": 284, "cost_micros": 23640, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" table comparing 2005 and 2010:\n\n- Shopping: 10% to 10% (no change)\n- Fitness: 5% to 6% (+1%)\n- With family and friends: 21% to 17% (-4%)\n- Eating out: 10% to [value not shown]\n- Hobbies: 2% to 4% (+2%)\n- Net surfing: 3% to 4% (+1%)\n- Travelling: 5% to [value not shown]\n- Reading: 10% to [value not shown]\n- Watching films: 20% to [value not shown]\n\nFrom the visible data, Hobbies shows the largest increase at 2 percentage points (from 2% to 4%).\n\nAnswer: Hobbies", "citations": [], "input_tokens": 4368, "output_tokens": 197, "cost_micros": 16059, "latency_ms": 5109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "hobbies", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the table \"Time spent on weekends\" comparing 2005 and 2010:\n\n- With family and friends: 35 \u2192 21 (decrease of 14)\n- Watching films: 20 \u2192 22 (increase of 2)\n- Reading: 10 \u2192 10 (no change)\n- Travelling: 5 \u2192 6 (increase of 1)\n- Net surfing: 3 \u2192 4 (increase of 1)\n- Hobbies: 2 \u2192 4 (increase of 2)\n- Eating out: 10 \u2192 17 (increase of 7)\n- Fitness: 5 \u2192 6 (increase of 1)\n- Shopping: 10 \u2192 10 (no change)\n\nThe category with the most increase is \"Eating out\" with an increase of 7.\n\nAnswer: Eating out", "citations": [], "input_tokens": 9758, "output_tokens": 213, "cost_micros": 32469, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about time spent on weekends from 2005 to 2010.Let me continue reading the document to find information about time spent on weekends:This doesn't seem to be the right content. Let me search through more of the document to find information about time spent on weekends:Great! I found a reference to \"Time spent on weekends\" at line 2446. Let me read that section:Perfect! I found the data. Let me analyze the percentages for each category from 2005 to 2010:\n\nFrom the OCR text (lines 2402-2424):\n\n**2005 \u2192 2010 comparisons:**\n- Shopping: 10% \u2192 10% (increase: 0%)\n- Fitness: 5% \u2192 6% (increase: 1%)\n- With family and friends: 21% \u2192 17% (decrease: -4%)\n- Eating out: 10% \u2192 10% (increase: 0%)\n- Hobbies: 2% \u2192 4% (increase: 2%)\n- Net surfing: 3% \u2192 6% (increase: 3%)\n- Travelling: 5% \u2192 10% (increase: 5%)\n- Reading: 10% \u2192 4% (decrease: -6%)\n- Watching films: 20% \u2192 35% (increase: 15%)\n\nThe category with the most increase from 2005 to 2010 is **Watching films** with an increase of 15 percentage points (from 20% to 35%).\n\nAnswer: Watching films", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42125, "error": null, "extra": {"thread_id": 1919, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5fc07b74eefd45dcbf475865f8a3bf94", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "watching films", "normalised_gold": "eating out"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at page 13, I can see an infographic titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO (Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization).\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A world map with numbers indicating participants by region:\n - 43 (North America)\n - 5 (Central/South America)\n - 130 (Europe - shown as the largest red circle)\n - 80 (Asia/Middle East region)\n - 77 (Africa/Asia region)\n - 30 (Africa)\n - 25 (Asia-Pacific region)\n\nBased on the map visualization, Europe has the largest circle with 130 participants, which is the highest number shown.\n\nAnswer: Europe", "citations": [], "input_tokens": 24369, "output_tokens": 216, "cost_micros": 76347, "latency_ms": 10344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at the document, I can see a section titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO PREPARATORY COMMISSION.\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (NGO, Station Operator, Ministry of FA, etc.)\n- Regional breakdown showing numbers: 98, 55, 43, 80, 66, 20, 77, 28, 30, 80, 8, 1\n\nI can see there's a map visualization with numbers distributed across different regions. Looking at the geographic distribution:\n- The number 98 appears to be positioned over Asia\n- 80 appears twice in different locations\n- Other numbers are distributed across continents\n\nBased on the positioning on the world map in the infographic, the largest number (98) appears to be associated with the Asian region.\n\nAnswer: Asia", "citations": [], "input_tokens": 5203, "output_tokens": 229, "cost_micros": 19044, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants by continent for the CTBTO Advanced Science Course.\n\nLooking at the document, I can see a visualization titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO Preparatory Commission. It shows:\n\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (University, Research Institute, National Data Centre, etc.)\n\nHowever, I do not see any breakdown of participants by continent in this visualization or anywhere else in the document. The document shows the total number of participants (425) and their institutional affiliations, but does not provide geographical/continental distribution data.\n\nAnswer: Information not provided in the document", "citations": [], "input_tokens": 6455, "output_tokens": 152, "cost_micros": 21645, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "information not provided in document", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the CTBTO Advanced Science Course data on page 12, I can see a visualization showing participants from 43 countries with numbers:\n\n- 77\n- 130\n- 80\n- 55\n- 66\n\nThe chart shows \"registered institutional affiliation: 425\" total participants and \"participants from 43 countries.\"\n\nThe number 130 appears to be the largest among the values shown in the visualization, which represents participants by region/continent. Based on the positioning and context of the chart layout, this would represent Asia.\n\nAnswer: Asia", "citations": [], "input_tokens": 4363, "output_tokens": 125, "cost_micros": 14964, "latency_ms": 4905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the \"ADVANCED SCIENCE COURSE at a glance\" section in the document, I can find the \"Participants by Region\" table which shows:\n\n- North America: 43\n- Central America: 20\n- South America: 8\n- Europe: 130\n- Africa (West/Central): 77\n- Africa (South): 30\n- Middle East/Asia: 80\n- South East Asia/Oceania: 28\n\nEurope has the highest number with 130 registered participants.\n\nAnswer: Europe", "citations": [], "input_tokens": 9753, "output_tokens": 120, "cost_micros": 31059, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about CTBTO registered participants for advanced science courses by continent.Let me search for information about CTBTO and advanced science course participants by continent.Let me read the entire document to find charts or tables about participant registration by continent.Let me continue reading to find information about CTBTO:Let me search for \"participant\" to find relevant information:Let me look at later sections of the document:Let me search more specifically:Let me continue reading the document to find the CTBTO-related content:Great! I found the CTBTO content. Let me read that specific section more carefully:I can see the CTBTO Advanced Science Course infographic, but I need to see if there's a visualization showing participant numbers by continent. Let me look for the visual description of this image:That's a different image. Let me search for more info about the CTBTO figure. Let me look for text that mentions continents or regions in the numbers shown:The numbers I see in the CTBTO section are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These numbers appear to be on a chart/map. Let me check if there's a visual description for the CTBTO image. Let me search around that area:Let me look for an embedded image description that might be for the CTBTO figure. The figure ends at line 2239. Let me check if there's a visual description after that:Let me look at what comes before the CTBTO figure to find if there's an embedded image marker:I notice Image18.jpg is about Indonesia entrepreneurship, not CTBTO. Let me search for Image17:That's also not it. The CTBTO figure must be between Image17 and Image18. Let me look for any embedded image between lines 2000-2150:Now I see the CTBTO figure starts at line 2146. Based on what I've seen, the numbers shown are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These appear to be data points on what's likely a world map. Let me look for any visual description or search for these specific numbers in context. Let me search for \"world\" or \"map\":Looking at the numbers from the CTBTO figure more carefully:\n- 43\n- 130 (appears as \"A0 130\" which might be \"Africa 130\")\n- 80\n- 66\n- 20\n- 77\n- 28\n- 30\n- 8\n- 80\n- 98\n\nThe \"A0 130\" at line 2216 could potentially be \"Africa 130\". Let me check if there are continent names mixed in with these numbers. Let me search the exact context:Based on the data I can see from the CTBTO Advanced Science Course infographic, I have:\n- 425 registered participants from 105 countries\n- Numbers: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98\n\nThese numbers likely represent participants by geographic region/continent on a world map visualization. The largest number is 130, which likely corresponds to a continent. Without a clear visual description identifying which number belongs to which continent, I need to make an inference. Given that 130 is the highest value and appears with \"A0\" (possibly Africa or Asia), and considering typical patterns in international scientific courses, the highest participation often comes from either Europe or Asia.\n\nHowever, I cannot definitively determine from the OCR text alone which continent each number represents. Let me see if I can find more context by reading around these numbers more carefully:The OCR shows \"A0 130\" on line 2216. This could be \"Africa 130\" or \"Asia 130\" where the character wasn't recognized properly. Given that 130 is the highest number and that this appears to be showing registered participants by continent on a world map, I need to determine which continent this refers to.\n\nLooking at the pattern, if this is a world map with continental data, and 130 is preceded by what looks like \"A0\" (possibly \"Africa\" or \"Asia\" with poor OCR), I cannot make a definitive determination from the text alone.\n\nHowever, based on the structure and the fact that the highest number is 130, and without additional visual context to confirm which specific continent each number represents, I cannot provide a certain answer from this document.\n\nAnswer: Cannot be determined from the document text alone - the highest value is 130 but the continent labels are not clearly identifiable in the OCR text", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 144672, "error": null, "extra": {"thread_id": 1920, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b3899f4c01ab4b9084195ac54d3f04aa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1007, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document text alone highest value is 130 but continent labels are not clearly identifiable in ocr text", "normalised_gold": "europe"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that discusses the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking at page 8, I can see a slide titled \"Gestalt Principles of Visual Perception\" that includes information about Closure. The text states: \"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nBelow this text, there is an illustration showing incomplete shapes. Looking at the image, I can see:\n- An incomplete circle (dotted/dashed lines forming most of a circle)\n- An incomplete rectangle or square (dotted/dashed lines)\n- Another incomplete rectangular shape (dotted/dashed lines)\n\nThe shapes used to illustrate closure are geometric shapes with gaps or missing parts that our minds fill in to perceive complete shapes.\n\nAnswer: circle, rectangle, square", "citations": [], "input_tokens": 24379, "output_tokens": 216, "cost_micros": 76377, "latency_ms": 13483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "circle, rectangle, square", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the section about \"Closure\" in the Gestalt Principles of Visual Perception and identify what shapes are shown.\n\nLooking at the document, I can see the section on \"Gestalt Principles of Visual Perception\" that explains Closure:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nLooking at the visual examples provided in the Closure section of the slide, I can see incomplete shapes that our minds complete. The shapes shown appear to be:\n- A circle (incomplete/broken circle)\n- A square or rectangle (incomplete/broken square)\n- A triangle (incomplete/broken triangle)\n\nAnswer: circle, square, triangle", "citations": [], "input_tokens": 5213, "output_tokens": 183, "cost_micros": 18384, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "circle, square, triangle", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide about the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses Closure. The text states:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nThis section appears on a page that also discusses \"Continuity.\" The document indicates there is a figure on this page, but the actual visual content of the figure is not provided in the text transcription.\n\nHowever, based on the context and typical Gestalt closure demonstrations, and looking at the structure of the document, I cannot definitively identify the specific shapes used in the illustration from the provided text alone, as the figure content is not described in detail in the transcription.\n\nSince I cannot see the actual shapes in the figure that illustrates closure, I cannot provide an accurate answer based solely on what is visible in this document transcription.\n\nAnswer: Unable to determine from provided document", "citations": [], "input_tokens": 6465, "output_tokens": 251, "cost_micros": 23160, "latency_ms": 7265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided document", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that illustrates the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses \"Closure\". The text states:\n\n\"Closure: We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nHowever, I need to identify what shapes are actually shown in the visual illustration for this principle. Looking at the page layout, I can see there are visual examples accompanying the Gestalt principles, but the specific shapes used to illustrate closure are not clearly described in the extracted text.\n\nFrom the document structure, I can see that the Closure section appears on a page, but the specific shapes or visual elements used to demonstrate closure are not explicitly listed in the text content that was extracted.\n\nBased on typical Gestalt closure examples and what might be visible in such presentations, common shapes used include circles, triangles, or other geometric shapes that are incomplete but our minds perceive as complete. However, I cannot definitively identify the specific shapes from the text content provided.\n\nAnswer: I cannot determine the specific shapes from the provided document text", "citations": [], "input_tokens": 4373, "output_tokens": 284, "cost_micros": 17379, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i cannot determine specific shapes from provided document text", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for information about the Closure aspect of Gestalt Principle of Visual Perception...\n\nOn the page titled \"Gestalt Principles of Visual Perception\" that discusses Closure and Continuity, I can see the following description:\n\n\"The image illustrates the principle of **Closure** with a dashed outline of a circle and a dashed outline of a square, which the mind perceives as complete shapes despite the gaps.\"\n\nAnswer: circle, square", "citations": [], "input_tokens": 9763, "output_tokens": 105, "cost_micros": 30864, "latency_ms": 4812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "circle, square", "normalised_gold": "circle, rectangle"}} -{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the Gestalt Principle of Visual Perception and the closure aspect.Let me search for content about Gestalt Principle and closure:Now let me read the specific section around line 1631-1640 where closure is mentioned:Let me continue reading to see if there are visual examples for the closure principle:This doesn't seem to be the right section. Let me search more systematically for visual examples of the closure principle. Let me look at earlier sections:Perfect! I can see that Image13.jpg appears right before the Gestalt Principles section and shows \"dashed circle outline\" and \"dashed rectangle outline\". This image is likely illustrating the closure principle. Let me verify by reading a bit more context before the Gestalt section:Based on my reading of the document, Image13.jpg appears immediately before the Gestalt Principles of Visual Perception section discussing closure. The visual description clearly states that this image shows:\n- Left shape: dashed circle outline\n- Right shape: dashed rectangle outline\n\nThese dashed outlines (incomplete shapes) are used to illustrate the closure principle, where we perceive complete shapes even when they are not fully drawn.\n\nAnswer: circle, rectangle", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46890, "error": null, "extra": {"thread_id": 1921, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_76cfee4a20ba492dba921a5a3cdf2231", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 294, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "circle, rectangle", "normalised_gold": "circle, rectangle"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about voters' perceptions of mudslinging in the 2016 election compared to past elections.\n\nLooking through the document, I find a chart on page 7 titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more mudslinging than in past elections from 1992 to 2016.\n\nThe chart shows:\n- 1992: 68%\n- 1996: 49%\n- 2000: 46%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 90784, "output_tokens": 179, "cost_micros": 275037, "latency_ms": 41734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the chart titled \"More see 'mud-slinging,' less focus on issues\" on page 33 of the document, I can see the data points for \"More mudslinging than usual\" across different election years:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe document also explicitly states on page 6: \"Fully 92% of all voters - including 87% of Clinton supporters and 60% of Trump backers - say they were surprised by Trump's victory\" and \"Nearly three-quarters (73%) 0f all voters say there was more 'mudslinging' or negative campaigning compared with previous contests...\"\n\nHowever, looking at page 33 more carefully, it states: \"About nine-in-ten (92%) say there was more mudslinging or negative campaigning compared with previous contests, up from 68% who said that in 2012, up 38 points from 2008 (54% more negative) and 20 points higher than the previous high of 72% in 2004.\"\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 19137, "output_tokens": 292, "cost_micros": 61791, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on \"mudslinging\" or negative campaigning:\n\nOn page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing the percentage of voters who say there was more mudslinging than in past elections:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24416, "output_tokens": 147, "cost_micros": 75453, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking at the document, I can find a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" on page 6.\n\nThe chart shows percentages for different election years:\n- 1996: 46%\n- 2000: 49%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 21637, "output_tokens": 142, "cost_micros": 67041, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking through the document for information about mudslinging:\n\nOn page 5, there's a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a table showing:\n\n% of voters who say there was ___ than in past elections\n\n| Year | More mudslinging | Less mudslinging |\n| ---- | ---------------- | ---------------- |\n| 1992 | 68 | 16 |\n| 1996 | 49 | 36 |\n| 2000 | 46 | 34 |\n| 2004 | 72 | 14 |\n| 2008 | 54 | 27 |\n| 2012 | 68 | 19 |\n| 2016 | 92 | 4 |\n\nThe text also states: \"Fully 92% say there was more \"mudslinging\" or negative campaigning than in past elections \u2013 which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24377, "output_tokens": 286, "cost_micros": 77421, "latency_ms": 7157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about voters' perceptions of mudslinging compared to past elections.Let me search for content related to mudslinging:Let me read through the document to find the specific data about mudslinging. Let me start by reading the beginning sections:Let me continue searching for information about mudslinging. Let me read more of the document:Great! I found the relevant information. Let me continue reading this section to get the complete data:Perfect! I found the data about mudslinging. From the document, I can see the chart showing \"% of voters who say there was more mudslinging than in past elections\" with the following values across different election years:\n\n- 1992: 36%\n- 1996: 34%\n- 2000: 27%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe text also confirms: \"Fully 92% say there was more 'mudslinging' or negative campaigning than in past elections - which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39625, "error": null, "extra": {"thread_id": 1922, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_252b1c025dac41a5b4c7b4fbfefc6f87", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 214, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about whether men or women are more likely to think a female president will be elected in their lifetime.\n\nLooking through the document, I find on page 10 (document page 8) a section titled \"Will the country elect a female president in your lifetime?\"\n\nThe data shows:\n- All voters: No 17%, Yes 79%\n- Men: No 16%, Yes 81%\n- Women: No 18%, Yes 78%\n- Trump voters: No 17%, Yes 78%\n- Clinton voters: No 17%, Yes 81%\n\nComparing men (81% Yes) to women (78% Yes), more men think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 90776, "output_tokens": 163, "cost_micros": 274773, "latency_ms": 43483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on expectations of a female president by gender...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\"\n\nThe table shows:\n- Men who say \"Yes\": 81%\n- Women who say \"Yes\": 78%\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 19129, "output_tokens": 80, "cost_micros": 58587, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the table on page 8 titled \"Will the country elect a female president in your lifetime?\", I can find the breakdown by gender:\n\n- Men: 81% say yes\n- Women: 78% say yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 24408, "output_tokens": 64, "cost_micros": 74184, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on whether men or women think a female president will be elected in their lifetime...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\" showing:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 21629, "output_tokens": 80, "cost_micros": 66087, "latency_ms": 4219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to find the data about whether men or women think a female president will be elected in their lifetime.\n\nOn page 8, there is a table titled \"Will the country elect a female president in your lifetime?\" that shows:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nSince 81% > 78%, more men than women think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 24369, "output_tokens": 102, "cost_micros": 74637, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find information about opinions on whether a female president will be elected in a lifetime.Let me search for content related to female president and lifetime:Let me read the specific section around line 1095 to get the complete data:The text states \"There are no significant differences in these opinions among men and women,\" meaning men and women have similar percentages. Let me search for any table or chart that might have the exact percentages broken down by gender:I can see a table starting. Let me read more to see if it has the gender breakdown:Perfect! I found the table with the gender breakdown. Looking at the \"Yes\" column (indicating those who think a female president will be elected in their lifetime):\n\n- Men: 81%\n- Women: 78%\n\nAnswer: Men (81% vs 78%)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46859, "error": null, "extra": {"thread_id": 1923, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_47a779db7256446daa01071f4e8824a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "men 81 vs 78", "normalised_gold": "men"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll carefully examine the document to count all line plots.\n\nGoing through the document page by page:\n\n**Page 3:** Contains a line plot showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages from 1988-2016.\n\n**Page 5:** Contains a line plot showing \"Record share of voters say there was more campaign 'mudslinging'\" with two lines (More mudslinging and Less mudslinging) from 1992-2016.\n\n**Page 9:** Contains a line plot showing \"Half say they are happy Trump was elected president\" with percentages from 1996-2016.\n\n**Page 21:** Contains a line plot showing \"Will relations between Republicans and Democrats improve?\" with three lines from 1992-2016.\n\n**Page 24:** Contains two separate line plots:\n- One for Republican/Rep-leaning voters (More moderate vs More conservative) from 2008-2016\n- One for Democratic/Dem-leaning voters (More moderate vs More liberal) from 2008-2016\n\n**Page 27:** Contains two separate line plots showing grades for:\n- The Republican Party (1988-2016)\n- The Democratic Party (1988-2016)\n\n**Page 29:** Contains two separate line plots showing grades for:\n- The press (1988-2016)\n- The pollsters (1988-2016)\n\n**Page 31:** Contains a line plot showing \"For the first time in eight elections, most are dissatisfied with vote choices\" from 1988-2016.\n\n**Page 32:** Contains a line plot showing \"More see 'mud-slinging,' less focus on issues\" with two sets of lines from 1992-2016.\n\n**Page 33:** Contains two separate line plots showing \"Learning about the candidates and issues\" from 1988-2016.\n\n**Page 36:** Contains a line plot showing \"Most voters are confident own vote, national votes counted accurately\" from 2004-2016.\n\n**Page 37:** Contains a line plot showing \"Similar shares of Trump, Clinton voters very confident in national vote count\" from 2004-2016.\n\nTotal count:\nPage 3: 1\nPage 5: 1\nPage 9: 1\nPage ", "citations": [], "input_tokens": 90760, "output_tokens": 512, "cost_micros": 279960, "latency_ms": 41093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the number of line plots in this document.\n\nLet me go through the document systematically to identify line plots:\n\n1. Page 1: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with two lines (Winning candidate and Losing candidate) from 1988 to 2016.\n\n2. Page 6: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with two lines (More mudslinging and Less mudslinging) from 1992 to 2016.\n\n3. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows a line plot with two lines (Very/Fairly satisfied and Not very/Not at all satisfied) from 1988 to 2016.\n\n4. Page 32: Below the first plot on page 31, there's another line plot showing \"% of voters who say they were very/fairly satisfied with choice of presidential candidates\" with data points from 1988 to 2016 for winning and losing candidates.\n\n5. Page 33: \"More see 'mud-slinging,' less focus on issues\" - This contains TWO separate line plots:\n - First plot: Shows \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992 to 2016\n - Second plot: Shows discussion of issues \"Less than usual\" vs \"More than usual\" from 1992 to 2016\n\n6. Page 34: \"Learning about the candidates and issues\" - This contains TWO separate line plots:\n - First plot: \"Learned enough\" vs \"Did not learn enough\" from 1988 to 2016\n - Second plot: Presidential debates helpfulness from 1988 to 2016\n\nLet me recount to be sure:\n- Page 1: 1 line plot\n- Page 6: 1 line plot \n- Page 31: 2 line plots (one for all voters, one comparing winning/losing candidate voters)\n- Page 33: 2 line plots (mudslinging and issues discussion)\n- Page 34: 2 line plots (learning about candidates and debates)\n\nTotal:", "citations": [], "input_tokens": 19113, "output_tokens": 512, "cost_micros": 65019, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document to count all line plots (graphs that show data points connected by lines over time or categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This appears to be a line plot showing grades from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This is a line plot showing data from 1992-2016.\n\n3. Page 9: \"Half say they are happy Trump was elected president\" - This is a line plot showing data from 1996-2016.\n\n4. Page 24: Two separate line plots:\n - \"Most Republicans continue to say GOP should be more conservative\" (showing data from 2008-2016)\n - \"As the share of Democrats who want their party to be more liberal grows\" (showing data from 2008-2016)\n\n5. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This is a line plot showing data from 1988-2016.\n\n6. Page 32: Two separate line plots:\n - One showing \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992-2016\n - One showing discussion of issues \"Less than usual\" vs \"More than usual\" from 1992-2016\n\n7. Page 33: Two separate line plots:\n - One showing \"Learned enough\" vs \"Did not learn enough\" from 1988-2016\n - One showing helpfulness of debates from 1988-2016\n\n8. Page 34: \"More say press had 'too much' influence on outcome of the election\" - This is a line plot showing data from 1992-2016.\n\nTotal count: 1 + 1 + 1 + 2 + 1 + 2 + 2 + 1 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 24392, "output_tokens": 466, "cost_micros": 80166, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades...\" - This appears to be a bar chart showing percentages, not a line plot.\n\n2. Page 4: \"Large share of Trump voters are confident...\" - Bar chart\n \"How Clinton voters feel about 'giving Trump a chance'\" - Bar chart\n\n3. Page 5: \"% of voters saying Democratic leaders should...\" - Bar charts comparing 2016 and 2008\n\n4. Page 6: \"% of voters saying Donald Trump ___ appoint Democrats...\" - Table format\n \"% of voters who said Barack Obama ___ appoint Republicans...\" - Table format\n\n5. Page 7: \"Election report card: Barely passing grades\" - Table format\n \"Record share of voters say there was more campaign 'mudslinging'\" - This appears to be a LINE PLOT showing trend over years 1996-2016\n\n6. Page 8: \"Top reactions to Trump's victory\" - Bar chart\n\n7. Page 9: \"Trump's victory in a word\" - Word cloud/frequency table\n\n8. Page 10: \"Will the country elect a female president in your lifetime?\" - Bar chart\n\n9. Throughout the rest of the document, I see mostly bar charts, tables, and no additional line plots.\n\nLet me recheck page 7 more carefully. The chart showing \"More mudslinging\" and \"Less mudslinging\" from 1992-2016 appears to be displayed as a line plot with two lines tracking these measures over time.\n\nAfter careful review, I can identify 1 line plot in this document on page 7.\n\nAnswer: 1", "citations": [], "input_tokens": 21613, "output_tokens": 393, "cost_micros": 70734, "latency_ms": 11437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines over time or across categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with percentages over years from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with \"More mudslinging\" and \"Less mudslinging\" lines from 1992-2016.\n\n3. Page 9: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" (repeated from earlier) - This is a table showing A or B grades, labeled as \"Note: Percent of 'A' or 'B' grades\" - This appears to be the same as #1.\n\n4. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows two line plots tracking satisfaction levels from 1988-2016.\n\n5. Page 32: \"More see 'mud-slinging,' less focus on issues\" - This shows two separate line plots, one for mudslinging and one for discussion of issues from 1992-2016.\n\nLet me recount more carefully by looking at actual visual line plots:\n\n- Page 3: One line plot showing grades over time\n- Page 5: One line plot showing mudslinging trends\n- Page 31: Two line plots (satisfaction trends)\n- Page 32: Two line plots (mudslinging and issues discussion)\n\nTotal: 1 + 1 + 2 + 2 = 6 line plots\n\nAnswer: 6", "citations": [], "input_tokens": 24353, "output_tokens": 397, "cost_micros": 79014, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the report.Let me continue reading the document to see all the content and identify line plots:Let me continue reading to see if there are more charts/figures:Let me search for all figures/charts in the document to count line plots specifically:Now I need to look at the actual figures to determine which ones are line plots. Let me examine the key figure sections more carefully. Based on what I've already read, I saw one chart around line 798-853 that appears to be about \"mudslinging\" over time. Let me check the visual descriptions and content more carefully:This appears to be a line plot showing data over time (years 92, 96, 00, 04, 08, 12, 16). Let me check another figure:This appears to be another line plot showing data over time from 1996 to 2016. Let me check the first figure to see if it's a line plot:This first figure shows data for winning and losing candidates across years from 1988 to 2016. This appears to be a line plot with two lines (winning candidate and losing candidate). Let me check if there are any other potential line plots by examining more figures. Let me look at the figure around line 2240-2290:This appears to be a bar chart, not a line plot. Let me continue scanning through the document to identify all line plots. Let me check around line 2333:This appears to be a bar chart comparing 2016 and 2008 data, not a line plot. Based on my review of the document so far, I've identified:\n\n1. **Line plot 1** (lines 264-360): Shows grades for winning and losing candidates over time from 1988 to 2016 (two lines)\n2. **Line plot 2** (lines 798-848): Shows \"mudslinging\" over time with two lines (More mudslinging and Less mudslinging) from 1992 to 2016\n3. **Line plot 3** (lines 1209-1241): Shows happiness with election results from 1996 to 2016\n\nThese all show data trends over multiple time points, which is characteristic of line plots. Most other figures in the document appear to be bar charts showing comparisons between groups. Let me verify by checking if there are any other time-series charts I might have missed. Let me do a final check of the document to ensure I haven't missed any line plots.\n\nBased on my thorough review of the document, I found three distinct line plots that show data trends over time:\n\n1. A line plot showing grades for winning and losing candidates from 1988-2016\n2. A line plot showing perceptions of campaign mudslinging from 1992-2016 \n3. A line plot showing voter happiness with election results from 1996-2016\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 65172, "error": null, "extra": {"thread_id": 1924, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dcd15be6dac34256a7ae1a01a0998a5a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 476, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "8"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades information...\n\nOn page 3, there is a chart showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages of \"A\" or \"B\" grades on an A, B, C, D, F scale.\n\nThe chart shows:\n- Trump (2016): 30% gave A or B grades\n- Clinton (2016): 43% gave A or B grades\n\nThe document also states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nAdditionally, on page 26, there is a table showing average grades:\n- Trump: C- (average grade)\n- Clinton: C (average grade)\n\nSince C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 90768, "output_tokens": 246, "cost_micros": 275994, "latency_ms": 41342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the campaign grades data in the document:\n\nFor **Trump**:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor **Clinton**:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nTo find the median, I need to determine which grade represents the 50th percentile:\n\nFor Trump: 30% (A/B) + 19% (C) = 49%, then adding the next 15% (D) brings us to 64%. The 50th percentile falls in the D range.\n\nFor Clinton: 43% (A/B) + 20% (C) = 63%. The 50th percentile falls in the C range.\n\nThe document also explicitly states Trump receives a \"C-\" average grade while Clinton receives a \"C\" average grade, which is consistent with this analysis.\n\nAnswer: Clinton", "citations": [], "input_tokens": 19121, "output_tokens": 227, "cost_micros": 60768, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades:\n\nOn page 26, there is a table titled \"Election report card: Barely passing grades\" showing:\n- Trump: 30% gave A or B, Average grade: C-\n- Clinton: 43% gave A or B, Average grade: C\n\nThe document states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nClinton received an average grade of C, while Trump received an average grade of C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24400, "output_tokens": 176, "cost_micros": 75840, "latency_ms": 6358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Let me find the campaign grades for Trump and Clinton from the document.\n\nFrom the section \"Voters give Trump worse grades than they have for any winning candidate in recent decades\":\n\nFor Trump:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor Clinton:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nThe document also shows average grades:\n- Trump: C-\n- Clinton: C\n\nTo determine the median, I need to consider the distribution. With:\n- Trump: 30% (A/B), 19% (C), 15% (D), 35% (F)\n- Clinton: 43% (A/B), 20% (C), 16% (D), 21% (F)\n\nFor Trump, the cumulative percentages are: 30% at B or above, 49% at C or above, 64% at D or above. The median falls around the C grade.\n\nFor Clinton, the cumulative percentages are: 43% at B or above, 63% at C or above, 79% at D or above. The median falls around the C grade.\n\nHowever, since Clinton has a higher percentage (43%) receiving A or B compared to Trump (30%), and a lower percentage receiving F (21% vs 35%), Clinton's distribution is shifted toward higher grades. The average grades confirm this: Clinton received C while Trump received C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 21621, "output_tokens": 349, "cost_micros": 70098, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the campaign grades for Trump and Clinton.\n\nOn page 26-27, there is a table showing \"Voters grade the parties, press and pollsters quite negatively\" with the percentage of voters who give each a grade of A, B, C, D, or F:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n- Average grade: C-\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n- Average grade: C\n\nThe document explicitly states that Trump receives an average grade of C- and Clinton receives an average grade of C. Since C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24361, "output_tokens": 195, "cost_micros": 76008, "latency_ms": 5969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about campaign grades for Trump and Clinton.Let me search for \"campaign grade\" in the document to find relevant information.Let me search for \"grade\" to find relevant sections.Now let me read the specific sections that mention grades for Trump and Clinton. Let me look at the chunks around line 260 and 2800-2900.Good! Now let me look for more specific information about median grades. Let me check the section around line 2800-2900 where grades are discussed in detail.Perfect! I found the information. Let me look at the table that shows the grade distribution to calculate the median. From lines 2820-2838, I can see:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n\nThe document also explicitly states:\n- Trump receives a C- grade on average (line 2801-2802)\n- Clinton's average grade is a C (line 2897)\n\nTo find the median, I need to look at where the 50th percentile falls:\n\n**Trump:**\n- 0-30%: A or B\n- 31-49%: C\n- 50-64%: D (the 50th percentile falls here)\n- 65-100%: F\n\n**Clinton:**\n- 0-43%: A or B\n- 44-63%: C (the 50th percentile falls here)\n- 64-79%: D\n- 80-100%: F\n\nSo Trump's median grade is D, and Clinton's median grade is C.\n\nAnswer: Clinton", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45171, "error": null, "extra": {"thread_id": 1925, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5b7e95c94e72489983c39189c2251166", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 287, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to search through the document for charts related to mudslinging.\n\nLooking through the document, I can find references to mudslinging in the following locations:\n\n1. On page 5 (document page 7), there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more or less mudslinging than in past elections from 1992 to 2016.\n\n2. On page 32 (document page 34), there is another chart with the same title \"More see 'mud-slinging,' less focus on issues\" which shows similar data about mudslinging perceptions.\n\nLet me verify these are distinct charts:\n- The first chart on page 5 shows data points: 68, 49, 34, 72, 54, 68, 92 for \"More mudslinging\" \n- The second chart on page 32 shows the same data\n\nThese appear to be the same chart repeated in different sections of the document.\n\nTherefore, there is 1 unique chart related to mudslinging (though it appears twice in the document).\n\nAnswer: 1", "citations": [], "input_tokens": 90760, "output_tokens": 261, "cost_micros": 276195, "latency_ms": 37187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages from 1992-2016 of voters who say there was more or less mudslinging than in past elections.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows the same data about mudslinging over time (1992-2016) plus a separate section about discussion of issues.\n\nLet me verify if these are two separate charts or if one is a repeat:\n- Page 6 shows the mudslinging chart in the context of the executive summary\n- Page 33 shows the same mudslinging data but combined with another chart about discussion of issues in a single figure\n\nThese appear to be presenting the same mudslinging data, but in different contexts. The page 33 version combines it with the issues discussion data.\n\nCounting distinct charts that contain mudslinging data:\n1. The chart on page 6 (standalone mudslinging chart)\n2. The chart on page 33 (mudslinging combined with issues discussion)\n\nSince the question asks about charts \"related to mudslinging,\" both of these count as they both contain mudslinging data.\n\nAnswer: 2", "citations": [], "input_tokens": 19113, "output_tokens": 323, "cost_micros": 62184, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 5, there is a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a chart showing \"% of voters who say there was ___ than in past elections\" with \"More mudslinging\" and \"Less mudslinging\" data from years 92, 96, 00, 04, 08, 12, 16.\n\n2. On page 32, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" showing \"% of voters who say there was ... More mudslinging than usual\" and \"Less mudslinging than usual\" from years 92, 96, 00, 04, 08, 12, 16.\n\nThese appear to be the same chart repeated in different sections of the document (one in the executive summary and one in the detailed findings).\n\nLet me verify if there are any other mudslinging-related charts... No, I only find these two instances, which appear to be the same chart shown in different parts of the document.\n\nSince the question asks how many charts are related to mudslinging, and there are 2 instances of mudslinging charts in the document (even though they show the same data), the answer would be 2.\n\nHowever, if we're counting unique charts, it would be 1 chart that appears twice.\n\nLooking at the context of the question, it seems to be asking for the count of chart occurrences in the document, which would be 2.\n\nAnswer: 2", "citations": [], "input_tokens": 24392, "output_tokens": 372, "cost_micros": 78756, "latency_ms": 10358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts in the document that are related to \"mudslinging.\"\n\nLet me search through the document for references to mudslinging:\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows percentages of voters who say there was more or less mudslinging than in past elections from 1996-2016.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows percentages of voters who say there was more mudslinging than usual and less mudslinging than usual from 1992-2016.\n\nLet me verify there are no other charts about mudslinging by carefully reviewing the document again.\n\nAfter reviewing the document thoroughly, I can confirm there are 2 charts specifically related to mudslinging:\n- One on page 6 showing the record share saying there was more mudslinging\n- One on page 33 showing mud-slinging trends and comparison with issue discussion\n\nAnswer: 2", "citations": [], "input_tokens": 21613, "output_tokens": 237, "cost_micros": 68394, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/tables in the document that are related to \"mudslinging\" or negative campaigning.\n\nLet me search through the document:\n\n1. On page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" - This shows data about mudslinging from 1992-2016.\n\n2. On page 32, there is a chart titled \"More see 'mud-slinging,' less focus on issues\" - This shows data about mudslinging and discussion of issues from 1992-2016.\n\nThese appear to be the only two charts specifically related to mudslinging in the document. The first chart shows the percentage of voters who say there was more mudslinging than in past elections over different election years. The second chart also shows mudslinging data alongside data about discussion of issues.\n\nAnswer: 2", "citations": [], "input_tokens": 24353, "output_tokens": 194, "cost_micros": 75969, "latency_ms": 8342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find charts related to mudslinging.Let me read the full document to search for content related to mudslinging and identify charts:Let me continue reading to see if there are more charts related to mudslinging:Based on my thorough reading of the document, I found the following charts related to mudslinging:\n\n1. **Lines 788-853 (chunk 49482)**: A chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages of voters who said there was more or less mudslinging than in past elections, with data from years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\n2. **Lines 3933-4048 (chunk 49595)**: A chart in the section \"More see 'mud-slinging,' less focus on issues\" showing percentages of voters who say there was \"More mudslinging than usual\" and \"Less mudslinging than usual\" across election years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\nThese appear to be two representations of the same data - one earlier in the document and one later in a more detailed section about campaign evaluations.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62437, "error": null, "extra": {"thread_id": 1926, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bd535bded05431a9423ba96684e000e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 344, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically looking for percentages who feel \"uneasy\" and \"excited.\"\n\nLooking through the document, I can find on page 6 (document page 8) a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows voter reactions.\n\nThe chart shows:\n- 53% say Trump's election makes them feel \"uneasy\"\n- 51% say it makes them feel \"hopeful\"\n- 41% say \"sad\"\n- 41% say \"scared\"\n- 36% say \"proud\"\n- 31% say \"angry\"\n\nI don't see \"excited\" listed as one of the emotional reactions measured. The document shows \"hopeful,\" \"proud,\" \"uneasy,\" \"sad,\" \"scared,\" and \"angry\" but not \"excited.\"\n\nFor \"uneasy,\" the answer is 53%.\n\nSince \"excited\" is not measured in this survey, I cannot provide a percentage for it.\n\nAnswer: 53% uneasy, excited not measured", "citations": [], "input_tokens": 90783, "output_tokens": 245, "cost_micros": 276024, "latency_ms": 34905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for voters' reactions to Trump's victory:\n\nFrom page 7, there is a table titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n- Uneasy: 53%\n\nFrom page 8, there is a word cloud table showing one-word reactions. Under \"Trump voters,\" I can see:\n- Excited: 16 (this is the actual number of respondents who mentioned this word, not a percentage)\n\nHowever, the question asks for percentages. Looking at page 11, there is a more detailed table showing emotional reactions:\n\"% of voters who say the election of Donald Trump makes them feel ...\"\n- Uneasy: 53%\n\nThe document does not provide \"excited\" as a percentage in the main emotional reactions table on pages 7 and 11. The word \"excited\" only appears in the word cloud with an actual count of 16 mentions from Trump voters, but this is not presented as a percentage of all voters.\n\nBased on the data provided in percentage form:\n- Uneasy: 53%\n- Excited: Not provided as a percentage of all voters\n\nAnswer: 53%, Not provided", "citations": [], "input_tokens": 19136, "output_tokens": 269, "cost_micros": 61443, "latency_ms": 9281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not provided", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, on page 6 (and repeated on page 10), there is a section titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo \"uneasy\" is 53%.\n\nHowever, I don't see \"excited\" listed as one of the reaction options in this chart.\n\nLooking at page 7, there is a word cloud section showing individual words mentioned by Trump and Clinton voters. For Trump voters, \"Excited\" appears with 16 mentions, but this is not a percentage - these are actual counts of how many people mentioned that specific word.\n\nThe main emotional reactions chart on page 6 does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nSince \"excited\" is not measured as a percentage in the same way as \"uneasy,\" I can only provide the percentage for \"uneasy\" which is 53%.\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24415, "output_tokens": 331, "cost_micros": 78210, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel \u2026\n\n| Feeling | % of Voters |\n| ------- | ----------- |\n| Uneasy | 53 |\n| Hopeful | 51 |\n| Proud | 36 |\n| Scared | 41 |\n| Sad | 41 |\n| Angry | 31 |\n\nI can see that 53% feel \"uneasy.\"\n\nFor \"excited,\" I need to look at the word cloud section titled \"Trump's victory in a word\" which shows responses from Trump voters and Clinton voters. Under Trump voters, I can see:\n- 16 Excited\n\nHowever, this appears to be a count (16 respondents), not a percentage. The note states \"Figures show actual number of respondents who offered each response; these numbers are not percentages.\"\n\nLooking back at the main emotional reactions table on the previous page, \"excited\" is not listed as one of the specific emotions measured in the survey question about how Trump's election makes them feel.\n\nBased on the available data in the document:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the emotional reactions survey (only appears as a word count of 16 in the open-ended response)\n\nAnswer: 53%, not measured as percentage", "citations": [], "input_tokens": 21636, "output_tokens": 364, "cost_micros": 70368, "latency_ms": 11468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not measured as percentage", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find information about emotional reactions to Trump's election on page 10:\n\n\"Voters express a mix of emotional reactions to the election of Donald Trump. On the positive side, 51% say that Trump's election makes them feel hopeful; somewhat fewer say it makes them feel proud (36%).\n\nOn the negative side, 53% say Trump's election makes them feel uneasy. About four-in-ten say his election makes them feel sad (41%) or scared (41%), and 31% say the election of Trump makes them feel angry.\"\n\nSo 53% felt \"uneasy.\"\n\nLooking at the table on page 6 titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" and the word cloud on page 7, I can see the following reactions listed:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nIn the word cloud on page 7 showing \"Trump's victory in a word,\" I can see \"Excited\" listed among Trump voters with 16 mentions, but this is showing the actual number of respondents, not percentages.\n\nThe main table showing percentage reactions does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nBased on the available data:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the survey\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24376, "output_tokens": 401, "cost_micros": 79143, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the specific percentages for \"uneasy\" and \"excited\" reactions to Trump's victory.Let me read the specific section with the data about voter reactions to see the percentages clearly.Based on the document, I can see the percentages clearly in the figure at lines 890-916:\n\n- **Uneasy: 53%**\n- **Excited: 16** (this appears to be a count in the open-ended word table, not a percentage)\n\nLet me verify if there's a percentage for \"excited\" elsewhere in the document.The document shows a figure with percentages of voter reactions to Trump's victory. The measured reactions with percentages are:\n- Hopeful: 51%\n- Proud: 36%\n- **Uneasy: 53%**\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\n\"Excited\" appears in a different table showing word counts from open-ended responses (16 mentions from Trump voters), not as a percentage measurement of all voters.\n\nBased on the document, the percentage for \"uneasy\" is **53%**. There is no percentage given for \"excited\" as a reaction category - it only appears as a word count (16) in the open-ended responses table.\n\nAnswer: 53%, 16", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44733, "error": null, "extra": {"thread_id": 1927, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5850dcc4518c4b98b48b2eae8c928f95", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 326, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "16, 53", "normalised_gold": "1 4, 53"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically the percentage who felt \"angry.\"\n\nLooking through the document, I find a section on page 10 titled \"Emotional reactions to Trump's election\" with a chart showing \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\"\n\nThe chart shows percentages of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nAnswer: 31", "citations": [], "input_tokens": 90769, "output_tokens": 150, "cost_micros": 274557, "latency_ms": 34717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's victory.\n\nLooking through the document for information about emotional reactions to Trump's election:\n\nOn page 7, there is a section titled \"Election reactions: Nearly all Trump supporters feel 'hopeful'\" which includes a chart showing \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\"\n\nThe chart shows the percentage of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states: \"About half of voters (53%) say his election makes them feel 'uneasy,' while nearly as many (51%) say it makes them feel 'hopeful'. Smaller shares say his election triumph makes them feel 'scared', 'sad' (41% each), 'proud' (36%) or 'angry' (31%).\"\n\nAnswer: 31", "citations": [], "input_tokens": 19122, "output_tokens": 243, "cost_micros": 61011, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document for information about voters' reactions to Trump's victory.\n\nOn page 6 (and repeated on page 10), there is a chart titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows the percentage of voters who say the election of Donald Trump makes them feel various emotions.\n\nAccording to this chart:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states that 31% of voters say Trump's election makes them feel \"angry.\"\n\nAnswer: 31", "citations": [], "input_tokens": 24401, "output_tokens": 157, "cost_micros": 75558, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's election.\n\nLooking through the document for information about voters' emotional reactions to Trump's victory...\n\nOn page 12 (in the section \"Voters express a mix of emotional reactions to the election of Donald Trump\"), I find a table showing:\n\n\"% of voters who say the election of Donald Trump makes them feel \u2026\"\n\nThe feelings listed include:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo 31% of voters say the election of Trump makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 21622, "output_tokens": 166, "cost_micros": 67356, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for emotional reactions to Trump's victory...\n\nOn page 6, I find the section \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel ...\n\n| Reaction | % |\n| -------- | -- |\n| Hopeful | 51 |\n| Proud | 36 |\n| Uneasy | 53 |\n| Sad | 41 |\n| Scared | 41 |\n| Angry | 31 |\n\nThe table clearly shows that 31% of voters say Trump's election makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 24362, "output_tokens": 166, "cost_micros": 75576, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about voter reactions to Trump's victory.Let me search for sections that discuss reactions or emotions about Trump's victory.Great! I found a reference to \"angry\" in the context of reactions. Let me read the specific chunk that contains this information.Perfect! I found the information. According to the document, 31% of voters say Trump's election makes them feel \"angry\".\n\nAnswer: 31", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32359, "error": null, "extra": {"thread_id": 1928, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4e0d0df3d4f749b8a5376414cce75543", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 148, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find data from 2015 about how many people believed the government was doing very/fairly well in reducing the threat of terrorism.\n\nLooking at page 2, I can see a chart titled \"For first time since 9/11, government's job rating on terrorism turns negative\" which shows the percentage who say the government is doing \"Very/Fairly well\" in reducing the threat of terrorism.\n\nFrom the chart on page 2, at the end point in 2015 (specifically December 2015), I can see:\n- Total: 46%\n\nLooking at page 10-11, there's a detailed breakdown by party. The chart on page 10 shows \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" with partisan breakdowns.\n\nFrom the chart on page 10, for 2015 (the rightmost data point):\n- Republican: 27%\n- Democrat: 64%\n- Independent: 44%\n\nSo for the question asking about adults (total), Republicans, and Democrats in 2015:\n- Adults (Total): 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 58172, "output_tokens": 269, "cost_micros": 178551, "latency_ms": 22812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentage of adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section \"Views of how the government is handling the terrorist threat\" on page 10-11.\n\nThe document states:\n- Total (all adults): 46% say the government is doing very/fairly well\n- Democrats: 64% say the government is doing very/fairly well \n- Republicans: 27% say the government is doing very/fairly well\n\nThis data is from the December 2015 survey (conducted Dec. 8-13, 2015).\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 13406, "output_tokens": 163, "cost_micros": 42663, "latency_ms": 5468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for the specific data on government performance in reducing the threat of terrorism in 2015:\n\nFrom the chart on page 10 titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" showing percentages saying government is doing very/fairly well in reducing the threat of terrorism:\n\nFor December 2015:\n- Total/All adults: 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 19266, "output_tokens": 113, "cost_micros": 59493, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentages of surveyed adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" on page 10.\n\nThe chart shows \"% saying govt is doing very/fairly well in reducing the threat of terrorism\" for December 2015:\n- Total (all adults): 46%\n- Republican: 27%\n- Democrat: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 14501, "output_tokens": 140, "cost_micros": 45603, "latency_ms": 5233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on how well the government is doing in reducing the threat of terrorism in 2015:\n\nFrom the table on page 10 titled \"How well is govt doing reducing threat of terrorism?\" and the section \"Views of how the government is handling the terrorist threat\" on pages 10-11:\n\nIn December 2015:\n- Total: 46% said very/fairly well\n- Republican: 27% said very/fairly well \n- Democrat: 64% said very/fairly well\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 18633, "output_tokens": 125, "cost_micros": 57774, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the specific data about beliefs regarding government performance in reducing terrorism threats in 2015.Let me search for data about government performance in reducing terrorism threats:I can see a figure showing data about \"How well is govt doing reducing threat of terrorism?\" with percentages for \"Very/Fairly well\" across years. I can see 46 for 2015. Now let me look for the breakdown by party affiliation (Republicans and Democrats):Let me search more specifically for partisan breakdown of government performance on terrorism:I found a reference to partisan data. Let me read that section:The figure I found shows partisan data over time. Let me read more context around this section to understand the 2015 data points:Perfect! I found the data. The text clearly states:\n\n- Overall in 2015: 46% say government is doing very or fairly well\n- Democrats in 2015: 64% say government is doing at least fairly well \n- Republicans in 2015: 27% say government is doing very or fairly well\n\nThis is also confirmed by the figure showing the data points for year '15' with Democrat at 64, Republican at 27.\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42844, "error": null, "extra": {"thread_id": 1929, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cb2b43cade5c41b98919c13d86621c55", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 281, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nLooking at page 15, I can see a chart titled \"ISIS rises higher atop public's threat list\" showing various threats with their percentages in December 2015 and August 2014, plus the change.\n\nThe threats that increased (positive change) from August 2014 to December 2015 are:\n- ISIS: +16 points (67% to 83%)\n- Iran's nuclear program: +3 points (59% to 62%)\n- North Korea's nuclear program: +2 points (57% to 59%)\n- China's emergence as a world power: +1 point (48% to 49%)\n- Global climate change: +1 point (48% to 49%)\n\nNow I need to find the R-D difference for each of these threats. Looking at page 15, there's another chart showing \"Partisan differences in views of top threats to U.S.\" with R-D diff column:\n\n- Iran's nuclear program: R+27\n- ISIS: R+14\n- China's emergence as a world power: R+18\n- North Korea's nuclear program: R+13\n- Global climate change: D+51 (this is actually a Democratic advantage, so -51 for R-D)\n\nAmong the threats that increased, Iran's nuclear program has the biggest R-D difference at R+27.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 58152, "output_tokens": 342, "cost_micros": 179586, "latency_ms": 35328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find which threats had bigger percentages in December 2015 compared to August 2014, and then determine which has the largest Republican-Democrat difference.\n\nLooking at the table \"ISIS rises higher atop public's threat list\" on page 14:\n\nThreats that increased from August 2014 to December 2015:\n1. ISIS: 67% \u2192 83% (+16)\n2. Iran's nuclear program: 59% \u2192 62% (+3)\n3. North Korea's nuclear program: 57% \u2192 59% (+2)\n4. China's emergence as a world power: 48% \u2192 49% (+1)\n5. Global climate change: 48% \u2192 49% (+1)\n\nNow I need to find the R-D differences for these threats from the table \"Partisan differences in views of top threats to U.S.\" on page 15:\n\n1. ISIS: R-D difference = +14 (93% Republican - 79% Democrat)\n2. Iran's nuclear program: R-D difference = +27 (79% Republican - 52% Democrat)\n3. North Korea's nuclear program: R-D difference = +13 (70% Republican - 57% Democrat)\n4. China's emergence as a world power: R-D difference = +18 (62% Republican - 44% Democrat)\n5. Global climate change: R-D difference = -51 (22% Republican - 73% Democrat = -51, but this is negative)\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 percentage points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 13386, "output_tokens": 380, "cost_micros": 45858, "latency_ms": 8984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nFrom page 14, the table \"ISIS rises higher atop public's threat list\" shows changes from August 2014 to December 2015:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n\nFrom page 15, the table \"Partisan differences in views of top threats to U.S.\" shows R-D differences in December 2015:\n- ISIS: 93% (R) - 79% (D) = +14 R-D difference\n- Iran's nuclear program: 79% (R) - 52% (D) = +27 R-D difference\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13 R-D difference\n- China's emergence as a world power: 62% (R) - 44% (D) = +18 R-D difference\n\nAmong threats that increased from August 2014 to December 2015, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 19246, "output_tokens": 314, "cost_micros": 62448, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the threats that increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" (page 14), the threats that increased are:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence: +1 point\n- Global climate change: +1 point\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" (page 15), the R-D differences are:\n- ISIS: 93% (R) - 79% (D) = +14\n- Iran's nuclear program: 79% (R) - 52% (D) = +27\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13\n- China's emergence: 62% (R) - 44% (D) = +18\n\nAmong the threats that increased from August 2014, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 14481, "output_tokens": 271, "cost_micros": 47508, "latency_ms": 7327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find threats where concern increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" on page 14, I can see the changes from August 2014 to December 2015:\n\n- ISIS: +16 points (83% in Dec 2015 vs 67% in Aug 2014)\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n- Israeli-Palestinian conflict: -5 points (decreased)\n- Growing authoritarianism in Russia: -11 points (decreased)\n\nSo the threats that increased are: ISIS, Iran's nuclear program, North Korea's nuclear program, China's emergence, and global climate change.\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" on page 15, the R-D differences in December 2015 are:\n\n- ISIS: +14 (93% Rep - 79% Dem)\n- Iran's nuclear program: +27 (79% Rep - 52% Dem)\n- North Korea's nuclear program: +13 (70% Rep - 57% Dem)\n- China's emergence as a world power: +18 (62% Rep - 44% Dem)\n- Global climate change: -51 (22% Rep - 73% Dem) - Note: This is negative, meaning Democrats see it as more of a threat\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 18613, "output_tokens": 402, "cost_micros": 61869, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that appears most relevant based on the priority list: the ISIS and terrorism release from December 2015.Let me continue reading to find the data about threats and R-D differences:I can see the document mentions ISIS increased from 67% in August 2014 to 83% as a major threat. Let me continue reading to find data about other threats and their R-D differences:Let me search for data about threats to well-being and R-D differences. I need to find a table or chart comparing different threats:Good! I found references to \"R-D diff\" in the document. Let me read the section around line 1364:This table shows the \"most important problem\" but I need to find the table about \"threats to well-being\" comparing 2015 to August 2014. Let me continue searching:Perfect! I found the section about threats. Let me read the full table around line 1974-2076:Great! Now I need to see the partisan breakdown table with R-D differences. Let me continue reading:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1930, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd9ddaac9b56498ab35c8337203470ec", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 315, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document that appears most relevant based on priority list isis and terrorism release from december 2015 let me continue reading to find data about threats and r d differences i can see document mentions isis increased from 67 in august 2014 to 83 as major threat let me continue reading to find data about other threats and their r d differences let me search for data about threats to well being and r d differences i need to find table or chart comparing different threats good i found references to r d diff in document let me read section around line 1364 this table shows most important problem but i need to find table about threats to well being comparing 2015 to august 2014 let me continue searching perfect i found section about threats let me read full table around line 1974 2076 great now i need to see partisan breakdown table with r d differences let me continue reading", "normalised_gold": "iran s nuclear program"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll go through the document systematically to count all bar charts.\n\nLooking through each page:\n\nPage 1: Title page - no charts\n\nPage 2: \n- One line chart showing \"Views of Government's Handling of Terrorism\"\n- No bar charts\n\nPage 3:\n- One line chart showing partisan gaps\n- One pie chart showing scrutiny of Muslims\n- No bar charts\n\nPage 4:\n- One table showing party preferences on various issues\n- No bar charts\n\nPage 5:\n- One line chart showing views on Islam and violence over time\n- One line chart showing partisan divide\n- One table with demographic data\n- No bar charts\n\nPage 6:\n- Multiple horizontal bar charts showing \"Should Muslims be subject to greater scrutiny\" broken down by different groups\n- Count: approximately 3-4 bar charts on this page\n\nPage 7:\n- Multiple horizontal bar charts continuing the scrutiny question by demographics\n- Count: approximately 3-4 bar charts on this page\n\nPage 8:\n- One table showing \"Most important problem facing nation\"\n- No bar charts\n\nPage 9:\n- One table showing partisan differences\n- No bar charts\n\nPage 10:\n- One line chart showing government ratings over time by party\n- No bar charts\n\nPage 11:\n- One table showing demographic views\n- No bar charts\n\nPage 12:\n- One line chart showing concerns about anti-terrorism policies\n- No bar charts\n\nPage 13:\n- One line chart showing partisan views on anti-terrorism policies\n- One table\n- No bar charts\n\nPage 14:\n- Multiple horizontal bar charts showing ISIS as threat\n- Count: approximately 2 bar charts\n\nPage 15:\n- Multiple horizontal bar charts showing partisan differences in threats\n- Count: approximately 2 bar charts\n\nPage 16:\n- Line charts showing views of U.S. campaign\n- No bar charts\n\nPage 17:\n- Horizontal bar charts showing current assessments\n- Count: approximately 2 bar charts\n\nPage 18:\n- Multiple horizontal bar charts about concerns and ground troops\n- Count: approximately 3-4 bar charts\n\nPage 19:\n- Line chart and horizontal bar charts\n- Count: approximately 2 bar charts\n\nPage 20:\n- Line charts showing views on military force\n-", "citations": [], "input_tokens": 58124, "output_tokens": 512, "cost_micros": 182052, "latency_ms": 17578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with bar-like elements showing percentages, but it's formatted as a comparative table.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table.\n\n8. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n9. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n10. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n11. Page 9: \"More Republicans than Democrats cite immigration, terrorism as top problems\" - This is a table.\n\n12. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n13. Page 11: \"Evaluations of the government's job reducing the threat of terrorism\" - This is a table.\n\n14. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n15. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n16. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n17. Page 14: \"ISIS rises higher atop", "citations": [], "input_tokens": 13358, "output_tokens": 512, "cost_micros": 47754, "latency_ms": 11733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart, not a bar chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart, not a bar chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with horizontal bars showing differences.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - Line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - Line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This shows horizontal bar comparisons.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - Horizontal bars.\n\n9. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - Line chart.\n\n10. Page 12: \"More concern with country's protection than civil liberties\" - Line chart.\n\n11. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - Line chart.\n\n12. Page 14: \"ISIS rises higher atop public's threat list\" - Horizontal bars.\n\n13. Page 15: \"Partisan differences in views of top threats to U.S.\" - Horizontal bars.\n\n14. Page 16: \"Most continue to support U.S. military campaign against ISIS\" - Line chart.\n\n15. Page 17: \"Few say current military effort in Iraq and Syria is going well\" - Horizontal bars.\n\n16. Page 17: \"More long-term optimism in ISIS fight\" - Vertical bars.\n\n17. Page 18: \"Bigger concern: Will U.S. go too far or not far enough in Iraq and Syria?\" - Table format with categories.\n\n18. Page ", "citations": [], "input_tokens": 19218, "output_tokens": 512, "cost_micros": 65334, "latency_ms": 13172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully go through the document to count all bar charts.\n\nLet me examine each chart in the document:\n\n1. Page 2: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line graph, not a bar chart.\n\n2. Page 3: \"Most say more scrutiny of Muslims not warranted\" - This is a bar chart showing percentages for Republicans, Independents, and Democrats.\n\n3. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a bar chart comparing Democratic Party vs Republican Party on various issues.\n\n4. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line graph.\n\n5. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a bar chart showing Republican, Independent, and Democrat responses.\n\n6. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table, not a bar chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a bar chart showing responses by different political groups.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - This is a bar chart showing responses by different demographic groups.\n\n9. Page 8: \"Most important problem facing nation\" - This is a table, not a bar chart.\n\n10. Page 9: \"Most important problem facing nation\" (partisan breakdown) - This is a table, not a bar chart.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a bar chart showing percentages by party affiliation.\n\n12. Page 11: \"Older, less educated give more negative ratings\" - This is a bar chart showing responses by age and education.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line graph.\n\n14. Page 13: Bar chart showing partisan views on anti-terrorism policies - This is a line graph.\n\n15. Page 13: \"Young adults more concerned about civil liberties\" - This is a bar chart showing responses by age group.\n\n16. Page 14: \"", "citations": [], "input_tokens": 14453, "output_tokens": 512, "cost_micros": 51039, "latency_ms": 11922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "16", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This appears to be a simple text display with percentages, not a chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a table, not a bar chart.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n9. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n10. Page 9: \"Most important problem facing nation\" - This is a table.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n12. Page 11: \"How well is govt doing reducing threat of terrorism?\" - This is a table.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n14. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n15. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n16. Page 14: \"ISIS rises higher atop public's threat list\" - This is a table.\n\n17. Page 15: \"Partisan differences in views of top threats to U.S.\" - This is a table.", "citations": [], "input_tokens": 18585, "output_tokens": 512, "cost_micros": 63435, "latency_ms": 12656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the bar charts in the report.Let me continue reading to count all the bar charts in the document.Let me search for \"figure\" tags and examine the visual content descriptions more carefully:I found 31 figures in the document. Now I need to examine each one to determine which are bar charts. Let me read through the document systematically to identify the type of each chart. Based on what I've already read, I can see several line charts and some other visualizations. Let me look for more specific patterns that indicate bar charts.\n\nLooking at the content I've already read, I can identify the following charts:\n\n1. Lines 188-253: Line chart showing government's job rating on terrorism over time\n2. Lines 306-354: Line chart showing partisan gap in views of whether Islam encourages violence \n3. Lines 372-413: Pie chart showing views on Muslim scrutiny\n4. Lines 436-505: Horizontal bar chart comparing parties on various issues (terrorist threat, gun control, economy, immigration, climate change, environment)\n5. Lines 563-617: Line chart about Islam and violence over time\n6. Lines 618-657: Line chart showing partisan differences over time\n7. Lines 937-1006: Horizontal bar chart showing Muslim scrutiny views by demographic groups\n8. Lines 1055-1193: Horizontal bar chart showing scrutiny views by various demographics\n9. Lines 1508-1563: Line chart showing government ratings on terrorism by party\n10. Lines 1738-1802: Line chart about civil liberties concerns\n11. Lines 1850-1905: Line chart showing partisan views on anti-terror policies\n12. Lines 2004-2072: Vertical bar chart comparing threat levels (ISIS, Iran, North Korea, etc.)\n13. Lines 2117-2210: Horizontal bar chart showing partisan differences in threat views\n14. Lines 2239-2304: Line chart about military campaign approval\n15. Lines 2353-2425: Two separate charts about military campaign assessment\n\nLet me carefully review the entire document to count specifically the bar charts (not line charts or pie charts).\n\nBased on my thorough reading of the document, I can identify the following **bar charts**:\n\n1. **Page 4** (lines 436-505): Horizontal bar chart - \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - comparing Democratic Party vs Republican Party on 6 issues\n2. **Page 6** (lines 937-1006): Horizontal bar chart - \"Should Muslims be subject to greater scrutiny because of their religion?\" - showing responses by party/ideology\n3. **Page 7** (lines 1055-1193): Horizontal bar chart - \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - showing responses by demographics\n4. **Page 14** (lines 2004-2072): Mixed chart showing ISIS and other threats with numerical comparisons\n5. **Page 15** (lines 2117-2210): Horizontal bar chart - \"Partisan differences in views of top threats to U.S.\"\n\nAfter careful examination, the document contains **5 bar charts**.\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46078, "error": null, "extra": {"thread_id": 1931, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6dc6e43bf0ee4e80af05e0ae2c870d11", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 404, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically page 17 (document page 18), I can find information about public attitudes regarding the ISIS fight.\n\nThe chart titled \"More long-term optimism in ISIS fight\" shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria.\n\nThe text confirms: \"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nAnswer: Two-thirds believe the U.S. and allies will succeed", "citations": [], "input_tokens": 58160, "output_tokens": 159, "cost_micros": 176865, "latency_ms": 22375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Let me search the document for information about people's attitudes regarding the ultimate success of the U.S. and its allies in the fight against ISIS.\n\nI found the relevant section on page 17, which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also notes this represents an increase: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Long-term optimism that U.S. and allies will succeed", "citations": [], "input_tokens": 13394, "output_tokens": 182, "cost_micros": 42912, "latency_ms": 6734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "long term optimism that u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, on page 17, there is a section titled \"More long-term optimism in ISIS fight\" which shows:\n\n\"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail. The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Will definitely or probably succeed", "citations": [], "input_tokens": 19254, "output_tokens": 118, "cost_micros": 59532, "latency_ms": 4703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "will definitely or probably succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically the section titled \"More long-term optimism in ISIS fight\" on page 16, I can find the answer.\n\nThe document shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria, compared to only 27% who said they will \"Definitely/Probably fail.\"\n\nThe text states: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: They will succeed/be successful", "citations": [], "input_tokens": 14489, "output_tokens": 141, "cost_micros": 45582, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4444444444444445, "method": "str_norm", "normalised_pred": "they will succeed be successful", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document for information about public attitudes on the ultimate success of the U.S. and its allies against ISIS in 2015...\n\nFrom page 17, I can see the section \"Current assessments of U.S. military campaign, prospects for success\" which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also shows that \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Two-thirds believe U.S. and allies will succeed", "citations": [], "input_tokens": 18621, "output_tokens": 191, "cost_micros": 58728, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about public attitudes toward the U.S. and its allies' efforts against ISIS in 2015.Let me continue reading to find the specific information about public attitudes:Let me search for information about attitudes toward the fight against ISIS and whether the U.S. and allies will succeed:Perfect! I found the information. Let me read a bit more to confirm the complete data:Based on the document, I found the answer. The document states that although few say the current military effort in Iraq and Syria is going well (only 35% in December 2015 say it's going very/fairly well), when it comes to long-term outlook, two-thirds (66%) believe the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria.\n\nAnswer: Two-thirds (66%) believe the U.S. and its allies will succeed", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43858, "error": null, "extra": {"thread_id": 1932, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a011d8590eae4d86a7883730ba9de008", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.26666666666666666, "method": "str_norm", "normalised_pred": "two thirds 66 believe u s and its allies will succeed", "normalised_gold": "will definitely probably succeed"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1 on page 26:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nand\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 59872, "output_tokens": 134, "cost_micros": 181626, "latency_ms": 31250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 14:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 32, the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 19062, "output_tokens": 151, "cost_micros": 59451, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 18:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 31, we can see that the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22767, "output_tokens": 155, "cost_micros": 70626, "latency_ms": 4282, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the output dimension of the linear_class layer in the DETR PyTorch inference code.\n\nLooking at the code in Listing 1:\n\n```python\ndef __init__(self, num_classes, hidden_dim, nheads,\n num_encoder_layers, num_decoder_layers):\n super().__init__()\n # ... other layers ...\n self.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe linear_class layer is defined as `nn.Linear(hidden_dim, num_classes + 1)`, which means it takes `hidden_dim` as input and outputs `num_classes + 1` dimensions.\n\nThen looking at line 33:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe model is instantiated with `num_classes=91`.\n\nTherefore, the output dimension of the linear_class layer is:\nnum_classes + 1 = 91 + 1 = 92\n\nAnswer: 92", "citations": [], "input_tokens": 19551, "output_tokens": 270, "cost_micros": 62703, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 15:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 33:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe `linear_class` layer has an output dimension of `num_classes + 1`, where `num_classes=91`.\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22903, "output_tokens": 155, "cost_micros": 71034, "latency_ms": 4562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the DETR document to find the output dimension of the linear_class layer in the PyTorch inference code.Let me search for \"linear_class\" in the document to find the PyTorch inference code:Let me search for PyTorch code or inference code in the document:Now let me read the section with the PyTorch inference code around line 2864:Perfect! I found the PyTorch inference code. Looking at line 2898, I can see:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 2915, the model is instantiated with:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nSo the output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39343, "error": null, "extra": {"thread_id": 1933, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e081bc008e9a4c03bbbaf39b68e10bd7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 162, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about missed instances when the number of visible instances is 60.\n\nLooking at Figure 12 on page 25, I can see a graph titled \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nThe graph shows three colored lines representing different categories:\n- Dog (blue line)\n- Person (orange line)\n- Apple (green line)\n\nThe x-axis shows \"Number of visible instances\" and the y-axis shows \"% of missed instances.\"\n\nAt the point where the number of visible instances = 60, I need to compare the values for each category:\n\nLooking at the graph at x=60:\n- The dog (blue) line appears to be at approximately 20-25%\n- The person (orange) line appears to be at approximately 15-20%\n- The apple (green) line appears to be at approximately 10-15%\n\nAt 60 visible instances, the dog category shows the highest percentage of missed instances.\n\nAnswer: dog", "citations": [], "input_tokens": 59873, "output_tokens": 231, "cost_micros": 183084, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, I can see a graph plotting \"% of missed instances\" (y-axis) against \"Number of visible instances\" (x-axis) for three categories: dog, person, and apple.\n\nAt 60 visible instances, I need to identify which line/category has the highest percentage of missed instances:\n\n- The dog line (appears to be one color)\n- The person line (appears to be another color) \n- The apple line (appears to be a third color)\n\nFrom the graph, at the 60 visible instances mark, the \"apple\" category appears to have the highest percentage of missed instances, showing approximately 20-25% missed instances, while \"dog\" and \"person\" show lower percentages (around 10-15%).\n\nAnswer: apple", "citations": [], "input_tokens": 19063, "output_tokens": 203, "cost_micros": 60234, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nLooking at the graph in Figure 12, I can see three lines representing different categories: dog, person, and apple.\n\nAt 60 visible instances (x-axis), I need to compare the percentage of missed instances (y-axis) for each category:\n\n- Dog (appears to be around 20-25%)\n- Person (appears to be around 15-20%)\n- Apple (appears to be the highest, around 30-35%)\n\nThe apple category clearly shows the highest percentage of missed instances at 60 visible instances.\n\nAnswer: apple", "citations": [], "input_tokens": 22768, "output_tokens": 160, "cost_micros": 70704, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, it shows a graph with \"Number of visible instances\" on the x-axis (from 0 to 100) and \"% of missed instances\" on the y-axis (from 0 to 70). The graph shows three lines for different categories: dog, apple, and one other category.\n\nAt the point where the number of visible instances = 60, I need to identify which category line is highest (meaning the largest proportion of missed instances).\n\nFrom the graph, at 60 visible instances:\n- The \"dog\" line (appears to be one line)\n- The \"apple\" line (appears to be another line)\n- There appears to be a third line\n\nLooking at the vertical position at x=60, the \"dog\" line appears to be the highest among the three categories shown, indicating the largest proportion of missed instances at that point.\n\nAnswer: dog", "citations": [], "input_tokens": 19552, "output_tokens": 226, "cost_micros": 62046, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 12 (the table on page 25), I need to find the number of missed instances for each category when there are 60 visible instances.\n\nFrom the table:\n- dog: 5 missed instances\n- person: 22 missed instances \n- apple: 15 missed instances\n\nOut of 60 visible instances:\n- dog: 5/60 = 8.3%\n- person: 22/60 = 36.7%\n- apple: 15/60 = 25%\n\nThe person category has the largest proportion of missed instances.\n\nAnswer: person", "citations": [], "input_tokens": 22904, "output_tokens": 139, "cost_micros": 70797, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "person", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about visible instances and missed instances by category.Let me search for content about \"missed instances\" and look for figures or charts.Let me read the section with Figure 12 which discusses missed instances:Perfect! I found Figure 12 which shows the chart. Let me read the data more carefully. The chart shows:\n- Y-axis: % of missed instances (0 to 70)\n- X-axis: Number of visible instances (20 to 100)\n- Three categories plotted: dog, person, and apple\n\nAt 60 visible instances, I need to identify which category has the largest proportion of missed instances. From the text data shown:\n- Lines 2795-2796: dog\n- Lines 2797-2798: person \n- Lines 2801-2802: apple\n\nThe chart shows these three categories, and I need to determine which has the highest value at x=60. Based on the chart layout and the typical pattern where lines are listed in the legend in order of their values, and given that \"dog\" is listed first in the legend (line 2795), followed by \"person\" (line 2797), and then \"apple\" (line 2801), this typically indicates their relative positions on the chart.\n\nLet me look for any additional data that might give exact values:Based on the chart data from Figure 12, I can see:\n- Y-axis values listed: 70, 60, 50, 40, 30, 20, 10, 0\n- X-axis values: 20, 40, 60, 80, 100\n- Three categories in the legend: dog, person, apple\n\nThe legend positioning typically indicates the order of lines on the chart. In this case, at x=60 (60 visible instances), \"dog\" appears to be listed first in the legend (lines 2795-2796), suggesting it has the highest proportion of missed instances at that point, followed by \"person\" and then \"apple\".\n\nGiven the layout where:\n- \"dog\" is listed at the top of the legend\n- \"person\" is in the middle\n- \"apple\" is at the bottom\n\nAnd the typical convention where legend items are ordered by their y-values at key points or overall prominence, \"dog\" would have the largest proportion of missed instances when the number of visible instances equals 60.\n\nAnswer: dog", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47125, "error": null, "extra": {"thread_id": 1934, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39684984e24b48b0a29495ffed5edb23", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 304, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 on page 13, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP50, \u0394, APS, APM, APL\n\nThe rows show different combinations:\n- Row 1: X (class), X (\u21131), blank (no GIoU): AP=35.8, AP50=57.3\n- Row 2: X (class), blank (no \u21131), X (GIoU): AP=39.9, AP50=61.6\n- Row 3: X (class), X (\u21131), X (GIoU): AP=40.6, AP50=61.6\n\nThe question asks for DETR with L1 loss and WITHOUT GIoU loss, which corresponds to the first row where class=X, \u21131=X, and GIoU is blank.\n\nAnswer: 57.3", "citations": [], "input_tokens": 59883, "output_tokens": 274, "cost_micros": 183759, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three different configurations:\n\n1. First row: class \u2713, L1 \u2717, GIoU \u2713 \u2192 AP: 35.8, AP50: 57.3\n2. Second row: class \u2713, L1 \u2713, GIoU \u2717 \u2192 AP: 39.9, AP50: 61.6\n3. Third row (baseline): class \u2713, L1 \u2713, GIoU \u2713 \u2192 AP: 40.6, AP50: 61.6\n\nThe question asks for the configuration with L1 loss and without GIoU loss, which corresponds to the second row.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19073, "output_tokens": 223, "cost_micros": 60564, "latency_ms": 5608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three rows with different loss combinations:\n\n1. Row 1: class \u2612, l1 \u2612, GIoU \u2610 (class and L1, but no GIoU)\n - AP: 35.8\n - AP50: 57.3\n\n2. Row 2: class \u2612, l1 \u2610, GIoU \u2612 (class and GIoU, but no L1)\n - AP: 39.9\n - AP50: 61.6\n\n3. Row 3 (baseline): class \u2612, l1 \u2612, GIoU \u2612 (all three losses)\n - AP: 40.6\n - AP50: 61.6\n\nThe question asks for DETR with L1 loss and without GIoU loss, which corresponds to the first row in Table 4.\n\nAnswer: 57.3", "citations": [], "input_tokens": 22778, "output_tokens": 278, "cost_micros": 72504, "latency_ms": 7140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see the ablation study for different loss combinations:\n\nThe table shows three rows:\n1. class \u2713, L1 \u2717, GIoU \u2713: AP = 35.8, AP50 = 57.3\n2. class \u2713, L1 \u2713, GIoU \u2717: AP = 39.9, AP50 = 61.6\n3. class \u2713, L1 \u2713, GIoU \u2713: AP = 40.6, AP50 = 61.6 (baseline)\n\nThe second row shows the model with L1 loss but without GIoU loss (class \u2713, L1 \u2713, GIoU \u2717).\n\nThe AP50 value for this configuration is 61.6.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19562, "output_tokens": 256, "cost_micros": 62526, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP\u2085\u2080, \u0394, AP\u209b, AP\u2098, AP\u2097\n\nThe rows show different combinations:\n1. Row with \u2713 for class and \u21131, but no GIoU: AP = 35.8, AP\u2085\u2080 = 57.3\n2. Row with \u2713 for class and GIoU, but no \u21131: AP = 39.9, AP\u2085\u2080 = 61.6\n3. Row with \u2713 for all three (class, \u21131, GIoU): AP = 40.6, AP\u2085\u2080 = 61.6\n\nThe first row corresponds to the model with L1 loss but without GIoU loss, which shows AP\u2085\u2080 = 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 22914, "output_tokens": 285, "cost_micros": 73017, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for the AP50 value of DETR with L1 loss and without GIoU loss in the provided document.Let me read the table that shows the effect of loss components:Perfect! I found Table 4 which shows the effect of loss components on AP. Looking at the table:\n\n- The first row shows: class=\u2612, l1=\u2612, GIoU=(empty), which means L1 loss is used but GIoU loss is not used\n- For this configuration, the AP50 value is 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32109, "error": null, "extra": {"thread_id": 1935, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_984a5f35790043fbba33381ecf31e0b6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 on page 22, which shows the \"Architecture of DETR's transformer,\" I can see the Multi-Head Self-Attention layers are shown in the diagram.\n\nThe Multi-Head Self-Attention boxes in the diagram are colored pink/salmon.\n\nAnswer: Pink (or salmon)", "citations": [], "input_tokens": 59883, "output_tokens": 71, "cost_micros": 180714, "latency_ms": 31750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "pink or salmon", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 \"Architecture of DETR's transformer\" in the document, I need to identify the color of the Multi-Head Self-Attention layer.\n\nIn the diagram, the Multi-Head Self-Attention layer appears in both the Encoder and Decoder sections. These layers are shown in a light orange or peach color.\n\nAnswer: Orange (or peach/light orange)", "citations": [], "input_tokens": 19073, "output_tokens": 89, "cost_micros": 58554, "latency_ms": 3953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange or peach light orange", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I can see the architecture diagram of DETR's transformer.\n\nThe Multi-Head Self-Attention layer boxes in the diagram are colored blue.\n\nAnswer: Blue", "citations": [], "input_tokens": 22778, "output_tokens": 48, "cost_micros": 69054, "latency_ms": 4203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I need to identify the color of the Multi-Head Self-Attention layer in the architecture diagram.\n\nIn the figure, the Multi-Head Self-Attention layer appears in the Encoder section of the architecture.\n\nAnswer: Orange", "citations": [], "input_tokens": 19562, "output_tokens": 62, "cost_micros": 59616, "latency_ms": 3406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, which shows the architecture of DETR's transformer:\n\nThe Multi-Head Self-Attention layers are shown in the diagram boxes in the Encoder and Decoder sections.\n\nAnswer: The diagram does not specify colors for the layers in Fig 10", "citations": [], "input_tokens": 22914, "output_tokens": 65, "cost_micros": 69717, "latency_ms": 3734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "diagram does not specify colors for layers in fig 10", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 10 and identify the color of the Multi-Head Self-Attention layer in DETR's transformer architecture.Let me search for \"Fig 10\" or \"Figure 10\" in the document:Let me search for references to transformer architecture figures:Great! I found that Figure 10 shows the \"Architecture of DETR's transformer\" at line 2409. Let me read that section:I can see the text content of Figure 10, but I need to look for color information. The document mentions it's embedded as an image. Let me search for more detailed description of the figure or any color information:The visual description I found was for a different image (zebras). Let me go back and look more carefully at the Figure 10 architecture diagram. Let me search around that area more carefully:I see the text extracted from Figure 10, but the visual description that follows (zebras) is clearly for a different image (Im7.png). Let me search for visual descriptions that might be associated with the transformer architecture diagram. Let me look before Figure 10:Let me search for where there might be an embedded image description for Figure 10. Let me check if there's an image description before line 2400:Let me search for image descriptions or embedded images related to Figure 10. Let me check what comes right before the figure starts:Let me look more carefully. The embedded image Im7.png comes AFTER Figure 10. Let me check if there's an embedded image BEFORE Figure 10 that might contain the actual architecture diagram:The document doesn't seem to have a visual description embedded for Figure 10's architecture diagram itself within the accessible text. However, looking at the structure, it appears Figure 10 is presented as a figure with text labels but the actual image with colors might not have been described in the OCR/visual description section.\n\nLet me search more broadly for any mentions of colors in the architecture or in figures:Let me search for other color terms that might be in Figure 10:These color mentions don't seem to be about Figure 10. Let me try to find if there's an image file reference specifically for the transformer architecture. Let me look for \"Im\" images around Figure 10:Image Im8.png is not Figure 10 either. Let me look between Figure 10 text and Im7.png more carefully. Actually, let me check if there's an embedded image right after the
tag for Figure 10. Let me look at the exact area around the figure closing:So Figure 10 ends at line 2485, and then Im7.png starts at line 2489, which is clearly about zebras, not the transformer architecture. The Figure 10 text content doesn't include a visual description with colors. Let me search for if there's any embedded image metadata that might describe colors for transformer diagrams. Let me try searching for \"green\" or \"red\" along with terms near Figure 10 context:Let me try a different approach. Perhaps the PDF was processed and the image containing Figure 10 wasn't given a separate visual description. Let me look for if there's any image file reference or visual annotation BEFORE Figure 10 that might be the diagram itself:It seems the document processing didn't include a detailed visual description with colors for Figure 10's architecture diagram. This could mean the image was processed but only the text labels were extracted, not the visual styling information like colors of the boxes.\n\nLet me try searching the entire document for any reference to colors of \"Self-Attention\" layers or transformer layers:Based on my thorough search through the document, I cannot find any visual description that specifies the color of the Multi-Head Self-Attention layer in Figure 10. The document contains the text content and labels from Figure 10, but the visual styling information (colors of boxes/components) was not captured in the OCR or visual description process.\n\nWithout access to the actual image file or a visual description that includes color information, I cannot determine from this document what color the Multi-Head Self-Attention layer is depicted as in Figure 10.\n\nAnswer: Color information not available in the provided document text.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 122281, "error": null, "extra": {"thread_id": 1936, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_be29632a506941cfa603de6bebd1c2eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 844, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color information not available in provided document text", "normalised_gold": "red"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "original_error": "JSONDecodeError: Expecting value: line 169 column 1 (char 924)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 36889, "error": null, "raw_text_chars": 1214}]}, "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure, specifically the number of multi-head self-attention layers.\n\nLet me search through the document for information about DETR's architecture.\n\nFrom page 2, I can see that DETR has a transformer encoder-decoder architecture.\n\nOn page 10 (Section 4.1), I find: \"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nLooking at the detailed architecture description on page 22 (Section A.3) and Figure 10, I can see that:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing multi-head self-attention AND multi-head attention (cross-attention)\n\nFrom page 9-10, the baseline model uses \"6 encoder, 6 decoder layers\"\n\nEach encoder layer has one multi-head self-attention layer.\nEach decoder layer has one multi-head self-attention layer and one multi-head cross-attention layer.\n\nSo the total number of multi-head self-attention layers (not counting cross-attention) is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 59866, "output_tokens": 318, "cost_micros": 184368, "latency_ms": 36875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the number of multi-head self-attention layers in DETR's default structure.\n\nLooking at the document, I need to identify the default configuration of DETR.\n\nFrom Section 4 (Experiments), specifically the \"Technical details\" subsection:\n\"we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nHowever, I need to understand the architecture better. Looking at Section 3.2 and Figure 10:\n\n1. The **encoder** has 6 layers, and each encoder layer contains one multi-head self-attention module (as stated: \"Each encoder layer has a standard architecture and consists of a multi-head self-attention module and a feed forward network (FFN)\")\n\n2. The **decoder** has 6 layers. Looking at the description and Figure 10, each decoder layer contains:\n - Multi-head self-attention\n - Multi-head attention (encoder-decoder attention)\n\nSo each decoder layer has one multi-head self-attention layer.\n\nFrom the default configuration mentioned in the experiments:\n- 6 encoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n- 6 decoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19056, "output_tokens": 334, "cost_micros": 62178, "latency_ms": 8546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about DETR's default structure regarding multi-head self-attention layers.\n\nFrom Section 4.1 \"Comparison with Faster R-CNN\":\n\"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nFrom Section 4.2 \"Ablations\":\n\"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\nFrom the architecture description in Section 3.2:\n- The transformer encoder has multiple layers, each containing a multi-head self-attention module\n- The transformer decoder has multiple layers, each containing multi-head self-attention and encoder-decoder attention mechanisms\n\nSo the default DETR structure has:\n- 6 encoder layers (each with 1 multi-head self-attention layer) = 6 layers\n- 6 decoder layers (each with 1 multi-head self-attention layer) = 6 layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22761, "output_tokens": 271, "cost_micros": 72348, "latency_ms": 7187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and the number of multi-head self-attention layers.\n\nLooking through the document:\n\n1. In Section 4 \"Experiments\" under \"Technical details\", it states:\n\"We use scale augmentation... we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\n2. In Section A.3 \"Detailed architecture\", the document describes the transformer architecture with both encoder and decoder layers.\n\n3. From the architecture description and Figure 10:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing:\n - Multi-head self-attention\n - Multi-head attention (encoder-decoder attention)\n\n4. From the technical details in Section 4:\n- 6 encoder layers (each with multi-head self-attention)\n- 6 decoder layers (each with multi-head self-attention)\n\nSo the total number of multi-head self-attention layers would be:\n- 6 encoder layers \u00d7 1 self-attention layer each = 6\n- 6 decoder layers \u00d7 1 self-attention layer each = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19545, "output_tokens": 287, "cost_micros": 62940, "latency_ms": 7234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and count the multi-head self-attention layers.\n\nFrom the document, I can find the architecture details:\n\n1. In Section 4.2 (Ablations), it states: \"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\n2. Looking at Figure 10 and Section A.3 (Detailed architecture), the structure shows:\n - Encoder has N layers, each with Multi-Head Self-Attention\n - Decoder has M layers, each with Multi-Head Self-Attention AND Multi-Head Attention (cross-attention)\n\n3. From the baseline model description: \"we choose a model with 6 transformer and 6 decoder layers\"\n\n4. In the encoder: Each of the 6 encoder layers contains 1 multi-head self-attention layer = 6 layers\n\n5. In the decoder: Each of the 6 decoder layers contains 1 multi-head self-attention layer = 6 layers\n (Note: The decoder also has multi-head cross-attention, but that's different from self-attention)\n\nTotal multi-head self-attention layers = 6 (encoder) + 6 (decoder) = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22897, "output_tokens": 303, "cost_micros": 73236, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll search for information about DETR's structure in the provided document.Now I found the key information. Let me search for more details about the architecture to confirm the total number of self-attention layers.Let me check the decoder structure to understand how many self-attention layers are in the decoder:Now let me look at the supplementary material section that describes the architecture in detail:Based on the document, I can see that:\n\n1. The default DETR model has **6 encoder layers** (line 1054, 1073: \"6 transformer and 6 decoder layers\" and \"6 encoder, 6 decoder layers\")\n\n2. Each encoder layer has a **multi-head self-attention module** (line 624)\n\n3. The default DETR model has **6 decoder layers** (line 1054, 1073)\n\n4. Each decoder layer has **multi-head self-attention** (line 2403-2404: \"multiple multi-head self-attention and decoder-encoder attention\")\n\nSo the total number of multi-head self-attention layers in DETR's default structure is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61719, "error": null, "extra": {"thread_id": 1937, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_12c4837a12434eeca194f2ee2772f40e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 320, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this InVue 2021 Product Catalog.\n\nLooking through the document systematically:\n\n1. **Series 2865** (page 9) - For iPad Pro, iPad Air, iPad, iPad mini 4\n - Multi-position security system with sensors\n\n2. **OnePOD** (page 10) - For iPad Pro, iPad Air, iPad, iPad mini 4\n - POD display solution\n\n3. **Series 960** (page 12) - For iPad Pro, iPad Air, iPad, iPad mini 4\n - POD with exposed cable\n\n4. **iPad + Keyboard Solutions** (pages 17-19):\n - Standard Solution for iPad Pro + Keyboards (page 18)\n - High Security Solution for iPad Pro + Keyboards (page 19)\n\n5. **iPad Digital Display** (pages 15-16) - Bracketed pedestal solution\n\nSo counting the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. iPad + Keyboard Standard Solution\n5. iPad + Keyboard High Security Solution\n6. iPad Digital Display\n\nAnswer: 6", "citations": [], "input_tokens": 96851, "output_tokens": 271, "cost_micros": 294618, "latency_ms": 54405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this Apple catalog document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** (pages 3-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 6-9) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (pages 10-12) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 13) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 14) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 15-16) - A bracketed pedestal solution for iPads as digital displays\n\n7. **iPad + Keyboard Solutions** (pages 17-19):\n - iPad Pro + Keyboards Standard Solution (page 18)\n - iPad Pro + Keyboards High Security Solution (page 19)\n\nLet me count the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nAnswer: 8", "citations": [], "input_tokens": 11786, "output_tokens": 335, "cost_micros": 40383, "latency_ms": 6532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays\n\n7. **iPad Pro + Keyboards Standard Solution** (page 11) - For iPad Pro with Magic Keyboard and Smart Keyboard\n\n8. **iPad Pro + Keyboards High Security Solution** (page 12) - For iPad Pro with keyboards\n\nSo there are **8** different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 18388, "output_tokens": 271, "cost_micros": 59229, "latency_ms": 6733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions mentioned in the document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without Keyboard) on page 38\n\n2. **OnePOD** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 40\n\n3. **Series 960** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 41\n\n4. **iPad Digital Display** - mentioned as a \"Bracketed pedestal for utilizing iPads as digital displays on Apple Fixtures\" on pages 11-12\n\n5. **iPad Pro + Keyboards Standard Solution** - mentioned on page 11 for \"iPad Pro, Magic Keyboard, Smart Keyboard and Apple Pencil\"\n\n6. **iPad Pro + Keyboards High Security Solution** - mentioned on page 12\n\nSo I can identify 6 different iPad solutions in the document:\n- Series 2865 (for standard iPads)\n- OnePOD (for standard iPads)\n- Series 960 (for standard iPads)\n- iPad Digital Display\n- iPad Pro + Keyboards Standard Solution\n- iPad Pro + Keyboards High Security Solution\n\nAnswer: 6", "citations": [], "input_tokens": 12620, "output_tokens": 289, "cost_micros": 42195, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in the document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays on Apple Fixtures\n\n7. **iPad + Keyboard Solutions - Standard Solution** (page 11) - For iPad Pro with Magic Keyboard, Smart Keyboard and Apple Pencil\n\n8. **iPad + Keyboard Solutions - High Security Solution** (page 12) - For iPad Pro with Magic and Smart Folio keyboards\n\nSo there are 8 different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 16954, "output_tokens": 288, "cost_micros": 55182, "latency_ms": 7030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for iPad solutions in the provided document.Let me search for \"solution\" to find where iPad solutions are mentioned:Now let me read the table of contents section more carefully to see the iPad solutions listed:I can see there are two main sections: \"iPhone & iPad Solutions\" and \"iPad + Keyboard Solutions\". Let me now read through these sections to count the individual iPad solutions. Let me start with the \"iPhone & iPad Solutions\" section:Let me read the document more systematically. Let me check the pages 1-10 which should cover the \"iPhone & iPad Solutions\" section:Let me search for specific product series names to count the solutions:Let me look more systematically at the document. Let me read pages that should list individual iPad solutions:I can see Series 2865, OnePOD, and Series 960 mentioned. Let me continue reading to find all the different solutions and then look at the iPad + Keyboard Solutions section:Let me search for more product names. Let me look for what comes after Series 960:Good, I found \"One60 Tethered\". Let me continue:Good, I found \"One90QR\". Let me continue searching through the document:Good, I found \"iPad Digital Display\". Now let me check if there are more iPhone & iPad solutions and then look at the iPad + Keyboard Solutions section. Let me look at page 10 where iPad + Keyboard Solutions should start:Good! I found \"iPad Pro + Keyboards Standard Solution\". Let me continue to find the next one:Good! I found \"iPad Pro + Keyboards High Security Solution\". Now let me continue to see if there are more iPad solutions. Let me check what comes on page 12 and beyond, which should start MacBook Solutions (page 13 according to the table of contents):Perfect! Now I can see that page 13 starts MacBook Solutions, which means the iPad + Keyboard Solutions section ends at page 12. Let me now compile all the iPad solutions I found. Let me verify by checking if there are any other iPad product names I might have missed. Let me look at pages 10-11 more carefully:Now let me look more carefully at page 9 to see if there are more solutions listed on that introductory page:Good. Now let me compile the complete list. I need to be systematic. Let me re-examine the section headers carefully. Let me look at what page 9 shows at the bottom to clarify:\n\nActually, looking back at my findings, page 9 seems to be a transition page showing the \"iPad + Keyboard Solutions\" section header with compatibility information. Pages 10-11 then have two specific solutions.\n\nLet me now count all the different iPad solutions I found in the document:\n\n**From \"iPhone & iPad Solutions\" section (pages 1-8):**\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n\n**From \"iPad + Keyboard Solutions\" section (pages 10-11):**\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nThat gives me a total of **8** different iPad solutions.\n\nBut wait, let me verify this more carefully. OnePOD was mentioned on page 2 in the product comparisons - let me verify if it's a distinct solution:Yes, OnePOD is a distinct solution. Let me also verify the others are all distinct iPad solutions and not just iPhones. Let me check the compatibility sections more carefully. Looking back at my readings, all of these products list iPad compatibility:\n\n- Series 2865: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- OnePOD: There's a specific OnePOD section for iPad ordering\n- Series 960: Compatible with iPads\n- One60 Tethered: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- One90QR: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- iPad Digital Display: Specifically for iPads on Apple Fixtures\n- iPad Pro + Keyboards Standard Solution: For iPad Pro with keyboards\n- iPad Pro + Keyboards High Security Solution: For iPad Pro with keyboards\n\nAll 8 are indeed iPad solutions. \n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 126703, "error": null, "extra": {"thread_id": 1938, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_18872fac42c746e097bc8836213e95a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 870, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features are listed but not shown in the illustration.\n\nLooking at page 15 (document page 22), I can see the \"MacBook High Security Solution\" section.\n\nThe \"FEATURES AND BENEFITS\" lists 6 numbered features:\n\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow looking at the illustrations on the same page, I can see:\n- Image showing features 3, 6, 1, 6, and 2 labeled\n- Image showing feature 5 labeled\n\nLet me verify which features are NOT shown in the illustrations:\n- Feature 1: Shown (labeled in bottom left image)\n- Feature 2: Shown (labeled in bottom left image)\n- Feature 3: Shown (labeled in bottom left image)\n- Feature 4: NOT shown in any illustration\n- Feature 5: Shown (labeled in top right image)\n- Feature 6: Shown (labeled in bottom left image)\n\nAnswer: 1", "citations": [], "input_tokens": 96859, "output_tokens": 327, "cost_micros": 295482, "latency_ms": 47640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which feature numbers are mentioned in the text but not shown in the illustration.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section.\n\nThe FEATURES AND BENEFITS list shows:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to look at the illustration to see which numbers are labeled in the image.\n\nFrom the illustration, I can see the following numbers are clearly marked:\n- 1 (pointing to the rear anchor area)\n- 2 (pointing to the cable/tether area)\n- 3 (pointing to bracket/arm area)\n- 5 (pointing to the metal tabs)\n- 6 (appears twice, pointing to the molded feet)\n\nThe numbers NOT shown in the illustration are:\n- 4 (Ability to power/alarm MacBook with USB-C Port)\n\nAnswer: 4", "citations": [], "input_tokens": 11794, "output_tokens": 307, "cost_micros": 39987, "latency_ms": 8657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed in text with what's shown in the illustration.\n\nLooking at pages 14-17, I find the MacBook High Security Solution on page 15.\n\nThe text lists these FEATURES AND BENEFITS:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration to see which features are labeled. The illustration shows numbered callouts (1, 2, 3, 6) pointing to different parts of the MacBook security solution.\n\nFrom the visible callouts in the illustration:\n- Feature 1 is shown\n- Feature 2 is shown\n- Feature 3 is shown\n- Feature 6 is shown\n\nFeatures NOT shown in the illustration:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nTherefore, 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 18396, "output_tokens": 323, "cost_micros": 60033, "latency_ms": 9530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed with what's shown in the illustration.\n\nLooking at page 15, I can see the \"HIGH SECURITY EXCEPTION PROGRAM - MACBOOK\" section titled \"MacBook High Security Solution\".\n\nThe FEATURES AND BENEFITS section lists 6 numbered features:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration on that page to see which features are NOT shown visually. Looking at the image description \"delete alarm\" and \"return\" text visible, this appears to be showing a MacBook with the security solution.\n\nFrom the numbered features:\n- Feature 4 (USB-C Port) - this is an internal capability, not visually shown in an illustration\n- Feature 5 (Metal tabs with rubber pads preventing closing) - this would be visible if shown\n- Feature 6 (Stable, molded feet) - these would be underneath and likely not visible in a standard product photo\n\nWithout being able to see the exact details of the illustration clearly, but based on typical product photography, features that are functional/internal capabilities (like feature 4 - power/alarm ability) or underneath components (like feature 6 - molded feet) are typically not shown in product illustrations.\n\nBased on the context and typical product documentation, features 4 and 6 would most likely not be presented in the illustration, giving us 2 features.\n\nAnswer: 2", "citations": [], "input_tokens": 12628, "output_tokens": 418, "cost_micros": 44154, "latency_ms": 12765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features listed are NOT shown in the illustrations.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section with:\n\n**FEATURES AND BENEFITS** (numbered 1-6):\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe page shows 4 images with numbered callouts:\n- Image 1 (Top Left): Shows callout 5\n- Image 2 (Top Right): Shows callout 5 (close-up)\n- Image 3 (Bottom Left): Shows callouts 1, 2, 3, and 6\n- Image 4 (Bottom Right): Shows callout 2 (close-up)\n\nFeatures shown in illustrations:\n- Feature 1: YES (shown in Image 3)\n- Feature 2: YES (shown in Images 3 and 4)\n- Feature 3: YES (shown in Image 3)\n- Feature 4: NO (not shown in any image)\n- Feature 5: YES (shown in Images 1 and 2)\n- Feature 6: YES (shown in Image 3)\n\nOnly Feature 4 (Ability to power/alarm MacBook with USB-C Port) is NOT presented in the illustrations.\n\nAnswer: 4", "citations": [], "input_tokens": 16962, "output_tokens": 396, "cost_micros": 56826, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about MacBook high security solution and identify which features are not presented in the illustration.Let me search for \"security\" in the document to find the relevant section:Let me search for \"MacBook\" and \"high security\" together to find the specific section:Now let me read the specific section about \"MacBook High Security Solution\":Perfect! I found the MacBook High Security Solution section. Now let me read it more carefully to see the full list of features and the illustration. Let me go back to line 3132:I can see the features listed are numbered 1-6:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe illustration shows numbers 1, 2, 3, and 6 marked on the MacBook keyboard image in the figure. The OCR text shows these numbers visible in the illustration. However, I notice the visual description after the figure is describing a smartwatch, which seems incorrect. Let me check if there's a better description of the MacBook illustration by reading a bit more context around this section.\n\nLooking at the figure content (lines 3159-3225), I can see the illustration has:\n- Number 1 appears twice (lines 3185, 3189, 3223)\n- Number 2 appears twice (lines 3137, 3179, 3191, 3217)\n- Number 3 appears twice (lines 3173, 3215)\n- Number 6 appears three times (lines 3213, 3221)\n\nFrom the text, the 6 features are listed, and from the illustration markers I can identify which numbers are shown:\n- 1 is shown (rigid anchor design)\n- 2 is shown (steel cable)\n- 3 is shown (bracket bar arms)\n- 6 is shown (stable molded feet)\n\nThe features that are NOT shown in the illustration are:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nSo 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46969, "error": null, "extra": {"thread_id": 1939, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_abe777eda57d4187b59395c83dff7b8b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 346, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about \"One40\"...\n\nI cannot find any mention of \"One40\" in this Apple Solutions 2021 Product Catalog from InVue. The document discusses various security solutions for Apple products including:\n\n- Series 2865\n- OnePOD (One55, One60, One65)\n- Series 960\n- W2000\n- WS2\n- HSW100\n- One60 Wearable\n- OnePOD Wearable\n\nBut there is no product called \"One40\" mentioned in this catalog.\n\nAnswer: Cannot determine - product not found in document", "citations": [], "input_tokens": 96873, "output_tokens": 141, "cost_micros": 292734, "latency_ms": 38656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine product not found in document", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and whether it can only be used for Apple Watch.\n\nOn page 21, in the \"OnePOD Wearable / Apple Watch Solutions\" section, under \"FEATURES AND BENEFITS,\" it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is mentioned in the context of Apple Watch solutions. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nSearching through the rest of the document, One40 is only mentioned in this one location in relation to Apple Watch compatibility. There is no indication that One40 is used for any other products in this catalog.\n\nHowever, the question asks if it can \"only\" be used for Apple Watch. The document doesn't explicitly state that One40 cannot be used for other purposes - it simply doesn't mention One40 being used for anything else in this particular catalog.\n\nGiven that this is specifically an Apple catalog and One40 is only mentioned in the Apple Watch section without any other applications shown, and based solely on what is presented in this document:\n\nAnswer: yes", "citations": [], "input_tokens": 11808, "output_tokens": 265, "cost_micros": 39399, "latency_ms": 8577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and what products it can be used with.\n\nOn page 21, under \"OnePOD Wearable\" section for Apple Watch Solutions, it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is compatible with the OnePOD Wearable system for Apple Watch. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nLooking through the rest of the document, I can see that One40 is only mentioned in the context of Apple Watch solutions. There is no mention of One40 being used for iPhones, iPads, or other Apple products in their respective sections.\n\nAnswer: No", "citations": [], "input_tokens": 18410, "output_tokens": 179, "cost_micros": 57915, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I need to search the document for information about \"One40\" and what products it can be used with.\n\nLooking through the document, I can find mentions of \"One40\" on page 21 in the OnePOD Wearable section:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis appears in the context of Apple Watch solutions, but it only states compatibility with these stands. It doesn't state that One40 can ONLY be used for Apple Watch or that it's exclusive to Apple Watch.\n\nThe document doesn't provide enough information to definitively say whether One40 can only be used for Apple Watch or if it has other uses beyond what's mentioned in this catalog.\n\nHowever, since the question asks for a direct yes/no answer based on what's in the document, and the document only shows One40 mentioned in the Apple Watch context without explicitly stating it's exclusive to Apple Watch, I cannot confirm the statement is true.\n\nAnswer: No", "citations": [], "input_tokens": 12642, "output_tokens": 221, "cost_micros": 41241, "latency_ms": 6593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Let me search through the document for information about One40 and its compatibility.\n\nLooking at the Apple Watch Solutions section, I can see mentions of:\n- W2000\n- WS2\n- OnePOD Wearable (compatible with One55, One60, One65 and One40)\n- One60 Wearable (High Security)\n- HSW100\n\nOn page 21, under \"OnePOD Wearable\" features and benefits, it states:\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is a stand that is compatible with the OnePOD Wearable system for Apple Watch. However, the document does not explicitly state that One40 can ONLY be used for Apple Watch. The document doesn't provide comprehensive information about all possible uses of the One40 stand.\n\nBased on the limited information provided in the document, I cannot definitively confirm that One40 is exclusively for Apple Watch only.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 16976, "output_tokens": 239, "cost_micros": 54513, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "yes"}} -{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about One40 and its compatibility.Let me search for \"One40\" in the document.Now let me read the section around line 4032 to get more context about One40.This section is about Apple Watch products. Let me search for more information about One40 to see if it's used with other products.Let me search for other mentions of One40 in the document to see if it's used with other products besides Apple Watch.Let me search for \"One55\" or \"One60\" to understand what these stands are used for, which will help determine if One40 is only for Apple Watch.Good! I can see One55 is used for iPhone and iPad. Now let me check around line 1666 to see what products use these stands.Let me look for what products are associated with One40 specifically. Let me search for \"One40\" more broadly in the document.Let me search the entire document more carefully. I'll look for sections that mention what One40 is used for.I notice the table only shows One55, One65, and One60, but not One40. Let me look at sections that specifically discuss iPhone and iPad product ordering to see if One40 is mentioned there.I can see iPhone ordering uses One55, One60, and One65, but not One40. Let me check iPad ordering and then look specifically for what products use One40.iPad also uses One55, not One40. Now let me search for a broader section around the Apple Watch mention to understand the context better. Let me look for tables or product ordering sections related to Apple Watch and One40.Let me search for Apple Watch product ordering section specifically.Now let me read the Apple Watch product ordering section around line 6290.I can see the Apple Watch product ordering sections, but they don't mention One40 in the stand options. They mention DBD910-W, DBD600-W, DBD650-W, AC271-W, and ZW2000 for Apple Watch. Let me go back to where One40 was mentioned (line 4032) and read more context to understand what section that is part of.Perfect! Now I have clear context. The section starting at line 3973 is titled \"ONEPOD WEARABLE\" and is under \"/ Apple Watch Solutions\" (line 3974). The section at line 4021-4060 describes the OnePOD Wearable product which:\n\n1. Has \"Harness bands secure Apple Watch to sensor\" (line 4028)\n2. Is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\" (line 4032)\n3. Has \"OEM charging compatibility for Apple Watch models\" (line 4035)\n4. Shows compatibility with various Apple Watch Series (lines 4043-4059)\n\nThis clearly indicates that One40 is mentioned specifically in the Apple Watch section. However, the statement says One40 is compatible with the OnePOD Wearable sensor/product along with One55, One60, and One65 stands. Let me verify if One40 stand is used for other products as well by searching more thoroughly.One40 is mentioned only once in the entire document, and it's in the Apple Watch section. However, I notice that it says the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\". This means the Apple Watch sensor can work with multiple stand types including One40, but it doesn't necessarily mean One40 can ONLY be used for Apple Watch.\n\nLet me check if there's a definition or description of what One40 is elsewhere, or if it appears in any product tables.Let me look at what the table at line 1666 shows - it lists One55, One65, and One60 but notably does NOT include One40. This suggests One40 might be a different or less common stand type. Let me see if I can find any section that specifically describes One40 as a product.The table comparison shows One55, One65, and One60, but NOT One40. This table appears on page 3 under \"/ iPhone & iPad Solutions\" and \"ONEPOD\u2122\" headers (lines 1615-1616).\n\nBased on my search:\n1. One40 is mentioned only ONCE in the entire document (line 4032)\n2. It's mentioned in the \"Apple Watch Solutions\" section under \"OnePOD Wearable\" product description\n3. The comparative table of stands (lines 1658-1730) for iPhone/iPad products shows One55, One65, and One60, but NOT One40\n4. iPhone ordering sections show One55, One60, One65 but not One40\n5. iPad ordering sections show One55 but not One40\n6. Apple Watch ordering sections don't explicitly list One40 in their product codes\n\nThe document states that the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands.\" This indicates One40 CAN be used with Apple Watch, but since One55, One60, and One65 are used for multiple products (iPhone, iPad) and are also compatible with Apple Watch, it's possible One40 could also be used for other products.\n\nHowever, the fact that:\n- One40 appears nowhere else in the document\n- It's not in the comparison table\n- It's not in iPhone/iPad ordering sections\n- It only appears in the Apple Watch compatibility statement\n\nThis strongly suggests One40 is specifically or primarily designed for Apple Watch, though I cannot definitively say it can ONLY be used for Apple Watch based solely on this document.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 125969, "error": null, "extra": {"thread_id": 1940, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_274957bdc18c45a58945780a57a038df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 928, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the paper to identify the function names with blue background in the program execution.\n\nLooking at Figure 1 (Overview of PROGRAMFC model) on page 2, I can see the \"Functions\" column which shows the program execution functions. The functions shown with blue background are:\n\n1. Fact Checker (appears twice - for S1 and S3)\n2. QA Model (for S2)\n3. Logical Reasoner (for S4)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 65107, "output_tokens": 141, "cost_micros": 197436, "latency_ms": 20703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names with blue background in the \"Functions\" column of the Program Execution section.\n\nFrom the figure, I can see the following functions are called in the reasoning program:\n- S1: Verify (Fact Checker)\n- S2: Question (QA Model)\n- S3: Verify (Fact Checker)\n- S4: Predict (Logical Reasoner)\n\nThe functions with blue background are:\n- Fact Checker\n- QA Model\n- Logical Reasoner\n\nHowever, the question asks for \"function names\" which appear to be the ACTION names in the program format. Looking at the actual function calls in the program:\n- Verify\n- Question\n- Predict\n\nIn ascending alphabetical order:\n\nAnswer: Predict, Question, Verify", "citations": [], "input_tokens": 26912, "output_tokens": 192, "cost_micros": 83616, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I can see the \"Functions\" column which shows the sub-task functions that are called during program execution.\n\nThe functions shown with blue backgrounds in the figure are:\n- Fact Checker (called in steps S1 and S3 for the Verify function)\n- QA Model (called in step S2 for the Question function)\n- Logical Reasoner (called in step S4 for the Predict function)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 29934, "output_tokens": 135, "cost_micros": 91827, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names that appear with blue background in the \"Functions\" column during program execution.\n\nFrom the figure, I can see the following functions listed in the execution steps:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, examining the actual function names called in the \"Reasoning Program\" column:\n- FACT_1 uses a fact verification function\n- ANSWER_1 uses Question (QA)\n- FACT_2 uses a fact verification function\n- PREDICTED_LABEL uses Predict (logical reasoning)\n\nLooking at Section 3.4 which describes the sub-task functions, the three function names are:\n1. QUESTION\n2. VERIFY\n3. PREDICT\n\nThese correspond to the blue-backgrounded function types shown in Figure 1.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 22321, "output_tokens": 230, "cost_micros": 70413, "latency_ms": 7172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names shown in the \"Functions\" column that correspond to the program execution steps.\n\nFrom the figure, the functions shown are:\n- Fact Checker (for S1 and S3)\n- QA Model (for S2)\n- Logical Reasoner (for S4)\n\nHowever, looking more carefully at the actual function names used in the reasoning program section and throughout the paper, the standardized function names are:\n- VERIFY (for fact checking)\n- QUESTION (for question answering)\n- PREDICT (for logical reasoning)\n\nThese are the three sub-task functions described in Section 3.4.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 29236, "output_tokens": 174, "cost_micros": 90318, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I can see Figure 1 in the document. Let me search for information about the program execution functions with blue backgrounds. I need to look more carefully at the figure description and any additional details about the model architecture.Let me look at the actual figure more carefully. I need to examine the visual description and the figure content in detail to identify functions with blue backgrounds.I can see the figure content, but I need to look for more detailed visual information about the figure. Let me search for image descriptions or additional figure details.Let me look for any embedded images or visual descriptions that might contain details about the colors in Figure 1.Let me search for more embedded images near Figure 1 to see if there's a better visual description.Let me look for the actual embedded image description near Figure 1 more carefully. Let me search around line 408 where Im5.png is mentioned.Let me search for more specific information about the visual elements of Figure 1. Let me look for descriptions that might mention colors or boxes in the figure.Let me look for image descriptions near the figure that might contain more details about the visual layout. Let me read more around the figure area to find additional embedded images or descriptions.Based on Figure 1 in the document, I can see the \"Functions\" column which shows the program execution functions. Looking at the structure, I can see:\n- S1: Verify \u2192 Fact Checker\n- S2: Question \u2192 QA Model \n- S3: Verify \u2192 Fact Checker\n- S4: Predict \u2192 Logical Reasoner\n\nHowever, I need to find which ones have blue backgrounds. Let me search more carefully for visual information about the figure or look for any other embedded images that might be Figure 1 itself.From Figure 1, I can see the functions column showing:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, the text-based XML doesn't explicitly specify which ones have blue backgrounds. Let me search the document for any other references to colors or visual styling in Figure 1, or look for the actual Figure 1 image description more thoroughly. Let me search for \"Fact Checker\", \"QA Model\", \"Logical Reasoner\" to understand the context better.Based on my analysis of Figure 1 from the document, I can see the \"Functions\" column contains:\n- Fact Checker (appears for S1 and S3 - Verify operations)\n- QA Model (appears for S2 - Question operation)\n- Logical Reasoner (appears for S4 - Predict operation)\n\nGiven the typical visualization patterns in academic papers where functions are color-coded, and looking at the structure where the three main function types are displayed, the functions with blue background would likely be the distinct function types shown in the \"Functions\" column. Without explicit color information in the XML, but based on the structure and common visualization practices, the three function types are: \"Fact Checker\", \"Logical Reasoner\", and \"QA Model\".\n\nIn alphabetical order, these would be:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 80155, "error": null, "extra": {"thread_id": 1941, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6bd06724d924284a60194ba98ad4765", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 579, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to find how many prompt examples are shown in the paper's main body (not appendices).\n\nLet me search through the document for prompt examples:\n\n1. **Figure 1** (page 2): Shows a complete example of the PROGRAMFC model with a reasoning program for the claim about James Cameron and Interstellar. This includes the full program structure.\n\n2. **Figure 2** (page 5): Shows \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the .\" This shows one example of a reasoning program for the James Cameron/Interstellar claim.\n\n3. **Figure 3** (page 5): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book).\n\n4. **Section 3.4** (page 4): Shows prompt templates for the QUESTION, VERIFY, and PREDICT functions in text form.\n\nLooking at Figure 2 specifically, it shows one complete example of a prompt with the reasoning program for the James Cameron claim.\n\nIn the main body (before Appendices which start at page 14), the primary prompt example shown is in Figure 2, which displays one reasoning program example. The text also mentions using \"K = 20\" in-context examples (page 4), but these full 20 examples are not shown in the main body - they are referenced to be in Appendix D.\n\nThe main body shows **1** complete prompt example explicitly in Figure 2.\n\nAnswer: 1", "citations": [], "input_tokens": 65062, "output_tokens": 356, "cost_micros": 200526, "latency_ms": 28827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLet me go through the paper systematically:\n\n1. **Figure 2** (page 6985): Shows one prompt template for Codex used to generate reasoning programs. This includes:\n - Task instruction\n - One example showing a claim about James Cameron and Interstellar\n - Mentions \"more in-context examples here\"\n - Input claim placeholder\n This counts as **1 prompt example**.\n\n2. **Figure 3** (page 6985): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book). These show prompt templates for FLAN-T5 with question-answering format. This counts as **1 prompt example** (showing variations for different settings).\n\n3. **Section 3.4** (page 6984): Shows the VERIFY module prompt:\n ```\n EVIDENCE\n Q: Is it true that CLAIM ? True or False? The answer is:\n ```\n This counts as **1 prompt example**.\n\n4. The paper mentions prompts for baselines in Appendix A, but those are not in the main body.\n\n5. The detailed prompts in Appendix D and E are explicitly in appendices, not the main body.\n\nLet me recount more carefully:\n- Figure 2: 1 prompt template\n- Figure 3: 3 variations of the same prompt structure (but essentially 1 prompt design)\n- Section 3.4 VERIFY: 1 prompt\n\nLooking at the question more carefully - it asks for \"prompt examples\" shown in the main body. The main body ends before the appendices.\n\nIn the main body:\n1. Figure 2 shows one complete prompt example\n2. Figure 3 shows prompt variations (3 settings)\n3. Section 3.4 shows the VERIFY prompt\n\nIf counting each distinct prompt shown: Figure 2 (1), Figure 3 (3 variations but let me check if they're separate examples), Section 3.4 (1).\n\nActually, looking at Figure 3 more carefully, it shows one prompt design with three different implementations for three settings. The question", "citations": [], "input_tokens": 26867, "output_tokens": 512, "cost_micros": 88281, "latency_ms": 15515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in the appendices).\n\nLooking through the paper:\n\n1. **Figure 2** (page 6985) shows a prompt template for generating reasoning programs with Codex. This includes:\n - A task instruction\n - One complete example showing the claim about James Cameron and the director of Interstellar\n - A note saying \"(...more in-context examples here...)\"\n - An input claim placeholder\n\nThis appears to be showing **1 complete example** in the prompt template.\n\n2. **Figure 1** (page 6982) shows an overview diagram with the same example claim about James Cameron, but this is showing the system architecture rather than being a separate prompt example.\n\n3. **Section 3.4** describes the prompt formats for different sub-task functions (QUESTION, VERIFY, PREDICT), but these are described in text format rather than being full examples.\n\n4. **Section 4.4** mentions prompts for closed-book fact-checking and states \"The detailed prompting templates are given in Appendix E\" - so the full examples are in the appendix, not the main body.\n\nIn the main body, I can only count **1** complete prompt example that is actually shown (the one in Figure 2 about James Cameron and Interstellar).\n\nAnswer: 1", "citations": [], "input_tokens": 29889, "output_tokens": 308, "cost_micros": 94287, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the document systematically:\n\n1. **Figure 1**: This shows an overview of the model with what appears to be a simplified example of the reasoning process for the claim about James Cameron and Christopher Nolan. This shows one example of how the program works but not a full prompt template.\n\n2. **Figure 2**: This is explicitly labeled as \"The Codex prompt template used to generate reasoning programs\" and shows:\n - A task instruction\n - One complete in-context example (the James Cameron/Interstellar claim)\n - A note that there are \"more in-context examples here\"\n - A prompt template for the input claim\n\n So this shows **1 complete prompt example** in the template.\n\n3. **Figure 3**: This shows the implementation of the question-answering sub-task function with different prompt formats for different settings (Gold Evidence, Open-book, Closed-book). These are partial prompts for sub-tasks, showing **3 prompt format variations**.\n\n4. In the text around Section 3.4, there are descriptions of prompts:\n - For QUESTION (closed-book): \"Q: QUESTION ? The answer is:\" - **1 prompt**\n - For QUESTION (other settings): \"EVIDENCE Q: QUESTION ? The answer is:\" - **1 prompt**\n - For VERIFY: \"EVIDENCE Q: Is it true that CLAIM ? True or False? The answer is:\" - **1 prompt**\n\nHowever, I need to be careful about what counts as \"shown\" vs just described in text.\n\nLooking more carefully at what is explicitly displayed in figures/boxes in the main body:\n- Figure 1: Shows the example execution but not a full prompt\n- Figure 2: Shows 1 complete prompt example\n- Figure 3: Shows 3 prompt format examples (though these are sub-task prompts)\n\nThe question asks for \"prompt examples\" in the main body. The most prominent and complete prompt example shown is in Figure 2, which displays **1** complete example.\n\nAnswer: 1", "citations": [], "input_tokens": 22276, "output_tokens": 485, "cost_micros": 74103, "latency_ms": 14203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the main body of the paper:\n\n1. **Figure 2** (page 6984): Shows the Codex prompt template used to generate reasoning programs. This shows one prompt example with the claim about James Cameron and Interstellar.\n\n2. **Figure 3** (page 6985): Shows implementation of question-answering sub-task function with different prompt formats for three settings (Gold Evidence, Open-book, Closed-book). This shows prompt examples for the QA function.\n\n3. **Section 3.4** discusses the VERIFY and PREDICT functions with their prompt formats shown inline in the text.\n\nLet me count more carefully:\n\nIn **Figure 2**: This shows 1 complete prompt template example.\n\nIn **Section 3.4** (page 6984):\n- QUESTION function shows prompts for closed-book and other settings (2 prompt formats)\n- VERIFY function shows 1 prompt format\n\nSo in the main body, I can identify:\n- Figure 2: 1 prompt example\n- Section 3.4 for QUESTION: Shows prompt formats but these are templates\n- Section 3.4 for VERIFY: Shows 1 prompt template\n\nActually, looking at Figure 2 more carefully, it shows one example claim with its program within a larger prompt template that includes \"(... more in-context examples here ...)\".\n\nThe question asks for \"prompt examples\" - I should count distinct prompt templates or examples shown.\n\nIn the main body:\n1. Figure 2 - shows the program generation prompt template (1)\n2. Figure 3 - shows QA implementation prompts (multiple formats shown)\n3. Section 3.4 text - shows VERIFY prompt format (1)\n\nBut Figure 2 actually shows a full prompt with one complete example visible.\n\nLooking at this more carefully, Figure 2 shows **one** concrete example in the prompt template.\n\nAnswer: 1", "citations": [], "input_tokens": 29191, "output_tokens": 456, "cost_micros": 94413, "latency_ms": 14032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find how many prompt examples are shown in the paper's main body.Let me continue reading to see the document structure and find prompt examples:Let me search for prompt examples in the document:Now let me look at Figure 2 and Figure 3 which show the prompt templates:Let me search for \"Figure\" to understand how many figures/examples are in the main body:Let me check where the appendix starts to understand what's in the main body vs appendix:Let me look for where the paper sections end and appendix begins:Now let me search for where the appendix starts:Good! So \"## A Implementation Details about the Baselines\" starts the appendix. Let me find the line number:So the appendix starts at line 2301. Now let me search for all figures and tables to count prompt examples in the main body (before line 2301):I can see Figure 2 shows a prompt template with one example shown (the James Cameron / Interstellar example), and it says \"( ... more in-context examples here .. . )\" indicating there are more but not shown. Let me look at Figure 6 which shows an error case:Let me look before this to see the actual example in Figure 6:Good! Figure 6 shows one example. Now let me check if there are any other figures or examples shown in the main body. Let me look for examples in sections discussing the prompts:Now let me look at the Limitations section which might have another example:Good! I found another example in the Limitations section - the \"Aristotle couldn't have used a laptop\" example. Let me now carefully count all prompt/program examples shown in the main body (before line 2301 where Appendix starts):\n\n1. **Figure 1** (line 335-402) - Shows the \"James Cameron and Interstellar director\" example with the full reasoning program\n2. **Figure 2** (line 747-770) - Shows the same \"James Cameron and Interstellar director\" example as part of the prompt template \n3. **Figure 6** (line 1441-1464) - Shows the \"Emery ghost town\" error case example\n4. **Limitations section** (line 1699-1706) - Shows the \"Aristotle couldn't have used a laptop\" example\n\nHowever, I need to be careful - Figure 1 and Figure 2 show the SAME example (James Cameron/Interstellar). So let me count unique examples:\n\n1. James Cameron/Interstellar example (shown in both Figure 1 and Figure 2)\n2. Emery ghost town example (Figure 6)\n3. Aristotle laptop example (Limitations section)\n\nThat's **3 unique prompt/program examples** shown in the main body of the paper.\n\nWait, let me re-read the question: \"How many prompts examples are shown in the paper's main body?\" - this could mean distinct prompt templates or distinct claim examples with programs. Let me verify by looking at what constitutes an example.\n\nLooking back at the figures:\n- Figure 2 explicitly shows a prompt template with ONE example shown (the James Cameron one) plus \"( ... more in-context examples here .. . )\"\n- Figure 6 shows ONE error case example\n- Limitations shows ONE example\n\nSo there are **3 prompt examples** shown in the main body.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 87217, "error": null, "extra": {"thread_id": 1942, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fcb323f039bb45c4bc9d5aa08b03f871", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 646, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at page 6985 (which is page 5 of the document), I can see Figure 2 which shows a prompt template for Codex. The figure contains in-context examples demonstrating the program generation format.\n\nIn the figure, I can see:\n1. One complete example starting with \"# The claim is that Both James Cameron and the director of the film Interstellar were born in Canada.\" with a full program definition.\n2. After that, there is text \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples.\n3. Then \"# The claim is that \" followed by \"def program():\" which is the prompt for the input.\n\nSo there is 1 complete in-context example explicitly shown in full in this figure, with an indication that more examples would be included in the actual prompt.\n\nAnswer: 1", "citations": [], "input_tokens": 65078, "output_tokens": 222, "cost_micros": 198564, "latency_ms": 13453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at Figure 2 on page 6985, which shows \"The Codex prompt template used to generate reasoning programs\", I can see:\n\n1. One complete example about James Cameron and the director of Interstellar (showing the full program with fact_1, Answer_1, fact_2, and label)\n\n2. After that, there's a notation \"( ... more in-context examples here . . . )\" indicating additional examples are included but not shown\n\n3. Then there's the prompt for the input claim\n\nSo the figure explicitly shows **1** complete in-context example in full detail, though it indicates more examples are present in the actual implementation (they're just not displayed in the figure).\n\nAnswer: 1", "citations": [], "input_tokens": 26883, "output_tokens": 187, "cost_micros": 83454, "latency_ms": 7171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at page 6985, I can see Figure 2, which shows \"The Codex prompt template used to generate reasoning programs.\"\n\nThe figure contains:\n1. A task instruction at the top\n2. One complete example showing:\n - A claim: \"Both James Cameron and the director of the film Interstellar were born in Canada.\"\n - A program with:\n - fact_1 = Verify(\"James Cameron was born in Canada.\")\n - Answer_1 = Question(\"Who is the director of the film Interstellar?\")\n - fact_2 = Verify(\"{Answer_1} was born in Canada.\")\n - label = Predict(fact_1 and fact_2)\n3. A note saying \"( ... more in-context examples here .. . )\"\n4. A prompt for the input claim\n\nSo there is **1** complete in-context example fully shown in this figure.\n\nAnswer: 1", "citations": [], "input_tokens": 29905, "output_tokens": 242, "cost_micros": 93345, "latency_ms": 7921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at the document, page 6985 corresponds to the section showing Figure 2, which displays the Codex prompt template used to generate reasoning programs.\n\nIn Figure 2, I can see:\n1. A task instruction at the top explaining the three functions that can be called\n2. One complete example showing:\n - The claim about \"Both James Cameron and the director of the film Interstellar were born in Canada\"\n - The complete program with multiple steps (fact_1, Answer_1, fact_2, label)\n3. A note saying \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples are present but not shown\n4. The input prompt template at the bottom\n\nThe figure shows 1 complete in-context example explicitly displayed in full, with an indication that more examples exist but are not shown in the figure.\n\nAnswer: 1", "citations": [], "input_tokens": 22292, "output_tokens": 222, "cost_micros": 70206, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the figure on page 6985 (Figure 2: The Codex prompt template), I can see the prompt template structure.\n\nThe template shows:\n1. A task instruction at the top\n2. One complete example showing a claim about James Cameron and the Interstellar director, with its full program implementation\n3. A note \"(... more in-context examples here ...)\" indicating additional examples would go here\n4. The input claim placeholder at the bottom\n\nSo there is **1** complete in-context example explicitly shown in the figure, though the text indicates more examples would be included in the actual prompt (the paper mentions using K=20 examples total).\n\nAnswer: 1", "citations": [], "input_tokens": 29207, "output_tokens": 151, "cost_micros": 89886, "latency_ms": 7562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 6985 and count the complete in-context examples shown.Let me search for page 6985 in the document:Let me read through the document to find the page number references:I can see the document is from ACL conference pages 6981-7004. Let me search for figures and read the content more systematically:Now let me look at Figure 2 which seems to be the prompt template. Let me read around line 769 where Figure 2 is mentioned:I can see Figure 2 template ends around line 770. Now I need to find what page this corresponds to. Let me check if there are any page markers. Let me look for the beginning of the document to understand the structure:Good! I can see page numbers now. Line 327 shows \"\". Now let me search for page 6985:Great! Page 6985 starts at line 902. Let me read the content around that line to find the figure at the top of the page:I can see that at the top of page 6985 (starting at line 902), there's a table (Table 1), not a figure. Let me continue reading to see if there's a figure further down on this page:I see Figure 4 is on page 6986 (line 1108). Let me look back more carefully. The question asks about the figure at the TOP of page 6985. Let me check if there's a figure that spans from page 6984 to 6985, or if I need to look more carefully at what's immediately after the page break at line 902:So at the top of page 6985 (line 902-906), there's a Table 1, not a figure. Let me check what's on page 6984 to see if there's a figure that might be spanning or near the top of 6985:Let me read around line 744 to see what's on page 6984:I can see that Figure 2 is on page 6984 (lines 747-769), and it shows the Codex prompt template. Looking at the content of Figure 2, I can see:\n\n- Line 747-750: Task instruction\n- Lines 752-762: One complete in-context example showing the claim about James Cameron and the director of Interstellar\n- Line 764: \"( ... more in-context examples here .. . )\" - indicating there are more examples\n- Lines 766-767: The prompt for the input claim\n\nThe figure caption at line 769 says \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the .\"\n\nHowever, I see only ONE complete example shown explicitly (lines 752-762), and then it says \"more in-context examples here\" without showing them.\n\nWait, let me re-read the question. It asks about \"the figure at the top of page 6985\". Let me check if Figure 2 actually spans to page 6985 or if there's another figure. Looking at the structure:\n- Page 6984 starts at line 744\n- Figure 2 content is at lines 747-769\n- Page 6985 starts at line 902\n\nSo Figure 2 is entirely on page 6984, not 6985. At the top of page 6985 (line 902-906) there's Table 1, not a figure. Let me search more carefully for any figures that might be between these pages or check if I'm missing something:Wait, I think I need to reconsider. Let me look at the actual visual structure. Lines 747-769 show Figure 2 which is on page 6984. Let me check if the complete Figure 2 example I see is showing all the in-context examples or if there's more detail I should look at. Let me read the full Figure 2 content more carefully:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 91078, "error": null, "extra": {"thread_id": 1943, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_479f734c9e744aafac61b60b60655bab", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 680, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Reasoning program evaluation for incorrectly-predicted examples from each hop length in HOVER\":\n\nFor semantic errors:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 65070, "output_tokens": 105, "cost_micros": 196785, "latency_ms": 20235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs across HOVER 2-hop, 3-hop, and 4-hop claims:\n\nFor semantic errors, the three types and their totals are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 26875, "output_tokens": 120, "cost_micros": 82425, "latency_ms": 4672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, which shows the error analysis for reasoning programs on the HOVER dataset broken down by hop length (2-hop, 3-hop, 4-hop):\n\nThe three types of semantic errors are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29897, "output_tokens": 116, "cost_micros": 91431, "latency_ms": 5218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs on the HOVER dataset, I need to examine the semantic error breakdown across the three hop types (2-hop, 3-hop, and 4-hop).\n\nThe semantic errors are broken down into three categories:\n- Token: 8% (2-hop) + 20% (3-hop) + 18% (4-hop) = 46% total\n- Structure: 19% (2-hop) + 13% (3-hop) + 57% (4-hop) = 89% total\n- Subtask: 2% (2-hop) + 5% (3-hop) + 2% (4-hop) = 9% total\n\nAnswer: Subtask", "citations": [], "input_tokens": 22284, "output_tokens": 180, "cost_micros": 69552, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 which shows the error analysis for the HOVER dataset across 2-hop, 3-hop, and 4-hop claims:\n\n**Subtask errors:**\n- 2-hop: 2%\n- 3-hop: 5%\n- 4-hop: 2%\n- Total: 9%\n\n**Token errors:**\n- 2-hop: 8%\n- 3-hop: 20%\n- 4-hop: 18%\n- Total: 46%\n\n**Structure errors:**\n- 2-hop: 19%\n- 3-hop: 13%\n- 4-hop: 57%\n- Total: 89%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29199, "output_tokens": 171, "cost_micros": 90162, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about semantic errors in the HOVER dataset.Let me search for information about semantic errors and HOVER dataset in the document:Let me search for tables or figures that show error analysis:Now let me read the section with Table 2 that shows the error analysis:Perfect! I found Table 2 which shows the error analysis for the HOVER dataset. Let me examine the semantic error types and their percentages:\n\nFrom the table, the semantic errors are broken down into three types:\n- Token: 8% (2-hop), 20% (3-hop), 18% (4-hop) = Total: 46%\n- Structure: 19% (2-hop), 13% (3-hop), 57% (4-hop) = Total: 89%\n- Subtask: 2% (2-hop), 5% (3-hop), 2% (4-hop) = Total: 9%\n\nThe semantic error type with the lowest total percentages across all HOVER dataset hop lengths is \"Subtask\" with 2% + 5% + 2% = 9%.\n\nAnswer: Subtask", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41280, "error": null, "extra": {"thread_id": 1944, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_964d1076448248edb0a0c213d4b3aab0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 on page 7, I can see the retrieval recall@10 comparison:\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nFEVEROUS-S has the highest ProgramFC retrieval recall@10 at 85.65.\n\nNow I need to find the InstructGPT Self-Ask performance on FEVEROUS-S in the closed-book setting.\n\nLooking at Table 3 on page 8, which shows closed-book setting results:\n- InstructGPT - Self-Ask on FEVEROUS: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 65104, "output_tokens": 223, "cost_micros": 198657, "latency_ms": 22157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. What is the InstructGPT Self-Ask performance on that dataset in the closed-book setting\n\nFirst, let me look at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ProgramFC = 77.13\n- HOVER (3-hop): ProgramFC = 73.18\n- HOVER (4-hop): ProgramFC = 85.65\n- FEVEROUS-S: ProgramFC = 76.25\n\nThe highest ProgramFC retrieval recall@10 is 85.65 for HOVER (4-hop).\n\nNow, I need to find the InstructGPT Self-Ask performance on HOVER 4-hop in the closed-book setting from Table 3:\n\nLooking at Table 3 (Closed-book setting):\n- InstructGPT - Self-Ask on HOVER 4-hop: 52.45\n\nHowever, I notice the column headers are \"2-hop\", \"HOVER 3-hop\", \"4-hop\", and \"FEVEROUS\". The 4-hop column shows 52.45 for Self-Ask.\n\nAnswer: 52.5", "citations": [], "input_tokens": 26909, "output_tokens": 292, "cost_micros": 85107, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ~73.18\n- HOVER (3-hop): ~77.13\n- HOVER (4-hop): ~85.65\n- FEVEROUS-S: ~76.25\n\nThe highest ProgramFC retrieval recall@10 is for HOVER (4-hop) at approximately 85.65.\n\nNow looking at Table 3 (Closed-book setting) for InstructGPT with Self-Ask on HOVER (4-hop):\nThe Self-Ask row shows: 52.45 for HOVER (4-hop)\n\nAnswer: 52.5", "citations": [], "input_tokens": 29931, "output_tokens": 198, "cost_micros": 92763, "latency_ms": 9953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall at 10\n2. The InstructGPT model with Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 (mentioned in the text on page 7), which discusses retrieval performance, I need to find the retrieval recall@10 values. However, the actual figure is not fully detailed in the text.\n\nFrom Table 3 on page 7, I can see closed-book fact-checking results. The table shows performance across HOVER (2-hop, 3-hop, 4-hop) and FEVEROUS datasets.\n\nLooking at Table 3:\n- The models listed include: InstructGPT (with variants Direct, ZS-CoT, CoT, Self-Ask), Codex, FLAN-T5, and ProgramFC\n- Self-Ask row shows: 51.54, 51.47, 52.45, 56.82\n\nThe text mentions that Figure 5 compares retrieval performance, and discusses that \"iterative retrieval guided by the reasoning program yields better results.\" However, without seeing the actual Figure 5 data clearly showing which dataset has the highest recall@10 for ProgramFC, I need to infer from the context.\n\nBased on the text discussing HOVER and FEVEROUS-S datasets, and looking at Table 3's structure with columns for \"2-hop\", \"3-hop\", \"4-hop\", and what appears to be FEVEROUS, the Self-Ask scores are: 51.54, 51.47, 52.45, 56.82.\n\nThe highest value for Self-Ask appears to be 56.82, which corresponds to the FEVEROUS column.\n\nAnswer: 56.8", "citations": [], "input_tokens": 22318, "output_tokens": 404, "cost_micros": 73014, "latency_ms": 14375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 (Retrieval recall@10):\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is FEVEROUS-S at 85.65.\n\nFrom Table 3 (Closed-book setting macro-F1 scores), looking at InstructGPT with Self-Ask on FEVEROUS:\n- Self-Ask: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 29233, "output_tokens": 186, "cost_micros": 90489, "latency_ms": 6452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I'll help you find this information from the document. Let me start by reading the relevant document that was identified as most relevant.Let me continue reading to find information about ProgramFC retrieval recall and InstructGPT performance with Self-Ask.Let me search for \"recall\" to find tables with retrieval metrics:Good! I found information about retrieval recall@10. Let me read the section with Figure 5 and the surrounding content:Perfect! I can see from Figure 5 that the ProgramFC retrieval recall@10 values for different datasets are:\n- HOVER (2-hop): 73.18\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 51.33\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is **85.65** for **FEVEROUS-S**.\n\nNow I need to find the performance of InstructGPT with Self-Ask in the closed-book setting on FEVEROUS-S. Let me search for tables with these results:Let me read Table 1 which shows performance on FEVEROUS-S:This table shows results but doesn't specifically mention InstructGPT with Self-Ask in a closed-book setting. Let me search for \"closed-book\" or \"closed book\":Let me read the section about closed-book setting evaluation:Perfect! I found Table 3 which shows the closed-book setting results. Looking at the table:\n\nFor **FEVEROUS** (which corresponds to FEVEROUS-S in this context):\n- InstructGPT with Self-Ask: **56.82**\n\nLet me verify this is indeed FEVEROUS-S by checking if they use the same notation:The table says \"FEVEROUS\" but earlier in the document they established that they use FEVEROUS-S (the sentence-only subset). Let me verify by checking the dataset description section again:Yes, they use FEVEROUS-S throughout the paper. In Table 3, \"FEVEROUS\" refers to FEVEROUS-S. \n\nNow let me confirm the answer:\n1. From Figure 5, the highest ProgramFC retrieval recall@10 is **85.65** for FEVEROUS-S\n2. From Table 3, InstructGPT with Self-Ask in the closed-book setting on FEVEROUS achieves **56.82**\n\nBut let me double-check this value is actually 56.82 by reading that specific cell again:Perfect! Confirmed. The row for \"Self-Ask\" shows:\n- HOVER 2-hop: 51.54\n- HOVER 3-hop: 51.47\n- HOVER 4-hop: 52.45\n- FEVEROUS: 56.82\n\nThe answer is **56.82**, which in 1 decimal format is **56.8**.\n\nWait, let me reconsider. The question asks for \"1 decimal\" which means I should provide it with 1 decimal place. 56.82 rounded to 1 decimal place would be 56.8.\n\nHowever, looking at the exact value in the table, it shows 56.82. Since they're asking for float format with 1 decimal, I should present it as 56.8.\n\nAnswer: 56.8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84281, "error": null, "extra": {"thread_id": 1945, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f2cf8d23b121431e8f0ffe2a73635d4e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 567, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to FLAN-T5-small.\n\nLooking at Figure 4 on page 7, which shows \"F1 score for fact-checking with gold evidence using FLAN-T5 (blue line) and PROGRAMFC (green line) for language models of increasing sizes.\"\n\nThe figure shows three graphs for:\n1. HOVER (2-hop)\n2. HOVER (3-hop)\n3. HOVER (4-hop)\n\nFor FLAN-T5-small (80M), I can read the values:\n- HOVER (2-hop): FLAN-T5 is around 47.75, ProgramFC is around 64.35\n- HOVER (3-hop): FLAN-T5 is around 49.29, ProgramFC is around 62.23\n- HOVER (4-hop): FLAN-T5 is around 48.59, ProgramFC is around 62.46\n\nThe differences (ProgramFC - FLAN-T5) are:\n- HOVER (2-hop): 64.35 - 47.75 = 16.6\n- HOVER (4-hop): 62.46 - 48.59 = 13.9\n- HOVER (3-hop): 62.23 - 49.29 = 12.9\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 65112, "output_tokens": 350, "cost_micros": 200586, "latency_ms": 12921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4, which shows F1 scores for fact-checking with gold evidence using FLAN-T5 and PROGRAMFC for different model sizes including FLAN-T5-small (80M):\n\nFor HOVER 2-hop:\n- FLAN-T5-small: approximately 47.75\n- ProgramFC with FLAN-T5-small: approximately 64.35\n- Difference: 64.35 - 47.75 = 16.6\n\nFor HOVER 3-hop:\n- FLAN-T5-small: approximately 48.59\n- ProgramFC with FLAN-T5-small: approximately 61.36\n- Difference: 61.36 - 48.59 = 12.8\n\nFor HOVER 4-hop:\n- FLAN-T5-small: approximately 49.29\n- ProgramFC with FLAN-T5-small: approximately 63.05\n- Difference: 63.05 - 49.29 = 13.8\n\nSorting in descending order: 16.6, 13.8, 12.8\n\nAnswer: 16.6, 13.8, 12.8", "citations": [], "input_tokens": 26917, "output_tokens": 288, "cost_micros": 85071, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 13 8, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to find the F1 score differences between ProgramFC and FLAN-T5 using FLAN-T5-small (80M) for gold evidence setting across the three HOVER datasets.\n\nFrom Figure 4:\n- HOVER (2-hop): ProgramFC \u2248 62.23, FLAN-T5 \u2248 47.75, difference = 14.5\n- HOVER (3-hop): ProgramFC \u2248 61.36, FLAN-T5 \u2248 48.59, difference = 12.8\n- HOVER (4-hop): ProgramFC \u2248 63.39, FLAN-T5 \u2248 49.29, difference = 14.1\n\nSorting in descending order:\n\nAnswer: 14.5, 14.1, 12.8", "citations": [], "input_tokens": 29939, "output_tokens": 219, "cost_micros": 93102, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 14 1, 14 5", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence...\n\nIn Figure 4 (Section 4.2), the document discusses comparing performance across different model sizes, including FLAN-T5-small. The text mentions:\n\n\"Our results show that the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nHowever, Figure 4 is referenced but the actual figure with specific F1 scores is not fully visible in the provided text. The document shows results for different hop counts (2-hop, 3-hop, 4-hop) on HOVER dataset in the gold evidence setting.\n\nFrom Table 1, I can see overall results but these are for FLAN-T5-XXL, not FLAN-T5-small.\n\nThe text in Section 4.2 discusses Figure 4 which would contain the specific comparisons with FLAN-T5-small, but the exact numerical values for the F1 score differences are not explicitly stated in the text portions provided.\n\nWithout access to the actual Figure 4 with the specific numerical values, I cannot provide the exact F1 scores requested.\n\nAnswer: Unable to determine from provided text", "citations": [], "input_tokens": 22326, "output_tokens": 321, "cost_micros": 71793, "latency_ms": 9483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided text", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence.\n\nFrom Figure 4, I can extract the following F1 scores for ProgramFC (green line) with FLAN-T5-small (80M):\n- HOVER 2-hop: 64.35\n- HOVER 3-hop: 62.23\n- HOVER 4-hop: 62.46\n\nAnd for FLAN-T5-small (80M):\n- HOVER 2-hop: 47.75\n- HOVER 3-hop: 49.29\n- HOVER 4-hop: 48.59\n\nThe differences (ProgramFC - FLAN-T5-small):\n- HOVER 2-hop: 64.35 - 47.75 = 16.6\n- HOVER 4-hop: 62.46 - 48.59 = 13.9\n- HOVER 3-hop: 62.23 - 49.29 = 12.9\n\nSorting in descending order with one decimal place:\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 29241, "output_tokens": 279, "cost_micros": 91908, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to Flan-T5-small.Let me search for tables containing F1 scores for ProgramFC with gold evidence and Flan-T5-small:Let me read the section with Figure 4 and look for tables with F1 scores:Let me search for tables with gold evidence:Let me read Table 1 which contains F1 scores with gold evidence:Now I have the table data. I can see:\n- FLAN-T5 Gold scores: 73.69 (HOVER 2-hop), 65.66 (HOVER 3-hop), 58.08 (HOVER 4-hop), 90.81 (FEVEROUS-S)\n- ProgramFC (N=5) Gold scores: 75.65 (HOVER 2-hop), 68.48 (HOVER 3-hop), 66.75 (HOVER 4-hop), 92.69 (FEVEROUS-S)\n\nHowever, I need to check if these are specifically for Flan-T5-small. Let me look at Figure 4 data more carefully, as it mentions different sizes of Flan-T5:Looking at the data structure in Figure 4, I need to identify which values correspond to Flan-T5-small (80M) for ProgramFC. The chart shows alternating values. Let me read more of this section:Based on Figure 4 data, I can see there are three charts (HOVER 2-hop, 3-hop, 4-hop) with values at different model sizes (80M, 250M, 780M, 3B, 11B). \n\nLooking at the structure:\n- The first chart (HOVER 2-hop) has values from lines 1130-1148\n- The second chart (HOVER 3-hop) has values from lines 1152-1188 \n- The third chart (HOVER 4-hop) has values from lines 1192-1210\n\nFor 80M (FLAN-T5-small), looking at the ProgramFC values (green line):\n- HOVER 2-hop: 75.65 (line 1132)\n- HOVER 3-hop: 68.48 (line 1156)\n- HOVER 4-hop: 58.08 (line 1198)\n\nWait, let me verify this more carefully by looking at the pattern. The chart shows alternating FLAN-T5 and ProgramFC values. Let me trace through more carefully:\n\nFor HOVER 2-hop at 80M:\n- Lines around 1130-1135 show values near 76-77\n- This corresponds to 80M (first position)\n\nFor HOVER 3-hop at 80M:\n- Starting around line 1152-1158\n- Values around 67-69\n\nFor HOVER 4-hop at 80M:\n- Starting around line 1192-1198\n- Values around 56-58\n\nLooking at the caption again: \"FLAN-T5 (blue line) and PROGRAMFC (green line)\"\n\nFrom the table structure and comparing with Table 1 values (FLAN-T5 has 73.69 for 2-hop, ProgramFC N=5 has 75.65), I can deduce:\n\nFor 80M (FLAN-T5-small) with gold evidence, ProgramFC scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48 \n- HOVER 4-hop: 58.08\n\nIn descending order: 75.65, 68.48, 58.08\n\nHowever, I should check if there are more datasets. Let me verify if FEVEROUS-S is included in this figure:Figure 4 only shows HOVER 2-hop, 3-hop, and 4-hop. Let me check if there's a figure or table specifically showing FEVEROUS-S scores broken down by model size. But based on Table 1, the FEVEROUS-S score for ProgramFC (N=5) with gold evidence is 92.69, and this appears to be using FLAN-T5-XXL based on the context.\n\nLet me search for more information about which model size was used for the main results:Now I need to carefully parse Figure 4 data. The figure shows three charts for HOVER 2-hop, 3-hop, and 4-hop. Looking at the data structure, I see values followed by model sizes (80M, 250M, 780M, 3B, 11B) repeated three times.\n\nLet me trace through the data more systematically. From line 1130 onwards, reading in groups:\n\nHOVER 2-hop chart appears to show pairs of values for each model size (FLAN-T5 then ProgramFC based on the caption mentioning blue and green lines):\n- At 80M: 76.11, 75.65\n- Then 77.62\n\nLet me look for a clearer pattern by examining all the values more carefully:Good! I found key information at lines 1385-1388: \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nThis confirms that Figure 4 shows results using different-sized FLAN-T5 models. Now let me parse the Figure 4 data correctly. Based on the structure and comparing with Table 1:\n\nFrom Table 1, I know:\n- FLAN-T5 gold evidence scores: 73.69 (2-hop), 65.66 (3-hop), 58.08 (4-hop)\n- ProgramFC (N=5) gold evidence scores: 75.65 (2-hop), 68.48 (3-hop), 66.75 (4-hop)\n\nThese match values in Figure 4 data. Now looking at the chart data structure more carefully, the values appear to be organized by model size. Let me trace the 80M (FLAN-T5-small) values for ProgramFC:\n\nLooking at lines 1130-1246, the pattern seems to show data points for both lines (FLAN-T5 and ProgramFC) across multiple model sizes for three different charts.\n\nFrom the visible data and comparing with Table 1 values at line 1148 (73.69) which is FLAN-T5 for HOVER 2-hop, and line 1132 (75.65) which matches ProgramFC (N=5) for HOVER 2-hop:\n\nFor FLAN-T5-small (80M) ProgramFC with gold evidence:\n- HOVER 2-hop: 72.56 (line 1140)\n- HOVER 3-hop: 68.55 (line 1154) \n- HOVER 4-hop: 63.39 (line 1194)\n\nWait, let me reconsider. Looking at the x-axis labels (80M, 250M, 780M, 3B, 11B) appearing three times (once per chart), and the fact that there are two lines per chart (FLAN-T5 and ProgramFC), I need to identify which values correspond to which.\n\nLet me look for clearer structure or additional tables:Figure 4 only shows HOVER datasets (2-hop, 3-hop, 4-hop), not FEVEROUS-S. Let me carefully re-parse the figure data. \n\nLooking at the chart data structure: there are 3 separate line charts, each showing F1 scores for 5 different model sizes. Each chart has 2 lines (FLAN-T5 and ProgramFC).\n\nThe data flows:\n- Lines 1130-1148: HOVER 2-hop chart data\n- Lines 1152-1188: HOVER 3-hop chart data \n- Lines 1192-1210: HOVER 4-hop chart data\n\nFor each chart, there should be approximately 10 data points (2 lines \u00d7 5 model sizes).\n\nLet me map this out for HOVER 2-hop (starting line 1130):\nLooking at the pattern and knowing from Table 1 that ProgramFC (N=5) gets 75.65 for 2-hop (line 1132), this should be for the largest model (11B based on Table 1 context).\n\nActually, let me reconsider the entire approach. The question asks for F1 scores for \"ProgramFC on different datasets with gold evidence in relation to Flan-T5-small\". \n\nThis might mean: the F1 scores when ProgramFC uses Flan-T5-small as its sub-task solver, across different datasets.\n\nFrom Figure 4 caption and the data structure, for 80M (FLAN-T5-small) as the sub-task solver in ProgramFC:\n\nLooking at line 1140 (72.56), 1154 (68.55), 1194 (63.39) - these appear to be the first ProgramFC values in each chart.\n\nBut I need to verify this interpretation. Let me check if there are explicit values stated in the text:From the text at lines 1368-1388, it discusses Figure 4 and mentions that \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers\" achieves certain performance. This confirms Figure 4 shows ProgramFC results when using different sizes of FLAN-T5 as sub-task solvers.\n\nNow I need to carefully extract the FLAN-T5-small (80M) ProgramFC values from Figure 4 data. Let me re-examine the data structure.\n\nBased on the chart showing 5 model sizes (80M, 250M, 780M, 3B, 11B) and 2 lines (FLAN-T5 and ProgramFC), there should be interleaved or grouped values.\n\nLooking at lines 1128-1248, I see numeric values followed by model size labels. The pattern suggests the chart data is organized with Y-axis values first, then X-axis labels.\n\nFor a line chart with 5 points on each of 2 lines, I'd expect 10 Y values per chart. Let me count the values before the first \"80M\" marker at line 1218:\n\nFrom 1130-1216, there appear to be values organized in groups. \n\nLet me try a different approach - look at the actual layout. The values from 1130-1148 before seeing the next section, and knowing Table 1 shows ProgramFC (N=5) has 75.65 for HOVER 2-hop with presumably the larger model:\n\nFirst chart (HOVER 2-hop) - 5 model sizes, 2 lines = 10 points\nLet me identify based on comparing with known Table 1 values. Line 1148 shows 73.69, which exactly matches FLAN-T5 HOVER 2-hop gold from Table 1. This suggests this is for the larger model (XXL/11B).\n\nLooking backward from there and forward, the organization seems to show alternating or separate line data. Since line charts typically show both lines across all X values, let me map:\n\nValues around lines 1130-1148 for HOVER 2-hop\nValues around lines 1152-1188 for HOVER 3-hop\nValues around lines 1192-1210 for HOVER 4-hop\n\nFor 80M (first model size), I need the first pair of values (FLAN-T5 and ProgramFC) for each chart.\n\nGiven the complexity and my uncertainty about the exact data structure, let me look for any other tables or explicit statements:Looking at this continuous stream helps! Let me parse it:\n\n\"80 76.11 75.65 77.62 80 80 72.56 77.07 70 71.69 73.69 70 67.88 68.55 68.48 69.56 70 68.37 68.56 66.75 68.18 64.35 68.24 62.23 66.89 62.46 60 60 63.05 65.07 65.66 60 61.36 63.39 56.58 58.08 50 50 50 47.75 49.29 48.59 40 40 40 80M 250M 780M 3...\"\n\nThe \"80\" values appear to be Y-axis scale markers. Let me focus on the actual data values and model sizes.\n\nParsing more carefully:\n- After the initial scale markers, values like 76.11, 75.65, 77.62 appear\n- Then 72.56, 77.07\n- Then 71.69, 73.69\n- Etc.\n\nKnowing from Table 1 that:\n- FLAN-T5 HOVER 2-hop gold = 73.69 \n- ProgramFC (N=5) HOVER 2-hop gold = 75.65\n\nI can see 73.69 and 75.65 in the data stream. These are likely for the XXL model since Table 1 results use the larger model.\n\nIf the data is organized as alternating FLAN-T5/ProgramFC for each model size from 80M to 11B, then:\n- First pair (80M): 76.11 (FLAN-T5), 75.65 (ProgramFC)? But 75.65 matches Table 1 ProgramFC which should be for the larger model...\n\nThis is confusing. Let me try another interpretation: perhaps all FLAN-T5 values come first, then all ProgramFC values for each chart.\n\nOr perhaps the values are arranged as: HOVER 2-hop FLAN-T5 (all 5 sizes), HOVER 2-hop ProgramFC (all 5 sizes), then HOVER 3-hop data, etc.\n\nWithout seeing the actual visual chart, this is challenging. Let me look for any other references or tables that might clarify:From the stream, 68.48 from Table 1 (ProgramFC N=5 HOVER 3-hop gold) appears right after 68.55.\n\nLet me hypothesize: if the chart shows both lines across all model sizes, and the data is organized left-to-right, then for each X position (model size) moving left to right, we have interleaved FLAN-T5 and ProgramFC Y values.\n\nFor HOVER 2-hop at 80M (first model size):\n- Looking at the early values: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69...\n\nOne pattern could be: pairs of (FLAN-T5, ProgramFC) for 80M, then for 250M, etc.\n- 80M: (76.11, 75.65)? But this doesn't align with Table 1 where 75.65 is the high-model result...\n\nOr: all 5 FLAN-T5 points, then all 5 ProgramFC points.\n\nLet me try: \nHOVER 2-hop FLAN-T5: 76.11, 77.62, 72.56, 71.69, 73.69 (for 80M, 250M, 780M, 3B, 11B)\nHOVER 2-hop ProgramFC: 75.65, 77.07, ... (for 80M, 250M, 780M, 3B, 11B)\n\nBut wait, 73.69 matches Table 1 FLAN-T5 which should be for 11B based on Table 1 being the best model. So if 73.69 is the 5th FLAN-T5 value (11B), then:\nHOVER 2-hop FLAN-T5: 76.11 (80M), 77.62 (250M), 72.56 (780M), 71.69 (3B), 73.69 (11B)\n\nThat doesn't make sense as performance should generally increase with model size, but we see 76.11 \u2192 77.62 \u2192 72.56 which decreases.\n\nLet me reconsider: perhaps the visual layout in the PDF is organized differently than the XML text extraction shows. The values might not be in simple left-to-right order.\n\nGiven the time constraints and complexity, let me make my best educated guess based on patterns:\n\nLooking at Table 1 reference values and Figure 4 caption mentioning these are results across different model sizes, and considering that:\n1. Figure 4 shows HOVER 2-hop, 3-hop, 4-hop only (not FEVEROUS-S)\n2. The question asks for ProgramFC scores with gold evidence for Flan-T5-small across different datasets\n\nThe likely answer based on extracting what appear to be the first ProgramFC values in each section that would correspond to the smallest model (80M):\n- HOVER 2-hop: 72.56\n- HOVER 3-hop: 68.55\n- HOVER 4-hop: 63.39\n\nIn descending order: 72.56 \u2192 68.55 \u2192 63.39\n\nBut let me verify one more time by looking at the complete pattern:Now I see the pattern more clearly! The model size labels (80M, 250M, 780M, 3B, 11B) appear THREE times (lines 1218-1246), once for each of the three charts (HOVER 2-hop, 3-hop, 4-hop).\n\nSo the data structure is:\n- Lines 1130-1148: HOVER 2-hop chart Y-axis values\n- Lines 1152-1188: HOVER 3-hop chart Y-axis values\n- Lines 1192-1210: HOVER 4-hop chart Y-axis values\n- Lines 1218-1246: X-axis labels (model sizes) for all three charts\n\nFor each chart with 2 lines and 5 X-values, I'd expect about 10 Y-values.\n\nHOVER 2-hop (lines 1130-1148): 76.11, 75.65, 77.62, (80, 80 are axis markers), 72.56, 77.07, (70), 71.69, 73.69, (70)\n\nRemoving axis markers: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69\n\nThat's 7 values. For 2 lines with 5 points each = 10 values, so I might be missing some or there's overlap.\n\nLooking at known reference: 73.69 (Table 1 FLAN-T5 2-hop) and 75.65 (Table 1 ProgramFC 2-hop) both appear in this section.\n\nIf arranged as: FLAN-T5 first 5 values, then ProgramFC 5 values:\nFLAN-T5: 76.11 (80M), 75.65 (250M), 77.62 (780M), 72.56 (3B), 77.07 (11B)? \nBut 73.69 should be FLAN-T5 and it's not in this sequence...\n\nAlternative: interleaved pairs:\n- (76.11, 75.65) = (FLAN-T5, ProgramFC) at 80M\n- (77.62, 72.56) = (FLAN-T5, ProgramFC) at 250M \n- (77.07, 71.69) = (FLAN-T5, ProgramFC) at 780M\n- (73.69, ???) = (FLAN-T5, ProgramFC) at some size\n\nBut this only gives 4 pairs...\n\nLet me try yet another approach. Reading the values without the axis scale markers (70, 80):\n76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69 for HOVER 2-hop\n\nIf this represents 2 lines of 5 points each minus 3 values (perhaps some overlap at endpoints or data points), let me see if the pattern makes sense as:\n\nLine 1 (FLAN-T5): 71.69, 73.69, 72.56, 77.07, 76.11 (in size order 80M to 11B)? Reverse order?\nLine 2 (ProgramFC): 75.65, 77.62, ..., ..., ... \n\nThis is getting too speculative. Let me use logical reasoning instead:\n\n1. Table 1 shows ProgramFC (N=5) achieves 75.65, 68.48, 66.75 for HOVER 2/3/4-hop with gold evidence\n2. These are the best results, so likely using the largest model (11B/XXL)\n3. Figure 4 shows how performance varies with model size for both FLAN-T5 and ProgramFC\n4. The text states that FLAN-T5-small (80M) ProgramFC can achieve comparable performance to the large model for 4-hop claims\n\nLooking at 4-hop values (lines 1192-1210): 61.36, 63.39, 56.58, 58.08, (50s), 47.75, 49.29, 48.59, (40s)\n\n58.08 appears here and also matches Table 1 FLAN-T5 4-hop gold.\n66.75 (ProgramFC 4-hop from Table 1) does NOT appear in this 4-hop section, which is strange...\n\nUnless Table 1 uses N=5 program aggregation while Figure 4 uses N=1? Let me check:\n\nLooking back at Table 1, there's both \"ProgramFC (N=1)\" and \"ProgramFC (N=5)\". \n\nProgramFC (N=1) gold scores from Table 1:\n- HOVER 2-hop: 74.10\n- HOVER 3-hop: 66.13\n- HOVER 4-hop: 65.69\n\nProgramFC (N=5) gold scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48\n- HOVER 4-hop: 66.75\n\nLet me check if 74.10, 66.13, or 65.69 appear in Figure 4 data... No, they don't appear in the extracted values.\n\nGiven this complexity and the fact that the visual chart layout isn't perfectly captured in the XML extraction, let me make my best inference:\n\nFor FLAN-T5-small (80M) as sub-task solvers in ProgramFC with gold evidence:\n- The first ProgramFC value in each chart section should correspond to 80M\n- Based on positioning and eliminating known FLAN-T5 values from Table 1:\n\nHOVER 2-hop: 72.56\nHOVER 3-hop: 68.55\nHOVER 4-hop: 63.39\n\nIn descending order: 72.6, 68.6, 63.4 (to one decimal place)\n\nAnswer: 72.6, 68.6, 63.4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 174562, "error": null, "extra": {"thread_id": 1946, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b81fd9ccbe6f4839823c9313fb619eb2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2087, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "63 4, 68 6, 72 6", "normalised_gold": "62 2, 62 4, 64 3"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 on page 5, I can see the symbolic solvers used for different datasets:\n- Pyke: ProntoQA, ProofWriter\n- Prover9: FOLIO\n- python-constraint: LogicalDeduction\n- Z3: AR-LSAT\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section on page 6:\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAlso, Table 5 in Appendix B on page 14 confirms:\n- FOLIO has a Test Size of 204\n\nAnswer: 204", "citations": [], "input_tokens": 53261, "output_tokens": 253, "cost_micros": 163578, "latency_ms": 13937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProntoQA and ProofWriter use Pyke\n- FOLIO uses Prover9\n- LogicalDeduction uses python-constraint\n- AR-LSAT uses Z3\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 22914, "output_tokens": 223, "cost_micros": 72087, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nFrom Table 1 in the document, I can see that Prover9 is used as the solver for the FOLIO dataset, which is categorized under \"First-Order Logic\" reasoning.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at Table 5 \"Statistics of the logical reasoning datasets\" in Appendix B:\n\nFOLIO:\n- Reasoning: FOL\n- Test Size: 204\n- #Opts: 3\n\nThe document also confirms in the main text (Section 4, Datasets): \"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 26784, "output_tokens": 232, "cost_micros": 83832, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProofWriter uses \"ProntoQA, Pyke\"\n- FOLIO uses \"Prover9\"\n- LogicalDeduction uses \"phn-constraint\"\n- AR-LSAT uses \"Z3\"\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the solutions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 20095, "output_tokens": 232, "cost_micros": 63765, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find which dataset uses Prover9 as the symbolic solver and how many examples are in its test set.\n\nFrom Table 1 (page 3810), I can see that FOLIO uses Prover9 as its symbolic solver under the \"FOL\" (First-Order Logic) problem type.\n\nFrom Table 5 in Appendix B (page 3819), which shows dataset statistics, I can see:\n- FOLIO has a Test Size of 204\n\nThis is also confirmed in the text on page 3811 where it states: \"We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 25913, "output_tokens": 159, "cost_micros": 80124, "latency_ms": 5094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about a dataset that uses Prover9 as the symbolic solver and determine how many examples are used for evaluation.Let me search for \"Prover9\" in the document to find the relevant information:Now let me search for sections that discuss datasets and evaluation:Let me read the section about datasets more carefully. Let me look for \"ProofWriter\" which seems to be mentioned and might use Prover9:Let me search more specifically for information about which dataset uses Prover9:I see \"First-order Logic Prover\" mentioned. Let me search for more specific information about datasets and their properties:Now let me look at the appendix section that might have more details about data statistics and which solver is used for which dataset:Let me search for tables or sections that describe which solver is used for each dataset:Great! I found a reference to Table 1 which summarizes the symbolic solvers used for each dataset. Let me read that table:Perfect! I found Table 1 which shows that **Prover9** is used as the symbolic solver for the **FOLIO** dataset. Now I need to find how many examples from FOLIO are used for evaluation. Let me go back to the section where FOLIO is described:Perfect! I found the answer. According to the document:\n\n1. From Table 1 (lines 846-849): **Prover9** is the symbolic solver used for the **FOLIO** dataset.\n2. From the dataset description (lines 1024-1026): \"We use the entire FOLIO test set for evaluation, consisting of **204 examples**.\"\n\nAnswer: 204", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64250, "error": null, "extra": {"thread_id": 1947, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e89f611945d040b28ca2dcfe99b6b2c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 410, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5 (Page 9):**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\" Netflix shows.\n- The predicted answer is shown as: B\n- No indication of whether this is correct or incorrect is given in the figure.\n\n**Figure 6 (Page 19):**\nThis shows 4 examples with predicted answers:\n\n1. **Example 1** (Wild turkeys): \n - Predicted answer: C \u2192 A\n - The arrow indicates the prediction changed from C to A\n\n2. **Example 2** (Legend of Zelda):\n - Predicted answer: A\n - No indication of correctness shown\n\n3. **Example 3** (Golf tournament):\n - Predicted answer: E \u2192 B\n - The arrow indicates the prediction changed from E to B\n\n4. **Example 4** (Restaurant/Subway):\n - Predicted answer: C \u2192 A\n - The arrow indicates the prediction changed from C to A\n\nLooking at the figure caption: \"Examples of generated symbolic representations and predicted answers. The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThe red and green markings indicate errors in the symbolic formulation, not whether the final answer was correct. The figure shows the symbolic representations had errors that were corrected, but doesn't explicitly state which final answers were correct.\n\nHowever, based on the examples shown, I cannot definitively determine from the information provided how many predictions were actually correct, as the figure focuses on showing the symbolic formulation errors rather than answer correctness.\n\nSince I cannot determine this with certainty from the document, I need to reconsider. The document shows 5 total examples (1 in Figure 5, 4 in Figure 6) but doesn't explicitly mark which final predictions were correct.\n\nAnswer: 0", "citations": [], "input_tokens": 53263, "output_tokens": 443, "cost_micros": 166434, "latency_ms": 15125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "answer_line", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThere is 1 example shown with:\n- Problem about \"Stranger Things\" and \"Black Mirror\"\n- Predicted answer: B\n- No indication that this is incorrect\n\n**Figure 6:**\nThere are 4 examples shown:\n\n**Example 1:** Wild turkeys problem\n- Predicted answer: \u20ac > A (this appears to be garbled text)\n- The arrow pointing to \"A\" suggests the correct answer should be A\n- The red marking indicates this is incorrect\n\n**Example 2:** Legend of Zelda problem\n- Predicted answer: A\n- No red markings indicating error\n- This appears correct\n\n**Example 3:** Golf tournament problem\n- Predicted answer: + + B (garbled)\n- The arrow pointing to \"B\" and red marking indicates this is incorrect\n\n**Example 4:** Restaurant/Subway problem\n- Predicted answer: ( + A (garbled)\n- The arrow pointing to \"A\" and red marking indicates this is incorrect\n\nFrom the caption of Figure 6: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis indicates that Figure 6 shows ERROR cases where Logic-LM made mistakes.\n\nFigure 5 shows a successful case (no red markings).\n\nSo:\n- Figure 5: 1 correct\n- Figure 6: 0 correct (all 4 are error examples)\n\nTotal correct: 1\n\nAnswer: 1", "citations": [], "input_tokens": 22916, "output_tokens": 365, "cost_micros": 74223, "latency_ms": 9516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to check each example in Figure 5 and Figure 6 to see if Logic-LM predicted the correct answer.\n\n**Figure 5:**\n- Problem about wild turkeys and Tom\n- Question: \"Is the following statement true, false, or uncertain? 'Black Mirror' is popular.\"\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: B\n- Looking at the logic: The problem states \"Karen does not download 'Black Mirror'\" and \"If and only if Karen binge-watches a Netflix show, she will download it.\" This means Karen doesn't binge-watch Black Mirror. Since \"If a Netflix show is popular, Karen will binge-watch it,\" this means Black Mirror is NOT popular.\n- So B (False) is correct. \u2713\n\n**Figure 6:**\n\n**Example 1:**\n- About Tom the wild turkey\n- Question: Is Tom an Ocellated wild turkey?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A (marked with ++)\n- Logic: Tom is a wild turkey but NOT Eastern, Osceola, Gould's, Merriam's, or Rio Grande. Since there are only 6 types and Tom is eliminated from 5, he must be Ocellated.\n- A (True) is correct. \u2713\n\n**Example 2:**\n- About Legend of Zelda\n- Question: Is it in the Top 10 list?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A\n- Logic: Legend of Zelda sold more than 1 million copies \u2192 selected into Top 10 list.\n- A (True) is correct. \u2713\n\n**Example 3:**\n- Golf tournament with 5 golfers\n- Question: Which finished third?\n- Options: (A) Rob, (B) Eve, (C) Eli, (D) Amy, (E) Dan\n- Predicted answer: B (marked with =>)\n- But there's an error in the constraints shown: \"Dan > Eve > Dan < Eve\" which is contradictory\n- The problem states: Dan finished 2nd, Dan", "citations": [], "input_tokens": 26786, "output_tokens": 512, "cost_micros": 88038, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\". The predicted answer is B, and there's no indication this is wrong, so I'll assume it's correct: **1 correct**\n\n**Figure 6 (labeled as \"Example Generations of LOGIC-LM\"):**\nThis contains 4 examples:\n\n1. **Example 1** (Wild turkey problem): \n - Predicted answer: C \u2192 A\n - The arrow notation indicates the prediction was C (Uncertain) but should have been A (True)\n - **Incorrect**\n\n2. **Example 2** (Legend of Zelda problem):\n - Predicted answer: A\n - No arrow or correction shown, indicating this is correct\n - **Correct**\n\n3. **Example 3** (Golf tournament problem):\n - Predicted answer: E \u2192 B\n - The arrow indicates the prediction was E (Dan finished third) but should have been B (Eve finished third)\n - **Incorrect**\n\n4. **Example 4** (Restaurant/Subway problem):\n - Predicted answer: C \u2192 A\n - The arrow indicates the prediction was C (Uncertain) but should have been A (True)\n - **Incorrect**\n\n**Total count:**\n- Figure 5: 1 correct\n- Figure 6: 1 correct (Example 2 only)\n\n**Total: 2 correct predictions**\n\nAnswer: 2", "citations": [], "input_tokens": 20097, "output_tokens": 361, "cost_micros": 65706, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many examples in Figure 5 and Figure 6 have Logic-LM predicting the correct answer.\n\n**Figure 5:**\n- Example 1: Predicted answer is B (shown as correct based on the context)\n- Count: 1 correct\n\n**Figure 6:**\n- Example 1: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\n- Example 2: Predicted answer is A (shown as correct, no corrections indicated)\n- Count: 1 correct\n\n- Example 3: Predicted answer changed from E to B. The arrows indicate E was wrong and B is the correction, meaning the final prediction is B (correct).\n- Count: 1 correct\n\n- Example 4: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\nTotal examples with correct predictions: 1 (Figure 5) + 4 (Figure 6) = 5\n\nAnswer: 5", "citations": [], "input_tokens": 25915, "output_tokens": 263, "cost_micros": 81690, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I'll read the document to find Figures 5 and 6 and count how many examples Logic-LM predicted correctly.Let me search for Figure 5 and Figure 6 in the document:Let me search for the actual content around these figures:Now let me look for Figure 6 in the appendix:I need to find the actual content of Figure 6 with the examples. Let me search more carefully:Now let me look back at Figure 5 to get the complete example:Perfect! Now I have both figures. Let me analyze them:\n\n**Figure 5** (lines 1612-1645): Contains 1 example about \"Black Mirror\" and \"Stranger Things\"\n- Predicted answer: B\n- I need to check if this is correct by looking at the problem logic\n\n**Figure 6** (lines 2993-3146): Contains 4 examples:\n1. Example 1 (lines 2994-3018): Wild turkey problem - Predicted answer: A (with ++ A notation, but marked with red X's indicating errors)\n2. Example 2 (lines 3021-3044): Legend of Zelda problem - Predicted answer: A\n3. Example 3 (lines 3047-3109): Golf tournament problem - Predicted answer: B (with => B notation, and red X's indicating errors)\n4. Example 4 (lines 3115-3146): Subway restaurant problem - Predicted answer: A (with <> A notation, and red X's with checkmarks indicating errors)\n\nAccording to the caption at line 3148-3149: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis means Figure 6 shows ERROR CASES where Logic-LM made mistakes. The red X's (\u2612) indicate incorrect segments.\n\nFor Figure 5, I need to verify if the answer is correct. Looking at the logic:\n- \"Black Mirror\" is a Netflix show\n- Karen does not download \"Black Mirror\"\n- If and only if Karen binge-watches a show, she will download it\n- Therefore, Karen does not binge-watch \"Black Mirror\"\n- If a Netflix show is popular, Karen will binge-watch it\n- Since Karen doesn't binge-watch \"Black Mirror\", it is NOT popular\n- Answer B (False) appears to be CORRECT\n\nSo the count is:\n- Figure 5: 1 example, 1 correct (Example about Black Mirror)\n- Figure 6: 4 examples, 1 correct (Example 2 - Legend of Zelda), 3 incorrect (Examples 1, 3, and 4 have red X marks)\n\nTotal correct: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54812, "error": null, "extra": {"thread_id": 1948, "search_space_id": 55, "answer_letter_strategy": "answer_line", "user_message_id": "msg_4ece07b065344ab588935735051340b9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 440, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\" starting on page 14:\n\nThe appendix states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThe sections are:\n- C.1 PrOntoQA Prompts (3 types: Standard, CoT, Logic-LM)\n- C.2 ProofWriter Prompts (3 types: Standard, CoT, Logic-LM)\n- C.3 FOLIO Prompts (3 types: Standard, CoT, Logic-LM)\n- C.4 LogicalDeduction Prompts (3 types: Standard, CoT, Logic-LM)\n- C.5 AR-LSAT Prompts (3 types: Standard, CoT, Logic-LM)\n\nEach subsection (C.1 through C.5) contains 3 prompt types (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM).\n\nSo there are 5 datasets \u00d7 3 prompt types = 15 prompt examples total.\n\nAnswer: 15", "citations": [], "input_tokens": 53247, "output_tokens": 276, "cost_micros": 163881, "latency_ms": 13389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\", I can see it covers prompts for different datasets:\n\n1. **C.1 PrOntoQA Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n2. **C.2 ProofWriter Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n3. **C.3 FOLIO Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n4. **C.4 LogicalDeduction Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\n5. **C.5 AR-LSAT Prompts** - Contains 3 prompt types:\n - Standard In-Context Learning (1 example)\n - Chain-of-Thought Prompting (1 example)\n - Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 22900, "output_tokens": 398, "cost_micros": 74670, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples given in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\":\n\n**C.1 PrOntoQA Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 LogicalDeduction Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompting methods = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 26770, "output_tokens": 323, "cost_micros": 85155, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in the Appendix section of the document.\n\nLooking at the Appendix sections:\n\n**Section C: Prompt Examples**\n\nThe document states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThen it provides:\n\n**C.1 PrOntoQA Prompts:**\n- Standard In-Context Learning (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 Logical Deduction Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count:\n- C.1: 2 examples\n- C.2: 3 examples\n- C.3: 3 examples\n- C.4: 3 examples\n- C.5: 3 examples\n\nTotal: 2 + 3 + 3 + 3 + 3 = 14 examples\n\nAnswer: 14", "citations": [], "input_tokens": 20081, "output_tokens": 407, "cost_micros": 66348, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C (Prompt Examples), I can see it's organized by dataset:\n\n**C.1 PrOntoQA Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.2 ProofWriter Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.3 FOLIO Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.4 LogicalDeduction Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.5 AR-LSAT Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 25899, "output_tokens": 346, "cost_micros": 82887, "latency_ms": 6280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about prompt examples in the Appendix.Let me search for the Appendix section in the document to find information about prompt examples.Now let me read the section with prompt examples to count them. Let me find where this content is located.Let me search for the Appendix C section with prompt examples more directly:Let me continue to search for all the sections to count the prompt examples. Based on what I've seen so far, I need to count the subsections in Appendix C.Perfect! I can see there are 5 subsections in Appendix C:\n- C.1 PrOntoQA Prompts\n- C.2 ProofWriter Prompts \n- C.3 FOLIO Prompts\n- C.4 LogicalDeduction Prompts\n- C.5 AR-LSAT Prompts\n\nEach subsection shows prompts for 3 different methods (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM), and according to the introduction text, each shows \"only a single demonstration for each setting.\"\n\nSo the count is: 5 datasets \u00d7 3 methods = 15 prompt examples in Appendix C.\n\nAnswer: 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1949, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_28321722a0164737934f82e2d1d378ae", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 318, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the performance comparison between Logic-LM and the two baseline models (Standard and CoT) when using GPT-4 as the base language model.\n\nLooking at Table 2, which shows the accuracy results for different models across five datasets, I can see the GPT-4 (gpt-4) columns:\n\n1. **PrOntoQA**: \n - Standard: 77.40\n - CoT: 98.79\n - Logic-LM: 83.20\n - Logic-LM does NOT outperform CoT (98.79 > 83.20), but does outperform Standard\n\n2. **ProofWriter**:\n - Standard: 52.67\n - CoT: 68.11\n - Logic-LM: 79.66\n - Logic-LM outperforms both baselines \u2713\n\n3. **FOLIO**:\n - Standard: 69.11\n - CoT: 70.58\n - Logic-LM: 78.92\n - Logic-LM outperforms both baselines \u2713\n\n4. **LogicalDeduction**:\n - Standard: 71.33\n - CoT: 75.25\n - Logic-LM: 87.63\n - Logic-LM outperforms both baselines \u2713\n\n5. **AR-LSAT**:\n - Standard: 33.33\n - CoT: 35.06\n - Logic-LM: 43.04\n - Logic-LM outperforms both baselines \u2713\n\nLogic-LM outperforms BOTH baseline models in 4 out of 5 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 53272, "output_tokens": 424, "cost_micros": 166176, "latency_ms": 23500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, I need to compare Logic-LM's performance against both Standard and CoT baselines for GPT-4 across all 5 datasets.\n\nFor GPT-4:\n\n1. **PrOntoQA**: \n - Logic-LM: 83.20\n - Standard: 77.40 \u2713\n - CoT: 98.79 \u2717\n - Logic-LM outperforms Standard only\n\n2. **ProofWriter**:\n - Logic-LM: 79.66\n - Standard: 52.67 \u2713\n - CoT: 68.11 \u2713\n - Logic-LM outperforms both\n\n3. **FOLIO**:\n - Logic-LM: 78.92\n - Standard: 69.11 \u2713\n - CoT: 70.58 \u2713\n - Logic-LM outperforms both\n\n4. **LogicalDeduction**:\n - Logic-LM: 87.63\n - Standard: 71.33 \u2713\n - CoT: 75.25 \u2713\n - Logic-LM outperforms both\n\n5. **AR-LSAT**:\n - Logic-LM: 43.04\n - Standard: 33.33 \u2713\n - CoT: 35.06 \u2713\n - Logic-LM outperforms both\n\nLogic-LM outperforms both baseline models in 4 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 22925, "output_tokens": 391, "cost_micros": 74640, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Table 2 to compare Logic-LM's performance against the two baselines (Standard and CoT) when using GPT-4.\n\nFrom Table 2, the GPT-4 results are:\n\n**PrOntoQA:**\n- Standard: 77.40\n- CoT: 98.79\n- Logic-LM: 83.20\n- Logic-LM does NOT outperform both (CoT is better)\n\n**ProofWriter:**\n- Standard: 52.67\n- CoT: 68.11\n- Logic-LM: 79.66\n- Logic-LM outperforms both \u2713\n\n**FOLIO:**\n- Standard: 69.11\n- CoT: 70.58\n- Logic-LM: 78.92\n- Logic-LM outperforms both \u2713\n\n**LogicalDeduction:**\n- Standard: 71.33\n- CoT: 75.25\n- Logic-LM: 87.63\n- Logic-LM outperforms both \u2713\n\n**AR-LSAT:**\n- Standard: 33.33\n- CoT: 35.06\n- Logic-LM: 43.04\n- Logic-LM outperforms both \u2713\n\nLogic-LM outperforms both baseline models on 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 26795, "output_tokens": 332, "cost_micros": 85365, "latency_ms": 7342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2 in the document, I need to compare Logic-LM's performance against Standard LLMs and Chain-of-Thought (CoT) for GPT-4 across all datasets.\n\nFrom Table 2, the GPT-4 results show:\n\n1. **PrOntoQA**: \n - Standard: 99.4%\n - CoT: 84.9%\n - Logic-LM: 100.0%\n - Logic-LM outperforms both \u2713\n\n2. **ProofWriter**:\n - Standard: 87.3%\n - CoT: 73.6%\n - Logic-LM: 99.0%\n - Logic-LM outperforms both \u2713\n\n3. **FOLIO**:\n - Standard: 66.7%\n - CoT: 61.8%\n - Logic-LM: 79.9%\n - Logic-LM outperforms both \u2713\n\n4. **LogicalDeduction**:\n - Standard: 11.3%\n - CoT: 57.7%\n - Logic-LM: 60.0%\n - Logic-LM outperforms both \u2713\n\n5. **AR-LSAT**:\n - Standard: 32.6%\n - CoT: 60.0%\n - Logic-LM: 39.8%\n - Logic-LM does NOT outperform CoT (39.8% < 60.0%) \u2717\n\nLogic-LM outperforms both baseline models in 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 20106, "output_tokens": 440, "cost_micros": 66918, "latency_ms": 8265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2, for GPT-4 (gpt-4):\n\n1. **PrOntoQA**: \n - Standard: 77.40\n - CoT: 98.79\n - Logic-LM: 83.20\n - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**:\n - Standard: 52.67\n - CoT: 68.11\n - Logic-LM: 79.66\n - Logic-LM outperforms both (79.66 > 68.11 > 52.67) \u2713\n\n3. **FOLIO**:\n - Standard: 69.11\n - CoT: 70.58\n - Logic-LM: 78.92\n - Logic-LM outperforms both (78.92 > 70.58 > 69.11) \u2713\n\n4. **LogicalDeduction**:\n - Standard: 71.33\n - CoT: 75.25\n - Logic-LM: 87.63\n - Logic-LM outperforms both (87.63 > 75.25 > 71.33) \u2713\n\n5. **AR-LSAT**:\n - Standard: 33.33\n - CoT: 35.06\n - Logic-LM: 43.04\n - Logic-LM outperforms both (43.04 > 35.06 > 33.33) \u2713\n\nLogic-LM outperforms both baselines on 4 datasets: ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT.\n\nAnswer: 4", "citations": [], "input_tokens": 25924, "output_tokens": 456, "cost_micros": 84612, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about Logic-LM's performance compared to baseline models when using GPT-4.Let me continue reading to find information about GPT-4 performance comparisons.Let me search for tables or sections that compare GPT-4 performance across datasets.Now let me read the table with GPT-4 results to count the datasets where Logic-LM outperforms baselines.Let me look at the table header to understand the structure better.Perfect! Now I have the complete table. Let me analyze the GPT-4 results:\n\nFor GPT-4 (the last 3 columns):\n- Standard, CoT, Logic-LM\n\nThe two baseline models are:\n1. Standard prompting\n2. Chain-of-thought (CoT) prompting\n\nI need to count how many datasets where Logic-LM (without self-refinement) outperforms BOTH baselines when using GPT-4.\n\nLet me check each dataset:\n\n1. **PrOntoQA**: Logic-LM = 83.20, Standard = 77.40, CoT = 98.79\n - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**: Logic-LM = 79.66, Standard = 52.67, CoT = 68.11\n - Logic-LM > Standard (79.66 > 52.67) \u2713\n - Logic-LM > CoT (79.66 > 68.11) \u2713\n - **Outperforms both** \u2713\n\n3. **FOLIO**: Logic-LM = 78.92, Standard = 69.11, CoT = 70.58\n - Logic-LM > Standard (78.92 > 69.11) \u2713\n - Logic-LM > CoT (78.92 > 70.58) \u2713\n - **Outperforms both** \u2713\n\n4. **LogicalDeduction**: Logic-LM = 87.63, Standard = 71.33, CoT = 75.25\n - Logic-LM > Standard (87.63 > 71.33) \u2713\n - Logic-LM > CoT (87.63 > 75.25) \u2713\n - **Outperforms both** \u2713\n\n5. **AR-LSAT**: Logic-LM = 43.04, Standard = 33.33, CoT = 35.06\n - Logic-LM > Standard (43.04 > 33.33) \u2713\n - Logic-LM > CoT (43.04 > 35.06) \u2713\n - **Outperforms both** \u2713\n\nLogic-LM (without self-refinement) outperforms both baseline models on 4 datasets when using GPT-4 as the base language model.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44437, "error": null, "extra": {"thread_id": 1950, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cfca2042672b455980d6e47d98e5f851", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nFrom Table 1 on page 5, I can see that for \"Deductive Reasoning\" problems (which corresponds to the LP - Logic Programming formulation), the solver used is **Pyke**.\n\nThis is further confirmed in Section 3.2 on page 5, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 53262, "output_tokens": 128, "cost_micros": 161706, "latency_ms": 10532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver implements the logic programming module.\n\nIn Table 1 on page 3810, the paper summarizes the symbolic solvers used for different problem types. For \"Deductive Reasoning\" which uses \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 22915, "output_tokens": 137, "cost_micros": 70800, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nIn Table 1 on page 3810, the paper shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems that use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 26785, "output_tokens": 150, "cost_micros": 82605, "latency_ms": 6202, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about the symbolic solver used for logic programming...\n\nIn Table 1 on page 3805, the document shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems (which correspond to the logic programming formulation mentioned in Figure 1), the solver listed is \"ProntoQA, Pyke\".\n\nAdditionally, in Section 3.2 and footnotes, the document references Pyke as the logic programming engine. In Section D (page 3823), it explicitly states: \"For PrOntoQA and ProofWriter, the Pyke logic programming engine returns the inferred value...\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 20096, "output_tokens": 158, "cost_micros": 62658, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver is used for the logic programming module.\n\nFrom Table 1 on page 3810, I can see that for \"Deductive Reasoning\" problems which use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states:\n\n\"**LP System.** For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 25914, "output_tokens": 143, "cost_micros": 79887, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the symbolic solver used for the logic programming module in Figure 1.Let me search for information about Figure 1 and the symbolic solver:Let me read the document content to find references to Figure 1 and the solver:Good! I can see Figure 1 is mentioned. Now let me search for specific mentions of the solver implementation:I can see references to solvers. Let me look at line 711 where it mentions \"logic programming engine\" and around line 823 where Table 1 is mentioned:Perfect! I found Table 1. Let me continue reading to see all the solvers listed:Perfect! I found the information. Looking at Table 1 and the description in section 3.2, I can see that:\n\n- For **Deductive Reasoning** (Logic Programming / LP), the solver used is **Pyke**\n- For **First-Order Logic** (FOL), the solver used is **Prover9**\n- For **Constraint Satisfaction** (CSP), the solver used is **python-constraint**\n- For **Analytical Reasoning** (SAT), the solver used is **Z3**\n\nNow, let me verify which one corresponds to the \"logic programming module\" mentioned in Figure 1. Based on the document:\n\n1. Figure 1 shows the overall LOGIC-LM framework with a \"Symbolic Reasoner\" component\n2. Section 3.2 describes the symbolic reasoner and mentions that for deductive reasoning using the Logic Programming (LP) language, they use **Pyke**\n3. Section 3.2.1 specifically states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nThe logic programming module shown in Figure 1 is implemented using **Pyke**.\n\nAnswer: Pyke", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58280, "error": null, "extra": {"thread_id": 1951, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_708b2982536441eabb41dbb732343ffa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the datasets table (Table 5 on page 14), I can see the following datasets with their number of options:\n\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options\n- AR-LSAT: 5 options\n\nAR-LSAT has 5 options for all questions. Looking at the references and dataset descriptions, AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions\" and the test set has \"231 multiple-choice questions.\"\n\nFrom the paper text: \"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 53272, "output_tokens": 258, "cost_micros": 163686, "latency_ms": 21859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 1 and the dataset descriptions in Section 4:\n\n- **PrOntoQA (2023)**: 2 options (True/False)\n- **ProofWriter (2021)**: 3 options\n- **FOLIO (2022)**: 3 options\n- **LogicalDeduction (2022)**: Variable options (3, 5, or 7 according to Table 5)\n- **AR-LSAT (2022)**: 5 options\n\nFrom the paper text (Section 4):\n\"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnd from Table 5, AR-LSAT has \"#Opts: 5\".\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 22925, "output_tokens": 253, "cost_micros": 72570, "latency_ms": 7828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for datasets proposed in 2022 with 5-option multiple-choice questions:\n\nFrom Table 5 (Dataset Statistics):\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (varies)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions.\"\n- Table 5 confirms AR-LSAT has 5 options (#Opts column)\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 26795, "output_tokens": 198, "cost_micros": 83355, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find datasets that:\n1. Were proposed in 2022\n2. Have all problems as multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the number of options (#Opts) for each dataset:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (variable)\n- AR-LSAT: 5 options\n\nFrom the Datasets section (Section 4), I can find when each dataset was created:\n- PrOntoQA: 2023 (Saparov and He, 2023)\n- ProofWriter: 2021 (Tafjord et al., 2021)\n- FOLIO: 2022 (Han et al., 2022)\n- LogicalDeduction: 2022 (from BigBench - Srivastava et al., 2022)\n- AR-LSAT: 2022 (Zhong et al., 2022)\n\nAmong the 2022 datasets:\n- FOLIO has 3 options\n- LogicalDeduction has variable options (3, 5, or 7)\n- AR-LSAT has 5 options\n\nOnly AR-LSAT consistently has 5 options for all questions.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 20106, "output_tokens": 343, "cost_micros": 65463, "latency_ms": 7766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the \"#Opts\" column which shows the number of options:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3,5,7 options (varying)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT is described as collecting \"all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\"\n- The citation is \"Zhong et al. 2022\"\n\nLooking at the references, AR-LSAT is cited as:\n\"Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, and Nan Duan. 2022. Analytical reasoning of text.\"\n\nAR-LSAT is the only dataset with consistently 5 options and was published in 2022.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 25924, "output_tokens": 307, "cost_micros": 82377, "latency_ms": 8733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about datasets used in the paper, focusing on those proposed in 2022 with multiple-choice questions having 5 options.Let me search for information about the datasets used in this paper, particularly focusing on those from 2022 and their characteristics.I can see references to datasets from 2022. Let me search for information about AR-LSAT and other datasets, particularly focusing on their characteristics regarding multiple-choice questions with 5 options.Let me search for more specific information about the datasets, particularly looking for details about multiple-choice options.Let me read more about each dataset to find their characteristics. Let me look at the section describing LogicalDeduction.Let me search for information about LogicalDeduction specifically and check the appendix for detailed data statistics.Let me search for \"Appendix B\" which was mentioned as containing detailed data statistics.Perfect! I found the information I need in Table 5. Let me verify the details about AR-LSAT:\n\nFrom the table:\n- AR-LSAT has 5 options (#Opts: 5)\n- AR-LSAT was proposed by Zhong et al., 2022\n\nLet me verify that AR-LSAT is specifically about logical reasoning and all problems are multiple-choice.Based on the document:\n\n1. AR-LSAT was proposed by Zhong et al., 2022\n2. It collects all **analytical logic reasoning questions** from the Law School Admission Test\n3. The test set has 231 **multiple-choice questions**\n4. From Table 5 (Appendix B), AR-LSAT has **5 options** (#Opts: 5)\n\nThis matches all the criteria in the question: proposed in 2022, logical reasoning problems, all multiple-choice questions with 5 options.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69453, "error": null, "extra": {"thread_id": 1952, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4c068961fe9145d382e57492c359de62", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 452, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about application periods for different groups of applicants in this undergraduate prospectus.\n\nLooking at page 25 (document page 47), I can see a table titled \"APPLICATION PERIOD AND FEE\" that shows different application groups and their respective application periods.\n\nFrom this table, I can see the following end dates for different application groups:\n\n1. Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End: 21 Feb 2024\n3. NUS High School Diploma: End: 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n5. Part-Time B.Eng: End: 15 Jan 2024\n6. Part-Time B.Tech in Computing: End: 15 Jan 2024\n\nComparing these dates, the latest end date is 19 Mar 2024, which applies to two groups:\n- Singapore-Cambridge GCE 'A' Level applicants\n- International Baccalaureate (IB) Diploma applicants\n\nAnswer: Singapore-Cambridge GCE 'A' Level applicants, International Baccalaureate (IB) Diploma applicants", "citations": [], "input_tokens": 60375, "output_tokens": 307, "cost_micros": 185730, "latency_ms": 53796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma applicants, singapore cambridge gce level applicants", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the APPLICATION PERIOD AND FEE table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n\n1. **Singapore-Cambridge GCE 'A' Level**: End: 19 Mar 2024\n2. **Diploma Awarded by a Polytechnic or equivalent**: End: 20 Jan 2024\n3. **NUS High School Diploma**: End: 19 Mar 2024\n4. **International Baccalaureate (IB) Diploma**: End: 19 Mar 2024\n5. **Part-Time B.Eng**: End date not fully specified (appears cut off)\n6. **Part-Time B.Tech in Computing**: End: 21 Feb 2024\n\nFrom the complete dates shown, the latest end date is **19 Mar 2024**, which applies to three groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, NUS High School Diploma, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 15947, "output_tokens": 244, "cost_micros": 51501, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, nus high school diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on pages 46-47:\n\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng and Part-Time B.Tech: End 15 Jan 2024\n\nThe latest end date is 19 March 2024, which applies to two groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 19386, "output_tokens": 173, "cost_micros": 60753, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on the document, I need to identify which application groups have the latest end date.\n\nFrom the \"APPLICATION PERIOD AND FEE\" table:\n\n1. Singapore-Cambridge GCE 'A' Level: End date is **19 Mar 2024**\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End date is 21 Feb 2024\n3. NUS High School Diploma: End date is 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End date is **19 Mar 2024**\n\nThe latest end date is 19 Mar 2024, which is shared by two groups:\n- Singapore-Cambridge GCE 'A' Level\n- International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 14659, "output_tokens": 220, "cost_micros": 47277, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application period table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma from Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng: End 15 Jan 2024\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 18593, "output_tokens": 183, "cost_micros": 58524, "latency_ms": 5078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about application periods for different groups of applicants.Let me search more specifically for dates and application-related content:Let me look at pages 46-47 which should contain the table with deadlines:Let me search for typical date formats and applicant categories:Great! I found the table. Let me read the specific section with the application periods:Perfect! I found the application period table. Now I can see the end dates for each group. Let me compare them:\n\n- Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End: 21 Feb 2024\n- NUS High School Diploma: End: 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n- Part-Time B.Eng: End: 15 Jan 2024\n- Part-Time B.Tech in Computing: 15 Jan 2024 (this appears to be both start and end date based on the context)\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n1. Singapore-Cambridge GCE 'A' Level\n2. International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44218, "error": null, "extra": {"thread_id": 1953, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4551739abb3d476cb8bf4c0348ee5447", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 278, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at page 3 of the document, which shows \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\", I need to examine each numbered reason and its corresponding image to find which one does not include any person.\n\nGoing through the reasons with their images:\n\n1. Interdisciplinary Learning - shows a group of students\n2. Space to Innovate - shows people in a collaborative setting\n3. Industry-ready - shows a person at a desk\n4. Immersion in Research - shows a person in a lab setting (Rolls-Royce)\n5. Internship Opportunities - shows the \"AMD\" text/logo area with people\n6. Global Exposure - shows a person in a winter setting\n7. Be Among the Top Ranked - shows students walking\n8. Prestigious Scholarships and Comprehensive Financial Aid - shows students\n9. Most Beautiful Campus - shows an aerial view of the campus with greenery and pathways\n10. Vibrant Campus Life - shows students/people\n\nReason #9 \"Most Beautiful Campus\" shows an aerial photograph of the campus featuring buildings, green spaces, and pathways, but does not show any people in the image.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 60381, "output_tokens": 271, "cost_micros": 185208, "latency_ms": 35592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to identify which reason's corresponding figure/photo does not include any person.\n\nGoing through each reason with its associated image:\n\n1. Interdisciplinary Learning - has people\n2. Space to Innovate - has people \n3. Industry-ready - has a photo credited to Rolls-Royce showing industrial equipment/machinery without visible people\n4. Immersion in Research - has people\n5. Prestigious Scholarships - not clearly shown with people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - appears to be text/rankings\n8. Most Beautiful Campus - shows campus architecture/scenery\n9. Internship Opportunities - has people\n10. Vibrant Campus Life - has people\n\nThe reason labeled \"3 Industry-ready\" with the photo credit to Rolls-Royce shows industrial/laboratory equipment without any visible people in the image.\n\nAnswer: Industry-ready", "citations": [], "input_tokens": 15953, "output_tokens": 236, "cost_micros": 51399, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "industry ready", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section, I need to examine each reason and its corresponding figure to identify which one does not include any person.\n\nGoing through the reasons:\n1. Interdisciplinary Learning - has figures/images with people\n2. Space to Innovate - has people in the image\n3. Industry-ready - has people\n4. Immersion in Research - has people (photo credit: Rolls-Royce)\n5. AMD Internship Opportunities - has people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - this appears to be primarily text/rankings focused\n8. Prestigious Scholarships and Comprehensive Financial Aid - appears to be icons/symbols\n9. Most Beautiful Campus - this is described as about the campus itself\n10. Vibrant Campus Life - has people\n\nLooking at reason #9 \"Most Beautiful Campus\" - the description states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world.\" This reason is about the campus architecture and grounds themselves, and the corresponding figure would logically show the campus landscape/buildings without people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 19392, "output_tokens": 273, "cost_micros": 62271, "latency_ms": 8437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section and examine the figures/images associated with each reason to identify which one does not include any person.\n\nFrom the document, the top 10 reasons are listed on pages 2-3:\n\n1. Be Among the Top Ranked\n2. Space to Innovate\n3. Industry-ready\n4. Prestigious Scholarships and Comprehensive Financial Aid\n5. Most Beautiful Campus\n6. Vibrant Campus Life\n7. Internship Opportunities\n8. Immersion in Research\n9. (appears to have a photo credit: Rolls-Royce)\n10. Vibrant Campus Life (repeated)\n\nThe document mentions \"Photo credit: Rolls-Royce\" which suggests there's an image for reason #9 \"Immersion in Research.\"\n\nLooking at the layout, reason #5 is \"Most Beautiful Campus\" - this reason would typically show campus buildings and architecture rather than people, as it's about the physical beauty of the campus itself.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 14665, "output_tokens": 246, "cost_micros": 47685, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to examine the images associated with each reason.\n\nThe 10 reasons are:\n1. Interdisciplinary Learning\n2. Space to Innovate\n3. Industry-ready\n4. Immersion in Research\n5. Internship Opportunities\n6. Global Exposure\n7. Be Among the Top Ranked\n8. Prestigious Scholarships and Comprehensive Financial Aid\n9. Most Beautiful Campus\n10. Vibrant Campus Life\n\nLooking at the corresponding figures/images described:\n- Reason #4 \"Immersion in Research\" includes a photo credit to Rolls-Royce, and the image appears to show industrial/research equipment rather than people.\n- Reason #9 \"Most Beautiful Campus\" states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world\" and the cover page shows \"a large, modern building with a curved facade featuring prominent vertical wooden louvers or fins\" with \"green landscaping visible at the base\" - this is an architectural shot without people visible.\n\nThe most likely answer based on the description is reason #9, as it explicitly describes a campus building without mentioning any people in the scene.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 18599, "output_tokens": 300, "cost_micros": 60297, "latency_ms": 9328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the top 10 reasons why NTU smart campus is for you and identify which reason has a corresponding figure without any person.Let me search for \"top 10 reasons\" or \"smart campus\" in the document:Let me read the section around line 448-488 where the \"TOP 10\" appears:Good! I can see the beginning of the top 10 reasons. Let me continue reading to see all 10 reasons and their corresponding figures:Let me continue reading to see all the images associated with each reason:Now let me go back and carefully read the section with the 10 reasons to identify each reason and its associated image description. Let me re-read more carefully:Based on my reading of the document, I can now identify the 10 reasons and their corresponding figures. Let me analyze the images:\n\n1. **Interdisciplinary Learning** (Reason 1) - Image shows a female researcher/scientist in a laboratory (person present)\n\n2. **Space to Innovate** (Reason 2) - Image shows a person positioned on left side operating equipment in a laboratory (person present)\n\n3. **Industry-ready** (Reason 3) - Image shows six young adults standing in a horizontal line (people present)\n\n4. **Immersion in Research** (Reason 4) - Image shows a single person standing next to AMD logo (person present)\n\n5. **Internship Opportunities** (Reason 5) - The image associated with reasons 5-10 appears to be Im5.jpg\n\n6. **Global Exposure** (Reason 6) - Image (Im5.jpg) shows a single person standing on snowy terrace/viewing area with ch\u00e2teau backdrop (person present)\n\n7. **Be Among the Top Ranked** (Reason 7) - This appears to share imagery with the ranking pages\n\n8. **Prestigious Scholarships and Comprehensive Financial Aid** (Reason 8) - Part of the same figure group\n\n9. **Most Beautiful Campus** (Reason 9) - The next distinct image is Im7.jpg which shows an aerial view of a modern curved/spiral building with NO PEOPLE mentioned in the description - only architecture and landscape features\n\n10. **Vibrant Campus Life** (Reason 10) - Part of the combined figure\n\nThe reason that does not include any person in the corresponding figure is **Reason 9: Most Beautiful Campus** - the image shows only an aerial view of the building architecture and landscape with no people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48717, "error": null, "extra": {"thread_id": 1954, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_939ae241eba1446aa240613de9b11e4b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 370, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for images of students playing badminton and identify their major.\n\nLooking through the pages, I can see several student testimonials with photos and their majors listed.\n\nOn page 22 (document page 41), there are two images at the bottom of the page showing students in athletic settings. The text mentions:\n\n\"\u2014 JADE LIM SI MIN\nPhilosophy, Class of 2023\n\nNTU has been a springboard for my growth as a student leader. Through the Student Leadership Development Programme, Varsity Sports Association and FISU World Forum, NTU has equipped me with the leadership skills necessary to succeed both in my sport and beyond.\"\n\nAnd:\n\n\"\u2014 GRACE CHUA HUI ZHEN\nMedicine, Class of 2023\n\nNTU has allowed me to pursue my two passions: academics and athletics. From letting me participate in competitions to offering generous subsidies to giving encouragement, I really feel grateful for the school's the tremendous support.\"\n\nThe image on the right shows what appears to be a badminton player in action. Based on the layout and the caption, this appears to be Grace Chua Hui Zhen from Medicine.\n\nAnswer: Medicine", "citations": [], "input_tokens": 60367, "output_tokens": 265, "cost_micros": 185076, "latency_ms": 45125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images or mentions of students playing badminton.\n\nLooking through the document, I can find several student testimonials with their names, majors, and years listed. Let me check for any mention of badminton or sports activities.\n\nOn page 41, there is a section about \"SPORTS @ NTU\" with several images. I can see mentions of:\n- JADE LIM SI MIN, Philosophy, Class of 2023 - with text about varsity sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - with text about academics and athletics\n\nHowever, I need to look more carefully at the actual images in the document. The document mentions \"Griffin at Lee Wee Nam Library\" as an image caption on page 42.\n\nLooking more carefully at the sports section and student profiles, I can see references to students involved in sports, but I need to identify specifically which student's picture shows them playing badminton.\n\nUpon careful review of the document, I cannot definitively identify from the text alone which specific student's picture shows them playing badminton, as the document doesn't explicitly describe the content of each photograph beyond the captions with names and majors.\n\nHowever, based on the context in the sports section where student testimonials are shown, and the typical layout of such brochures, if there is a badminton-related image, it would likely be associated with one of the students featured in the sports section.\n\nAnswer: I cannot determine this from the text provided in the document.", "citations": [], "input_tokens": 15939, "output_tokens": 339, "cost_micros": 52902, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i cannot determine this from text provided in document", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images of someone playing badminton and identify which major that student belongs to.\n\nLooking through the document, I can see several student photos with their names and majors mentioned:\n\n1. Nick Erwin Chia Zhiyang - Renaissance Engineering Programme, Year 4\n2. Gladys Phua Min - Communication Studies, Year 2\n3. Elijah Adam Bin Abdul Haady Aldrin - Chemistry & Biological Chemistry, Year 1\n4. Joey Tay - NTU-NIE TSP, Year 2\n5. Stella Chua Zhi Yun - Mechanical Engineering, Year 3\n6. Seth Low - Communication Studies, Year 4\n7. Balaji Srinivasan - Biological Sciences and Psychology, Year 4\n8. Muhamed Shamir Jaffar Hussein - MBBS, Year 4\n9. Wang Shi Lin Nicole - Business, Year 3\n10. Ruth Koh - Sport Science & Management, Year 3\n\nOn page 41, there is a section about sports and I can see images related to sports activities. Looking at the bottom of page 41, there is a photo with Grace Chua Hui Zhen from Medicine, Class of 2023, and the image appears to show her playing badminton (based on the context of sports activities and the layout).\n\nAnswer: Medicine", "citations": [], "input_tokens": 19378, "output_tokens": 320, "cost_micros": 62934, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for any images or references to badminton playing.\n\nLooking through the document, I can see several student profiles with quotes and their majors listed, but I need to find one specifically associated with a badminton image.\n\nOn page 41, I can see there are sports-related images and student profiles. The document shows:\n\n- JADE LIM SI MIN, Philosophy, Class of 2023 - associated with FISU WORLD FORUM and sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - mentioned in relation to academics and athletics\n\nHowever, I cannot definitively identify which specific image shows badminton or which student's picture is specifically playing badminton from the text content provided. The document mentions \"SPORTS @ NTU\" section and various athletic activities, but the actual image descriptions are not clear enough in the text to determine which major the badminton player belongs to.\n\nBased on the limited visual descriptions available in the text, I cannot provide a definitive answer about which student major is shown playing badminton.\n\nAnswer: Cannot be determined from the provided document text", "citations": [], "input_tokens": 14651, "output_tokens": 250, "cost_micros": 47703, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided document text", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the badminton image...\n\nOn page 41, there is a caption under an image that states:\n\n> GRACE CHUA HUI ZHEN\n> Medicine, Class of 2023\n\nThe caption appears below text mentioning \"a student playing badminton in a competition.\"\n\nAnswer: Medicine", "citations": [], "input_tokens": 18585, "output_tokens": 74, "cost_micros": 56865, "latency_ms": 5875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about a student playing badminton in the brochure document.Let me search for \"badminton\" in the document:Now let me read the context around the badminton image to find the student's major:Let me look earlier in the document to see the context. The badminton image appears on page about \"Communication-Focused\". Let me search for more context around this section:Let me look at what comes before this section to identify which school/major this is part of:I can see this is about CoHASS (College of Humanities, Arts, and Social Sciences). Let me search for where specific majors or student testimonials are mentioned near the badminton image:I see Seth Low is from Communication Studies. Let me check if the badminton image is associated with him or if there's another student profile. Let me look at the pages more carefully:Let me look at the earlier mention at line 2200 to see if there's another student:That's not the badminton image. Let me look more systematically. The badminton image appears around line 3343. Let me check what's before and after to see which student profile or section it's associated with:The badminton image appears on page 20 (based on the page numbers), and the text after it (page 21) shows Seth Low from Communication Studies. But let me look more carefully at the layout. Let me check the section just before the badminton image to see which school section it belongs to:I see on page 19 there's a quote from Seth Low, Communication Studies. Now let me look at page 20 where the badminton image is:Let me look at the structure better. The Im1.jpg is on page 19 (Seth Low's image), then page 20 starts with CoHASS section. The badminton image Im0.jpg should be on page 20. Let me check the page markers more carefully:So page 19 ends at line 3127, then the next page marker is page 21 at line 3387. So the badminton image (around line 3343) is on page 20 which doesn't have an explicit marker. Let me look at the entire section more carefully to see which school this belongs to:Now I see WKWSCI (Wee Kim Wee School of Communication and Information) section starts around line 3548, but the badminton image is earlier around line 3343. Let me trace back to see which section the badminton image actually belongs to. Let me read backwards from line 3320:Perfect! Now I can see the structure. The badminton image (Im0.jpg) appears under the \"Communication-Focused\" section which is part of the CoHASS (College of Humanities, Arts and Social Sciences) overview on page 20. This section appears before the individual school pages. Then on page 21, we see Seth Low from Communication Studies.\n\nSo the badminton image is associated with Communication Studies, which is evident from:\n1. It's on the CoHASS overview page (page 20)\n2. The \"Communication-Focused\" heading right before the badminton image\n3. Seth Low, Communication Studies student, appears on the next page (21)\n4. WKWSCI (Wee Kim Wee School of Communication and Information) is listed as one of the schools under CoHASS\n\nAnswer: Communication Studies", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 83500, "error": null, "extra": {"thread_id": 1955, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2258a5b949624b49af1a1b98a8b2e791", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 658, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "communication studies", "normalised_gold": "medicine"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document to identify which colleges/schools/institutes feature Year 3 students on their cover pages or promotional sections.\n\nLooking through the document:\n\n1. Page 10 (College of Engineering section) - Features **Stella Chua Zhi Yun, Mechanical Engineering, Year 3**\n\n2. Page 12 (College of Humanities, Arts and Social Sciences) - Features **Seth Low, Communication Studies, Year 4** (not Year 3)\n\n3. Page 14 (College of Science) - Features **Balaji Srinivasan, Biological Sciences and Psychology, Year 4** (not Year 3)\n\n4. Page 16 (Lee Kong Chian School of Medicine) - Features **Muhamed Shamir Jaffar Hussein, MBBS, Year 4** (not Year 3)\n\n5. Page 17 (Nanyang Business School) - Features **Wang Shi Lin Nicole, Business, Year 3**\n\n6. Page 18 (National Institute of Education) - Features **Ruth Koh, Sport Science & Management, Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 60377, "output_tokens": 272, "cost_micros": 185211, "latency_ms": 42937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to look through the document to identify which colleges/schools/institutes feature Year 3 students on their cover/featured pages.\n\nLet me examine each section:\n\n1. **College of Engineering** (page 16-17): Features STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21): Features SETH LOW, Communication Studies, Year 4 (not Year 3)\n\n3. **College of Science** (page 24-25): Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not Year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28-29): Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not Year 3)\n\n5. **Nanyang Business School** (page 30-31): Features WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33): Features RUTH KOH, Sport Science & Management, Year 3\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 15949, "output_tokens": 282, "cost_micros": 52077, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the document to identify which colleges/schools/institutes feature year 3 students on their cover pages.\n\nLooking through the document:\n\n1. **College of Engineering** (page 16) - Features STELLA CHUA ZHI YUN, Mechanical Engineering, **Year 3**\n\n2. **College of Humanities, Arts and Social Sciences** (page 20) - Features SETH LOW, Communication Studies, Year 4 (not year 3)\n\n3. **College of Science** (page 24) - Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28) - Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not year 3)\n\n5. **Nanyang Business School** (page 30) - Features WANG SHI LIN NICOLE, Business, **Year 3**\n\n6. **National Institute of Education** (page 32) - Features RUTH KOH, Sport Science & Management, **Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 19388, "output_tokens": 278, "cost_micros": 62334, "latency_ms": 6905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document for references to Year 3 students who appear as student models on cover pages or in promotional materials.\n\nLooking through the document, I can find the following Year 3 students quoted/featured:\n\n1. Page 16 - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3 - This is from the **College of Engineering** section\n\n2. Page 30 - WANG SHI LIN NICOLE, Business, Year 3 - This is from the **Nanyang Business School** section (she appears twice on this page)\n\n3. Page 33 - RUTH KOH, Sport Science & Management, Year 3 - This is from the **National Institute of Education** section (she appears twice on this page)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 14661, "output_tokens": 193, "cost_micros": 46878, "latency_ms": 6233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify which colleges/schools/institutes feature Year 3 students on their cover/promotional pages in this prospectus.\n\nLet me search through the document for mentions of students with their year level and their associated colleges/schools:\n\n1. **College of Engineering** (page 16-17):\n - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21):\n - SETH LOW, Communication Studies, Year 4\n\n3. **College of Science** (page 24-25):\n - BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4\n\n4. **Lee Kong Chian School of Medicine** (page 28-29):\n - MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4\n\n5. **Nanyang Business School** (page 30-31):\n - WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33):\n - RUTH KOH, Sport Science & Management, Year 3\n\nFrom this analysis, the colleges/schools/institutes that use Year 3 students as their cover page student models are:\n- College of Engineering (Stella Chua)\n- Nanyang Business School (Wang Shi Lin Nicole)\n- National Institute of Education (Ruth Koh)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 18595, "output_tokens": 352, "cost_micros": 61065, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find which colleges/schools/institutes from NTU use year 3 students as cover page student models.Let me search for information about cover page student models and year 3 students.Let me search for \"Year 3\" with capital letters and look at the beginning of the document which might contain information about cover pages or student models.Now let me search for information about cover pages and models. Let me read the beginning of the document more carefully:Let me search more specifically for information about models or photos. Let me look for text that mentions students being models or featured on covers:Let me look at the end of the document, which often contains credits or acknowledgments about models or photos:Let me search for the total lines in this document and read from the end:Let me read closer to the end of the document where credits or acknowledgments are typically found:Let me continue reading towards the very end where photo credits or acknowledgments might be:Let me search near the very end of the document:The document ended without showing photo credits. Let me search more specifically for mentions of colleges and Year 3 together. Let me look at those specific line numbers from the grep results:Good! I found one: Stella Chua Zhi Yun is from Mechanical Engineering, Year 3. Now let me check the other instances:Good! Wang Shi Lin Nicole is from Business (Nanyang Business School), Year 3. Now let me check the other instances:Good! Ruth Koh is from Sport Science & Management (National Institute of Education), Year 3. Now let me check the next instance:This is another mention of Ruth Koh. Now let me look more carefully at the context. I need to understand which of these students are featured on \"cover pages\" of their respective schools/colleges. Let me search for any section that lists or describes cover page models:None of these matches refer to \"cover page\" in the context I'm looking for. Let me look at the actual pages where these students appear to see if they are featured as cover page models. Let me check around the Mechanical Engineering section:Now let me look before this section to see if there's a cover page for College of Engineering:Perfect! I can see that Stella Chua Zhi Yun (Mechanical Engineering, Year 3) is featured on the cover page for the College of Engineering section. Now let me check the other sections:Great! I can see Wang Shi Lin Nicole (Business, Year 3) is featured on the cover page for Nanyang Business School. Now let me check the NIE section:Perfect! I can see Ruth Koh (Sport Science & Management, Year 3) is featured on the cover page for the National Institute of Education (NIE) section.\n\nNow let me check if there are any other colleges/schools that I might have missed. Let me look at the table of contents and see what major sections exist:Let me check if there's a cover page model for College of Humanities, Arts and Social Sciences (CoHASS):I can see Seth Low (Communication Studies, Year 4) is mentioned but not Year 3. Let me check the College of Science:I can see Balaji Srinivasan (Biological Sciences and Psychology, Year 4) is mentioned for College of Science, but not Year 3. Let me check if there's a cover page before the College of Science section:So for College of Science, the cover page model is Balaji Srinivasan (Year 4), not Year 3. Let me also check Lee Kong Chian School of Medicine:Let me check the cover page before Lee Kong Chian School of Medicine:So for Lee Kong Chian School of Medicine, the cover page model is Muhamed Shamir Jaffar Hussein (MBBS, Year 4), not Year 3.\n\nNow let me systematically check all the major schools/colleges. Based on my review:\n\n**Year 3 students as cover page models:**\n1. **College of Engineering** - Stella Chua Zhi Yun (Mechanical Engineering, Year 3)\n2. **Nanyang Business School** - Wang Shi Lin Nicole (Business, Year 3)\n3. **National Institute of Education** - Ruth Koh (Sport Science & Management, Year 3)\n\nLet me verify there are no other colleges I missed by checking the table of contents more carefully:Based on the table of contents, the main colleges/schools are:\n1. College of Engineering (has Year 3 student - Stella Chua)\n2. Colleges of Humanities, Arts and Social Sciences (has Year 4 student - Seth Low)\n3. Colleges of Science (has Year 4 student - Balaji Srinivasan)\n4. Lee Kong Chian School of Medicine (has Year 4 student - Muhamed Shamir)\n5. Nanyang Business School (has Year 3 student - Wang Shi Lin Nicole)\n6. National Institute of Education (has Year 3 student - Ruth Koh)\n\nSo the colleges/schools/institutes that use Year 3 students as cover page models are:\n1. College of Engineering\n2. Nanyang Business School\n3. National Institute of Education\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 154578, "error": null, "extra": {"thread_id": 1956, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_071293dfad4941b99ec3b52bc671c7da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1092, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the College of Humanities, Arts, and Social Sciences (CoHASS) and count how many schools are listed but not individually introduced in detail.\n\nFrom page 12, I can see the College of Humanities, Arts and Social Sciences section lists \"OUR SCHOOLS\":\n- School of Art, Design and Media\n- School of Humanities\n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 7 entities listed under \"OUR SCHOOLS\".\n\nLooking at pages 13-14, I can see detailed introductions for:\n1. School of Art, Design and Media (ADM) - page 13\n2. School of Humanities (SoH) - page 13\n3. School of Social Sciences (SSS@NTU) - page 13\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - page 13\n\nThese 4 schools have detailed \"ABOUT\" sections with descriptions of their programs and advantages on pages 22-23.\n\nThe entities that are listed but NOT individually introduced in detail are:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 3 entities.\n\nAnswer: 3", "citations": [], "input_tokens": 60364, "output_tokens": 324, "cost_micros": 185952, "latency_ms": 38015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools in the College of Humanities, Arts, and Social Sciences (CoHASS) and determine which ones are NOT introduced in detail.\n\nFrom page 20-21, the CoHASS section lists \"OUR SCHOOLS\":\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n\nIt also mentions:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow, let me check which schools get detailed individual introductions on pages 22-23:\n\n1. **School of Art, Design and Media (ADM)** - YES, has detailed section on page 22\n2. **School of Humanities (SoH)** - YES, has detailed section on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, has detailed section on page 23\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, has detailed section on page 23\n\nThe three centers/institutes mentioned (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences) are listed under \"OUR SCHOOLS\" but do NOT have individual detailed introductions.\n\nTherefore, 3 entities listed under CoHASS schools do not have individual detailed introductions.\n\nAnswer: 3", "citations": [], "input_tokens": 15936, "output_tokens": 340, "cost_micros": 52908, "latency_ms": 8296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools that belong to the College of Humanities, Arts and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFrom page 20, the document lists the schools under CoHASS:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow I need to check which schools have individual detailed introductions on pages 22-23 (the \"LEARN MORE ABOUT OUR SCHOOLS\" section):\n\nPage 22:\n- School of Art, Design and Media (ADM) - YES, has detailed introduction\n- School of Humanities (SoH) - YES, has detailed introduction\n\nPage 23:\n- School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n- Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nSchools that are NOT individually introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 19375, "output_tokens": 302, "cost_micros": 62655, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools within the College of Humanities, Arts, and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFirst, let me find the list of schools under CoHASS from page 20:\n\n**OUR SCHOOLS:**\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nThat's 7 schools total listed under CoHASS.\n\nNow let me check which schools have detailed individual introductions in the document (pages 21-23):\n\n1. **School of Art, Design and Media (ADM)** - YES, detailed introduction on page 22\n2. **School of Humanities (SoH)** - YES, detailed introduction on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, detailed introduction on page 22\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, detailed introduction on page 23\n\nThe following schools are listed but do NOT have individual detailed introductions:\n5. Nanyang Centre for Public Administration - NO detailed introduction\n6. NTU Centre for Contemporary Art Singapore - NO detailed introduction\n7. Centre for Liberal Arts and Social Sciences - NO detailed introduction\n\nTherefore, 3 schools are not individually introduced in detail.\n\nAnswer: 3", "citations": [], "input_tokens": 14648, "output_tokens": 358, "cost_micros": 49314, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify which schools from the College of Humanities, Arts, and Social Sciences (CoHASS) are listed, and then determine which ones are NOT introduced in detail.\n\nFrom the document, the CoHASS schools listed on page 20 are:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow, looking at the detailed introductions on pages 22-23 under \"LEARN MORE ABOUT OUR SCHOOLS\":\n1. School of Art, Design and Media (ADM) - YES, has detailed introduction\n2. School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n3. School of Humanities (SoH) - YES, has detailed introduction\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nThe schools that are NOT introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 18582, "output_tokens": 292, "cost_micros": 60126, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the College of Humanities, Arts, and Social Sciences and count which schools are not individually introduced in detail.Let me search for \"College of Humanities, Arts, and Social Sciences\" in the document:Let me try searching for variations:Let me read the section about the College of Humanities:Let me search for more information about the college structure. Let me look for \"COLLEGE OF HUMANITIES\" in caps:Found it! Let me read that section:Good! I can see the College of Humanities, Arts and Social Sciences has 4 schools listed:\n1. School of Art, Design and Media (page 22)\n2. School of Humanities (page 23)\n3. School of Social Sciences (page 23)\n4. Wee Kim Wee School of Communication and Information (page 24)\n\nNow let me check which ones are introduced in detail. Let me read pages 22-24:Let me search for where each school is detailed. Let me look at the actual page content around lines 3200-3300 where I saw \"School of Humanities\" mentioned:Good! I can see the CoHASS section lists their schools. Now let me find where individual schools are detailed. Let me search for detailed sections of each school:Let me read the ADM section:Great! Now let me check for Wee Kim Wee School:Perfect! Now let me also check if there are any other schools in CoHASS that I might have missed. Let me go back to the list to verify:I see that the College of Humanities, Arts and Social Sciences has the following units listed:\n- School of Art, Design and Media\n- School of Humanities \n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow let me check if the last three (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) have detailed introductions. Let me search:So the three centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) are only listed in the OUR SCHOOLS section but don't have detailed individual introductions like the four main schools do.\n\nNow let me verify the total count by checking what the document considers as \"schools\" in CoHASS. The question asks specifically about \"schools\" - let me see if these centres are considered schools:\n\nLooking at the structure:\n- In the table of contents (lines 398-413), only 4 schools are listed under \"COLLEGES OF HUMANITIES, ARTS AND SOCIAL SCIENCES\":\n 1. School of Art, Design and Media (page 22)\n 2. School of Humanities (page 23)\n 3. School of Social Sciences (page 23)\n 4. Wee Kim Wee School of Communication and Information (page 24)\n\nAll 4 of these have detailed individual introductions as I found above.\n\nBut in the \"OUR SCHOOLS\" section (lines 3216-3236), it lists 7 entities:\n- 4 Schools (the ones mentioned above)\n- 3 Centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences)\n\nThe question asks about \"schools\" specifically. The centres are NOT called \"schools\" - they are called \"centres\". So the question is asking only about the 4 schools.\n\nSince all 4 schools (School of Art, Design and Media, School of Humanities, School of Social Sciences, and Wee Kim Wee School of Communication and Information) ARE individually introduced in detail, the answer is:\n\nAnswer: 0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97500, "error": null, "extra": {"thread_id": 1957, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_84cc170418784e7aaee4655dd9c795d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 722, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to analyze Figure 1 and find other figures that convey similar messages.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" across four datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10). The key message is that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries, especially in the low budget regime.\n\nNow let me look through the document for figures with similar messages:\n\n**Figure 6** (page 16): Shows \"Label diversity yields more performant and robust active querying strategies\" on PathMNIST, OrganAMNIST, and BloodMNIST. This compares strategies with and without label diversity, not the cold start problem specifically.\n\n**Figure 9** (page 20): Shows \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, focuses on label diversity rather than cold start.\n\n**Figure 4** (page 7): Shows \"Quantitative comparison of map-based querying strategies\" comparing easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast with random selection as baseline. This shows performance at low budgets and demonstrates the cold start problem.\n\n**Figure 5** (page 8): Shows \"On the importance of selecting a superior initial query\" with performance across active learning cycles starting from initial queries. This demonstrates how different initial query strategies perform, related to the cold start problem.\n\n**Figure 13** (page 24): Shows performance of active learning strategies with different initial query strategies on BloodMNIST, demonstrating the importance of initial query selection.\n\n**Figure 14** (page 24): Same as Figure 13 but for PathMNIST.\n\nFigures that convey the same core message as Figure 1 (that active learning fails at initial selection/cold start):\n- Figure 4: Shows the cold start problem through comparison with random\n- Figure 5: Shows importance of initial query selection\n- Figure 13: Shows initial query performance across cycles\n- Figure 14:", "citations": [], "input_tokens": 58482, "output_tokens": 512, "cost_micros": 183126, "latency_ms": 44218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" where most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. The figure has 4 subplots (a-d) for different datasets showing AUC performance vs. number of images.\n\nLet me search for other figures with similar messages:\n\n**Figure 6**: \"Label diversity yields more performant and robust active querying strategies\" - Shows 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) with 6 subplots each comparing strategies with/without diversity. This is about diversity improvement, not the cold start problem itself.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10 and CIFAR-10-LT. Also about diversity improvement rather than demonstrating the cold start problem.\n\n**Figure 4**: \"Quantitative comparison of map-based querying strategies\" - Shows random selection as a baseline and compares different strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across 4 datasets. This demonstrates that random selection is competitive and that hard-to-contrast performs best, which is related to the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST\" - This is about subsequent cycles after initial query.\n\n**Figure 14**: Similar to Figure 13 but for PathMNIST - Also about subsequent cycles.\n\n**Figure 5**: \"On the importance of selecting a superior initial query\" on OrganAMNIST - Shows active learning cycles but focuses on the importance of initial query selection.\n\nThe figures that most clearly convey the same core message as Figure 1 (that existing active querying strategies fail to outperform random selection at the start) are:\n\n- **Figure 4**: Directly compares strategies at low budgets and shows random selection as competitive baseline\n- Possibly Figure 6 and 9, but these focus more on the solution (diversity) rather than the", "citations": [], "input_tokens": 21463, "output_tokens": 512, "cost_micros": 72069, "latency_ms": 12625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1's main message is about the \"Cold start problem in vision active learning\" - showing that most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. It displays AUC performance curves across different numbers of images for multiple datasets.\n\nLet me go through the other figures to find similar messages:\n\n**Figure 2**: Shows label diversity of querying criteria - different message (about class distribution bias)\n\n**Figure 3**: Shows Dataset Maps and easy/hard-to-learn vs easy/hard-to-contrast data - different message (about data selection strategies)\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies, comparing hard-to-contrast with other strategies against random selection - **similar message** (comparing active learning strategies with random selection)\n\n**Figure 5**: Shows importance of selecting superior initial query on OrganAMNIST - **similar message** (about initial query performance across active learning cycles)\n\n**Figure 6**: Extended from Table 1, shows label diversity yields more performant strategies - different message (about adding label diversity)\n\n**Figure 7**: Continued from Figure 2, about label diversity - different message\n\n**Figure 8**: Visualization of K-means clustering - different message\n\n**Figure 9**: Similar to Figure 6, about diversity and performance - different message (about label diversity effect)\n\n**Figure 10**: Similar to Figure 2, about label diversity - different message\n\n**Figure 11**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 12**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 13**: Performance across active learning cycles on BloodMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\n**Figure 14**: Performance across active learning cycles on PathMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\nFigures that convey similar messages to Figure 1 (comparing active learning strategies, showing cold start problem):\n- Figure 4\n- Figure 5\n- Figure 13\n- Figure 14\n\nAnswer: 4", "citations": [], "input_tokens": 26468, "output_tokens": 506, "cost_micros": 86994, "latency_ms": 11765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey similar messages to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" - it demonstrates that most existing active querying strategies (like BALD, Consistency, etc.) are outperformed by random selection when selecting initial queries, particularly in low budget regimes.\n\nLet me go through the document to find figures with similar messages:\n\n**Figure 4**: Shows comparison of different querying strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across different datasets. This shows performance comparisons but focuses on the proposed method rather than demonstrating the cold start problem itself.\n\n**Figure 6**: Explicitly states it is \"Extended from Table 1\" and shows \"Label diversity yields more performant and robust active querying strategies.\" It compares active querying strategies with and without label diversity on PathMNIST, OrganAMNIST, and BloodMNIST. This is about the solution (label diversity) rather than demonstrating the cold start problem.\n\n**Figure 9**: States \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, this focuses on the solution rather than demonstrating the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST.\" This compares different strategies across active learning cycles but focuses on the full active learning procedure rather than specifically demonstrating the cold start problem.\n\n**Figure 14**: Similar to Figure 13, shows \"Performance of each active learning querying strategies with different initial query strategies on PathMNIST.\"\n\nLooking back at the document more carefully, none of the other figures directly replicate Figure 1's specific message about the cold start problem where active learning strategies fail to outperform random selection at the first choice. The other figures either:\n- Show solutions to the problem (Figures 6, 9)\n- Show active learning trajectories (Figures 13, 14)\n- Show other aspects like dataset maps or visualizations (Figures 3, 7, 8, 10, 11, 12)\n\nAnswer: 0", "citations": [], "input_tokens": 23168, "output_tokens": 504, "cost_micros": 77064, "latency_ms": 11578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1, which shows the \"Cold start problem in vision active learning\" - demonstrating that most existing active querying strategies are outperformed by random selection when selecting initial queries.\n\nLet me examine each figure:\n\n**Figure 1**: Shows cold start problem - active learning strategies perform worse than random selection at initial queries across multiple datasets.\n\n**Figure 2**: Shows label diversity of querying criteria - demonstrates class distribution bias in active querying strategies.\n\n**Figure 3**: Shows active querying based on Dataset Maps - compares easy/hard-to-learn vs easy/hard-to-contrast data.\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies - compares different strategies including hard-to-contrast vs random selection.\n\n**Figure 5**: Shows importance of selecting superior initial query - correlation analysis showing hard-to-contrast outperforms others across active learning cycles.\n\n**Figure 6**: Extended from Table 1 - shows label diversity yields more performant strategies, comparing with/without label diversity across budgets.\n\n**Figure 7**: Continued from Figure 2 - shows label diversity for OrganAMNIST and BloodMNIST.\n\n**Figure 8**: Visualization of K-means clustering and active selection using UMAP.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10-LT - shows diversity yields more performant strategies.\n\n**Figure 10**: Similar to Figure 2 but for CIFAR-10 and CIFAR-10-LT - shows label diversity.\n\n**Figure 11**: Similar to Figure 3 - Active querying based on Dataset Maps for PathMNIST and OrganAMNIST.\n\n**Figure 12**: Similar to Figure 3 - Active querying based on Dataset Maps for CIFAR-10-LT.\n\n**Figure 13**: Performance of active learning strategies with different initial queries on BloodMNIST - shows correlation across cycles.\n\n**Figure 14**: Performance of active learning strategies with different initial queries on PathMNIST - shows correlation across cycles.\n\nFigures conveying a similar message to Figure 1 (showing the cold start problem where active learning strategies underperform random selection at the start):\n- **Figure ", "citations": [], "input_tokens": 37261, "output_tokens": 512, "cost_micros": 119463, "latency_ms": 12672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to understand the first figure and then identify how many other figures convey similar messages.Let me continue reading to find all figures in the document and understand what the first figure conveys.Now let me read more sections to understand all the figures and their messages better.Let me continue reading to see more figures.Let me continue reading to identify all figures.Let me search for more figures to get a complete count.Now let me understand what message Figure 1 conveys and identify similar figures. Based on my reading, let me analyze:\n\n**Figure 1** (lines 490-584): Shows four subplots (a-d) of line graphs plotting AUC vs Number of images, demonstrating the \"cold start problem\" where active querying strategies are outperformed by random selection in selecting initial queries. The message is: **existing active learning strategies perform worse than random selection at the beginning (cold start problem)**.\n\nNow let me identify which other figures convey similar messages:\n\n1. **Figure 4** (lines 1346-1455): Shows four subplots comparing map-based querying strategies with random selection as baseline, showing that random selection is \"a highly competitive baseline in cold start because it outperforms six popular active querying strategies as shown in Figure 1.\" This conveys the **same message about cold start problem**.\n\n2. **Figure 6** (lines 2545-2805): Shows plots about label diversity yielding more performant strategies - this is about **solutions**, not about demonstrating the cold start problem itself.\n\n3. **Figure 7** (lines 2840-2920): Shows bar charts about label diversity - this is about **class distribution bias**, related but different message.\n\n4. **Figure 9** (lines 3454-3640): Similar to Figure 6, about diversity yielding better strategies - this is about **solutions**.\n\n5. **Figure 10** (lines 3689-3800): Bar charts about label diversity - about **class distribution bias**.\n\nLet me verify by reading more carefully about what each figure demonstrates:Based on my thorough analysis of the document, Figure 1's main message is: **Active learning strategies are outperformed by random selection at the cold start (initial query selection) due to the cold start problem.**\n\nFigures that convey this same or very similar message:\n- **Figure 4**: Explicitly shows random selection as a competitive baseline that outperforms popular active querying strategies in the cold start problem, directly referencing Figure 1.\n\nAll other figures (2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14) convey different messages:\n- Figures 2, 7, 10: Show class distribution bias\n- Figures 3, 11, 12: Show dataset maps for selecting data\n- Figure 5, 13, 14: Show importance of superior initial query across learning cycles\n- Figures 6, 9: Show that diversity improves querying strategies\n- Figure 8: Shows visualization of clustering\n\nTherefore, **1 figure** (Figure 4) conveys a similar message as Figure 1.\n\nAnswer: 1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78500, "error": null, "extra": {"thread_id": 1958, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cace0f3b35134ef58541b082bc26d8e7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 533, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1, which shows the \"Cold start problem in vision active learning,\" I can see performance comparisons on different datasets including CIFAR-10 (subplot d). The figure shows that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries.\n\nLooking at Table 1, which compares different querying strategies on CIFAR-10-LT at 10% and 20% budgets:\n- At 10% budget: Random achieves 91.6\u00b11.1\n- At 20% budget: Random achieves 93.1\u00b10.6\n\nComparing with other strategies at these budgets:\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%, strategies that outperform random (91.6): VAAL (92.6), Entropy (92.0), Margin (91.7), BALD (92.8) = 4 strategies\n\nAt 20%, strategies that outperform random (93.1): Consistency (93.4), VAAL (93.7), Margin (93.2), Coreset (93.6) = 4 strategies\n\nHowever, the question asks generally how many strategies outperform random selection. Looking at the paper's proposed \"Ours\" strategy (hard-to-contrast), the results show it significantly outperforms random", "citations": [], "input_tokens": 58490, "output_tokens": 512, "cost_micros": 183150, "latency_ms": 41641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10, I can see performance curves for different strategies. The document mentions CIFAR-10-LT specifically in several places.\n\nFrom Figure 4(d), I can see a comparison of different strategies on CIFAR-10-LT including:\n- Random (baseline, shown as dotted line)\n- Easy-to-learn\n- Hard-to-learn\n- Easy-to-contrast\n- Hard-to-contrast\n\nLooking at the quantitative results in Figure 4(d) for CIFAR-10-LT at different budget levels (20.0% and 30.0% with 2481 and 3721 images), I can observe which strategies perform above the random baseline.\n\nFrom the text in section 3.2: \"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nLooking at Figure 4(d) more carefully, I can see that at the budget levels shown:\n1. Hard-to-contrast (performs best, clearly above random)\n2. Easy-to-learn (appears to perform above random)\n3. Hard-to-learn (appears close to or slightly above random)\n4. Easy-to-contrast (appears below random)\n\nFrom the visual inspection of Figure 4(d), it appears that 2-3 strategies outperform random selection, with hard-to-contrast being the clear winner, and easy-to-learn also performing above the baseline.\n\nBased on the figure, I count: Hard-to-contrast and Easy-to-learn as clearly outperforming random selection.\n\nAnswer: 2", "citations": [], "input_tokens": 21471, "output_tokens": 478, "cost_micros": 71583, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows the cold start problem on CIFAR-10, I can see performance curves for different strategies compared to random selection. However, the question asks specifically about CIFAR-10-LT.\n\nLet me check the text more carefully. In Section 3.2, it states:\n\n\"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nThis indicates that the \"hard-to-contrast\" strategy (which is the authors' proposed method) outperforms random selection.\n\nLooking at Figure 1(d) which shows CIFAR-10 (not CIFAR-10-LT, but related), most of the active learning strategies shown (BALD, Consistency, Coreset, Margin, VAAL, Entropy) appear to perform worse than or similar to random selection at the initial stages (the \"cold start\" problem that the paper discusses).\n\nThe main point of the paper is that existing active learning strategies fail to outperform random selection at the cold start (initial query selection), which is why they propose their hard-to-contrast method.\n\nLooking at Table 1 and Table 7, which compare different strategies on CIFAR-10-LT with different budget levels, at low budgets (10%, 20%), the proposed method with diversity enforcement shows the best performance. However, most existing strategies WITHOUT diversity enforcement perform poorly or fail to select all classes (marked as \"-\").\n\nBased on the paper's main finding that existing active querying strategies are \"outperformed by random selection\" at the cold start (as stated in Figure 1 caption and throughout the introduction), and that only their proposed \"hard-to-contrast\" method significantly outperforms random selection on CIFAR-10-LT, the answer appears to be:\n\nAnswer: 1", "citations": [], "input_tokens": 26476, "output_tokens": 501, "cost_micros": 86943, "latency_ms": 12703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many active learning strategies outperform random selection on CIFAR-10-LT in the cold start problem (initial query selection).\n\nLooking at Figure 1 in the document, which illustrates the \"Cold start problem in vision active learning,\" I can see panel (d) shows results for CIFAR-10-LT.\n\nThe document states in the caption of Figure 1: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries... However, some classes are not selected by active querying strategies due to selection bias, so their results are not presented in the low budget regime.\"\n\nThe document consistently emphasizes that existing active learning strategies fail to outperform random selection at the cold start (initial query stage). The main contribution of the paper is proposing a new strategy (hard-to-contrast) that does outperform random selection.\n\nLooking at Table 1, for CIFAR-10-LT at 10% and 20% budgets:\n- Random: 91.6\u00b11.1 (10%), 93.1\u00b10.6 (20%)\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%: VAAL (92.6), Entropy (92.0), BALD (92.8), and Margin (91.7) appear close or slightly better than Random (91.6)\nAt 20%: VAAL (93.7), Consistency (93.4), Coreset (93.", "citations": [], "input_tokens": 23176, "output_tokens": 512, "cost_micros": 77208, "latency_ms": 12390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10-LT:\n\nThe figure shows performance curves for different strategies:\n- Random (baseline)\n- BALD (Kirsch et al., 2019)\n- Consistency (Gao et al., 2020)\n- Coreset (Sener et al., 2017)\n- Margin (Balcan et al., 2007)\n- VAAL (Sinha et al., 2019)\n- Entropy (Wang et al., 2014)\n\nThe text states: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries, since random selection is i.i.d. to the entire dataset.\"\n\nFrom Figure 1(d), I can see that at the initial query stage (low budget regime), the random selection baseline appears to outperform most other strategies. The figure caption states \"Active learning fails to select data as effectively as random selection at the first choice.\"\n\nHowever, looking more carefully at the results, I need to determine which strategies actually outperform random at the cold start (initial query selection).\n\nAccording to the paper's main findings and Figure 4, the proposed \"hard-to-contrast\" strategy significantly outperforms random selection on CIFAR-10-LT. The paper states this is their contribution - addressing the cold start problem where existing strategies fail to beat random.\n\nFrom the various results shown, only the authors' proposed strategy (hard-to-contrast) consistently outperforms random selection at the initial query stage on CIFAR-10-LT.\n\nAnswer: 1", "citations": [], "input_tokens": 37269, "output_tokens": 407, "cost_micros": 117912, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about strategies that outperform the random selection baseline on CIFAR-10-LT.Let me search for content related to CIFAR-10-LT and performance comparisons:Let me read through the document more systematically to find tables or figures with results:Let me search for tables that compare strategies on CIFAR-10-LT:Perfect! I found Table 1. Let me continue reading to see the complete table for CIFAR-10-LT results:Now I can see Table 1 clearly. Let me analyze the CIFAR-10-LT columns (10% and 20% budget). The table shows performance with and without enforcing label diversity. Looking at the CIFAR-10-LT columns:\n\nFor the 10% budget (column 9), I need to compare each strategy's score with Random baseline (91.6\u00b11.1):\n- Random (baseline): 91.6\u00b11.1\n- Consistency with diversity (\u2713): 91.4\u00b11.1 - DOES NOT outperform\n- Consistency without diversity (\u2717): 67.1\u00b117.1 - DOES NOT outperform\n- VAAL with diversity (\u2713): 92.6\u00b10.2 - OUTPERFORMS\n- VAAL without diversity (\u2717): - (missing)\n- Margin with diversity (\u2713): 91.7\u00b10.9 - OUTPERFORMS (slightly)\n- Margin without diversity (\u2717): 81.9\u00b10.8 - DOES NOT outperform\n- Entropy with diversity (\u2713): 92.0\u00b11.2 - OUTPERFORMS (slightly)\n- Entropy without diversity (\u2717): 65.6\u00b115.6 - DOES NOT outperform\n- Coreset with diversity (\u2713): 91.5\u00b10.4 - DOES NOT outperform\n- Coreset without diversity (\u2717): 65.9\u00b115.9 - DOES NOT outperform\n- BALD with diversity (\u2713): 92.8\u00b10.1 - OUTPERFORMS\n- BALD without diversity (\u2717): 64.9\u00b114.9 - DOES NOT outperform\n\nLooking at 10% budget, strategies that outperform random (91.6):\n1. VAAL with diversity: 92.6\n2. Margin with diversity: 91.7\n3. Entropy with diversity: 92.0\n4. BALD with diversity: 92.8\n\nThat's 4 strategies at 10% budget.\n\nFor the 20% budget (column 10), comparing with Random baseline (93.1\u00b10.6):\n- Random: 93.1\u00b10.6\n- Consistency with diversity: 93.4\u00b10.2 - OUTPERFORMS\n- Consistency without diversity: 88.6\u00b10.3 - DOES NOT outperform\n- VAAL with diversity: 93.7\u00b10.4 - OUTPERFORMS\n- VAAL without diversity: - (missing)\n- Margin with diversity: 93.2\u00b10.2 - OUTPERFORMS (slightly)\n- Margin without diversity: 86.3\u00b10.3 - DOES NOT outperform\n- Entropy with diversity: 91.9\u00b11.3 - DOES NOT outperform\n- Entropy without diversity: 86.4\u00b10.2 - DOES NOT outperform\n- Coreset with diversity: 93.6\u00b10.2 - OUTPERFORMS\n- Coreset without diversity: 86.9\u00b10.1 - DOES NOT outperform\n- BALD with diversity: 90.8\u00b12.4 - DOES NOT outperform\n- BALD without diversity: 84.7\u00b10.6 - DOES NOT outperform\n\nAt 20% budget, strategies that outperform random (93.1):\n1. Consistency with diversity: 93.4\n2. VAAL with diversity: 93.7\n3. Margin with diversity: 93.2\n4. Coreset with diversity: 93.6\n\nThat's 4 strategies at 20% budget.\n\nBut the question asks about \"CIFAR-10-LT\" in general. Let me count unique strategies (considering both budgets):\n\nStrategies that outperform at 10%: VAAL, Margin, Entropy, BALD (all with diversity)\nStrategies that outperform at 20%: Consistency, VAAL, Margin, Coreset (all with diversity)\n\nCombined unique strategies: VAAL, Margin, Entropy, BALD, Consistency, Coreset = 6 strategies\n\nBut I should check if the question means at ANY budget level or at BOTH budget levels. Looking at Figure 1d in the document might also help clarify. Let me check that figure.I notice Figure 1(d) is labeled \"CIFAR-10\" not \"CIFAR-10-LT\". But the question specifically asks about CIFAR-10-LT. Let me look more carefully at Table 1. The table clearly has a column header \"CIFAR-10-LT\" with two sub-columns (10% and 20%).\n\nBased on Table 1, I need to count how many strategies (considering different variations - with/without diversity) outperform the random baseline on CIFAR-10-LT. Let me be more systematic:\n\nAt 10% budget, Random = 91.6\u00b11.1:\n1. Consistency (\u2713): 91.4 - NO\n2. Consistency (\u2717): 67.1 - NO \n3. VAAL (\u2713): 92.6 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 91.7 - YES\n6. Margin (\u2717): 81.9 - NO\n7. Entropy (\u2713): 92.0 - YES\n8. Entropy (\u2717): 65.6 - NO\n9. Coreset (\u2713): 91.5 - NO\n10. Coreset (\u2717): 65.9 - NO\n11. BALD (\u2713): 92.8 - YES\n12. BALD (\u2717): 64.9 - NO\n\nAt 10%: 4 strategies outperform random\n\nAt 20% budget, Random = 93.1\u00b10.6:\n1. Consistency (\u2713): 93.4 - YES\n2. Consistency (\u2717): 88.6 - NO\n3. VAAL (\u2713): 93.7 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 93.2 - YES\n6. Margin (\u2717): 86.3 - NO\n7. Entropy (\u2713): 91.9 - NO\n8. Entropy (\u2717): 86.4 - NO\n9. Coreset (\u2713): 93.6 - YES\n10. Coreset (\u2717): 86.9 - NO\n11. BALD (\u2713): 90.8 - NO\n12. BALD (\u2717): 84.7 - NO\n\nAt 20%: 4 strategies outperform random\n\nThe question asks \"How many strategies outperform the random selection baseline on CIFAR-10-LT?\" \n\nLooking at both budget levels (10% and 20%), and counting each unique strategy configuration:\n- At 10%: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713) = 4 \n- At 20%: Consistency(\u2713), VAAL(\u2713), Margin(\u2713), Coreset(\u2713) = 4\n\nIf we count unique strategy configurations across both budgets: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713), Consistency(\u2713), Coreset(\u2713) = 6 total\n\nBut the most straightforward interpretation is: at a given budget level. Since the table shows two budget levels and the question doesn't specify which, I should look at what makes most sense. Given that 4 strategies outperform at each budget level separately, and 6 unique strategy variations outperform across at least one budget level, the answer is likely **4** (at each specific budget) or **6** (total unique strategies that ever outperform).\n\nHowever, re-reading the question more carefully - it asks how many strategies outperform, not how many strategy-variants. If we count base strategies (ignoring the diversity modifier):\n- VAAL outperforms (with diversity at both budgets)\n- Margin outperforms (with diversity at both budgets) \n- Entropy outperforms (with diversity at 10%)\n- BALD outperforms (with diversity at 10%)\n- Consistency outperforms (with diversity at 20%)\n- Coreset outperforms (with diversity at 20%)\n\nThat would be 6 unique base strategies.\n\nBut looking at the data more carefully at the initial query (lowest budget), which is what the paper focuses on for \"cold start\", the answer is most likely **4** for the 10% budget.\n\nActually, let me reconsider - in Table 1, the \"Unif.\" column indicates whether uniform/diversity enforcement is applied. Each strategy has 2 rows (with and without diversity). So we have 7 base strategies tested: Random, Consistency, VAAL, Margin, Entropy, Coreset, BALD.\n\nIf I count how many of these 7 base strategies (in any configuration) outperform random at either budget:\n- Consistency: NO at 10%, YES at 20% (with diversity)\n- VAAL: YES at 10%, YES at 20% (with diversity)\n- Margin: YES at 10%, YES at 20% (with diversity)\n- Entropy: YES at 10%, NO at 20%\n- Coreset: NO at 10%, YES at 20% (with diversity)\n- BALD: YES at 10%, NO at 20%\n\nCount = 6 base strategies outperform random in at least one configuration at at least one budget level.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 81906, "error": null, "extra": {"thread_id": 1959, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7833996f4234f1db0fa9af29b8ebd6c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 805, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7, which show label diversity comparisons across different datasets (PathMNIST, OrganAMNIST, BloodMNIST), I can see the class distributions for different querying strategies.\n\nFrom these figures, VAAL consistently shows the poorest label diversity across datasets:\n- In PathMNIST (Figure 2), VAAL heavily selects from the \"muscle\" class while largely ignoring \"mucus\" and \"mucosa\" classes\n- In OrganAMNIST (Figure 7), VAAL prefers \"femur-right\" and \"platelet\" classes while largely ignoring lung, liver and monocyte classes\n- In BloodMNIST (Figure 7), VAAL shows similarly poor label diversity with heavy bias toward specific classes\n\nThis is further supported by Table 1, where VAAL frequently has missing results (marked as \"-\") because it fails to sample at least one data point for each class.\n\nAnswer: VAAL", "citations": [], "input_tokens": 58491, "output_tokens": 229, "cost_micros": 178908, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7 in the document, which show label diversity comparisons across different datasets, I can observe the class distribution patterns for various active querying strategies.\n\nFrom the figures, VAAL consistently shows highly imbalanced distributions with strong bias towards specific classes:\n- In PathMNIST (Figure 2): VAAL heavily favors muscle class while largely ignoring mucus and mucosa\n- In OrganAMNIST (Figure 7a): VAAL prefers femur-right while ignoring lung and liver\n- In BloodMNIST (Figure 7b): VAAL favors platelet while ignoring other classes\n\nThe document also states: \"For example, VAAL prefers selecting data in the muscle class, but largely ignores data in the mucus and mucosa classes.\"\n\nTable 2 further confirms this, showing VAAL has very poor class coverage (0.11-0.30) compared to other methods at low budgets.\n\nAnswer: VAAL", "citations": [], "input_tokens": 21472, "output_tokens": 230, "cost_micros": 67866, "latency_ms": 25187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity figures and tables in the document:\n\nIn Figure 2 (PathMNIST), VAAL shows extremely poor label diversity with an entropy of only 2.800, heavily biased toward the muscle class while largely ignoring mucus and mucosa classes.\n\nIn Figure 7 (OrganAMNIST and BloodMNIST), VAAL again shows the poorest label diversity, preferring specific classes like femur-right and platelet while ignoring others.\n\nIn Table 2 (class coverage), VAAL consistently has the lowest class coverage across datasets:\n- PathMNIST: 0.11 (lowest)\n- OrganAMNIST: 0.18 (lowest)\n- BloodMNIST: 0.13 (lowest)\n- CIFAR-10-LT: 0.30 (tied for lowest)\n\nThe document also notes that VAAL results are often missing (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 26477, "output_tokens": 247, "cost_micros": 83136, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Class coverage of selected data\" across different datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10-LT), I can see the class coverage values for different querying strategies.\n\nVAAL consistently shows the lowest class coverage values across the datasets:\n- PathMNIST: 0.11 at 0.00015%, 0.11 at 0.00030%, 0.18 at 0.001%, 0.18 at 0.002%\n- OrganAMNIST: 0.13 at 0.001%, 0.13 at 0.002%\n- BloodMNIST: values not explicitly shown but indicated as poor\n- CIFAR-10-LT: 0.30 at 0.2%, 0.30 at 0.3%\n\nAdditionally, Table 1 shows that VAAL has many missing results (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 23177, "output_tokens": 263, "cost_micros": 73476, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity analysis in the document, I need to examine Figure 2, Table 2, Figure 7, and Figure 10 which show class distributions and coverage across different querying strategies.\n\nFrom Figure 2 (PathMNIST), VAAL shows extremely poor label diversity - it selects 25% from muscle class but only 1% and 2% from mucosa and mucus classes respectively, with an entropy of 2.800 (lowest among all strategies).\n\nFrom Table 2 (class coverage):\n- VAAL consistently shows the poorest class coverage across datasets\n- PathMNIST (0.00015%): VAAL covers only 0.11 (11% of classes)\n- OrganAMNIST (0.001%): VAAL covers only 0.18 (18% of classes) \n- BloodMNIST (0.001%): VAAL covers only 0.13 (13% of classes)\n- CIFAR-10-LT (0.2%): VAAL covers only 0.30 (30% of classes)\n\nFigure 7 confirms VAAL selects 100% from femur-right and platelet while selecting 0% from most other classes.\n\nFigure 10 shows VAAL fails to select any samples from 9 out of 10 classes in CIFAR-10-LT.\n\nAnswer: VAAL", "citations": [], "input_tokens": 37270, "output_tokens": 330, "cost_micros": 116760, "latency_ms": 9437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find information about querying criteria and label diversity across datasets.Let me continue reading the document to find information about querying criteria and label diversity:Let me search for information about label diversity across different querying criteria in the document:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28125, "error": null, "extra": {"thread_id": 1960, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9f3482197c3247b0aae1aed4062d9a6a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 96, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read priority document to find information about querying criteria and label diversity across datasets let me continue reading document to find information about querying criteria and label diversity let me search for information about label diversity across different querying criteria in document", "normalised_gold": "vaal"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each page systematically.\n\nPage 1 (Figure 1): Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. This appears to be 4 figures with multiple lines each.\n\nPage 2 (Figure 1 continued): Same figure from page 1.\n\nPage 4 (Figure 2): Shows a bar chart comparing class distributions, not line plots.\n\nPage 5 (Figure 3): Shows three panels with scatter plots (Dataset Maps), not line plots.\n\nPage 7 (Figure 4): Shows 4 subplots (a-d), each containing line plots with error bars comparing different strategies. This is 4 figures with multiple lines each.\n\nPage 8 (Figure 5): Shows 10 subplots arranged in 2 rows, each containing multiple line plots. This is 10 figures with multiple lines each.\n\nPage 16 (Figure 6): Shows 18 subplots (6 strategies \u00d7 3 datasets), each containing line plots. This is 18 figures with multiple lines each.\n\nPage 17 (Figure 7): Shows bar charts, not line plots.\n\nPage 18 (Figure 8): Shows UMAP visualizations (scatter plots), not line plots.\n\nPage 20 (Figure 9): Shows 12 subplots (6 strategies \u00d7 2 datasets), each containing line plots. This is 12 figures with multiple lines each.\n\nPage 21 (Figure 10): Shows bar charts, not line plots.\n\nPage 22 (Figure 11): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 23 (Figure 12): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 24 (Figure 13 and 14): Each figure shows 10 subplots with line plots. This is 20 figures with multiple lines each.\n\nCounting the figures that contain line plots:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal: 4", "citations": [], "input_tokens": 58479, "output_tokens": 512, "cost_micros": 183117, "latency_ms": 36375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure systematically.\n\n**Figure 1** (page 2): Shows 4 subplots (a-d), each containing multiple line plots comparing different methods. Each subplot has multiple lines.\n\n**Figure 2** (page 4): This is a bar chart showing class distributions, not line plots.\n\n**Figure 3** (page 5): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 4** (page 8): Shows 4 subplots (a-d), each containing multiple line plots (4 different methods plotted).\n\n**Figure 5** (page 9): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 6** (page 16): Shows 3 rows \u00d7 6 columns = 18 subplots, each containing line plots (with and without diversity).\n\n**Figure 7** (page 17): These are bar charts showing class distributions, not line plots.\n\n**Figure 8** (page 18): These are scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9** (page 20): Shows 2 rows \u00d7 6 columns = 12 subplots, each containing line plots.\n\n**Figure 10** (page 21): These are bar charts showing class distributions, not line plots.\n\n**Figure 11** (page 22): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 12** (page 23): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 13** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 14** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\nNow let me count the total number of individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 4 panels \n- Figure 5: 10 panels\n- Figure 6: 18 panels\n- Figure 9: 12 panels\n- Figure 13: 10 panels", "citations": [], "input_tokens": 21460, "output_tokens": 512, "cost_micros": 72060, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document page by page:\n\n**Figure 1 (page 2)**: Contains 4 line plots (a, b, c, d) showing cold start problem comparisons.\n\n**Figure 2 (page 3)**: This is a bar chart showing label diversity, not line plots.\n\n**Figure 3 (page 5)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 4 (page 7)**: Contains 4 line plots (a, b, c, d) comparing map-based querying strategies.\n\n**Figure 5 (page 8)**: Contains 10 line plots (2 rows \u00d7 5 columns) showing training from scratch and fine-tuning results.\n\n**Figure 6 (page 16)**: Contains 18 line plots (3 rows \u00d7 6 columns) showing extended results from Table 1.\n\n**Figure 7 (page 17)**: Contains bar charts, not line plots.\n\n**Figure 8 (page 18)**: Contains scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9 (page 20)**: Contains 12 line plots (2 rows \u00d7 6 columns) showing results on CIFAR-10 and CIFAR-10-LT.\n\n**Figure 10 (page 21)**: Contains bar charts, not line plots.\n\n**Figure 11 (page 22)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 12 (page 23)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 13 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for BloodMNIST results.\n\n**Figure 14 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for PathMNIST results.\n\nTotal count:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal =", "citations": [], "input_tokens": 26465, "output_tokens": 512, "cost_micros": 87075, "latency_ms": 8219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document:\n\n**Figure 1**: Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. Each subplot has multiple lines representing different strategies.\n\n**Figure 4**: Shows 4 subplots for different datasets (PathMNIST, OrganAMNIST, BloodMNIST, CIFAR-10-LT), each with multiple line plots.\n\n**Figure 6**: Shows 6 subplots (one for each strategy: BALD, Consistency, Coreset, Margin, VAAL, Entropy) across 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) = 18 subplots total, each containing 2 line plots (with and without diversity).\n\n**Figure 9**: Shows 6 subplots (one for each strategy) for 2 datasets (CIFAR-10 and CIFAR-10-LT) = 12 subplots total, each containing 2 line plots.\n\n**Figure 13**: Shows performance plots for BloodMNIST with two parts (a) and (b). Each part shows 6 subplots (Random, Entropy, Margin, BALD, Coreset, and appears to show multiple strategies). Each subplot contains multiple line plots showing different initial query strategies.\n\n**Figure 14**: Shows performance plots for PathMNIST with two parts (a) and (b). Similar structure to Figure 13, with 6 subplots in each part, each containing multiple line plots.\n\nLet me count more carefully by looking at each figure:\n\n- Figure 1: 4 subplots \u00d7 ~7 lines each = 28 line plots\n- Figure 4: 4 subplots \u00d7 4 lines each = 16 line plots\n- Figure 6: 18 subplots \u00d7 2 lines each = 36 line plots\n- Figure 9: 12 subplots \u00d7 2 lines each = 24 line plots\n- Figure 13: 2 parts \u00d7 6 subplots \u00d7 ~4 lines = ", "citations": [], "input_tokens": 23165, "output_tokens": 512, "cost_micros": 77175, "latency_ms": 10296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure.\n\n**Figure 1:** Shows 4 subplots (a-d) for PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10. Each subplot contains multiple line plots comparing different strategies. This appears to be 4 plots with multiple lines each.\n\n**Figure 2:** This is a table showing label diversity, not line plots.\n\n**Figure 3:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 4:** Shows 4 sets of comparisons (a-d) with 2 budget levels each. Each comparison shows line plots. This gives us 8 line plot panels.\n\n**Figure 5:** Shows 2 subplots (a-b) for \"Training from scratch\" and \"Fine-tuning from self-supervised pre-training\". Each contains multiple line plots.\n\n**Figure 6:** Shows a 3\u00d76 grid (3 datasets \u00d7 6 methods) = 18 line plot panels.\n\n**Figure 7:** This is a table, not line plots.\n\n**Figure 8:** Shows UMAP visualizations (scatter plots), not line plots.\n\n**Figure 9:** Shows 2 rows \u00d7 6 columns = 12 line plot panels.\n\n**Figure 10:** This is a table, not line plots.\n\n**Figure 11:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 12:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 13:** Shows 2 subplots (a-b) with multiple line plots each.\n\n**Figure 14:** Shows 2 subplots (a-b) with multiple line plots each.\n\nNow let me count more carefully by counting individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 8 panels (4 datasets \u00d7 2 budgets)\n- Figure 5: 2 panels\n- Figure 6: 18 panels (3 datasets \u00d7 6 methods)\n- Figure 9: 12 panels (2 datasets \u00d7 6 methods)\n- Figure 13: 2 panels\n- Figure 14: 2 panels", "citations": [], "input_tokens": 37258, "output_tokens": 512, "cost_micros": 119454, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the paper.Let me continue reading to see the full document and search for figures/plots:Now let me search for mentions of plots, charts, or examine the figures more carefully:I can see Figure 1 has 4 subfigures (1a, 1b, 1c, 1d) and they appear to be line plots. Let me continue examining other figures:Figure 2 appears to be a bar chart/heatmap showing class distributions. Let me check Figure 3:Figure 3 appears to be scatter", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46530, "error": null, "extra": {"thread_id": 1961, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5cc38c9bf2aa4c2ba5f55758d03cf5bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 228, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the optimizers used in this research by searching through the document.\n\nLooking at the implementation configurations section (Appendix A), specifically:\n\n1. In Table 3a (MedMNIST pre-training), the optimizer is listed as \"SGD\"\n2. In Table 3b (CIFAR-10-LT pre-training), the optimizer is listed as \"SGD\"\n3. In Table 6 (Benchmark settings), the optimizer is listed as \"SGD\"\n\nThroughout all the experimental settings described in the document, SGD (Stochastic Gradient Descent) is consistently used as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 58486, "output_tokens": 152, "cost_micros": 177738, "latency_ms": 32796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the optimizers used in the research.\n\nIn Table 3 (page 14), which shows \"Contrastive learning settings on MedMNIST and CIFAR-10-LT,\" I can see:\n\nFor both MedMNIST pre-training (Table 3a) and CIFAR-10-LT pre-training (Table 3b):\n- optimizer: SGD\n\nIn Table 6 (page 15), which shows \"Benchmark settings,\" the optimizer listed is also:\n- optimizer: SGD\n\nThroughout the document, only SGD (Stochastic Gradient Descent) is mentioned as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 21467, "output_tokens": 164, "cost_micros": 66861, "latency_ms": 6155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (pages 13-14), which shows contrastive learning settings:\n- For MedMNIST pre-training: optimizer = SGD\n- For CIFAR-10-LT pre-training: optimizer = SGD\n\nIn Table 6 (page 15), which shows benchmark settings:\n- For training MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 26472, "output_tokens": 120, "cost_micros": 81216, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (page 12), the contrastive learning settings show:\n- optimizer: SGD\n- optimizer momentum: 0.9\n\nIn Table 6 (page 13), the benchmark settings show:\n- optimizer: SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 23172, "output_tokens": 72, "cost_micros": 70596, "latency_ms": 3764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (Contrastive learning settings), the optimizer is listed as **SGD** for both MedMNIST and CIFAR-10-LT pre-training.\n\nIn Table 6 (Benchmark settings), the optimizer is also listed as **SGD** for training MedMNIST, CIFAR-10, and CIFAR-10-LT.\n\nAnswer: SGD", "citations": [], "input_tokens": 37265, "output_tokens": 106, "cost_micros": 113385, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the optimizers used in the research.Let me search for more specific terms related to optimizers:Good! I found SGD. Let me read the sections that mention optimizer settings to get a complete picture:Let me check the benchmark settings table as well:Perfect! I found the optimizer information. Based on the document, I can see that SGD (Stochastic Gradient Descent) is consistently used as the optimizer across different experimental settings:\n\n1. Table 3(a) - MedMNIST pre-training: optimizer = SGD\n2. Table 3(b) - CIFAR-10-LT pre-training: optimizer = SGD \n3. Table 6 - Benchmark settings for MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD (Stochastic Gradient Descent)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42718, "error": null, "extra": {"thread_id": 1962, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eae58ea384884f52967fd1489c25db07", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 204, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "sgd stochastic gradient descent", "normalised_gold": "sgd"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 which shows the Dataset Maps for PathMNIST:\n\nThe Data Map by ground truth (panel b) shows data points that are clearly stratified by classes, with distinct vertical bands representing different classes across the confidence axis.\n\nThe Data Map by pseudo-labels (panel c) shows data points that are more continuously distributed across the confidence range, without the same degree of class stratification.\n\nFrom the visual representation, the Data Map by ground truth appears to have data points spread more widely across different confidence levels (from approximately 0.0 to 1.0), while the pseudo-labels map shows a somewhat more compressed distribution in terms of confidence values.\n\nAnswer: Data Map by ground truth", "citations": [], "input_tokens": 58503, "output_tokens": 153, "cost_micros": 177804, "latency_ms": 44546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "data map by ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3b shows the Data Map by ground truth with confidence values ranging from approximately 0.2 to 1.0\n- Figure 3c shows the Data Map by pseudo-labels with confidence values ranging from approximately 0.0 to 0.6\n\nThe map by ground truth shows a wider spread of confidence values across a larger range (approximately 0.8 units) compared to the pseudo-labels map (approximately 0.6 units), and the data points in the ground truth map appear more dispersed across different confidence levels.\n\nAnswer: ground truth", "citations": [], "input_tokens": 21484, "output_tokens": 149, "cost_micros": 66687, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 11(b) shows the Data Map by ground truth\n- Figure 11(c) shows the Data Map by pseudo-labels\n\nExamining the confidence (y-axis) distribution in both maps:\n\nIn the ground truth map (11b), the data points are stratified by classes and spread across different confidence levels, with points distributed from approximately 0.2 to 1.0.\n\nIn the pseudo-labels map (11c), the data points are more clustered, with most points concentrated in a narrower confidence range, roughly between 0.2 and 0.9.\n\nThe ground truth map shows a wider spread of confidence values across the vertical axis compared to the pseudo-labels map.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 26489, "output_tokens": 184, "cost_micros": 82227, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nExamining the y-axis (confidence) range in both maps:\n\nIn Figure 3(b) (ground truth), the confidence values span approximately from 0.2 to 1.0, showing data points distributed across a wide range with clear stratification by classes.\n\nIn Figure 3(c) (pseudo-labels), the confidence values span approximately from 0.0 to 0.6, showing a more compressed distribution.\n\nThe map based on ground truths displays a larger spread in confidence values from low to high, indicating greater variance in confidence compared to the pseudo-labels map which shows a more compressed range.\n\nAnswer: ground truth", "citations": [], "input_tokens": 23189, "output_tokens": 198, "cost_micros": 72537, "latency_ms": 6797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine the data maps for PathMNIST to compare the confidence variance between the map by ground truth and the map by pseudo-labels.\n\nLooking at Figure 3 in the document, which shows \"Active querying based on Dataset Maps\":\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nFrom the visual comparison of these two maps:\n- The map by ground truth (3b) shows confidence values ranging roughly from 0.0 to 1.0\n- The map by pseudo-labels (3c) shows confidence values also ranging roughly from 0.0 to 1.0\n\nLooking more carefully at the spread of points:\n- In the ground truth map (3b), the confidence values appear to span a wider range vertically (approximately 0.0 to 1.0)\n- In the pseudo-labels map (3c), the confidence values appear more compressed, with most points concentrated between roughly 0.1 and 0.6\n\nThis indicates that the map by ground truth has a larger variance in confidence values compared to the map by pseudo-labels.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 37282, "output_tokens": 267, "cost_micros": 115851, "latency_ms": 7093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about PathMNIST dataset maps and confidence variance.Let me search for \"PathMNIST\" in the document to find the relevant section.Let me search for \"dataset map\" or look at figures related to PathMNIST.Good! I found references to Dataset Maps. Let me read the section around line 680-681 and look at Figure 3.Let me continue reading to see the full figure and find PathMNIST specifically.This figure appears to be about BloodMNIST based on the cell types mentioned. Let me search for figures with PathMNIST.Perfect! I found Figure 11 which includes PathMNIST dataset maps. Let me read that section.Perfect! I can see the PathMNIST dataset maps in Figure 11. Now I need to look at the actual visual representation or description to understand which map has larger confidence variance. Let me continue reading to see if there's more detail about the variance.I can see the figure caption and layout, but I need to look at the actual scatter plots more carefully. Looking at the text I've read:\n\nFor PathMNIST (lines 3829-3883):\n- (b) Data Map by ground truth - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n- (c) Data Map by pseudo-labels - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n\nThe figure shows the confidence ranges from 0.0 to 1.0 on the y-axis for both maps. However, I need to check if there's additional information about which map has larger variance. Let me look at the visual descriptions of the actual figure images.Let me look for the visual description of Figure 11 specifically, which should contain information about the PathMNIST maps.Let me search for image descriptions related to Figure 11's scatter plots to find information about confidence variance.I need to look more carefully at the PathMNIST data in Figure 11. Let me look for visual descriptions of the actual scatter plots. Let me search for image descriptions that might contain the scatter plot information.Looking at the data from PathMNIST in Figure 11:\n- Panel (b) shows \"Data Map by ground truth\" with confidence ranging from 0.0 to 1.0 (as I can see from lines 3831-3873)\n- Panel (c) shows \"Data Map by pseudo-labels\" with confidence ranging from 0.0 to 1.0\n\nThe confidence is on the y-axis. Looking at the values, I see for panel (b) the y-axis goes from 0.0 to ~0.9, and for panel (c) it goes from 0.0 to 1.0 (full range). The question asks about \"confidence variance\" which would be the spread/range of confidence values on the y-axis.\n\nHowever, I need to look more carefully. Let me check if there are image descriptions that describe the actual scatter plot distributions.Let me look at the image descriptions around the PathMNIST figure more carefully. Let me search for image descriptions near Figure 11.Let me look for scatter plot images that are specifically associated with PathMNIST. Let me check around the chunks where PathMNIST appears in Figure 11.Let me look for the actual scatter plot images. Let me search for specific image descriptions that mention PathMNIST scatter plots with confidence and variability axes.Let me search more specifically for scatter plots with confidence and variability axes that relate to PathMNIST.Good! I found a scatter plot description. Let me look for more scatter plots, especially those that might be for PathMNIST ground truth vs pseudo-labels maps. Let me continue searching.Good! I found another scatter plot. Let me continue reading this one.I'm finding scatter plots but I need to identify which ones specifically correspond to PathMNIST panel (b) ground truth and panel (c) pseudo-labels from Figure 11. Let me look for scatter plots with specific axis ranges that match what I saw earlier for PathMNIST.\n\nLooking back at the PathMNIST data from lines 3829-3883, I saw:\n- The y-axis (confidence) goes from 0.0 to at least 0.8\n- Panel (b) shows variability on x-axis with range mentioned as 0.2, 0.4\n- Panel (c) also shows variability on x-axis with range 0.2, 0.4\n\nLet me search for images that might be specifically labeled or that have characteristics matching PathMNIST.I found Im5.jpg which is a scatter plot with confidence 0.0-1.0 and variability 0.0-0.4. This could be one of the PathMNIST maps. Let me look for more scatter plots around Figure 11 area. Let me check if there are descriptions that tell me which image corresponds to which panel of Figure 11.\n\nActually, looking back at the text structure, the figure content is laid out sequentially. From lines 3805-3884, I saw Figure 11 with PathMNIST having:\n- (a) Overall distribution (line 3881)\n- (b) Data Map by ground truth (line 3883)\n- (c) Data Map by pseudo-labels (line 3884)\n\nThe OCR text I saw showed axes values. Let me look more carefully at Im24.jpg and Im29.jpg which I found, as well as other scatter plots, to see if I can determine which represents ground truth vs pseudo-labels.\n\nLet me look for a pattern. Based on the figure caption at line 3805, panels (b) and (e) are ground truth maps, and panels (c) and (f) are pseudo-label maps. Since PathMNIST is (a,b,c) and OrganAMNIST is (d,e,f), I need to find the two scatter plot images that follow the PathMNIST overall distribution.\n\nLet me look at the description of Im24.jpg more carefully (lines 7044-7077) - it shows confidence 0.0-1.0 and variability 0.0-~0.5, with an inverse relationship.\n\nAnd Im29.jpg (lines 7158-7194) shows confidence 0.0-1.0 and variability 0.0-~0.6, with points spread from (0.1, 0.0) to (0.6, 0.7).\n\nNow let me check Im5.jpg (lines 4897-4932) - it shows confidence 0.0-1.0 and variability 0.0-0.4, with a **negative correlation** (upper left high confidence/low variability to lower right low confidence/high variability).\n\nThis is key! The description says Im5.jpg shows data points concentrated in \"Upper left region (low variability, high confidence)\" going to \"Lower right region (high variability, low confidence)\" with \"increased scatter/spread of points in middle and lower portions of curve.\"\n\nNow let me check Im24.jpg and Im29.jpg again to see which shows more spread/variance in confidence.\n\nIm24.jpg (lines 7067-7076): \"Greater scatter/dispersion visible at intermediate confidence values (0.2-0.8)\" and \"Points more tightly clustered near confidence extremes (approaching 0.0 and 1.0)\"\n\nIm29.jpg (lines 7181-7194): \"Some scatter/dispersion around the main curve throughout the distribution\"\n\nLet me look for more scatter plot images that might be the actual PathMNIST panels. Let me check if there are images with specific patterns that would indicate stratification by class (ground truth) vs mixed patterns (pseudo-labels).Perfect! The caption says that ground truth maps have \"data are stratified by classes in the 2D space\" - meaning the points would be grouped/clustered by class. This would create a MORE spread out or varied pattern compared to pseudo-labels which would be more mixed/homogeneous.\n\nBased on the figure captions, the ground truth map shows stratification by classes, which would likely result in **larger variance** in confidence values since different classes cluster at different confidence levels.\n\nLet me search for visual descriptions that mention clustering or stratification patterns in scatter plots.I found very useful information! Looking at lines 5699-5716, there's a description of a clustered scatter plot which might be relevant. But more importantly, I need to focus on the actual PathMNIST dataset maps.\n\nThe key insight from the caption is that ground truth maps show \"data are stratified by classes in the 2D space\" - this means points are separated/grouped by class, which would naturally create **more variance** in confidence values across the entire dataset map, since different classes would cluster at different confidence levels.\n\nIn contrast, pseudo-label maps would show a more homogeneous distribution since pseudo-labels don't perfectly separate classes and mix them together, resulting in **less variance** in confidence values.\n\nBased on the figure caption (line 3805) stating that ground truth maps have data \"stratified by classes\" creating distinct groupings at different confidence levels, the ground truth map would have **larger confidence variance**.\n\nAnswer: ground truth", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 157108, "error": null, "extra": {"thread_id": 1963, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67e301ccb20c47e7a10fd97f2b39b6f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1340, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about Amazon-related datasets mentioned in the paper.\n\nFrom the document, I can identify the following Amazon-related datasets:\n\n1. **Amazon-beauty dataset** - mentioned multiple times throughout the paper as one of the two newly constructed datasets\n2. **Amazon-music dataset** - mentioned multiple times throughout the paper as the second newly constructed dataset\n\nLooking at Table 3 (Dataset Statistics), I can see three datasets are discussed:\n- Amazon-beauty\n- Amazon-music \n- Personality'18\n\nThe Personality'18 dataset is based on MovieLens, not Amazon.\n\nThe paper explicitly states: \"we construct two new domain-specific datasets: an Amazon-beauty and an Amazon-music dataset.\"\n\nTherefore, there are 2 Amazon-related datasets in total.\n\nAnswer: 2", "citations": [], "input_tokens": 26915, "output_tokens": 180, "cost_micros": 83445, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper also mentions Personality'18 dataset with 21,776 items, but this is a MovieLens dataset, not an Amazon dataset.\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 13244, "output_tokens": 132, "cost_micros": 41712, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Statistics of the three datasets used in our study), I can find:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper mentions two Amazon-related datasets: Amazon-beauty and Amazon-music.\n\nTotal items = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 16269, "output_tokens": 113, "cost_micros": 50502, "latency_ms": 5280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Dataset Statistics), I can find information about the datasets used in the study. The paper mentions three datasets:\n\n1. Amazon-beauty\n2. Amazon-music \n3. Personality2018 (which is a MovieLens dataset, not Amazon)\n\nFrom Table 3, I need to find the number of items for the Amazon datasets:\n- The table shows statistics for datasets but I need to locate the specific numbers for items\n\nLooking more carefully at the document, in Section 3.3 \"Dataset Statistics\":\n- It mentions \"Table 3 shows the final statistics of the datasets used in our study\"\n- It states \"The Personality2018 dataset contains the largest number of items and the smallest number of users\"\n- It mentions \"21,776 movies\" for Personality2018\n\nHowever, I cannot see Table 3 clearly displayed with all the numerical values in the provided text. Let me search more carefully...\n\nThe document mentions datasets but the actual Table 3 with complete statistics is not fully visible in the text provided. Without access to the complete Table 3 showing the number of items for Amazon-beauty and Amazon-music datasets, I cannot provide an accurate total.\n\nLooking through the entire document again, I can see references to Table 3 but the actual table with item counts is not included in the extracted text.\n\nSince I cannot find the specific numbers of items for the Amazon datasets in the provided document text, I cannot calculate the total accurately.\n\nAnswer: Unable to determine from the provided text as Table 3 statistics are not fully shown.\n\nWait, let me reconsider - the question asks for an integer answer, so I must find this information. Let me search more carefully for any mention of item counts...\n\nThe document does not provide the specific item counts for Amazon-beauty and Amazon-music datasets in the readable text provided. Without Table 3 being fully visible with those statistics, I cannot provide the accurate total.\n\nAnswer: 0", "citations": [], "input_tokens": 10434, "output_tokens": 451, "cost_micros": 38067, "latency_ms": 11968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study\":\n\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n- Personality'18: 21,776 items (but this is NOT an Amazon dataset, it's a MovieLens dataset)\n\nThe question asks specifically for \"Amazon related datasets\", so I should only count Amazon-beauty and Amazon-music.\n\nTotal items in Amazon datasets = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 14857, "output_tokens": 144, "cost_micros": 46731, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about Amazon-related datasets.Let me search for \"Amazon\" in the document to locate relevant information about datasets.Now let me read the sections that describe the datasets in detail to count the Amazon-related datasets:Let me continue reading to see more details about the datasets:Based on the document, I can see that the paper mentions Amazon-related datasets. Let me check the table more carefully and search for the total number of items:\n\nFrom Table 3 in the document, I can see three datasets are used in the study:\n1. **Amazon-beauty** - with 85 items\n2. **Amazon-music** - with 8,895 items \n3. Personality'18 - (this is not Amazon-related, it's a MovieLens dataset)\n\nThe paper constructed two Amazon-related datasets from the parent Amazon review dataset. Looking at the \"# of items\" row in Table 3:\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41483, "error": null, "extra": {"thread_id": 1964, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a498e50e54164511b090eb63e6f7d154", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the Receptiviti score example shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to find which dataset this corresponds to and what the average words per user is for that dataset.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study,\" I can see:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe User ID format A2GBIFL43U1LKJ appears to be from the Amazon dataset format (as shown in Table 1 with User ID A2SUAM1J3GNN38).\n\nHowever, Table 2 doesn't specify which dataset it's from. Looking at the text in Section 3.2, it states: \"For each user that wrote reviews in either of the two domains, we collect all his/her review texts and concatenate them together into a single document.\"\n\nThe text mentions selecting users for personality inference from both Amazon-beauty and Amazon-music datasets. Since Table 2 is just an example and doesn't specify which dataset, and the question asks about \"the dataset from which a data sample of a user's Receptiviti score is shown,\" I need to determine which dataset the example is from.\n\nGiven that the paper discusses both datasets and Table 2 appears to be a generic example, and considering the context of the paper focuses on both datasets equally, I should look for any other clues. However, without explicit information linking Table 2 to a specific dataset, and since the User ID format matches the Amazon dataset structure, the most reasonable interpretation is that this could be from either Amazon-beauty or Amazon-music.\n\nSince the question asks specifically about \"the dataset\" (singular) and Table 2 is presented as a single example, and given no other specific information, I cannot definitively determine which dataset it's from. However, looking at the document structure and flow, Table 2 appears early in the dataset construction section before domain", "citations": [], "input_tokens": 26939, "output_tokens": 512, "cost_micros": 88497, "latency_ms": 18092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with columns for User ID, AGR, CON, NEU, EXT, and OPEN. The user ID shown is \"A2GBIFL43U1LKJ\".\n\nLooking at Table 4, I can see review text examples with personality labels and scores. The table caption mentions \"The data sample of extreme personality cases to the annotators.\"\n\nNow I need to find which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see:\n\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon datasets (based on the naming convention).\n\nHowever, I need to determine which specific dataset (beauty or music) this example is from. Looking at the context around Table 2, it appears after the discussion of dataset construction and before the detailed statistics. The table is presented as a general example of the Receptiviti API output.\n\nLooking more carefully at the document flow:\n- Section 3.2 discusses dataset construction for both Amazon-beauty and Amazon-music\n- Table 2 is presented as \"An example of Receptiviti score for a specific, anonymized user\"\n- Table 3 immediately follows with statistics for both datasets\n\nSince Table 2 is presented as a general example without specifying which dataset it's from, and given that both Amazon datasets use similar user ID formats, I need to look for more context.\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" - this refers to Table 2. Without explicit identification of which dataset this user is from in Table 2, and given that the paper presents both datasets with their statistics in Table 3, the most reasonable interpretation is that this is asking about one of the Amazon datasets.\n\nSince the question asks for a specific answer and Table 2", "citations": [], "input_tokens": 13268, "output_tokens": 512, "cost_micros": 47484, "latency_ms": 13219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. Which dataset the data sample in Table 2 comes from\n2. The average words per user for that dataset\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nLooking at Table 1, I can see a similar user ID format (A2SUAM1J3GNN38) which appears to be from the Amazon dataset based on the format and fields shown (reviewerID, asin, etc.).\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see the \"Avg. words/user\" for each dataset:\n- Amazon-beauty: 990.48\n- Amazon-music: 466.43\n- Personality'18: - (no data)\n\nThe document mentions in Section 3.2 that they used the Receptiviti API to analyze personality from concatenated review texts. The example user ID format in Table 2 (A2GBIFL43U1LKJ) matches the Amazon dataset format.\n\nSince the paper constructs two Amazon datasets and Table 2 is presented early in the methodology section without specifying which domain, and given that Amazon-beauty is mentioned first throughout the paper and has more prominent results, the sample likely comes from Amazon-beauty dataset.\n\nHowever, looking more carefully at the context, the table appears as the first example when explaining the personality scores, and both datasets use the same methodology. The Amazon-music dataset has 466.43 average words per user.\n\nGiven the ambiguity, but noting that Amazon-beauty is discussed first and more extensively, and the table appears in the general methodology section:\n\nAnswer: 990.48", "citations": [], "input_tokens": 16293, "output_tokens": 400, "cost_micros": 54879, "latency_ms": 11655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find information about the average number of words per user used for experiments in the dataset.\n\nLet me search for relevant information about the dataset construction and word counts.\n\nFrom Section 3.2 \"Dataset Construction\":\n- The paper mentions they \"first extract the user reviews that are between 30 to 80 words\"\n- Then they \"concatenate all the valid review texts of each user and input their concatenation to the Receptiviti API\"\n\nFrom Section 3.2, it also states:\n\"The API can take a long piece of human-written text (more than 300 words), and output a faceted personality score\"\n\nThis indicates that they concatenated multiple reviews (each 30-80 words) to reach more than 300 words total per user.\n\nHowever, the question asks specifically about \"the average number of words per user used for the experiments in the dataset from which a data sample of a user's Receptiviti score is shown.\"\n\nLooking at Table 2, which shows \"User ID\" with personality scores, and Table 4, which shows \"Personality label\" with scores and review texts - these are examples of users with Receptiviti scores.\n\nThe key constraint mentioned is:\n- Individual reviews: 30 to 80 words\n- Concatenated text sent to API: more than 300 words\n\nThe paper doesn't provide an exact average number of words per user. However, it does specify that the concatenated reviews must be \"more than 300 words\" to use the Receptiviti API.\n\nSince the paper doesn't explicitly state the average number of words per user, but only mentions the minimum threshold of 300 words for the API, and the individual review range of 30-80 words, I cannot determine an exact average from the information provided.\n\nLet me re-read more carefully... The paper does not explicitly state the average number of words per user used in the experiments. It only mentions the requirements (30-80 words per review, and >300 words total for API input).\n\nWithout explicit information about the average number of words per user in the dataset, I cannot provide an accurate answer to this question based solely on the document.\n\nAnswer: Cannot be determined from the document provided.", "citations": [], "input_tokens": 10458, "output_tokens": 489, "cost_micros": 38709, "latency_ms": 13437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to identify which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see three datasets:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (not applicable)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon dataset format (starting with \"A\" followed by alphanumeric characters).\n\nLooking at Table 1, which shows \"An example of Receptiviti score for a specific, anonymized user\" and contains a reviewerID \"A2SUAM1J3GNN38\" - this is clearly an Amazon dataset format.\n\nHowever, I need to determine which Amazon dataset (beauty or music) the user in Table 2 belongs to. The document doesn't explicitly state which dataset Table 2's user is from.\n\nLooking more carefully at the document structure:\n- Table 1 shows an Amazon review example\n- Table 2 shows a Receptiviti score example\n- Table 3 shows statistics for all three datasets\n\nSince the document constructs two datasets (Amazon-beauty and Amazon-music) and Table 2 appears early in the dataset construction section (Section 3.2), and considering that both Amazon datasets were constructed using the same methodology, I need to check if there's any indication of which one is being referenced.\n\nLooking at Table 4, which shows \"data sample of extreme personality cases,\" these examples appear to be from the Amazon-beauty dataset based on the product descriptions (shampoo, shower gel, etc.).\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" referring to Table 2. Without explicit indication of which dataset Table 2's user belongs to, and given that the methodology section discusses both datasets together", "citations": [], "input_tokens": 14881, "output_tokens": 512, "cost_micros": 52323, "latency_ms": 13687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about the average number of words per user used for the experiments.Let me search for information about \"Receptiviti\" and \"words per user\" in the document.Let me read more of the document to find the relevant information. I'll search for sections that might contain dataset statistics or experimental setup.I found a key piece of information! Let me read around line 663 where it mentions \"Each user in the Amazon-music dataset has an average of 990.48 review words over all of\".Good! Now let me also check if there's similar information for the Amazon-beauty dataset, and let me look at the table and figure captions to find where Receptiviti scores are shown.Great! I found that Table 1 shows \"An example of Receptiviti score for a specific, anonymized user.\" Let me continue reading this table and find information about the dataset statistics, particularly for the Amazon-beauty dataset.Perfect! Now I need to find the statistics for the Amazon-beauty dataset. Let me look at Table 3 which shows \"the final statistics of the datasets.\"Excellent! I found Table 3 which shows the statistics for all three datasets. Now I need to identify which dataset the Receptiviti score example (Table 1) comes from.\n\nLooking at the document:\n- Table 1 (around line 581) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n- Table 3 (lines 689-732) shows the average words per user for each dataset:\n - Amazon-beauty: 990.48\n - Amazon-music: 466.43\n - Personality'18: -\n\nWait, I notice there's a discrepancy. Let me re-read the section more carefully. The text at line 662-664 says \"Each user in the Amazon-music dataset has an average of 990.48 review words\" but Table 3 shows Amazon-beauty has 990.48 and Amazon-music has 466.43.\n\nLet me read that section again more carefully:There seems to be an inconsistency in the text. Let me check the table more carefully again and see if the columns are correctly labeled:Looking at Table 3, the structure shows:\n- Row 1: Headers - Dataset | Amazon-beauty | Amazon-music | Personality'18\n- The text mentions 1,791 users for Amazon-music (which matches column 2: 1,791)\n- The text mentions 990.48 words/user and 51.01 words/review for Amazon-music\n\nBut in Table 3:\n- Column Amazon-music (2nd data column) shows: 1,791 users, 990.48 avg words/user\n- Wait, I need to re-check. Let me look again.\n\nActually, looking more carefully at the table structure:\n- Line 705: 1,791 is under Amazon-music column for # of users\n- Line 722-723: Avg. words/user shows 990.48 under Amazon-beauty and 466.43 under Amazon-music\n\nThere appears to be a mismatch between the text description and the table. However, the table columns might be swapped. Let me check by cross-referencing other values. The text says \"1,791 active users are selected for the Amazon-music dataset\" and in the table, 1,791 appears in the Amazon-music column.\n\nSo the table seems correct. The text at line 662-664 might have an error, or the columns in the table might be swapped.\n\nNow, to identify which dataset the Receptiviti score sample comes from, let me look more carefully at Table 1:Good! So I can see:\n- Table 1 (line 581) shows an Amazon review example with reviewText mentioning \"piano\" and \"old hymns\" - this appears to be from the music domain\n- Table 2 (line 635) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n\nThe review in Table 1 is clearly about music (piano, hymns), so it's from the Amazon-music dataset.\n\nNow, looking back at the data:\n- The text at lines 662-664 states: \"Each user in the Amazon-music dataset has an average of 990.48 review words\"\n- But Table 3 shows Amazon-music with 466.43 avg words/user\n\nLet me verify by checking if there might be swapped columns. Looking at # of users:\n- Text says 1,791 users for Amazon-music\n- Table shows 1,791 under Amazon-music column\nSo the Amazon-music column is correctly labeled.\n\nLet me check # of items:\n- Amazon-beauty should be smaller (beauty products vs music products)\n- Table shows 85 items for Amazon-beauty and 8,895 for Amazon-music\nThis makes sense - there would be more music items than beauty items.\n\nSo it appears there's an error in the text at line 663, where it says \"990.48\" but should say \"466.43\" based on Table 3. OR the table columns for Avg. words/user might be swapped.\n\nLet me verify using Avg. words/review:\n- Text says Amazon-music has \"averaging 51.01 words for each review\"\n- Table shows Amazon-beauty: 51.01, Amazon-music: 51.18\n\nThis suggests the columns might indeed be swapped for the word statistics! The text matches if we swap the columns.\n\nGiven that:\n1. The Receptiviti score sample (Table 1 and Table 2) shows a music review\n2. The text explicitly states the example is from Amazon-music with 990.48 avg words/user\n3. There appears to be a column swap in Table 3 for the word statistics\n\nThe answer should be **990.48** for the Amazon-music dataset.\n\nAnswer: 990.48", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 89094, "error": null, "extra": {"thread_id": 1965, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a03731abb68747a9a4c0bcb35d9412ce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 777, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the OCEAN personality scores for user A2GBIFL43U1LKJ and determine which personality trait has the highest score, then identify its color in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2 on page 4, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1 on page 5, in the \"Method 2: Soft-labeled personality\" section, the personality embedding matrix shows five rows for the OCEAN traits. The rows are colored differently:\n- Openness: appears to be gray/dark\n- Conscientiousness: appears to be gray/dark\n- Extroversion: appears to be gray/dark\n- Agreeableness: appears to be gray/dark\n- Neuroticism: appears to be gray/dark\n\nHowever, looking more carefully at the figure, the personality embedding matrix shows circles in different shades. For Extroversion (the third row), the circles appear to be a lighter shade or gray.\n\nAnswer: gray", "citations": [], "input_tokens": 26942, "output_tokens": 325, "cost_micros": 85701, "latency_ms": 14828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and determine which has the highest score, then identify its color in the soft-labeled personality embedding matrix shown in Figure 1.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, Method 2 shows the soft-labeled personality embedding matrix. The personality traits are listed in order as:\n- Openness (O) - shown in purple/dark blue\n- Conscientiousness (C) - shown in orange\n- Extroversion (E) - shown in green\n- Agreeableness (A) - shown in red\n- Neuroticism (N) - shown in purple\n\nSince Extroversion has the highest score for this user, I need to identify the color associated with Extroversion (E) in the embedding matrix diagram.\n\nAnswer: green", "citations": [], "input_tokens": 13271, "output_tokens": 301, "cost_micros": 44328, "latency_ms": 6467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "green", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 2, which shows \"An example of Receptiviti score for a specific, anonymized user\":\n\nUser ID: A2GBIFL43U1LKJ\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1, specifically the \"Method 2: Soft-labeled personality\" diagram, it shows the personality embedding matrix with different personality traits represented in different colors. From the figure, Extroversion is shown in purple/violet color in the personality embedding matrix.\n\nAnswer: Purple", "citations": [], "input_tokens": 16296, "output_tokens": 195, "cost_micros": 51813, "latency_ms": 6375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "purple", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and then identify which personality trait has the highest score, and finally determine what color represents that trait in the soft-labeled personality embedding visualization.\n\nFrom Table 2 in the document, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nNow I need to look at Figure 1 which shows the personality embedding methods. In the \"Method 2: Soft-labeled personality\" section, there is a personality embedding vector shown with the OCEAN scores {30,70,50,30,20} for an example user. The figure shows these are arranged in order: Neuroticism, Conscientiousness, Extraversion, Agreeableness, Openness.\n\nHowever, the document does not explicitly show colors associated with each personality trait in the soft-labeled personality embedding matrix visualization. The figure appears to be in grayscale in the provided document, and there is no color legend or color coding system described for the personality traits.\n\nAnswer: The document does not specify colors for personality traits in the embedding matrix.", "citations": [], "input_tokens": 10461, "output_tokens": 337, "cost_micros": 36438, "latency_ms": 8765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "document does not specify colors for personality traits in embedding matrix", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and identify which has the highest score, then determine what color represents that personality in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, specifically at Method 2 (Soft-labeled personality), I can see the personality embedding matrix shows five colored vectors. However, the figure in the document does not clearly show distinct colors for each personality trait in a way that I can definitively identify which color corresponds to Extroversion.\n\nThe figure shows a \"Personality embedding matrix\" but the specific color coding for each of the five OCEAN traits is not explicitly labeled or described in the document text.\n\nWithout clear color labels in the figure or explicit description in the text mapping colors to specific personality traits, I cannot determine the color with certainty.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 14884, "output_tokens": 303, "cost_micros": 49197, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the personality vector color for the specified user.Let me search for information about personality vectors, Receptiviti scores, and soft-labeled personality embeddings in the document.Now let me read the specific sections about the soft-labeled personality embedding matrix and look for figures or tables with user A2GBIFL43U1LKJ:Perfect! I found Table 2 which shows the Receptiviti scores for User A2GBIFL43U1LKJ. The scores are:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extraversion) at 54.39. Now I need to find the figure showing the soft-labeled personality embedding matrix to see what color corresponds to Extraversion.I can see the structure but I need to look at the actual image to identify the colors. Let me search for more information about the figure and its color coding:Let me look at the detailed visual description of Figure 1 with the personality embedding matrix:Perfect! I found the answer. According to the visual description of Figure 1, which shows the soft-labeled personality embedding matrix (Method 2), the personality traits are color-coded as follows:\n\n- Purple circles: Openness\n- Blue circles: Conscientiousness\n- **Brown/orange circles: Extraversion**\n- Green circles: Agreeableness\n- Black/gray circles: Neuroticism\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following Receptiviti scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39 (highest)\n- OPEN (Openness): 42.71\n\nThe highest Receptiviti score for User A2GBIFL43U1LKJ is Extraversion (EXT) at 54.39, and according to the figure's visual description, Extraversion is represented by brown/orange circles.\n\nAnswer: Brown/orange", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58234, "error": null, "extra": {"thread_id": 1966, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f42472e918374f5ab49ea4996d3a5acc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 362, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "brown orange", "normalised_gold": "orange"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores in the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods illustrated:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality \n- Method 3: Hard-coded personality\n\nIn Method 3 (Hard-coded personality), there is a \"Scaling\" step that shows personality scores with blue circles: 0.3 0.7 0.5 0.3 0.2\n\nLooking at Table 4 (data sample of extreme personality cases), I need to find which rows correspond to these scaled scores. The scaled values {0.3, 0.7, 0.5, 0.3, 0.2} represent the normalized OCEAN scores where:\n- Openness (O) = 0.3 \u2192 30\n- Conscientiousness (C) = 0.7 \u2192 70\n- Extroversion (E) = 0.5 \u2192 50\n- Agreeableness (A) = 0.3 \u2192 30\n- Neuroticism (N) = 0.2 \u2192 20\n\nThese are the scaled down values from the original scores {30, 70, 50, 30, 20} mentioned in the figure caption.\n\nSorting these in ascending order: 0.2, 0.3, 0.3, 0.5, 0.7\n\nOr in the original scale: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 26955, "output_tokens": 386, "cost_micros": 86655, "latency_ms": 14437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see that the example shows OCEAN scores of {30,70,50,30,20}. The figure shows different personality traits:\n- Openness (O): 30\n- Conscientiousness (C): 70\n- Extroversion (E): 50\n- Agreeableness (A): 30\n- Neuroticism (N): 20\n\nIn the figure, Conscientiousness is highlighted as the \"most salient personality\" with a score of 70, which appears to be the blue/highlighted vector in the personality embedding.\n\nLooking at Table 4 which shows \"The data sample of extreme personality cases to the annotators,\" I need to find entries related to Conscientiousness (which corresponds to the blue vector in the figure).\n\nThe Conscientiousness entries in Table 4 show personality scores of:\n- 75.38\n- 71.02\n\nArranging these in ascending order:\n\nAnswer: 71.02, 75.38", "citations": [], "input_tokens": 13284, "output_tokens": 265, "cost_micros": 43827, "latency_ms": 7109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "71 02, 75 38", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for a data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods shown:\n1. Method 1: Most salient personality - shows \"Conscientiousness\"\n2. Method 2: Soft-labeled personality - shows a personality embedding matrix with OCEAN scores\n3. Method 3: Hard-coded personality - shows scaled values\n\nIn Method 2 (Soft-labeled personality), there is a personality embedding matrix showing OCEAN scores in what appears to be blue-colored boxes:\n- Openness: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Neuroticism: 20\n\nThe figure caption confirms: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nThese scores in ascending order are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 16309, "output_tokens": 253, "cost_micros": 52722, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. The figure shows three methods for incorporating personality:\n\n1. Method 1: Most salient personality - selects conscientiousness (the highest score of 70)\n2. Method 2: Soft-labeled personality - shows a personality embedding vector with values 20\n3. Method 3: Hard-coded personality - shows scaling with values {0.3, 0.7, 0.5, 0.3, 0.2}\n\nThe figure caption states: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nLooking at the structure, the blue colored elements appear to be in Method 2 (Soft-labeled personality), which shows the personality embedding vector. However, I need to identify which specific values correspond to the blue vector.\n\nFrom the figure description and the example given, the OCEAN scores are:\n- Neuroticism: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Openness: 20\n\nIn ascending order, these scores are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 10474, "output_tokens": 345, "cost_micros": 36597, "latency_ms": 8702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. This appears to be the example being referenced.\n\nNow I need to check Table 4, which shows \"The data sample of extreme personality cases to the annotators.\" I need to identify which entries correspond to the blue personality vector in the model structure.\n\nFrom Figure 1, the example shows:\n- O (Openness): 30\n- C (Conscientiousness): 70\n- E (Extroversion): 50\n- A (Agreeableness): 30\n- N (Neuroticism): 20\n\nThe figure shows that Method 1 selects the maximum score (Conscientiousness = 70), which would be the \"blue\" personality vector in the embedding space.\n\nLooking at Table 4 for Conscientiousness entries, I see:\n- Conscientiousness: 75.38\n- Conscientiousness: 71.02\n\nHowever, I need to match the exact scores from the example in Figure 1. The scores in ascending order from the example {30,70,50,30,20} are:\n\n20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 14897, "output_tokens": 333, "cost_micros": 49686, "latency_ms": 8280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the personality scores for the blue personality vector in the extreme personality cases.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62187, "error": null, "extra": {"thread_id": 1967, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3a604ba7af854be1897689cf0dae5312", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find personality scores for blue personality vector in extreme personality cases", "normalised_gold": "71 02, 75 38"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 on page 6, which shows the distribution of personality traits in Amazon-beauty and Amazon-music datasets, I can see five personality traits displayed: OPEN, CON, EXT, AGR, and NEU.\n\nThe personality trait furthest to the left in the layout is OPEN (Openness).\n\nNow I need to find the highest hit rate (HR) for OPEN across all the datasets and metrics shown in the tables.\n\nLooking at Table 6 on page 8, which shows HR and NDCG results grouped by 5 personality traits:\n- For OPEN in Amazon-beauty: HR = 0.833 (+) and 0.750 (-)\n- For OPEN in Amazon-music: HR = 0.330 (+) and 0.313 (-)\n- For OPEN in Personality2018: HR = 0.535 (+) and 0.547 (-)\n\nThe highest hit rate for OPEN is 0.833 in the Amazon-beauty dataset.\n\nAnswer: 0.833", "citations": [], "input_tokens": 26925, "output_tokens": 268, "cost_micros": 84795, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.833", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate in the results.\n\nLooking at Figure 2 (Distribution of personality traits in Amazon-beauty and Amazon-music datasets), I need to identify which personality trait has its distribution furthest to the left. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU.\n\nFrom the figure description and visual analysis:\n- The median lines (red lines) show the central tendency of each distribution\n- According to the text in Section 5.2: \"agreeable people have the highest median score, and neurotic people have the lowest median score\"\n- This means NEU (Neuroticism) is furthest to the left (lowest scores)\n\nNow I need to find the highest hit rate for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows results grouped by personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty HR: 0.933 (with +) and 0.833 (with -)\n- Amazon-music HR: 0.397 (with +) and 0.254 (with -)\n- Personality2018 HR: 0.489 (with +) and 0.390 (with -)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 13254, "output_tokens": 324, "cost_micros": 44622, "latency_ms": 7905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify the personality trait located furthest to the left in Figure 2, then find its highest hit rate in the results tables.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait appears furthest to the left in the distribution plots. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU for both Amazon-beauty and Amazon-music datasets.\n\nFrom the figure, examining the distributions from left to right, NEU (Neuroticism) appears to have its distribution positioned furthest to the left, with the lowest median scores (indicated by the red line) compared to the other personality traits.\n\nNow I need to find the highest hit rate (HR) value for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows HR and NDCG results grouped by 5 personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (+) and 0.397 (-)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate value for NEU across all datasets is 0.933 (from Amazon-beauty with the + model).\n\nAnswer: 0.933", "citations": [], "input_tokens": 16279, "output_tokens": 313, "cost_micros": 53532, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that appears furthest to the left in Figure 2 (the distribution of personality traits), and then find its highest hit rate from the experimental results.\n\nLooking at Figure 2, I need to identify which personality trait appears furthest to the left. The figure shows distributions for five personality traits: OPEN (Openness), CON (Conscientiousness), EXT (Extroversion), AGR (Agreeableness), and NEU (Neuroticism).\n\nAccording to the text describing Figure 2: \"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nThis indicates that Neuroticism (NEU) has the lowest median score, meaning it would be positioned furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism (NEU) from Table 6, which shows the breakdown by personality traits.\n\nLooking at Table 6 in the document, I need to find the Hit Rate (HR) values for Neuroticism across the three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nHowever, Table 6 is not fully visible in the provided text. But I can see from the discussion in Section 5.4 that it mentions performance changes for different personality traits.\n\nLooking back at Table 5, which shows overall results, I need to check if there's personality-specific data. The table shows H@3, H@5, and H@10 values across different models and datasets.\n\nFrom the text in Section 5.4: \"In the Amazon-music dataset, openness (+27%), agreeableness (+10%), extroversion (+5%) improve while neuroticism (\u201318%) and conscientiousness (\u201312%) decreases.\"\n\nThis suggests neuroticism showed decreases in the Amazon-music dataset. However, I need to find the actual highest hit rate value for neuroticism from the tables.\n\nSince Table 6 details are not fully provided in the visible text, and Table 5 shows overall results rather than personality-specific breakdowns, I need to infer from the context. The highest hit rates in Table 5 are around 0.948-0.977 for H@10 in Amazon-beauty dataset.", "citations": [], "input_tokens": 10444, "output_tokens": 512, "cost_micros": 39012, "latency_ms": 11484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "6.0", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait has the lowest median score (furthest left).\n\nFrom the text in Section 5.2:\n\"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nSo Neuroticism (NEU) is the personality trait furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism in Table 6, which shows results grouped by personality traits.\n\nLooking at Table 6 for NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (both + and -)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for Neuroticism across all datasets is 0.933 (Amazon-beauty with personality information).\n\nAnswer: 0.933", "citations": [], "input_tokens": 14867, "output_tokens": 267, "cost_micros": 48606, "latency_ms": 8485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the distribution of personality traits figure and then identify the corresponding hit rate data.Let me search for figures related to personality traits distribution:Let me read around line 1011 where it mentions \"distribution of users' personalities\":Let me search for Figure 2 which shows the personality distribution:Now let me read Figure 2 in detail:Let me continue reading to see all the personality trait figures:Now let me search for Table 6 which shows the hit rates for different personality traits:Now I need to find which personality trait is \"furthest to the left\" in the distribution figure. Looking back at Figure 2, I need to examine the visual descriptions more carefully. Let me search for more details about each personality trait's distribution:The document states that \"neurotic people have the lowest median score\" (line 1509-1510). This means that NEU (Neuroticism) is the personality trait furthest to the left in the distribution. Now I need to find the highest hit rate for NEU from Table 6.\n\nLooking at Table 6 data I found earlier:\n- NEU (Neuroticism) row shows:\n - Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n - Amazon-music: HR = 0.397 (+) and 0.397 (-)\n - Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 70937, "error": null, "extra": {"thread_id": 1968, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1c93a76cd24f45808d3740ab6074704b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n\n1. Identify the model structure figure - This is Figure 1 on page 5\n2. Find the method at the bottom of this figure - This is \"NCF + Hard-coded personality\"\n3. Look at Table 5 (page 8) which shows results across the three datasets (Amazon-beauty, Amazon-music, and Personality2018)\n4. Find the highest metric value for NCF+Hard-Coded across all metrics\n\nFrom Table 5, for NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value is 0.977 for H@10 in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 26935, "output_tokens": 314, "cost_micros": 85515, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n1. Identify the method at the bottom of the model structure figure (Figure 1)\n2. Find its performance across the three datasets in Table 5\n3. Determine which metric has the highest value\n\nFrom Figure 1, the three methods shown are:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality \n- Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nLooking at Table 5, the row for \"NCF+Hard-Coded\" shows performance across three datasets (Amazon-beauty, Amazon-music, Personality2018) with metrics H@3, H@5, H@10, N@3, N@5, N@10.\n\nThe values for NCF+Hard-Coded are:\n- Amazon-beauty: 0.948, 0.961, 0.977, 0.849, 0.826, 0.848\n- Amazon-music: 0.175, 0.232, 0.345, 0.147, 0.160, 0.189\n- Personality2018: 0.503, 0.622, 0.758, 0.398, 0.447, 0.498\n\nThe highest value across all three datasets for this method is 0.977, which corresponds to H@10 (Hit Rate at 10) in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 13264, "output_tokens": 347, "cost_micros": 44997, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF + Hard-coded Personality model.\n\nFrom Table 5, examining the results for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018) and all metrics (H@3, H@5, H@10, N@3, N@5, N@10):\n\nAmazon-beauty: N@3 = 0.849 (highest value)\nAmazon-music: N@10 = 0.189 (highest value)\nPersonality2018: N@5 = 0.447 (highest value)\n\nThe metric that appears most frequently as the highest is NDCG-related metrics, with N@3 having the highest single value (0.849).\n\nAnswer: N@3", "citations": [], "input_tokens": 16289, "output_tokens": 205, "cost_micros": 51942, "latency_ms": 6843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF+Hard-Coded model.\n\nFrom Table 5, I need to find the highest metric value for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality 2018):\n\nAmazon-beauty: N@3 = 0.849 (highest)\nAmazon-music: N@10 = 0.189\nPersonality 2018: H@10 = 0.758\n\nThe highest value is 0.849 for N@3 (NDCG@3) in the Amazon-beauty dataset.\n\nAnswer: NDCG@3", "citations": [], "input_tokens": 10454, "output_tokens": 171, "cost_micros": 33927, "latency_ms": 6750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ndcg 3", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to identify the method at the bottom of the model structure figure and find its highest evaluation metric across the three datasets.\n\nLooking at Figure 1, the three methods shown are:\n1. Method 1: Most salient personality\n2. Method 2: Soft-labeled personality\n3. Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nNow I need to look at Table 5 to find the highest metric for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nFor NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value across all metrics and datasets for NCF+Hard-Coded is 0.977 for H@10 in Amazon-beauty.\n\nAnswer: H@10", "citations": [], "input_tokens": 14877, "output_tokens": 346, "cost_micros": 49821, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}} -{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find a model structure figure in this document first, then identify the method at the bottom of that figure, and finally check which evaluation metric has the highest number across three datasets for that method.Let me search for figures in the document more systematically:Great! I found Figure 1 showing the model structure. Now I can see that the three methods are:\n1. Method 1: Most salient personality - at the top\n2. Method 2: Soft-labeled personality - in the middle\n3. Method 3: Hard-coded personality - at the bottom\n\nThe method at the **bottom** is \"Hard-coded personality\" (Method 3). Now I need to find evaluation results for this method across the three datasets to determine which metric has the highest number.Now let me search for the experimental results tables:Let me continue searching for the results table:Perfect! Now I have found Table 5 which shows the evaluation results for all methods including \"NCF+Hard-Coded\" (the method at the bottom of the model structure figure) across the three datasets:\n\nFor **NCF+Hard-Coded** method across the three datasets:\n\n**Amazon-beauty:**\n- H@3: 0.948\n- H@5: 0.961 \n- H@10: 0.977\n- N@3: **0.849** \u2190 Highest\n- N@5: 0.826\n- N@10: 0.848\n\n**Amazon-music:**\n- H@3: 0.175\n- H@5: 0.232\n- H@10: 0.345\n- N@3: 0.147\n- N@5: 0.160\n- N@10: **0.189** \u2190 Highest\n\n**Personality2018:**\n- H@3: 0.503\n- H@5: 0.622\n- H@10: **0.758** \u2190 Highest\n- N@3: 0.398\n- N@5: 0.447\n- N@10: 0.498\n\nLooking at the highest value for each dataset:\n- Amazon-beauty: N@3 = 0.849\n- Amazon-music: N@10 = 0.189\n- Personality2018: H@10 = 0.758\n\nThe evaluation metric with the highest number across all three datasets for the Hard-coded method is **N@3** (with value 0.849 in Amazon-beauty).\n\nAnswer: N@3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 93687, "error": null, "extra": {"thread_id": 1969, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f41f50a7be1a46d59602bb6f59d5d5fd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 515, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3 on page 9, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV 50-shot:\n- Previous SoTA (line 4): 68.5(1.6)\n- The filter-then-rerank methods without ensemble are shown in the rows labeled \"+ Rerank (L)\"\n\nLooking at the three reranker options for 50-shot TACREV:\n- Vicuna-13B + Rerank (L): 70.8(1.6)\n- InstructGPT + Rerank (L): 72.3(1.4)\n- GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which refers to the \"+ Rerank (L)\" rows without the \"+ Ensemble (S)\" component.\n\nSince the question uses singular \"methods\" but there are multiple values, I should identify which one is the main result. Looking at the context, InstructGPT appears to be the primary model discussed in the main experiments (as mentioned in Section 5.3: \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nAnswer: 72.3", "citations": [], "input_tokens": 82081, "output_tokens": 358, "cost_micros": 251613, "latency_ms": 38407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) task, I need to find the 50-shot column.\n\nLooking at the rows for filter-then-rerank without ensemble:\n- Line 6: Vicuna-13B + Rerank (L): 70.8(1.6)\n- Line 7: InstructGPT + Rerank (L): 72.3(1.4)\n- Line 9: GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks about \"filter-then-rerank methods\" in general (plural), but I need to identify which specific method is being asked about. \n\nLooking at the context, when comparing with baseline methods, the paper typically refers to InstructGPT as the main reranker in their comparisons (as mentioned in section 5.3 where they state \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nFor 50-shot TACREV with InstructGPT + Rerank (L) without ensemble:\n\nAnswer: 72.3", "citations": [], "input_tokens": 35348, "output_tokens": 333, "cost_micros": 111039, "latency_ms": 9656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV dataset, I can see columns for 20-shot, 50-shot, and 100-shot settings.\n\nLooking at the 50-shot column for TACREV:\n\nThe rows showing filter-then-rerank methods without ensemble (marked as \"+ Rerank (L)\") are:\n- With Vicuna-13B as reranker: 70.8(1.6)\n- With InstructGPT as reranker: 72.3(1.4)\n- With GPT-4 as reranker: 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which means without the \"+ Ensemble (S)\" prefix. The previous SoTA method (line 4) shows 68.5(1.6) for comparison.\n\nSince there are three different LLM rerankers tested, and the question asks for \"filter-then-rerank methods\" in general, I should identify which specific result is being asked for. Looking at the context, when InstructGPT is used as the reranker (which appears to be the main model discussed in the results section based on Table 4 and other analyses), the performance is 72.3.\n\nAnswer: 72.3", "citations": [], "input_tokens": 46313, "output_tokens": 370, "cost_micros": 144489, "latency_ms": 11125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) at 50-shot setting:\n\nLooking at the rows with filter-then-rerank methods without ensemble:\n- Line 4: \"Previous SoTA\" (this is the baseline SLM)\n- Line 7: \"+ Rerank (L)\" with InstructGPT - this adds reranking to the previous SoTA without ensemble\n\nFrom Table 3, TACREV 50-shot column:\n- Previous SoTA: 68.5(1.6)\n- SLM + Rerank (L) with InstructGPT: 70.8(1.6)\n\nThe filter-then-rerank method without ensemble using InstructGPT shows 70.8.\n\nAnswer: 70.8", "citations": [], "input_tokens": 33414, "output_tokens": 254, "cost_micros": 104052, "latency_ms": 9844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "70.8", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nIn Table 3, under the TACREV (RE) 50-shot column:\n- Previous SoTA (SLM baseline): 72.6(1.5)\n- + Rerank (L) with Vicuna-13B: 73.8(1.7)\n- + Rerank (L) with InstructGPT: 75.4(1.5)\n- + Rerank (L) with GPT-4: 74.5(1.5)\n\nThe question asks for filter-then-rerank methods without ensemble. The \"+ Rerank (L)\" rows show the filter-then-rerank results without ensemble (the ensemble results are shown in the \"+ Ensemble (S) + Rerank (L)\" rows).\n\nSince there are three different rerankers tested, and the question asks for \"filter-then-rerank methods\" in general without specifying which LLM reranker, I should provide the InstructGPT result as it appears to be the primary comparison model used throughout the paper (it's the middle option and shows the best performance).\n\nAnswer: 75.4", "citations": [], "input_tokens": 45083, "output_tokens": 294, "cost_micros": 139659, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.4", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about filter-then-rerank methods performance on the 50-shot TACREV dataset in the provided document.Let me search for tables that contain both TACREV and 50-shot data:Perfect! I found Table 3 which contains the performance data. Let me look at the specific row for filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nFrom the table, I can see:\n- Column headers show: FewNERD (NER) with 5-shot, 10-shot, 20-shot, then TACREV (RE) with 20-shot, 50-shot, 100-shot\n- The filter-then-rerank method without ensemble is the \"+ Rerank (L)\" row (Line 7 as mentioned in the text)\n\nLooking at the data:\n- Under \"SLM\" section, the \"+ Rerank (L)\" row (line 2050-2060) shows the Vicuna-13B results\n- For TACREV 50-shot (5th data column), the value is: 70.8(1.6)\n\nLet me also check the InstructGPT rerank results:\n- \"+ Rerank (L)\" with InstructGPT (line 2080-2090) shows for TACREV 50-shot: 72.3(1.4)\n\nAnd GPT-4 rerank results:\n- \"+ Rerank (L)\" with GPT-4 (line 2111-2121) shows for TACREV 50-shot: 72.3(0.3)\n\nThe question asks for filter-then-rerank methods (w.o. ensemble), which would be the \"+ Rerank (L)\" rows. Looking at line 2084-2085, I can see the InstructGPT version shows 72.3 for 50-shot TACREV.\n\nHowever, I need to verify which specific implementation is being referred to. Let me check the context around line 1903 which mentions \"Lines 4 vs. 7\" for the comparison.\n\nBased on the table structure and the text mentioning \"Lines 4 vs. 7\" where Line 7 is the rerank method, and looking at the TACREV 50-shot column, the filter-then-rerank method (w.o. ensemble) with InstructGPT shows: 72.3\n\nAnswer: 72.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50031, "error": null, "extra": {"thread_id": 1970, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cec9e66ec5254f8aa0ad0e5be252d80b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 338, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left) in the document, which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset,\" the left panel shows \"ChatGPT's performance (F1 Score) across six instruction variants.\"\n\nThe red box in the left panel of Figure 3 highlights the instruction formats labeled I0 through I5 on the x-axis.\n\nBased on the context provided in Appendix E.2, these instruction variants range from simple to complex, with I0 being empty (no instruction) and I1 through I5 being increasingly detailed instructions for the NER task.\n\nAnswer: ChatGPT's performance (F1 Score) across six instruction variants", "citations": [], "input_tokens": 82074, "output_tokens": 166, "cost_micros": 248712, "latency_ms": 34046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "chatgpt s performance f1 score across six instruction variants", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants\" and displays \"ChatGPT's performance (F1 Score) across six instruction variants\" labeled I0 through I5.\n\nThe document provides the full text of these six instruction variants in Section E.2 \"Finding Better Instruction.\"\n\nThe instructions are:\n\n**I0:** [empty]\n\n**I1:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1]: [Definition_1], [Type_2]: [Definition_2], ..., [Type_N]: [Definition_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I3:** Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. The possible entity types are listed as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] identified_entity: [Entity_1]), ([Type_2] identified_entity: [Entity_2])'. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I4:** Assume you are an entity-instance annotator. Your objective is to perform a series of intricate steps for Named Entity Recognition. Firstly, you have to identify a particular word or phrase in the sentence that corresponds to an entity. Following this, classify the entity into one of the potential entity types. The potential entity types are provided as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] identified_entity: [Entity_", "citations": [], "input_tokens": 35341, "output_tokens": 512, "cost_micros": 113703, "latency_ms": 13984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.1702127659574468, "method": "str_norm", "normalised_pred": "type 1 sep identified entity entity", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"ChatGPT's performance (F1 Score) across six instruction variants,\" I need to find the instructions labeled I0 through I5.\n\nFrom Section E.2 \"Finding Better Instruction\" in the document, the six instruction variants are listed. The red box in Figure 3 (left) shows the x-axis labels I0, I1, I2, I3, I4, I5.\n\nThese correspond to:\n\n**I0**: [empty]\n\n**I1**: Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ... , [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2**: Same as I1 but with definitions added for each type\n\n**I3**: Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. [includes format specification]\n\n**I4**: Similar to I3 but with more elaborate language about \"intricate steps\"\n\n**I5**: Combines detailed task description with entity type definitions\n\nAnswer: Six instruction variants from empty (I0) to increasingly complex formats with definitions and detailed task descriptions (I1-I5)", "citations": [], "input_tokens": 46306, "output_tokens": 319, "cost_micros": 143703, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.06779661016949154, "method": "str_norm", "normalised_pred": "six instruction variants from empty i0 to increasingly complex formats with definitions and detailed task descriptions i1 i5", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), I can see it's showing \"Relationship between confidence scores and performance with/without LLM reranking.\"\n\nThe red box in the left figure appears to be highlighting the legend/experimental settings, which shows:\n\n**Answer: RoBERTa-large as filter and InstructGPT as reranker**", "citations": [], "input_tokens": 33407, "output_tokens": 81, "cost_micros": 101436, "latency_ms": 4281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.04081632653061225, "method": "str_norm", "normalised_pred": "roberta large as filter and instructgpt as reranker", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset\" with six instruction variants (I0-I5), I need to find the instruction text from the document.\n\nFrom Section E.2 \"Finding Better Instruction\", the six instruction variants for the NER task are listed. These are the instructions being tested in the red box of Figure 3 (left).\n\nAnswer: The six instruction variants I0 through I5 listed in Section E.2, ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "citations": [], "input_tokens": 45076, "output_tokens": 140, "cost_micros": 137328, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.17910447761194032, "method": "str_norm", "normalised_pred": "six instruction variants i0 through i5 listed in section e 2 ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the instructions corresponding to the settings shown in the red box.Let me search for Figure 3 in the document:Now let me read the section around Figure 3 to see the instruction variants and identify which ones are shown in the red box:I can see that Figure 3 Left shows ChatGPT's performance across six instruction variants labeled I0 through I5. I need to find the details about these instruction variants in Appendix E.2. Let me search for the appendix:Let me search for instruction variants more broadly:Let me search for the appendix sections with \"E.2\" or look for where instruction formats I0-I5 are defined:Let me look at the entire document more systematically. Let me search for sections starting with \"E.\" or \"Appendix\":Great! Now let me read section E.2 which should contain the instruction variants:Perfect! I found the six instruction variants (I0-I5). Now I need to look at Figure 3 more carefully to see which instructions are in the red box. Let me read around the figure again and look for any visual description of the red box:The image X14.png seems to be after Figure 3 in the document. Let me look for other embedded images that might be part of Figure 3:Let me look before Figure 3 to see if there's an image associated with the figure itself:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77483, "error": null, "extra": {"thread_id": 1971, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_538fbe85cd9b43f5a8b0d97c996a7e15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.08301886792452831, "method": "str_norm", "normalised_pred": "i ll read document to find figure 3 and identify instructions corresponding to settings shown in red box let me search for figure 3 in document now let me read section around figure 3 to see instruction variants and identify which ones are shown in red box i can see that figure 3 left shows chatgpt s performance across six instruction variants labeled i0 through i5 i need to find details about these instruction variants in appendix e 2 let me search for appendix let me search for instruction variants more broadly let me search for appendix sections with e 2 or look for where instruction formats i0 i5 are defined let me look at entire document more systematically let me search for sections starting with e or appendix great now let me read section e 2 which should contain instruction variants perfect i found six instruction variants i0 i5 now i need to look at figure 3 more carefully to see which instructions are in red box let me read around figure again and look for any visual description of red box image x14 png seems to be after figure 3 in document let me look for other embedded images that might be part of figure 3 let me look before figure 3 to see if there s image associated with figure itself", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find how many datasets are used for experiments in this paper.\n\nLooking at the document, in Section 3.1 \"Task, Dataset and Evaluation\", it states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe document then lists:\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD (3 datasets)\n- Relation Extraction (RE): TACRED, TACREV (2 datasets)\n- Event Detection (ED): ACE05, MAVEN, ERE (3 datasets)\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS (3 datasets)\n\nHowever, I need to count unique datasets. ACE05 and ERE appear in both ED and EAE tasks.\n\nCounting unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nThis gives us 9 unique datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 82066, "output_tokens": 265, "cost_micros": 250173, "latency_ms": 34217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the datasets used:\n\nFrom the Abstract and Introduction:\n- \"Through extensive experiments on nine datasets across four IE tasks\"\n\nFrom Section 3.1 (Task, Dataset and Evaluation):\n- \"We run experiments on nine widely-used datasets across four IE tasks.\"\n- The nine datasets are listed as:\n 1. CONLL03 (NER)\n 2. OntoNotes (NER)\n 3. FewNERD (NER)\n 4. TACRED (RE)\n 5. TACREV (RE)\n 6. ACE05 (ED)\n 7. MAVEN (ED)\n 8. ERE (ED)\n 9. ACE05, ERE, RAMS (EAE)\n\nWait, let me recount more carefully from the text:\n- NER: CONLL03, OntoNotes, FewNERD = 3 datasets\n- RE: TACRED, TACREV = 2 datasets\n- ED: ACE05, MAVEN, ERE = 3 datasets\n- EAE: ACE05, ERE, RAMS = 3 datasets\n\nHowever, ACE05 and ERE appear in multiple tasks. The document clearly states \"nine datasets\" multiple times.\n\nLooking at Table 6 which shows \"Statistics of nine datasets used\", I can count the distinct dataset names:\n1. CONLL\n2. OntoNotes\n3. FewNERD\n4. TACREV\n5. TACRED\n6. ACE05 (appears for both ED and EAE)\n7. MAVEN\n8. ERE (appears for both ED and EAE)\n9. RAMS\n\nThis confirms 9 distinct datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 35333, "output_tokens": 414, "cost_micros": 112209, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to identify all the datasets used across the four IE tasks.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n\n1. **Named Entity Recognition (NER)**: CONLL03, OntoNotes, FewNERD\n2. **Relation Extraction (RE)**: TACRED, TACREV\n3. **Event Detection (ED)**: ACE05, MAVEN, ERE\n4. **Event Argument Extraction (EAE)**: ACE05, RAMS, ERE\n\nCounting unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE, but counted once)\n- MAVEN\n- ERE (used in both ED and EAE, but counted once)\n- RAMS\n\nTotal unique datasets: 9\n\nThis is confirmed by the abstract which states \"extensive experiments on nine datasets across four IE tasks\" and multiple mentions throughout the paper of \"nine datasets.\"\n\nAnswer: 9", "citations": [], "input_tokens": 46298, "output_tokens": 251, "cost_micros": 142659, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the total number of datasets used in the experiments described in this paper.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\", the paper states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe four tasks and their associated datasets are:\n\n1. **Named Entity Recognition (NER)**: \n - CONLL03\n - OntoNotes\n - FewNERD\n\n2. **Relation Extraction (RE)**:\n - TACRED\n - TACREV\n\n3. **Event Detection (ED)**:\n - ACE05\n - MAVEN\n - ERE\n\n4. **Event Argument Extraction (EAE)**:\n - ACE05\n - ERE\n - RAMS\n\nCounting the unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE)\n- MAVEN\n- ERE (used in both ED and EAE)\n- RAMS\n\nThe paper explicitly states \"nine widely-used datasets\" and when counting unique dataset names, we have 9 distinct datasets (ACE05 and ERE are each used for two different tasks but are still single datasets).\n\nAnswer: 9", "citations": [], "input_tokens": 33399, "output_tokens": 309, "cost_micros": 104832, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for the datasets used in experiments:\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD\n- Relation Extraction (RE): TACRED, TACREV\n- Event Detection (ED): ACE05, MAVEN, ERE\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS\n\nCounting the unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nNote: ACE05 and ERE are used for both Event Detection and Event Argument Extraction tasks, but they are counted once as unique datasets.\n\nAnswer: 9", "citations": [], "input_tokens": 45068, "output_tokens": 207, "cost_micros": 138309, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the datasets used in the experiments.Let me search for a table or figure that lists all the datasets clearly.Perfect! I found Table 6 which clearly lists all the datasets. Let me count them:\n\nFrom the document, the paper uses experiments on **nine datasets** across four IE tasks:\n\n1. **Named Entity Recognition (NER)**: \n - CONLL03\n - OntoNotes\n - FewNERD\n\n2. **Relation Extraction (RE)**:\n - TACRED\n - TACREV\n\n3. **Event Detection (ED)**:\n - ACE05\n - MAVEN\n - ERE\n\n4. **Event Argument Extraction (EAE)**:\n - ACE05 (same dataset used for both ED and EAE)\n - RAMS\n - ERE (same dataset used for both ED and EAE)\n\nCounting unique datasets: CONLL03, OntoNotes, FewNERD, TACRED, TACREV, ACE05, MAVEN, ERE, RAMS = **9 datasets**\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43891, "error": null, "extra": {"thread_id": 1972, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_efab7508547a4ca893cf8d3cb1478856", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 235, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about the filter-then-rerank method and prompt templates...\n\nIn Table 21 (page 29), which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 82080, "output_tokens": 100, "cost_micros": 247740, "latency_ms": 31592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template used for the 'Contact.Meet' label in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 35347, "output_tokens": 142, "cost_micros": 108171, "latency_ms": 4609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for the 'Contact.Meet' event label in the filter-then-rerank paradigm.\n\nIn Table 21 (Templates for ACE05 dataset), which shows the templates used in the filter-then-rerank method, I can find:\n\nContact.Meet: \"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 46312, "output_tokens": 143, "cost_micros": 141081, "latency_ms": 6046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for 'Contact.Meet' used in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 33413, "output_tokens": 106, "cost_micros": 101829, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the template for 'Contact.Meet' in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find:\n\nContact.Meet | The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 45082, "output_tokens": 135, "cost_micros": 137271, "latency_ms": 4452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find information about the prompt template for label 'Contact.Meet' in the filter-then-rerank method.Let me search for information about \"Contact.Meet\" and \"filter-then-rerank\" in this document.Let me search for content with more context:Now I need to find the section that shows the prompt templates. Let me read around line 5446 where Contact.Meet was mentioned, and also look for tables or appendices with prompt templates:Perfect! I found the prompt template for Contact.Meet in Table 21 for ACE05 dataset. According to the document, the template for 'Contact.Meet' in the filter-then-rerank method is:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45328, "error": null, "extra": {"thread_id": 1973, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5466a03f139642c1a7d3c68dd6b01473", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the green module, then look for the 100-shot TACREV setting to calculate the absolute percentage drop.\n\nLooking through the document, I find Figure 6 on page 8, which shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" In this figure, I can see colored modules - orange for \"Easy Sample\" and pink for \"Hard Sample\". The diagram shows a filter (Small LM) and a reranker (Large LM).\n\nHowever, I need to look at Table 5 on page 9, which shows \"Ablation study on three datasets\" and examines the effectiveness of modules in the adaptive filter-then-rerank system. The table shows results for TACREV (100-shot).\n\nLooking at the table columns, I can see:\n- CoT (Chain of Thought)\n- Demo\n- LF (Label Filtering) \n- AD (Adaptive)\n\nThe green box in Figure 6 appears to correspond to the demonstration/demo component based on the visual representation.\n\nFrom Table 5, for TACREV (100-shot):\n- With all modules (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without Demo but with others (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe absolute drop is: 75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 82081, "output_tokens": 344, "cost_micros": 251403, "latency_ms": 36125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance impact of dropping the green module (demonstration) shown in Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can see the ablation results for different components on three datasets including TACREV (100-shot).\n\nThe table shows:\n- Full system (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9(2.4)\n- Without Demo (\u2717 Demo \u2717 CoT \u2713 LF \u2713 AD): 74.9(2.2)\n\nHowever, I need to isolate just the Demo effect. Looking more carefully:\n- With CoT and Demo (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9\n- Without Demo but with CoT (\u2713 CoT \u2717 Demo \u2713 LF \u2713 AD): This line doesn't exist\n\nBut I can see:\n- Without CoT but with Demo (\u2717 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.4\n- Without both CoT and Demo (\u2717 CoT \u2717 Demo \u2713 LF \u2713 AD): 74.9\n\nLet me look at the correct comparison for just dropping Demo:\nFrom the table, comparing rows where only Demo changes:\n- Row 1 (\u2713 CoT \u2713 Demo): 75.9\n- Row 2 (\u2717 CoT \u2713 Demo): 75.4\n- Row 3 (\u2717 CoT \u2717 Demo): 74.9\n\nThe drop from having Demo to not having Demo (when CoT is also removed) is:\n75.4 - 74.9 = 0.5\n\nHowever, to properly isolate the Demo effect with CoT present, I need to estimate. The pattern suggests:\n- Full system: 75.9\n- Without Demo (keeping other factors): approximately 75.4 - 75.9 range\n\nActually, looking at row 2 vs row 3: the difference is 75.4 -", "citations": [], "input_tokens": 35348, "output_tokens": 512, "cost_micros": 113724, "latency_ms": 11858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance drop when removing the green module (demonstration) from Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can find the relevant information:\n- The table shows ablation results on three datasets including TACREV (100-shot)\n- The rows show different configurations with checkmarks (\u2713) and crosses (\u2717)\n\nThe green module in Figure 6 represents the \"Demonstration\" component.\n\nFrom Table 5, for TACREV (100-shot):\n- With CoT \u2713, Demo \u2713, LF \u2713, AD \u2713: 75.9(2.4)\n- With CoT \u2717, Demo \u2717, LF \u2713, AD \u2713 (no demo): 74.9(2.2)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 46313, "output_tokens": 231, "cost_micros": 142404, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the \"green module,\" then look at the 100-shot TACREV setting to determine the performance drop.\n\nLooking at Figure 6 in the document, it shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" The green box is labeled as \"demos\" (demonstration examples).\n\nNow I need to find the ablation study that shows what happens when demos are removed. Looking at Table 5 (Ablation study), I can see different configurations where modules are removed:\n\nThe table shows results for FewNERD (20-shot), TACREV (100-shot), and ACE05 (20-shot).\n\nFor TACREV (100-shot):\n- Full system (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without CoT (\u2717 \u2713 \u2713 \u2713): 75.4(2.4)\n- Without Demo (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe \"Demo\" module corresponds to the green box in Figure 6. When we remove it (going from \u2717 \u2713 \u2713 \u2713 to \u2717 \u2717 \u2713 \u2713), the performance drops from 75.4 to 74.9.\n\nThe absolute drop is: 75.4 - 74.9 = 0.5\n\nAnswer: 0.5", "citations": [], "input_tokens": 33414, "output_tokens": 351, "cost_micros": 105507, "latency_ms": 9827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.5", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the \"green module\" in Figure 6 and its effect on the 100-shot TACREV setting.\n\nFrom Figure 6, the green box represents the **Demonstration** component in the reranker process.\n\nFrom Table 5 (Ablation Study), I can see the results when removing the Demo component:\n- The row \"[ ] [ ] [x] [x]\" indicates: no CoT, no Demo, with LF, with AD\n- For TACREV (100-shot): 74.9(2.2)\n- The full system \"[x] [x] [x] [x]\" achieves: 75.9(2.4)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 45083, "output_tokens": 198, "cost_micros": 138219, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and the information about dropping the green module in the 100-shot TACREV setting.Let me search for Figure 6 in the document:Now let me search for more context around Figure 6:Good! I found Figure 6. It mentions a green box representing \"demos\". Let me read the full content around Figure 6 and look for ablation studies or tables that show what happens when dropping the green module (demos):Perfect! Figure 6 shows that the green box represents \"Demonstration\" (demos). Now I need to find the ablation study or table that shows the performance drop when removing demos. Let me search for ablation or table related to this:Let me search for ablation study or a table showing what happens when demos are removed:Perfect! I found the ablation study in Table 5. Let me read that section:Let me search more specifically for Table 5:Great! I found Table 5. Let me continue reading to see all rows:Perfect! Now let me look for the explanation of what each column means. Let me read the section before Table 5:Let me find the section that explains the ablation study (section 5.5):Let me continue reading the ablation study explanation:I see the explanation was cut off. Let me search for where it continues:Let me look for text after Table 3 that continues the ablation study explanation:Perfect! Now I have the full explanation. Looking at Table 5, I need to identify which row corresponds to dropping the \"Demo\" (green module). Let me re-read Table 5 more carefully:Now let me understand the table structure. The columns are:\n- CoT (Chain of Thought)\n- Demo (Demonstration - the green module)\n- LF (Label Filtering)\n- AD (Adaptive)\n\nThe rows show different configurations with checkmarks (\u2713) meaning present and X (\u2717) meaning removed.\n\nLooking at the table:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 TACREV (100-shot) = 74.9\n- Row 4: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2612 AD \u2192 TACREV (100-shot) = 73.8\n- Row 5: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2717 AD \u2192 TACREV (100-shot) = 59.9\n\nWait, I need to understand the notation better. Let me look at the explanation again. From the text at lines 2189-2194:\n\n\"(2) Demo: We remove all examples, rendering the reranking a zero-shot problem.\"\n\nSo when Demo is removed (\u2717), all examples are removed. Looking at the table structure, I need to find the row where Demo changes from \u2713 to \u2717 while keeping other factors constant.\n\nThe best comparison to isolate the effect of Demo would be:\n- Row 1 (full model with all components): CoT \u2713, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.9\n- Row 2 (without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n\nTo find the effect of dropping Demo specifically, I should compare:\n- Row 2 (with Demo, without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n- Row 3 (without Demo and CoT): CoT \u2717, Demo \u2717, (different LF/AD settings) \u2192 74.9\n\nBut wait, the LF and AD settings change. Let me reconsider. Looking more carefully at the symbols, I see \"\u2612\" which might be different from \"\u2713\" and \"\u2717\".\n\nLet me look for a clearer comparison. The green module in Figure 6 is \"Demo\". To find the effect of dropping it:\n\nFrom the full model (row 1): 75.9\nTo without Demo - I need to find which row removes only Demo while keeping everything else.\n\nActually, looking at row 2 vs row 3:\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 74.9\n\nBut LF and AD also change, so this isn't a clean comparison.\n\nLet me think about this differently. According to the explanation text (lines 2197-2199):\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\"\n\nAnd (line 2199-2200):\n\"(2) Demos without explanations also contribute to performance improvement.\"\n\nSo the comparison should be:\n- With Demo + CoT (row 1): 75.9\n- Without CoT but with Demo (row 2): 75.4 \n- Without both CoT and Demo (row 3 onwards)\n\nThe cleanest way to see the effect of dropping Demo is to compare a configuration with Demo vs without Demo. Looking at row 2 (has Demo but no CoT) = 75.4, and the next logical step would be row 3 (no Demo, no CoT) = 74.9.\n\nBut let me also check if there's a better comparison. Actually, looking at the structure, row 3 onwards all have \"\u2717 \u2612\" for both CoT and Demo columns, meaning both are removed.\n\nSo the drop from having Demo (row 2: 75.4) to not having Demo (row 3: 74.9) = 75.4 - 74.9 = 0.5\n\nBut wait, in row 3, the LF and AD settings also changed (\u2612 instead of \u2713).\n\nLet me reconsider what \u2612 means. It might mean the opposite of the feature (e.g., if \u2713 means \"with\", then \u2612 might mean \"without\" for LF and AD columns).\n\nActually, based on the explanation:\n- LF (label filtering): \u2713 means using top-N labels, \u2717 means retaining all labels\n- AD (adaptive): \u2713 means only hard samples, \u2717 means all samples\n\nSo \u2612 might indicate the opposite setting.\n\nGiven the complexity, let me focus on finding where the Demo is specifically dropped. The clearest comparison would be between:\n- Row 1 (everything included): 75.9\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.9 - 74.9 = 1.0\n\nBut this includes the effect of removing CoT as well. To isolate Demo:\n- Row 2 (no CoT, yes Demo): 75.4\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.4 - 74.9 = 0.5\n\nHowever, LF and AD settings also changed between row 2 and 3.\n\nLet me look at this more systematically. Actually, I think I'm overcomplicating this. The question asks specifically about dropping \"the green module\" which is Demo. Looking at the full system (row 1) vs without demo, I need to find the appropriate ablation.\n\nActually, re-reading the table more carefully with the column headers having 2 cells labeled \"CoT Demo\", I think these might be a single column header spanning 2 rows. Let me re-examine:\n\nLooking at lines 2298-2303:\n```\n
\n```\n\nThis suggests \"CoT Demo\" is a single header spanning 2 columns. So the structure might be:\n- Column 1: CoT\n- Column 2: Demo\n- Column 3: LF\n- Column 4: AD\n\nWith this understanding:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, ? LF, ? AD \u2192 74.9\n\nSo dropping Demo (from row 2 to row 3) causes a drop from 75.4 to 74.9 = 0.5\n\nBut the LF and AD settings are unclear in row 3 with the \u2612 symbol.\n\nGiven the question asks specifically for dropping the green module (Demo), and looking at the cleanest comparison while keeping CoT removed:\n- With Demo (row 2): 75.4\n- Without Demo (row 3): 74.9\n- Drop: 75.4 - 74.9 = 0.5\n\nBut if we compare from the full model:\n- Full model (row 1): 75.9\n- Without Demo but with CoT - this row doesn't exist in the table\n\nActually, maybe I should look at row 5 which has everything removed. Let me reconsider all rows:\n1. All components: 75.9\n2. No CoT: 75.4\n3. No CoT, No Demo: 74.9\n4. No CoT, No Demo, No LF: 73.8\n5. Nothing (all removed): 59.9\n\nThe progression suggests cumulative removal. To isolate Demo's effect:\nRow 2 (75.4) - Row 3 (74.9) = 0.5\n\nWait, but I should verify this interpretation by looking at the explanation text again. From lines 2196-2200:\n\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\n(2) Demos without explanations also contribute to performance improvement.\"\n\nThis suggests:\n- Row 1 has Demo WITH CoT (explanations)\n- Row 2 has Demo WITHOUT CoT (no explanations) \n- Row 3 onwards has NO Demo\n\nSo the effect of dropping Demo entirely (going from row 2 to row 3): 75.4 - 74.9 = 0.5\n\nBut wait, there might be more to consider. Let me check if there's another row that might represent dropping just Demo while keeping CoT. Looking at the table again... no, there isn't such a row.\n\nGiven the structure and the question asking about dropping the green module (Demo), the most reasonable answer based on the ablation study is:\n\nFrom row 1 (full model with all components) = 75.9\nTo the configuration without Demo... I need to find which row that is.\n\nIf I assume row 3 still has other components but not Demo:\nRow 1: 75.9\nRow 3: 74.9\nDifference: 1.0\n\nBut this seems to also remove CoT. Let me think about what makes sense given Figure 6. The green module is specifically the \"Demonstration\" box shown feeding into the reranker.\n\nActually, perhaps the cleanest interpretation is:\n- Best system with Demo (row 1 or 2): max is 75.9 (row 1)\n- Best system without Demo (row 3, 4, or 5): max is 74.9 (row 3)\n- Absolute drop: 75.9 - 74.9 = 1.0\n\nBut this conflates CoT and Demo effects.\n\nHmm, let me reconsider the table structure once more. Maybe the header \"CoT Demo\" with colspan=\"2\" means there are two sub-columns, one for each. And the checkmarks below indicate their presence/absence.\n\nGiven the typical ablation study structure, each row progressively removes one more component. So:\n- Row 1: Everything (baseline) = 75.9\n- Row 2: Remove CoT = 75.4 (drop of 0.5)\n- Row 3: Remove Demo (CoT already removed) = 74.9 (additional drop of 0.5)\n- Row 4: Remove LF (CoT and Demo already removed) = 73.8 (additional drop of 1.1)\n- Row 5: Remove AD (everything removed) = 59.9 (additional drop of 13.9)\n\nIf we want the isolated effect of Demo, we need to compare configurations that differ only in Demo. But there's no row with CoT present and Demo absent.\n\nHowever, if we accept that row 2 \u2192 row 3 transition shows the effect of removing Demo (with CoT already removed), then the drop is 0.5.\n\nAlternatively, if the question wants the total contribution of Demo to the best system, we could argue:\n- Best with Demo: 75.9 (row 1)\n- Best without Demo: 74.9 (row 3)\n- Total drop attributable to absence of Demo: 1.0\n\nBut this includes the removal of CoT's contribution when combined with Demo removal.\n\nGiven typical ablation study interpretation and the question phrasing \"if I drop the green module,\" I believe the answer is the difference from the full system (row 1: 75.9) to the first row without Demo which is row 3 (74.9):\n\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 147812, "error": null, "extra": {"thread_id": 1974, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b54490e8642341dd973e7bd446513622", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1457, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to locate the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, Figure 1 appears on page 2. It shows \"An example of our SCITAB dataset\" with a reasoning graph on the right side.\n\nIn the reasoning graph on the right side of Figure 1, I can see:\n- Green rectangles (boxes): I can count \"Closed-domain knowledge: Table caption\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker: Supported\" - that's 5 green boxes\n- Grey rectangles: I can see grey/neutral colored boxes containing text like \"Productivity corresponds to Prod. column\", \"The number of random chance is 50%\", \"A's productivity is 57.5%\", and \"The subtraction result between 57.5% and 50% is 7.5%\" - that's 4 grey boxes\n\nWait, let me recount more carefully by looking at the colors:\n- Green boxes appear to be the operation/reasoning step boxes\n- Grey boxes appear to be the intermediate result/statement boxes\n\nCounting again in Figure 1's reasoning graph:\n- Green rectangles: 5 (the labeled reasoning steps)\n- Grey rectangles: 4 (the intermediate statements)\n\nIn descending order: [\"5\", \"4\"]\n\nAnswer: 5, 4", "citations": [], "input_tokens": 68164, "output_tokens": 313, "cost_micros": 209187, "latency_ms": 35953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4, 5", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1.\n\nLooking at Figure 1 on the first page, I can see a reasoning graph on the right side of the figure. Let me count the rectangles by color:\n\nGreen rectangles (these appear to represent supported claims or correct reasoning):\n1. \"A's productivity is 57.5%\"\n2. \"Productivity corresponds to Prod. column\"\n3. \"The number of random chance is 50%\"\n4. \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 4 green rectangles.\n\nGrey rectangles (these appear to represent refuted or incorrect claims):\n1. \"A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n\nThat's 1 grey rectangle.\n\nIn descending order: [\"4\", \"1\"]\n\nAnswer: 4, 1", "citations": [], "input_tokens": 31797, "output_tokens": 222, "cost_micros": 98721, "latency_ms": 7155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "1, 4", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the document, which is Figure 1 on the first page.\n\nLooking at Figure 1, I can see a reasoning graph on the right side. Let me count the rectangles by color:\n\nGreen rectangles (which appear to represent certain types of nodes in the reasoning graph):\n- \"Closed-domain knowledge: Table caption\"\n- \"Productivity corresponds to Prod. column\"\n- \"Commonsense knowledge\"\n- \"The number of random chance is 50%\"\n- \"Simple lookup\"\n- \"A's productivity is 57.5%\"\n- \"Subtraction\"\n- \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 8 green rectangles.\n\nGrey rectangles (which appear to represent the claims):\n- \"Supported Claim: A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n- \"Refuted Claim: A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n- \"Not Enough Info Claim: The low performance of \"to\" can be explained by the fact that it is responsible for only 4.6% of the inference in the training set.\"\n\nThat's 3 grey rectangles.\n\nIn descending order: 8, 3\n\nAnswer: 8, 3", "citations": [], "input_tokens": 44352, "output_tokens": 333, "cost_micros": 138051, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "3, 8", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, the first figure is Figure 1, which shows \"An example of our SCITAB dataset (left) and its corresponding reasoning graph (right).\"\n\nIn the reasoning graph (right side of Figure 1), I can see:\n- The graph contains nodes connected by arrows\n- There are rectangular boxes of different colors\n- Looking at the reasoning graph structure, I can identify green rectangles and grey rectangles\n\nCounting the rectangles in the reasoning graph:\n- Green rectangles: These appear to be the operation/function nodes in the graph. I can count: \"Lookup\", \"Background knowledge\", \"Commonsense knowledge\", \"Subtract\" - that's 4 green rectangles\n- Grey rectangles: These appear to be the value/result nodes. I can count: \"57.5%\", \"Prod.\", \"50%\", \"7.5%\" - that's 4 grey rectangles\n\nWait, let me recount more carefully by examining the figure description and structure:\n- Green rectangles (operations): 4\n- Grey rectangles (values): 4\n\nIn descending order: [\"4\", \"4\"]\n\nAnswer: 4, 4", "citations": [], "input_tokens": 28779, "output_tokens": 281, "cost_micros": 90552, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1 on page 1.\n\nLooking at Figure 1, I can see it contains:\n- A table on the left side\n- A reasoning graph on the right side with various nodes and connections\n\nIn the reasoning graph (right side), I can identify:\n- Grey rectangles: These appear to be nodes like \"Closed-domain knowledge\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker\"\n- Green rectangles: I need to look for any green-colored rectangles\n\nLet me count carefully:\n- Grey rectangles in the reasoning graph: I can see multiple grey nodes representing different types of operations and knowledge\n- Green rectangles: Looking at the figure, I don't see distinctly green rectangles in the traditional sense, but there appear to be some lighter/different colored elements\n\nActually, examining the figure more carefully:\n- There appear to be approximately 5 grey rectangular nodes in the reasoning graph\n- I don't clearly see green rectangles that are distinctly separate from the grey ones\n\nHowever, looking more carefully at the color scheme and the structure, it appears there are:\n- Grey rounded rectangles: approximately 5\n- There don't appear to be clearly distinct green rectangles\n\nBased on my examination, I count approximately 5 grey rectangles and 0 distinctly green rectangles in Figure 1.\n\nAnswer: 5, 0", "citations": [], "input_tokens": 36689, "output_tokens": 320, "cost_micros": 114867, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "0, 5", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper. Let me continue reading to locate Figure 1.Perfect! I found Figure 1. Now I need to examine this figure carefully to count the green and grey rectangles. Based on the content I can see, Figure 1 shows a reasoning graph with different colored rectangles representing different steps in the reasoning process. Let me read more carefully to identify all the rectangles and their colors.\n\nFrom what I can see in the figure description:\n- There's a table on the left\n- A reasoning graph on the right\n- The reasoning graph contains several boxes/rectangles with different steps\n\nLooking at the text content that describes the figure structure:\n1. \"Closed-domain knowledge: Table caption\" - appears to be one rectangle\n2. \"Commonsense knowledge\" - appears to be another rectangle\n3. \"Productivity corresponds to Prod. column\" - part of the reasoning\n4. \"Simple lookup\" - appears to be a step\n5. \"The number of random chance is 50%\" - another element\n6. \"Supported Claim\" - labeled element\n7. \"A's productivity is 57.5%\" - text element\n8. Various reasoning steps with operators (+, -, =, \u00d7, ?)\n9. \"Refuted Claim\" - labeled element\n10. \"Not Enough Info Claim\" - labeled element\n\nLet me search for more details about the actual visual representation or read the next section to see if there's a clearer description.Let me go back and read the figure description more carefully to understand the visual elements:It seems the content around line 632-646 is describing a green checkmark icon, which is not Figure 1. Let me look more carefully at the actual Figure 1 content. Let me re-read around the figure caption area:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55655, "error": null, "extra": {"thread_id": 1975, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b73a2e9dd2a74761aa49d98419f10801", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 323, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me search for more details about actual visual representation or read next section to see if there s clearer description let me go back and read figure description more carefully to understand visual elements it seems content around line 632 646 is describing green checkmark icon, which is not figure 1 let me look more carefully at actual figure 1 content let me re read around figure caption area", "normalised_gold": "1, 3"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to count the reasoning steps shown in the reasoning graph on the right side of the figure.\n\nThe reasoning graph shows the following steps:\n1. Closed-domain knowledge: Table caption\n2. Commonsense knowledge\n3. Simple lookup\n4. Subtraction\n\nThese are the four distinct reasoning steps required to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nAnswer: 4", "citations": [], "input_tokens": 68131, "output_tokens": 119, "cost_micros": 206178, "latency_ms": 30733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see a reasoning graph on the right side that shows the steps needed to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nThe reasoning graph shows the following steps:\n\n1. \"Closed-domain knowledge: Productivity corresponds to Prod. column\" (from table caption)\n2. \"Commonsense knowledge: The number of random chance is 50%\"\n3. \"Simple lookup: A's productivity is 57.5%\"\n4. \"Subtraction: The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese steps lead to verifying whether the claim is \"Supported\" or \"Refuted\".\n\nCounting the reasoning steps in the graph, I can identify 4 distinct reasoning steps.\n\nAnswer: 4", "citations": [], "input_tokens": 31764, "output_tokens": 225, "cost_micros": 98667, "latency_ms": 8390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the reasoning steps shown in the reasoning graph in Figure 1.\n\nLooking at Figure 1's reasoning graph (on the right side), I can identify the following reasoning steps:\n\n1. \"Closed-domain knowledge: Table caption\" - Productivity corresponds to Prod. column\n2. \"Commonsense knowledge\" - The number of random chance is 50%\n3. \"Simple lookup\" - A's productivity is 57.5%\n4. \"Subtraction\" - The subtraction result between 57.5% and 50% is 7.5%\n5. Final verification step - comparing the claim with the calculated result\n\nThe reasoning graph shows 5 distinct reasoning steps from the initial knowledge extraction to the final verification of the claim.\n\nAnswer: 5", "citations": [], "input_tokens": 44319, "output_tokens": 174, "cost_micros": 135567, "latency_ms": 6969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see it contains:\n- A table with columns: Cue, App., Prod., Cov.\n- A claim about \"A's productivity of 57.5%\"\n- A reasoning graph on the right side\n\nThe reasoning graph shows the verification process with several nodes connected by arrows. Let me count the reasoning steps by tracing through the graph:\n\n1. Start with the claim about A's productivity of 57.5%\n2. Look up \"A's productivity\" \u2192 refers to cell at last row and \"Prod.\" column\n3. Background knowledge: \"productivity\" corresponds to \"Prod.\" column\n4. Simple lookup: Get value 57.5% from the table\n5. Commonsense knowledge: \"random chance\" means 50% accuracy\n6. Numerical reasoning: Calculate difference 57.5% - 50% = 7.5%\n7. Compare calculated result with claimed value (7.5%)\n\nCounting the nodes in the reasoning graph shown in Figure 1, I can see there are 7 distinct reasoning steps involved in verifying this claim.\n\nAnswer: 7", "citations": [], "input_tokens": 28746, "output_tokens": 277, "cost_micros": 90393, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I need to count the reasoning steps shown in the reasoning graph on the right side.\n\nThe reasoning graph shows the following steps to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\":\n\n1. **Closed-domain knowledge**: Productivity corresponds to Prod. column (from table caption)\n2. **Commonsense knowledge**: The number of random chance is 50%\n3. **Simple lookup**: Finding the value 57.5% in the table (A's productivity)\n4. **Subtraction**: The subtraction result between 57.5% and 50% is 7.5%\n\nAnswer: 4", "citations": [], "input_tokens": 36656, "output_tokens": 169, "cost_micros": 112503, "latency_ms": 7671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned as most relevant to find Figure 1 and count the reasoning steps.Let me continue reading to find Figure 1:Now let me read the section around line 562 where Figure 1 is described:Let me read more to see the complete figure with all reasoning steps:Based on the document content, I can see Figure 1 shows a reasoning graph with the following steps:\n\n1. **Closed-domain knowledge: Table caption** - \"Productivity corresponds to Prod. column\"\n2. **Simple lookup** - \"A's productivity is 57.5%\"\n3. **Commonsense knowledge** - \"The number of random chance is 50%\"\n4. **Subtraction** - \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese 4 reasoning steps lead to verifying the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\" which is marked as \"Supported\".\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37062, "error": null, "extra": {"thread_id": 1976, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6471c23a58a448bd8c1bac8c5ba5da64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 190, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from the document.\n\nFrom Table 1 on page 4, I can see:\n\n**Wiki Table datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Table claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 68140, "output_tokens": 175, "cost_micros": 207045, "latency_ms": 33358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the number of claims in Wiki Table datasets versus Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 31773, "output_tokens": 182, "cost_micros": 98049, "latency_ms": 6016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 44328, "output_tokens": 174, "cost_micros": 135594, "latency_ms": 6858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 28755, "output_tokens": 183, "cost_micros": 89010, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 36665, "output_tokens": 174, "cost_micros": 112605, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about claims in Wiki Table datasets compared to scientific articles datasets.Perfect! I found the table with the statistics. Let me extract the specific information:\n\nFrom the table:\n- **Wiki Tables datasets**: \n - TabFact: 117,854 claims\n - FEVEROUS: 87,026 claims\n - Total: 117,854 + 87,026 = 204,880 claims\n\n- **Scientific Articles datasets**:\n - SEM-TAB-FACTS: 5,715 claims\n - SCITAB: 1,225 claims\n - Total: 5,715 + 1,225 = 6,940 claims\n\nDifference: 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36890, "error": null, "extra": {"thread_id": 1977, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eedb3672a2cb45bf898fdd6ba6d9a71c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 152, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the total number of claims in the dataset and the payment per claim annotation.\n\nFrom the document:\n\n1. Total number of claims in SCITAB: 1,225 claims (mentioned in the abstract and Table 1)\n\n2. Payment per claim annotation: 0.37 USD (mentioned in Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\")\n\nHowever, I need to consider that the annotation process involves multiple rounds and different types of claims:\n\nFrom Section 2.3, the manual verification process involves:\n- Verifying 872 false claims (counter-claims)\n- Verifying 900 NEI claims\n- Reviewing 872 real-world scientific claims\n\nThis suggests a two-round annotation process where each claim is annotated by two different annotators.\n\nLooking more carefully at the construction process:\n- 872 original claims were extracted\n- For each original claim, a counter-claim was generated (872 counter-claims)\n- 900 NEI claims were generated\n- After verification, the final dataset has 1,225 claims total\n\nThe annotation process in Section 2.3 states: \"We assign two different annotators to perform a two-round annotation for each claim\"\n\nSo the total annotations would be:\n- For supported/refuted claim pairs: 872 pairs need verification (2 rounds each)\n- For NEI claims: 900 claims need verification (2 rounds each)\n\nTotal annotations = (872 \u00d7 2) + (900 \u00d7 2) = 1,744 + 1,800 = 3,544 annotations\n\nTotal cost = 3,544 \u00d7 0.37 = 1,311.28 USD\n\nAnswer: 1311.28", "citations": [], "input_tokens": 68152, "output_tokens": 408, "cost_micros": 210576, "latency_ms": 38546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1311.28", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the annotation cost and the total number of claims in the dataset.\n\nFrom the document:\n\n1. **Annotation cost**: \"Each claim annotation is reimbursed at a rate of 0.37 USD\" (Section 2.3)\n\n2. **Total number of claims in SCITAB**: Looking at Table 1, the total number of claims in SCITAB is 1,225.\n\nHowever, I need to determine how many claims actually needed annotation. According to Section 2:\n\n- Section 2.1: 872 real-world scientific claims were filtered from SciGen\n- Section 2.2: They generated 872 false claims (counter-claims) and 900 NEI claims\n- Section 2.3: Manual verification was done on all these claims\n\nFrom Section 2.3 and Appendix D:\n- 872 false claims were verified\n- 900 NEI claims were verified (which resulted in 355 final NEI claims)\n- 872 original claims were also reviewed\n\nSo the total annotations performed:\n- False claim verification: 872 claims\n- NEI claim verification: 900 claims\n- Total annotations: 872 + 900 = 1,772 claims\n\nTotal cost = 1,772 claims \u00d7 0.37 USD per claim = 655.64 USD\n\nAnswer: 655.64", "citations": [], "input_tokens": 31785, "output_tokens": 318, "cost_micros": 100125, "latency_ms": 9562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "655.64", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. The total number of claims in the SCITAB dataset\n2. The payment rate per claim annotation\n\nFrom the document:\n\n**Total number of claims:**\nFrom Table 1 and Section 2.3: \"Total # of Claims: 1,225\"\n\n**Payment per claim:**\nFrom Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n**Calculation:**\nTotal cost = 1,225 claims \u00d7 0.37 USD per claim = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 44340, "output_tokens": 145, "cost_micros": 135195, "latency_ms": 5389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost for annotating all claims in the SCITAB dataset.\n\nFrom the document, I can find:\n\n1. **Total number of claims in SCITAB**: According to the abstract and Section 2, SCITAB contains **1,225 claims** (also written as \"1.2K expert-verified scientific claims\").\n\n2. **Cost per claim annotation**: According to Section 2.3 under \"Annotator Recruitment\":\n - \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n Total cost = Number of claims \u00d7 Cost per claim\n Total cost = 1,225 \u00d7 0.37\n Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 28767, "output_tokens": 183, "cost_micros": 89046, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost of annotating all claims in the SCITAB dataset.\n\nFrom the document:\n\n1. **Total number of claims in SCITAB**: According to Table 1, SCITAB has 1,225 total claims.\n\n2. **Payment per claim annotation**: From Section 2.3, it states \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n Total cost = Number of claims \u00d7 Cost per claim\n Total cost = 1,225 \u00d7 0.37\n Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 36677, "output_tokens": 152, "cost_micros": 112311, "latency_ms": 5469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant to find information about annotation costs for the proposed dataset.Let me search for information about annotation costs, dataset size, and pricing in the document.Let me read through the document more systematically. I'll start with sections that typically discuss dataset details and costs.Let me search for sections that discuss the dataset size and annotation costs. Let me look for keywords like \"price\", \"annotator\", \"pay\", etc.Let me search for numbers that might relate to costs:Let me read sections that might contain appendix or details about annotation costs:Let me look in later sections of the document where appendix or cost information might be:Let me search for \"Appendix\" section which might contain cost details:Let me try searching with content mode to find where these terms appear:Great! I found a reference to annotation cost. Let me read around line 974:Perfect! I found the annotation cost information. Now I need to find the total number of claims in the dataset. Let me search for that information:Perfect! Now I have all the information I need:\n- Total number of claims: 1,225\n- Cost per claim annotation: 0.37 USD\n\nLet me calculate the total cost:\nTotal cost = 1,225 \u00d7 0.37 = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97266, "error": null, "extra": {"thread_id": 1978, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77659594d74045e99872a87f698ab753", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 502, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about the distribution of reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3 on page 5, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset\":\n\nThe figure shows the percentage of claims for each number of reasoning steps (1-11).\n\nFrom the histogram, I can read the following percentages:\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20% (highest)\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1 on page 4, the total number of claims in SCITAB is 1,225.\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 68134, "output_tokens": 299, "cost_micros": 208887, "latency_ms": 34843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of claims with the highest reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the distribution of reasoning steps.\n\nThe x-axis shows reasoning steps from 1 to 11, and the y-axis shows the percentage.\n\nFrom the figure, I can read the following percentages:\n- 1 step: 1%\n- 2 steps: 7%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 6%\n- 9 steps: 5%\n- 10 steps: 3%\n- 11 steps: 2%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nThe document states there are 1,225 total claims in SCITAB (from Table 1).\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 31767, "output_tokens": 277, "cost_micros": 99456, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step percentage distribution in the SCITAB dataset from Figure 3.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 2%\n- 2 steps: 3%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 7%\n- 9 steps: 6%\n- 10 steps: 5%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nSince the dataset contains 1,225 claims total (as stated in the abstract and throughout the document), I need to calculate 20% of 1,225:\n\n1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 44322, "output_tokens": 243, "cost_micros": 136611, "latency_ms": 6812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the highest percentage of reasoning steps in the SCITAB dataset and determine how many claims fall into that category.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see:\n\n- The x-axis represents the reasoning steps in each claim\n- The y-axis represents the frequency for each reasoning step\n- The figure distinguishes between shallow claims (1-2 reasoning steps, highlighted in red) and deep claims (3+ reasoning steps, highlighted in blue)\n\nFrom the histogram in Figure 3, I need to identify which reasoning step has the highest frequency (highest bar).\n\nExamining the histogram carefully:\n- The highest bar appears to be at 4 reasoning steps\n- The frequency at 4 reasoning steps appears to be approximately 350-400 claims\n\nLooking more carefully at the visual representation, the bar at 4 reasoning steps is the tallest in the histogram, with a frequency of approximately 350 claims.\n\nAnswer: 350", "citations": [], "input_tokens": 28749, "output_tokens": 223, "cost_micros": 89592, "latency_ms": 7078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "350", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step with the highest percentage in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20%\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1, the total number of claims in SCITAB is 1,225.\n\nNumber of claims with 5 reasoning steps = 1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 36659, "output_tokens": 266, "cost_micros": 113967, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the most relevant document to find information about claims and reasoning steps in the author's proposed dataset.Let me continue reading to find information about the dataset and reasoning steps distribution.Great! I found a reference to a distribution histogram of reasoning steps. Let me read that specific section.Let me look at the earlier part where the figure caption was mentioned to understand the chart better.Perfect! Now I can see the histogram data. Let me read more to see all the percentage values clearly:From the histogram data I can see, the percentages are listed vertically and the reasoning steps go from 2 to 11. Looking at the data:\n\n- The percentages shown are: 20, 18, 20, 15, 15, 15, 6, 8, 7, 5, 3, 2, 1, 1\n\nThe highest percentages appear to be 20% which occurs twice. Let me look for more context to understand the exact mapping between reasoning steps and percentages. Let me search for more information about the total number of claims.Good! So SCITAB has 1,225 total claims. Now I need to figure out which reasoning step number has the highest percentage (20%), and calculate how many claims that represents. Looking at the histogram data again, I need to understand which reasoning steps correspond to the 20% values. Let me look for more details about the distribution.Now let me carefully re-examine the histogram data. The data shows reasoning steps 2-11 and their corresponding percentages. Looking at the visual structure again:I see the structure now. Lines 1182-1198 show \"2 3 4 5 6 7 8 9 10 11\" which are the reasoning steps on the x-axis. The percentages must be reading from the bars. Based on the structure, it appears:\n\nFrom the data, I can see percentages: 20, 18, 20, 15, 15, 15, 10, 6, 8, 7, 5, 5, 3, 2, 1, 1\n\nThe highest percentage is 20%, which appears twice in the data. With 1,225 total claims and 20% being the highest:\n- 20% of 1,225 = 0.20 \u00d7 1,225 = 245 claims\n\nHowever, I see 20% appears twice, which could mean there are two different reasoning step counts that each have 20%. Let me verify by checking if there's a table or more precise description.Based on the figure caption, the y-axis shows \"frequency\" which is shown as \"Percentages (%)\". The highest percentage is 20%, and since this appears to occur for specific reasoning step(s), I need to calculate the number of claims.\n\nWith 1,225 total claims in SCITAB and the highest percentage being 20%:\n20% \u00d7 1,225 = 245 claims\n\nAnswer: 245", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77500, "error": null, "extra": {"thread_id": 1979, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0b34f104b1a04155b78b4d63f4b337dd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the number of figures in the Appendix section of the paper.\n\nLooking through the document, I can see the Appendix starts at section A on page 13. Let me go through each appendix section and count the figures:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task (page 14)\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task (page 15)\n- Figure 7: The user interface for the refuted claim verification task (page 16)\n- Figure 8: Examples of post-annotation survey questions (page 17)\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: Refuted claims cases A to C (page 22)\n- Figure 10: Refuted claims cases D and E (page 23)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT (page 24)\n- Figure 12: Error Cases 4-6 for InstructGPT (page 25)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts (page 26)\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts (page 27)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures in the Appendix.\n\nAnswer: 10", "citations": [], "input_tokens": 68138, "output_tokens": 472, "cost_micros": 211494, "latency_ms": 35156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the paper.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- A.2 Claim Extraction Interface: Figure 5\n\n**Appendix B (Manual Claim Verification Procedure):**\n- B.2 NEI Claim Verification Interface: Figure 6\n- B.3 Refuted Claim Verification Interface: Figure 7\n- B.4 Annotation Post-Survey: Figure 8\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9 (refuted cases A to C)\n- Figure 10 (refuted cases D and E)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11 (Error Cases 1-3)\n- Figure 12 (Error Cases 4-6)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13 (Error Cases 1-3)\n- Figure 14 (Error Cases 4 and 5)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 31771, "output_tokens": 373, "cost_micros": 100908, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 44326, "output_tokens": 415, "cost_micros": 139203, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure)**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure)**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset)**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration)**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims)**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT)**\n- Figure 11: Error Cases 1-3 for InstructGPT\n- Figure 12: Error Cases 4-6 for InstructGPT\n\n**Appendix G (Error Cases for Program-of-Thoughts)**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts)**\n- No figures\n\nTotal figures in Appendix:\nFigure 5, 6, 7, 8, 9, 10, 11, 12, 13, 14\n\nAnswer: 10", "citations": [], "input_tokens": 28753, "output_tokens": 380, "cost_micros": 91959, "latency_ms": 8592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLet me go through the Appendix sections:\n\n**Appendix A** (Claim Extraction Procedure):\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B** (Manual Claim Verification Procedure):\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C** (Analysis of Refuted Reasons in the Sci-Fact dataset):\n- No figures\n\n**Appendix D** (Discussions on Human-Machine Collaboration):\n- No figures\n\n**Appendix E** (Case Study for Refuted Claims):\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F** (Error Cases for InstructGPT):\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G** (Error Cases for Program-of-Thoughts):\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H** (Prompts):\n- No figures\n\nTotal figures in Appendix:\n- Figure 5\n- Figure 6\n- Figure 7\n- Figure 8\n- Figure 9\n- Figure 10\n- Figure 11\n- Figure 12\n- Figure 13\n- Figure 14\n\nAnswer: 10", "citations": [], "input_tokens": 36663, "output_tokens": 427, "cost_micros": 116394, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find out how many figures are in the Appendix.Let me search for the Appendix section in the document.Now let me search for figures in the Appendix sections. Let me read the end of the document where appendices are typically located.Let me continue reading to find the Appendix section.Let me search for all figures in the appendix sections. Let me look for \"Figure\" in the context of Appendix.Let me read the document more systematically to find all appendix sections and count the figures. Let me search for specific appendix sections.Now I can see the appendix sections starting. Let me continue reading to find all figures in the appendices.Let me continue reading to find more figures in the appendix.Let me continue to look for all figures in the appendix. Let me read further.Let me continue reading to find all the figures.Let me continue reading to find more figures.Now let me search for the end of the document to make sure I've found all figures.Perfect! Now let me count all the figures I found in the Appendix sections. Based on my reading, I found the following figures in the appendices:\n\n**Appendix A:**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B:**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix E:**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F:**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G:**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\nTotal figures in the Appendix: 10\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 85187, "error": null, "extra": {"thread_id": 1980, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0161632b18b34ebfa7ee0421186ab320", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 490, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly at Figures 3, 7, and 9 which show the relative sizes of Swp (information flow from text part to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\n- In shallow layers, Swp is high while Spq is low\n- In deep layers, Spq becomes dominant and exceeds Swp\n\nThe document explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis means that Swp is NOT always greater than Spq - it depends on the layer depth.\n\nAnswer: no", "citations": [], "input_tokens": 41233, "output_tokens": 158, "cost_micros": 126069, "latency_ms": 32250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the relative sizes of Swp (information flow from text to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\nIn shallow layers (early layers), Swp is greater than Spq.\nIn deep layers (later layers), Spq becomes dominant and is greater than Swp.\n\nThe document explicitly states: \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince Swp is not always greater than Spq across all layers (it depends on which layer), the answer is no.\n\nAnswer: no", "citations": [], "input_tokens": 17763, "output_tokens": 200, "cost_micros": 56289, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 and Figure 7 in the document, which show the relative sizes of S_wp (information flow from text to label words), S_pq (information flow from label words to target position), and S_ww across different layers:\n\nIn shallow layers (layers close to input), S_wp is high while S_pq is low.\n\nIn deep layers (layers close to output), S_pq becomes dominant and exceeds S_wp.\n\nThe document explicitly states: \"In shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low, while S_wp, the information flow from the text part to label words is high; (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince S_wp is greater in shallow layers but S_pq becomes greater in deep layers, S_wp is NOT always greater than S_pq.\n\nAnswer: no", "citations": [], "input_tokens": 20121, "output_tokens": 222, "cost_micros": 63693, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly Section 2.1 and Figure 3, the metrics Swp (information flow from text part to label words) and Spq (information flow from label words to target position) are compared across different layers.\n\nThe text states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Swp is larger in shallow layers but Spq becomes larger in deep layers. Therefore, Swp is not always greater than Spq across all layers.\n\nAnswer: No", "citations": [], "input_tokens": 13762, "output_tokens": 132, "cost_micros": 43266, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the data presented in the document, particularly in Figure 3 and related figures showing $S_{wp}$, $S_{pq}$, and $S_{ww}$ values across different layers:\n\nIn shallow layers (e.g., layers 0-10), $S_{wp}$ (information flow from text to label words) is greater than $S_{pq}$ (information flow from label words to target position).\n\nHowever, in deep layers (e.g., layers 15-48), $S_{pq}$ becomes much larger than $S_{wp}$.\n\nFor example, in Figure 3(a) for SST-2:\n- Layer 0: $S_{wp}$ = 0.72, $S_{pq}$ = 0.15\n- Layer 20: $S_{wp}$ = 0.10, $S_{pq}$ = 0.90\n\nSince the relationship changes across layers, $S_{wp}$ is not **always** greater than $S_{pq}$.\n\nAnswer: no", "citations": [], "input_tokens": 23820, "output_tokens": 243, "cost_micros": 75105, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about information flow between text parts, label words, and target positions.Let me search for information about information flow between text parts, label words, and target positions.Let me search for specific terms related to the comparison:Let me read through the document more systematically to find tables or figures that might contain this information:Let me read Figure 3 and related sections since it mentions Swp, Spq, and Sww which seem to be related to information flow:Good! I found relevant information. Let me read more to understand the definitions and comparisons better:Perfect! Now I have the key information. Let me look at Figure 3 more carefully to see the actual values:Based on my reading of the document, I can now answer the question. The document clearly states:\n\nFrom the text (lines 842-851):\n- \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while **Swp, the information flow from the text part to label words is high**\"\n- \"in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one\"\n\nThe document also states in the figure caption (line 943): \"Initially, **Swp occupies a significant proportion**, but it gradually decays over layers, while **Spq becomes the dominant one**.\"\n\nThis indicates that:\n- In shallow layers: Swp (text part to label words) > Spq (label words to target position)\n- In deep layers: Spq (label words to target position) > Swp (text part to label words)\n\nSince Swp starts high and decays while Spq starts low and increases, across ALL layers (taking the mean), Swp is NOT always greater than Spq. In fact, Spq becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59108, "error": null, "extra": {"thread_id": 1981, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_31954715cb0b491eade4006731d43b22", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the related discussion in Section 2.1:\n\nThe text states: \"Figure 3 reveals that: (1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 41220, "output_tokens": 149, "cost_micros": 125895, "latency_ms": 31437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe information flow from label words to the target position (S_pq) is LOW in shallow layers, but becomes DOMINANT in deep layers. The document states: \"(1) in shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 17750, "output_tokens": 119, "cost_micros": 55035, "latency_ms": 4890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe text states: \"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 20108, "output_tokens": 140, "cost_micros": 62424, "latency_ms": 5203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and Section 2.1, I need to examine when the information flow from label words to the target position (Spq) dominates.\n\nFrom Figure 3 and the text in Section 2.1:\n- Initially (in shallow layers), Swp (information flow from text to label words) \"occupies a significant proportion\"\n- Spq (information flow from label words to target position) \"gradually\" becomes dominant\n- Spq becomes \"the dominant one\" but this happens over layers, not in all layers\n\nThe text explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Spq does NOT dominate in all layers - only in the deeper layers after the information aggregation phase in shallow layers is complete.\n\nAnswer: No", "citations": [], "input_tokens": 13749, "output_tokens": 198, "cost_micros": 44217, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the paper's findings in Section 2.1 and the results shown in Figures 3, 7, and 9:\n\nIn shallow layers, $S_{wp}$ (information flow from text to label words) is high while $S_{pq}$ (information flow from label words to target position) is low.\n\nIn deep layers, $S_{pq}$ becomes dominant while $S_{wp}$ decreases.\n\nThe paper explicitly states: \"in shallow layers, $S_{pq}$, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, $S_{pq}$, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 23807, "output_tokens": 171, "cost_micros": 73986, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to answer your question.Let me search for content related to \"information flow\" and \"label words\" in the document.Now let me read the specific sections that discuss whether information flow from label words dominates in all layers.Perfect! I found the answer. Let me read a bit more to confirm the complete picture.Based on the document, the answer is clear. The text states:\n\n\"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis shows that information flow from label words to the target position does NOT dominate in all layers - it is low in shallow layers and only becomes dominant in deep layers.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38452, "error": null, "extra": {"thread_id": 1982, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_91e767a2b44d476aa25ee429c43c2924", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 4, which shows the impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers:\n\nThe figure shows that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - has minimal impact on both Label Loyalty and Word Loyalty metrics for both GPT2-XL and GPT-J models. The bars for \"Random (Last)\" show loyalty values close to 100%, similar to the \"No Isolation\" baseline.\n\nThis is further confirmed in the text on page 4 which states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 41222, "output_tokens": 170, "cost_micros": 126216, "latency_ms": 33032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - shows loyalty values very close to the \"No Isolation\" baseline (around 100%). This indicates almost no impact on the model's behavior.\n\nAnswer: Yes", "citations": [], "input_tokens": 17752, "output_tokens": 101, "cost_micros": 54771, "latency_ms": 3967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that:\n\n- \"Random (Last)\" (randomly isolating non-label words in the last 5 layers) shows very high loyalty scores (close to 100%), nearly identical to \"No Isolation\"\n- This indicates minimal impact on the model's predictions\n\nThe text also confirms: \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 20110, "output_tokens": 134, "cost_micros": 62340, "latency_ms": 6030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 and the related text in section 2.2, the document shows that isolating label words within the first 5 layers has substantial impact, but isolation within the last 5 layers has negligible impact. The text also states that \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nFigure 4 and Figure 8 show that randomly isolating non-label words (represented by lighter colored lines) in the last layers results in very high loyalty scores (close to 100%), indicating almost no impact on the model's behavior.\n\nAnswer: yes", "citations": [], "input_tokens": 13751, "output_tokens": 139, "cost_micros": 43338, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see the results for \"Random (Last)\".\n\nThe table shows:\n- Label Loyalty (GPT2-XL) with Random (Last): 98\n- Word Loyalty (GPT2-XL) with Random (Last): 98\n- Label Loyalty (GPT-J) with Random (Last): 98\n- Word Loyalty (GPT-J) with Random (Last): 98\n\nThese values are nearly identical to the \"No Isolation\" baseline (100), indicating minimal impact.\n\nThe document states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 23809, "output_tokens": 191, "cost_micros": 74292, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about randomly isolating non-label words within the last 5 layers.Perfect! I found the answer. The document states that randomly isolating non-label words within the last 5 layers has \"inconsequential\" impact, which means \"almost no impact.\"\n\nAnswer: Yes", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40125, "error": null, "extra": {"thread_id": 1983, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c2144cf872eb43e78cc21f804b3432de", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with Vanilla In-Context Learning (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 41209, "output_tokens": 115, "cost_micros": 125352, "latency_ms": 29484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find the average improvement from the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 on the page discussing Anchor Re-weighting results:\n\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nThis is also confirmed in the text which states: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.7", "citations": [], "input_tokens": 17739, "output_tokens": 149, "cost_micros": 55452, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.7", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with the baseline vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract that mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 20097, "output_tokens": 141, "cost_micros": 62406, "latency_ms": 5483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the average improvement of the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 in the document, I can see the following results:\n\n**Vanilla In-Context Learning (1-shot per class):**\n- SST-2: 61.28\n- TREC: 57.56\n- AGNews: 73.32\n- EmoC: 15.44\n- Average: 51.90\n\n**Anchor Re-weighting (1-shot per class):**\n- SST-2: 90.07\n- TREC: 60.92\n- AGNews: 81.94\n- EmoC: 41.64\n- Average: 68.64\n\nThe improvement is calculated as:\n68.64 - 51.90 = 16.74\n\nThe document also states in the text: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 13738, "output_tokens": 229, "cost_micros": 44649, "latency_ms": 5717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to find the performance improvement of Anchor Re-weighting over vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract and Section 3.1 which mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 23796, "output_tokens": 148, "cost_micros": 73608, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the Anchor Re-weighting method and its performance improvement over vanilla ICL.Let me search for \"Anchor Re-weighting\" in the document to find the relevant information.Let me read the section with the results table to find the specific improvement values.Perfect! I found Table 1 which shows the performance comparison. Let me calculate the improvement:\n\nFrom the table:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement = 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35969, "error": null, "extra": {"thread_id": 1984, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25b28dfa8e5c4fbeb8c80f7d7650cb08", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 144, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I need to find information about anchor-only context compression speedup for GPT2-XL on AGNews.\n\nLooking at Table 3 on page 8, which shows \"Acceleration ratios of the Hiddenanchor method\":\n\nThe table shows:\n- Model: GPT2-XL\n- AGNews: 2.5\u00d7\n\nThis indicates that using the anchor-only context compression method (Hiddenanchor), GPT2-XL achieves a 2.5\u00d7 speedup on the AGNews dataset.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 41212, "output_tokens": 129, "cost_micros": 125571, "latency_ms": 31171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews using anchor-only context compression (Hiddenanchor method):\n\nIn Table 3 on the topic of \"Acceleration ratios of the Hiddenanchor method,\" the speedup ratio for GPT2-XL on AGNews is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 17742, "output_tokens": 90, "cost_micros": 54576, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup for GPT2-XL on AGNews...\n\nIn Table 3 (page 7), the acceleration ratios of the Hiddenanchor method are provided:\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 20100, "output_tokens": 86, "cost_micros": 61590, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews with anchor-only context compression...\n\nIn Table 3 (on page 8), which shows \"The speed-up ratio compared to the original ICL\", I can find the speedup values for different models and datasets.\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 13741, "output_tokens": 100, "cost_micros": 42723, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup on AGNews with GPT2-XL...\n\nIn Table 3 on page 11, the acceleration ratios of the Hidden_anchor method are presented:\n\n| Model | SST-2 | TREC | AGNews | EmoC |\n| ------- | ----- | ---- | ------ | ---- |\n| GPT2-XL | 1.1\u00d7 | 1.5\u00d7 | 2.5\u00d7 | 1.4\u00d7 |\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 23799, "output_tokens": 134, "cost_micros": 73407, "latency_ms": 5157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I'll search for information about anchor-only context compression and GPT2-XL speed up on AGNews in the provided document.Let me search for \"anchor\" and \"AGNews\" in the document to find the relevant information.Now let me look at Table 3 which shows acceleration ratios for the Hiddenanchor method:Perfect! I found the answer in Table 3. The table shows acceleration ratios of the Hiddenanchor method, and for GPT2-XL on AGNews, the speedup is 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36062, "error": null, "extra": {"thread_id": 1985, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c1692b1f5bfe4ec6aa79ada899da40bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 139, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the most confusing category of Abbreviation in the document.\n\nLooking at the confusion matrices in the document, particularly in Section 3.3 (Anchor Distances for Error Diagnosis) and the appendices, I can see confusion matrices for the TREC dataset.\n\nIn Figure 6 on page 8, there are two confusion matrices shown:\n- (a) Confusion matrix of Confusion^pred_ij\n- (b) Confusion matrix of Confusion_ij\n\nLooking at the Abbreviation row in both matrices, I need to identify which category Abbreviation is most confused with (the lowest confusion score, or highest confusion).\n\nFrom Figure 6(b), looking at the Abbreviation row:\n- Abbreviation-Abbreviation: 1 (diagonal, set to 1)\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n- Abbreviation-Person: 1\n- Abbreviation-Location: 1\n- Abbreviation-Number: 0.87\n\nThe lowest value (indicating highest confusion) in the Abbreviation row is with Description (0.76), followed by Entity (0.84).\n\nThis is also confirmed in the text on page 8 which states: \"Figure 6 shows that the proposed approximation metric, Confusion^pred_ij, can identify the most confusing case (Description-Entity) and performs reasonably well for highly confusing categories (Entity-Abbreviation, Description-Abbreviation).\"\n\nAnswer: Description", "citations": [], "input_tokens": 41202, "output_tokens": 351, "cost_micros": 128871, "latency_ms": 26937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the confusion matrix of Confusion_ij on the TREC dataset, I need to find which category has the highest confusion score with Abbreviation.\n\nFrom the confusion matrix in Figure 6(b), the values for Abbreviation's confusion with other categories are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nThe lowest value (highest confusion) is with Entity at 0.84.\n\nAnswer: Entity", "citations": [], "input_tokens": 17732, "output_tokens": 125, "cost_micros": 55071, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the actual confusion matrix (Confusionij) on the TREC dataset, I need to find which category Abbreviation is most confused with.\n\nFrom the confusion matrix in Figure 6(b), the Abbreviation row shows the following confusion scores with other categories:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nLower confusion scores indicate higher confusion between categories (as stated in the paper). The lowest score is 0.76 with Description, followed by 0.84 with Entity.\n\nAnswer: Description", "citations": [], "input_tokens": 20090, "output_tokens": 147, "cost_micros": 62475, "latency_ms": 5234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices in Figure 14, I need to identify which category is most confused with \"Abbreviation.\"\n\nIn the confusion matrices, lower values indicate higher confusion (as stated in the document: \"The closer Confusion_{ij} approximates 1, the less pronounced the confusion\").\n\nLooking at the \"Abbreviation\" row across the different matrices (M=5, 10, 20, 50, 100, 200), the lowest values (highest confusion) are:\n- Description: 0.31, 0.45, 0.52\n- Entity: 0.44, 0.49, 0.51\n\nThe \"Description\" category consistently shows the lowest values when compared with Abbreviation, indicating the highest degree of confusion.\n\nAnswer: Description", "citations": [], "input_tokens": 13731, "output_tokens": 174, "cost_micros": 43803, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices provided in the document, I need to find which category is most confused with \"Abbreviation.\"\n\nIn Figure 6(a), which shows the predicted confusion matrix ($\\text{Confusion}_{ij}^{\\text{pred}}$), the values for Abbreviation with other categories are:\n- Entity: 0.49\n- Description: 0.45\n- Person: 0.90\n- Location: 1.00\n- Number: 0.83\n\nLower values indicate higher confusion (as stated in the document: \"0 indicating the highest degree of category confusion\").\n\nIn Figure 6(b), which shows the actual confusion matrix ($\\text{Confusion}_{ij}$), the values for Abbreviation are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1.00\n- Location: 1.00\n- Number: 1.00\n\nIn both matrices, the lowest value (highest confusion) for Abbreviation is with \"Description\" (0.45 predicted, 0.76 actual), followed closely by \"Entity\" (0.49 predicted, 0.84 actual).\n\nAnswer: Description", "citations": [], "input_tokens": 23789, "output_tokens": 270, "cost_micros": 75417, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}} -{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the most confusing category of abbreviation.Let me search for information about \"confusing\" and \"abbreviation\" categories in the document.Let me read the specific chunk that mentions confusing categories and abbreviations:Perfect! I found the answer. Let me read a bit more context to confirm:Based on the document, the text clearly states that \"the most confusing case (Description-Entity)\" was identified. However, the question asks specifically about \"the most confusing category of Abbreviation.\"\n\nLooking at the confusion matrix data and the text, it mentions that the highly confusing categories involving Abbreviation are:\n- Entity-Abbreviation\n- Description-Abbreviation\n\nLet me check if there's more specific information about which is THE most confusing for Abbreviation:\n\nLooking at the confusion matrix values shown:\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n\nThe lower the value in a confusion matrix, the more confusing the pair. So Entity (0.84) is more confusing with Abbreviation than Description (0.76), but actually looking at this as a similarity matrix (as the caption says \"heatmaps display similarity\"), higher values mean more similar/confusing.\n\nBased on the text stating \"highly confusing categories (Entity-Abbreviation, Description-Abbreviation)\" with Entity-Abbreviation listed first, and the confusion matrix showing 0.84 for Abbreviation-Entity vs 0.76 for Abbreviation-Description:\n\nAnswer: Entity", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40344, "error": null, "extra": {"thread_id": 1986, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_052d25ab92e746d681f311675087569d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 227, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2, which shows the MIMIC-IT overview tree diagram, I can see three main branches: Perception, Reasoning, and Planning.\n\nAt the top of the Planning branch (shown in red/pink on the right side), the task labeled is \"Indoor Event Planning\".\n\nAnswer: Indoor Event Planning", "citations": [], "input_tokens": 55843, "output_tokens": 76, "cost_micros": 168669, "latency_ms": 49328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "indoor event planning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the MIMIC-IT overview with different branches representing fundamental capabilities, I need to identify the task at the top of the planning branch.\n\nIn the planning section (rightmost branch of the tree diagram), examining from top to bottom, the topmost task shown is \"Commonsense Reasoning\" with the example question \"What might this young boy learn from this experience? (LA)\"\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 21188, "output_tokens": 104, "cost_micros": 65124, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, which shows the MIMIC-IT overview with three main branches (Perception, Reasoning, and Planning), I can see the Planning branch on the right side.\n\nAt the top of the Planning branch, the task shown is \"Indoor Event Planning (IEP)\" with the question \"Can you suggest any recreational pursuits that can be enjoyed within this room?\"\n\nAnswer: Indoor Event Planning (IEP)", "citations": [], "input_tokens": 23513, "output_tokens": 95, "cost_micros": 71964, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "indoor event planning iep", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a planning branch or tree structure with tasks.\n\nIn Figure 1 (MIMIC-IT overview), I can see different capabilities organized into three main branches: Perception, Reasoning, and Planning.\n\nUnder the Planning branch, I can see two tasks listed:\n1. Egocentric Planning (E4D)\n2. Episode Memory (DC)\n\nLooking at the visual layout in Figure 1, \"Egocentric Planning\" appears to be positioned at the top of the Planning branch.\n\nAnswer: Egocentric Planning", "citations": [], "input_tokens": 20245, "output_tokens": 129, "cost_micros": 62670, "latency_ms": 5657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "egocentric planning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the task at the top of the Planning branch.\n\nThe Planning section shows three items:\n1. At the top: \"Is it safe to walk on the floor while the woman is cleaning? E4D\" \u2192 EVR [Egocentric Video Reasoning]\n2. In the middle: \"Can you suggest any ways to brighten up the room I'm in? E4D\" \u2192 EP [Egocentric Planning]\n3. At the bottom: \"Can you suggest any recreational pursuits that can be enjoyed within this room? IEP\" \u2192 IEPP [Indoor Event Planning]\n\nThe topmost task in the Planning branch is Egocentric Video Reasoning (EVR).\n\nAnswer: Egocentric Video Reasoning", "citations": [], "input_tokens": 24261, "output_tokens": 176, "cost_micros": 75423, "latency_ms": 6139, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "egocentric video reasoning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the planning branch of the tree and identify the task at the top.Let me search for content related to \"planning branch\" or \"tree\" to find the relevant figure or diagram:Let me search more specifically for \"tree\" and look at figures:Let me look at Figure 1 which mentions perception, reasoning, and planning:Good! I can see \"Planning\" section. Let me continue reading to see what tasks are under the Planning branch:Perfect! I can see the Planning section with its tasks. Looking at the structure, under \"Planning\" I can see:\n\n1. \"Commonsense Reasoning\" - \"What might this young boy learn from this experience? (LA)\"\n2. \"General Scene Difference\" \n3. \"Egocentric Planning\"\n4. \"Indoor Event Planning\"\n\nBut I need to see the visual tree structure more carefully. Let me look at the earlier part to understand the tree layout better:Now I can see the structure clearly. Looking at Figure 1, there's a tree structure with three main branches:\n1. Perception\n2. Reasoning\n3. Planning\n\nUnder the \"Planning\" branch, the first task/question that appears is: \"What might this young boy learn from this experience? (LA)\" which is labeled as \"Commonsense Reasoning\".\n\nSo the task at the top of the Planning branch is \"Commonsense Reasoning\".\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51937, "error": null, "extra": {"thread_id": 1987, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f3cc3b28e854493c927ab87e43d7d1d0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 285, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for bar plots.\n\nLooking through the figures:\n\n**Figure 1** (page 2): This shows example images and questions from the MIMIC-IT dataset. No bar plots here.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text. No bar plots here.\n\n**Figure 3** (page 5): This shows the Syphus pipeline overview. No bar plots here.\n\n**Figure 4** (page 7): This figure shows \"The data statistics of multi-modal in-context instruction-response pairs\" with:\n- (a) Instructions - shown as a sunburst/radial chart (not a bar plot)\n- (b) Responses - shown as a sunburst/radial chart (not a bar plot)\n- (c) Statistics of instructions and responses - This contains **4 bar plots**:\n 1. Instruction Length distribution\n 2. Response Length distribution\n 3. # Images distribution\n 4. # Related Instructions distribution\n\n**Figure 5** (page 8): This shows example responses from Otter with images and text. No bar plots here.\n\n**Figure 6** (page 10): This shows three subplots:\n- (a) Video understanding - **2 bar plots** (MSVD 0-shot and MSRVTT 0-shot)\n- (b) Vision-language model alignment - **1 bar plot** (Elo Rating)\n- (c) COCO caption - This is a line graph, not a bar plot\n\nTotal bar plots: 4 (from Figure 4c) + 2 (from Figure 6a) + 1 (from Figure 6b) = **7 bar plots**\n\nAnswer: 7", "citations": [], "input_tokens": 55838, "output_tokens": 405, "cost_micros": 173589, "latency_ms": 40905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots, including any subplots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text - no bar plots.\n\n**Figure 2**: Shows example images and text comparisons - no bar plots.\n\n**Figure 3**: Shows a pipeline diagram - no bar plots.\n\n**Figure 4**: This figure contains multiple subplots showing data statistics:\n- (a) Shows two circular/sunburst plots for instructions - not bar plots\n- (b) Shows two circular/sunburst plots for responses - not bar plots\n- (c) Shows four plots with statistics:\n 1. \"Instruction Length\" - appears to be a histogram/bar plot\n 2. \"Response Length\" - appears to be a histogram/bar plot\n 3. \"# Images\" - appears to be a histogram/bar plot\n 4. \"# Related Instructions\" - appears to be a histogram/bar plot\n\nSo Figure 4(c) contains 4 bar plots.\n\n**Figure 5**: Shows example images and responses - no bar plots.\n\n**Figure 6**: This figure contains three subfigures:\n- (a) \"Video understanding\" - shows a grouped bar chart with multiple bars\n- (b) \"Vision-language model alignment\" - shows a bar chart\n- (c) \"COCO caption\" - shows a line plot with points, not a bar plot\n\nSo Figure 6 has 2 bar plots (a and b).\n\nTotal count: 4 (from Figure 4c) + 2 (from Figure 6) = 6 bar plots.\n\nAnswer: 6", "citations": [], "input_tokens": 21183, "output_tokens": 367, "cost_micros": 69054, "latency_ms": 11281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count the bar plots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text describing different capabilities - no bar plots.\n\n**Figure 2**: Shows data format comparison with images and text - no bar plots.\n\n**Figure 3**: Shows the Sythus pipeline overview - no bar plots.\n\n**Figure 4**: This is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst plot showing root verb-noun pairs of instructions\n- (b) A circular/sunburst plot showing root verb-noun pairs of responses \n- (c) Four separate plots labeled \"Statistics of instructions and responses\":\n - Top left: A histogram/bar plot showing \"# Instructions\" vs \"Instruction Length\"\n - Top right: A histogram/bar plot showing \"# Responses\" vs \"Response Length\"\n - Bottom left: A histogram/bar plot showing \"# Instructions\" vs \"# Images\"\n - Bottom right: A histogram/bar plot showing \"# Instructions\" vs \"# Related Instructions\"\n\nSo in Figure 4(c), there are **4 bar plots**.\n\n**Figure 5**: Shows example responses with images and text - no bar plots.\n\n**Figure 6**: Contains three subplots:\n- (a) \"Video understanding\" - shows bar plots comparing VideoChatGPT and Otter on MSVD and MSRVTT for QA and Captioning. This contains **4 grouped bar comparisons** (which I count as bar plots).\n- (b) \"Vision-language model alignment\" - shows an Elo rating comparison with bars for different models - this is **1 bar plot**.\n- (c) \"COCO caption\" - shows a line plot, not a bar plot.\n\nSo Figure 6 has: 4 (from 6a) + 1 (from 6b) = **5 bar plots**.\n\n**Figure 7**: Shows text examples in multiple languages - no bar plots.\n\nTotal bar plots: 4 (from Figure 4c) + 5 (from Figure 6) = **9 bar plots**.\n\nAnswer: 9", "citations": [], "input_tokens": 23508, "output_tokens": 506, "cost_micros": 78114, "latency_ms": 14187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "arm": "llamacloud_basic_lc", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:13Z", "latency_ms": 11328, "error": null, "raw_text_chars": 1376}]}, "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots.\n\nLooking through the figures in the document:\n\n1. **Figure 1**: Shows example images with text descriptions - no bar plots\n2. **Figure 2**: Shows data format comparison with images and text - no bar plots\n3. **Figure 3**: Shows the Syphus overview diagram - no bar plots\n4. **Figure 4**: This appears to show statistics and analysis. Let me examine it carefully:\n - Part (a): Shows verb-noun structure for instructions - this appears to be a bar chart\n - Part (b): Shows verb-noun structure for responses - this appears to be a bar chart\n - Part (c): Shows \"Diversity in our dataset\" with what appears to be distribution plots\n\nLooking at Figure 4 more carefully:\n- (a) contains what looks like a horizontal bar chart showing verbs and nouns from instructions\n- (b) contains what looks like a horizontal bar chart showing verbs and nouns from responses\n- (c) contains multiple distribution plots showing lengths and counts\n\n5. **Figure 5**: Shows response examples - no bar plots\n6. **Figure 6**: The document mentions Fig. 6(b) and Fig. 6(c) but these are not fully visible in the provided pages\n\nFrom what I can see clearly in Figure 4:\n- Subfigure (a): 1 bar plot (showing instruction verb-noun structure)\n- Subfigure (b): 1 bar plot (showing response verb-noun structure)\n\nTotal visible bar plots: 2\n\nAnswer: 2", "citations": [], "input_tokens": 20240, "output_tokens": 354, "cost_micros": 66030, "latency_ms": 11328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count bar plots.\n\nLooking through the figures:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with three main branches (Perception, Reasoning, Planning). Not a bar plot.\n\n**Figure 2**: This shows data format comparisons with images and text examples. Not a bar plot.\n\n**Figure 3**: This is a flowchart showing the Sythus overview. Not a bar plot.\n\n**Figure 4**: This figure is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst chart showing instructions (verb-noun pairs)\n- (b) A circular/sunburst chart showing responses (verb-noun pairs)\n- (c) \"Statistics of instructions and responses\" which contains 4 separate bar plots:\n 1. Instruction Length vs # Instructions (bar plot)\n 2. Response Length vs # Responses (bar plot)\n 3. # Images vs # Instructions (bar plot)\n 4. # Related Instructions vs # Instructions (bar plot)\n\nSo Figure 4(c) contains **4 bar plots**.\n\n**Figure 5**: This shows example responses from Otter with images and text. Not a bar plot.\n\n**Figure 6**: This contains three parts:\n- (a) A table showing video understanding results. Not a bar plot.\n- (b) A table showing vision-language model alignment. Not a bar plot.\n- (c) A table showing COCO caption results. Not a bar plot.\n\nTotal count: 4 bar plots (all from Figure 4c)\n\nAnswer: 4", "citations": [], "input_tokens": 24256, "output_tokens": 385, "cost_micros": 78543, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count all bar plots (including subplots).Let me search for figures and plots in the document:Now let me read the actual content around the figures to see the descriptions of plots:Let me read more sections to find all the figures:Now let me look at Figure 4 more carefully to see if it contains bar plots:Good! I can see Figure 4 (c) has bar plots. Let me continue looking at Figure 6:Now I need to look at the actual visual description to understand what kind of plots Figure 6 has. Looking at the data in lines 1693-1823, I can see Figure 6 has three subplots (a), (b), and (c). Let me check if these are bar plots by examining the structure:\n\n- Figure 6(a): Shows \"MSVD 0-shot\" and \"MSRVTT 0-shot\" with \"QA\" and \"Captioning\" categories, comparing VideoChatGPT and Otter - this appears to be bar plots\n- Figure 6(b): Shows \"Elo Rating\" comparison among MM-GPT, Inst. BLIP, LLaVA, MiniGPT, and Otter - this appears to be a bar plot\n- Figure 6(c): Shows \"COCO caption\" with \"0-shot 4-shot 8-shot 16-shot\" comparing Otter and OpenFlamingo - this appears to be a line or bar plot\n\nNow let me search for more figures to make sure I don't miss any:Figure 7 is mentioned but let me check if it's a plot or just examples. From line 2292, Figure 7 seems to be just text examples for multi-lingual translation, not a plot.\n\nLet me search for any appendix figures or additional figures:Great! I found references to bar charts. Let me read those sections more carefully:These descriptions refer to the charts/bar plots that appear in Figure 4(c). Let me now carefully read to understand which images correspond to which figures. The Im1.png through Im4.png appear to be the four subplots in Figure 4(c).\n\nLet me search for Figure 6 visual descriptions:Let me look for the visual description of Figure 6:Let me search for visual descriptions that mention bar charts or plots more systematically by searching around the figure areas:Now let me check if there are any additional figures by searching for all figure captions:Good! So we have:\n- Figure 1: Overview (not a bar plot)\n- Figure 2: Data format comparison (not a bar plot)\n- Figure 3: Sythus overview (not a bar plot)\n- Figure 4: Data statistics with (a) and (b) showing verb-noun pairs, and (c) showing statistics with multiple subplots\n- Figure 5: Response examples (not a bar plot)\n- Figure 6: With (a), (b), and (c) subplots\n- Figure 7: Multi-lingual translation examples (not a bar plot)\n\nNow I need to count the bar plots more carefully. Let me re-examine Figure 4(c) and Figure 6:\n\nFrom my reading:\n- Figure 4(a): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles\n- Figure 4(b): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles \n- Figure 4(c): Contains 4 bar plot histograms based on the visual descriptions I found:\n 1. Instructions vs Instruction Length (Im1.png) - bar plot\n 2. Responses vs Response Length (Im2.png) - bar plot\n 3. Instructions vs # Images (Im3.png) - bar plot\n 4. Instructions vs # Related Instructions (Im4.png) - bar plot\n\nFor Figure 6, I need to examine what type of plots they are. Let me look at the text around Figure 6 more carefully. From the data I saw (lines 1693-1823), Figure 6 appears to have:\n- Figure 6(a): Shows QA and Captioning comparisons for MSVD and MSRVTT - likely bar plots (2 groups x 2 bars = 4 bars total, but this counts as 2 bar plots for the two datasets)\n- Figure 6(b): Shows Elo Rating comparison - likely a bar plot (1 bar plot)\n- Figure 6(c): Shows COCO caption with 0-shot, 4-shot, 8-shot, 16-shot - this could be a line plot or bar plot\n\nLet me search for more specific information about these plots. Let me check if there are visual descriptions for Figure 6 plots:The file ends at line 5199. Let me search systematically for any remaining bar plot descriptions:Based on my thorough analysis of the document, let me now count all the bar plots:\n\n**Figure 4(c)** contains 4 bar plot histograms:\n1. Instructions vs Instruction Length\n2. Responses vs Response Length \n3. Instructions vs # Images\n4. Instructions vs # Related Instructions\n\n**Figure 6** - I need to determine how many bar plots are in each subplot. Looking at the data structure:\n- Figure 6(a): Video understanding evaluation with MSVD 0-shot and MSRVTT 0-shot, each showing QA and Captioning - this appears to be 2 grouped bar charts (one for MSVD, one for MSRVTT)\n- Figure 6(b): Elo Rating comparison showing multiple models - this is 1 bar chart\n- Figure 6(c): COCO caption showing 0-shot through 16-shot results comparing Otter and OpenFlamingo - this could be either a grouped bar chart or line chart\n\nLooking at the structure in the text (lines 1816-1817: \"0-shot 4-shot 8-shot 16-shot\"), this suggests it's showing a progression, which is more typical of a line chart. However, I need to be careful.\n\nLet me reconsider the counting. The question asks for \"bar plots (including subplots)\". This means I should count each separate bar plot panel, not individual bars.\n\nFrom the evidence:\n- **Figure 4(c)**: 4 bar plot histograms (clearly described as histograms/bar charts in the visual descriptions)\n- **Figure 6(a)**: 2 bar plots (one for MSVD 0-shot, one for MSRVTT 0-shot, each comparing models on QA and Captioning)\n- **Figure 6(b)**: 1 bar plot (Elo Rating comparison)\n- **Figure 6(c)**: Likely 1 chart but unclear if it's a bar plot or line plot based on the \"0-shot 4-shot 8-shot 16-shot\" progression\n\nTotal count depends on whether Figure 6(c) is a bar plot. Given the context of comparing few-shot performance across different numbers of shots, and looking at typical machine learning papers, this is often shown as either a line chart or grouped bar chart.\n\nHowever, examining the OCR text more carefully from chunk 152307 (lines 1816-1822), the layout shows:\n- (a) Video understanding\n- (b) Vision-language model alignment \n- (c) COCO caption\n\nAnd earlier in the caption it mentions \"CIDEr\" metric with values like \"60.8\", \"55\", etc. with labels for different shot counts.\n\nGiven the uncertainty, let me make my best assessment: Figure 6(c) showing few-shot learning progression is more likely to be a line plot given it shows a progression/trend across shot counts.\n\n**Final count:**\n- Figure 4(c): 4 bar plots\n- Figure 6(a): 2 bar plots\n- Figure 6(b): 1 bar plot\n- Figure 6(c): Not a bar plot (likely line plot)\n\n**Total: 7 bar plots**\n\nWait, let me reconsider Figure 6(a). Looking at the text structure again (lines 1693-1806), it shows:\n- MSVD 0-shot with QA and Captioning\n- MSRVTT 0-shot with QA and Captioning\n- These are comparing VideoChatGPT vs Otter\n\nThis could be:\n- 2 separate bar plots (one for MSVD, one for MSRVTT), OR\n- 1 bar plot with 2 groups\n\nTypically in papers, when multiple datasets are shown side-by-side in subfigure (a), they would be considered separate bar plots. So I'll count them as 2.\n\n**Final answer: 7**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 164109, "error": null, "extra": {"thread_id": 1988, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_98fcc3ce4d2f4d98b3c04b41720c35ca", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1170, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 titled \"Sythus overview\" on page 5, I can see the cold start process.\n\nThe figure shows a \"Cold Start\" stage that comes before the main Sythus pipeline. The cold start stage shows:\n- In-context examples (document icon at top)\n- An arrow pointing down to \"Cold Start\"\n- ChatGPT being queried during this stage\n- This then leads to \"Step 1: System Message + visual annotation\"\n\nThe cold start stage is described in the text as being used to \"identify the optimal system message and in-context example for querying instruction-response pairs in a given dataset.\"\n\nAnswer: In-context examples", "citations": [], "input_tokens": 55843, "output_tokens": 153, "cost_micros": 169824, "latency_ms": 41358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "in context examples", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage appears before the main pipeline steps (Step 1-4).\n\nThe cold start stage shows interactions with ChatGPT to identify the optimal system message and in-context examples. The figure shows arrows connecting to \"System Message\" and \"In-context examples\" components.\n\nAnswer: Identify optimal system message and in-context example", "citations": [], "input_tokens": 21188, "output_tokens": 91, "cost_micros": 64929, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "identify optimal system message and in context example", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage shows a flow diagram where the first step involves \"System Message\" with an arrow pointing to ChatGPT.\n\nAnswer: System Message", "citations": [], "input_tokens": 23513, "output_tokens": 50, "cost_micros": 71289, "latency_ms": 4468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 (Sythus overview), I can see the pipeline stages.\n\nThe cold start stage is shown at the top, and the first step within it involves prompting ChatGPT to generate system messages and in-context examples.\n\nAnswer: System Message", "citations": [], "input_tokens": 20245, "output_tokens": 62, "cost_micros": 61665, "latency_ms": 4858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, I can see the cold start process and the subsequent steps.\n\nThe cold start section shows \"In-context examples\" which feeds into Step 1.\n\nStep 1 is labeled as \"System Message + visual annotation\" which leads to the \"Prompt\".\n\nAnswer: System Message + visual annotation", "citations": [], "input_tokens": 24261, "output_tokens": 80, "cost_micros": 73983, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message visual annotation", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure about cold start and identify the first step.Let me search for \"cold start\" in the document to locate the relevant figure.Good! I found a reference to \"Cold Start\" with steps. Let me read the section around line 759-798 (chunk 152250) where the figure appears.Perfect! I can see the figure clearly shows the steps of the cold start process. According to the figure, the steps are:\n\n- Step 1: System Message\n- Step 2: Generate instruction-response pairs\n- Step 3: Filtering\n- Step 4: Translation\n\nThe first step of cold start is \"System Message\".\n\nAnswer: System Message", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 29859, "error": null, "extra": {"thread_id": 1989, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39c9f325d0c8483fb88a1b48780b1918", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figures in the document, particularly Figure 1 and Figure 5 which show examples from the MIMIC-IT dataset and Otter's capabilities:\n\nIn Figure 1, there are examples showing egocentric video reasoning with what appears to be soccer/football scenes.\n\nIn Figure 5, the egocentric visual assistant examples at the bottom show scenes from what appears to be soccer/football, including references to passing the ball, wearing jerseys, and the goalkeeper saving the ball.\n\nAnswer: Soccer/Football", "citations": [], "input_tokens": 55848, "output_tokens": 115, "cost_micros": 169269, "latency_ms": 35250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples showing sports.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an \"Egocentric Visual Assistant\" example with text that says \"Hey Otter, what should I do?\" and the response mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\"\n\nThis clearly refers to soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 21193, "output_tokens": 102, "cost_micros": 65109, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, particularly in Figure 5 under \"Egocentric Visual Assistant,\" I can see an image showing a soccer/football scene from a first-person perspective.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 23518, "output_tokens": 52, "cost_micros": 71334, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, I need to identify what sport is shown in the MIMIC-IT dataset examples and Otter's response examples.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an example labeled \"Egocentric Visual Assistant\" at the bottom. The text mentions \"guiding users to land a small aircraft.\"\n\nAdditionally, in the Ego4D (E4D) section's in-context examples (Table 5), there are questions about:\n- \"What color of jersey does my teammates wear?\" with the response \"Black, as you pass the ball to a player wearing black jersey\"\n- \"Does our team goal?\" with the response about a goalkeeper saving the ball\n\nThis indicates soccer/football is being discussed.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 20250, "output_tokens": 180, "cost_micros": 63450, "latency_ms": 7734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples that show sports.\n\nIn Figure 5, under the \"Egocentric Visual Assistant\" section, there are two screenshots. The right screenshot shows \"A person playing soccer with a chat overlay\" and includes the text \"User: Hey Otter, what should I do? OTTER-E: Pass the ball *to the middle* and run to the gate, looking for a shooting opportunity.\"\n\nAnswer: Soccer", "citations": [], "input_tokens": 24266, "output_tokens": 102, "cost_micros": 74328, "latency_ms": 6890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "soccer", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document you mentioned to find information about the visual examples in MIMIC-IT and Otter.Let me search for visual examples and figures in the document to find information about sports shown in MIMIC-IT and Otter examples.Let me read more of the document to find figures and visual examples:Let me continue reading to find specific visual examples that show sports:Found \"tennis\"! Let me search for the context around this mention:Perfect! Let me read that section to get more context about the tennis visual example:Now let me search for where this image appears in relation to MIMIC-IT or Otter examples. Let me look earlier in the document to understand the context of these figures:Let me search backwards to find where these images are part of a figure showing examples:Let me search for \"Otter\" in the document to find where these visual examples are shown:Now let me look at Figure 5, which shows \"Otter's response examples\":Great! I found it. In the Egocentric Visual Assistant section of Figure 5, there's a conversation where Otter-E says \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\" This clearly refers to soccer/football. Let me verify by looking at the visual description of the image that accompanies this text:Perfect! I found the answer. The visual description clearly states this is an \"Outdoor soccer/football field with green grass\" with players wearing blue athletic shirts. The Otter-E response also mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity,\" which confirms this is soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 123078, "error": null, "extra": {"thread_id": 1990, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e493d9b5d4804d67aac4ae04786b3ef7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at Figure 7 on page 16, which shows \"In-context examples for multi-lingual translation query,\" I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 55842, "output_tokens": 108, "cost_micros": 169146, "latency_ms": 37125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nAnswer: 8", "citations": [], "input_tokens": 21187, "output_tokens": 113, "cost_micros": 65256, "latency_ms": 4327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 23512, "output_tokens": 102, "cost_micros": 72066, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 7 which shows \"In-context examples for multi-lingual translation query\", I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 20244, "output_tokens": 108, "cost_micros": 62352, "latency_ms": 4578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7 on page 16, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 24260, "output_tokens": 107, "cost_micros": 74385, "latency_ms": 4422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about multi-lingual translation query and count the languages in the in-context examples.Now I can see references to the multi-lingual translation query. Let me read the specific section that mentions Figure 7 and the languages.Perfect! I found the information. Let me now look at Figure 7 to see the actual in-context examples and count the languages.Perfect! I can now see Figure 7 which shows the in-context examples for multi-lingual translation query. Let me count the languages shown:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nThe document clearly shows 8 languages in the in-context examples for multi-lingual translation query in Figure 7.\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44640, "error": null, "extra": {"thread_id": 1991, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25a64843d98447e9baa03cf538d93723", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 212, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll go through the document to count all the line plots shown.\n\nLooking through the figures in the document:\n\n**Figure 1** (page 2): This shows a complex diagram with multiple examples of different tasks and image types, but does not appear to contain line plots.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text, but no line plots.\n\n**Figure 3** (page 5): This shows the Sythus pipeline overview with icons and flowchart elements, but no line plots.\n\n**Figure 4** (page 7): This contains:\n- (a) A sunburst chart showing instructions\n- (b) A sunburst chart showing responses \n- (c) Multiple bar charts/histograms and what appears to be **line plots** showing statistics\n\nLooking at Figure 4(c) more carefully, there appear to be line plots showing \"# Instructions\" and \"# Responses\" over \"Instruction Length\" and \"Response Length\", plus what looks like a line plot for \"# Related Instructions\".\n\n**Figure 5** (page 8): Shows example images with text responses, not line plots.\n\n**Figure 6** (page 10): This contains three subfigures:\n- (a) Bar charts for video understanding\n- (b) Bar chart for vision-language model alignment\n- (c) Contains **line plots** showing CIDEr scores across different shot settings (0-shot, 4-shot, 8-shot, 16-shot) comparing Otter and OpenFlamingo - this appears to show 2 line plots\n\n**Figure 7** (page 16): Shows text examples in multiple languages, no line plots.\n\nCounting the line plots:\n- Figure 4(c): appears to show 2-3 line plots\n- Figure 6(c): shows 2 line plots (one for Otter, one for OpenFlamingo)\n\nTotal: approximately 4-5 line plots visible in Figure 4(c) and 2 in Figure 6(c) = approximately 6-7 total.\n\nLooking more carefully at Figure 4(c), I can see there are line plots for instruction length distribution, response length distribution, and related instructions. Figure 6(c) clearly shows 2 ", "citations": [], "input_tokens": 55833, "output_tokens": 512, "cost_micros": 175179, "latency_ms": 42171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots.\n\nLooking through the figures in the document:\n\n**Figure 1**: This shows example images with text descriptions - not line plots.\n\n**Figure 2**: Shows example data formats with images and text - not line plots.\n\n**Figure 3**: Shows a pipeline/flowchart diagram - not line plots.\n\n**Figure 4**: This figure contains multiple subplots:\n- (a) A circular/sunburst chart showing root verb-noun pairs\n- (b) Another circular/sunburst chart showing root verb-noun pairs\n- (c) Four separate line plots showing:\n 1. Instruction Length distribution\n 2. Response Length distribution\n 3. # Images distribution\n 4. # Related Instructions distribution\n\nSo Figure 4(c) contains **4 line plots**.\n\n**Figure 5**: Shows example images with text responses - not line plots.\n\n**Figure 6**: This figure contains three subplots:\n- (a) Two grouped bar charts for \"Video understanding\" (MSVD 0-shot and MSRVTT 0-shot)\n- (b) A bar chart showing \"Human evaluation comparison\" with Elo Ratings\n- (c) A line plot showing \"COCO caption\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\nSo Figure 6(c) contains **1 line plot**.\n\n**Total line plots**: 4 (from Figure 4c) + 1 (from Figure 6c) = **5 line plots**\n\nAnswer: 5", "citations": [], "input_tokens": 21178, "output_tokens": 363, "cost_micros": 68979, "latency_ms": 10016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing data points connected by lines).\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows sample images with text descriptions - not a line plot.\n\n**Figure 2**: Shows a comparison of data formats with images and text - not a line plot.\n\n**Figure 3**: Shows a flowchart/pipeline diagram - not a line plot.\n\n**Figure 4**: Contains multiple subfigures:\n- (a) A circular/sunburst diagram showing verb-noun pairs\n- (b) Another circular/sunburst diagram showing verb-noun pairs\n- (c) Contains **4 line plots**: \n 1. \"# Instructions\" vs \"Instruction Length\"\n 2. \"# Responses\" vs \"Response Length\"\n 3. \"# Instructions\" vs \"# Images\"\n 4. \"# Instructions\" vs \"# Related Instructions\"\n\n**Figure 5**: Shows example images with captions and chat interfaces - not a line plot.\n\n**Figure 6**: Contains three subfigures:\n- (a) Bar chart for video understanding - not a line plot\n- (b) Bar chart for vision-language model alignment - not a line plot\n- (c) Shows **1 line plot**: \"CIDEr\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\n**Figure 7**: Shows text examples in multiple languages - not a line plot.\n\nTotal line plots found:\n- Figure 4(c): 4 line plots\n- Figure 6(c): 1 line plot\n\nAnswer: 5", "citations": [], "input_tokens": 23503, "output_tokens": 370, "cost_micros": 76059, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to search through the document for line plots/graphs.\n\nLooking through the document, I can identify the following figures with line plots:\n\n1. Figure 4 - This shows multiple visualizations including what appears to be bar charts and word clouds showing verb-noun structures and dataset statistics. The caption mentions \"Fig. 4 (a)\" for instructions and \"Fig. 4 (b)\" for responses, and \"Fig. 4 (c)\" for diversity metrics. These appear to be bar charts and word frequency visualizations, not line plots.\n\n2. Figure 6 - The text references \"Fig. 6(b)\" and \"Fig. 6(c)\" where:\n - Fig. 6(b) is described as showing Elo ratings (likely a bar chart or ranking visualization)\n - Fig. 6(c) is described as showing \"COCO caption (CIDEr) few-shot evaluation\" - this is explicitly described as comparing performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot, 32-shot), which would typically be shown as a line plot\n\nBased on the text descriptions, Figure 6(c) showing the few-shot learning performance across different numbers of shots (0, 4, 8, 16, 32) would be presented as a line plot to show the progression of performance.\n\nHowever, I cannot actually see the figures in this document to count them definitively. Based on the textual descriptions alone, there appears to be at least 1 line plot (Figure 6c showing few-shot performance).\n\nAnswer: 1", "citations": [], "input_tokens": 20235, "output_tokens": 361, "cost_micros": 66120, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing lines connecting data points over continuous axes).\n\nLooking through the figures in the document:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with different capabilities (Perception, Reasoning, Planning) - not a line plot.\n\n**Figure 2**: Shows data format comparisons with images and text examples - not a line plot.\n\n**Figure 3**: Shows a flowchart of the Syphus pipeline - not a line plot.\n\n**Figure 4**: This figure shows multiple visualizations of data statistics:\n- (a) Shows a circular/sunburst chart for Instructions (verb-noun pairs)\n- (b) Shows a circular/sunburst chart for Responses (verb-noun pairs)\n- (c) Shows \"Statistics of instructions and responses\" with what appears to be 4 small plots showing:\n - Instruction Length vs # Instructions\n - Response Length vs # Responses\n - # Images vs # Instructions\n - # Related Instructions vs # Instructions\n\nThese plots in Figure 4(c) appear to be line plots showing distributions/relationships.\n\n**Figure 5**: Shows example responses from Otter with images and text - not a line plot.\n\n**Figure 6**: Shows three tables labeled (a), (b), and (c) with numerical results - these are tables, not line plots.\n\n**Figure 7**: Shows text examples for multi-lingual translation - not a line plot.\n\nCounting the line plots in Figure 4(c): There are 4 small line plots showing different statistics.\n\nAnswer: 4", "citations": [], "input_tokens": 24251, "output_tokens": 364, "cost_micros": 78213, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}} -{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to count the line plots.Let me search for figures and plots in the document by reading through it systematically:Now let me read the actual content of these figures to understand their structure and count line plots:Let me continue to check all figures systematically:Let me search more specifically for line plots or line charts:Let me look at Figure 6 more carefully as it seems to contain multiple subfigures with plots:Based on the data I see in Figure 6(c), it appears to show different shot settings (0-shot, 4-shot, 8-shot, 16-shot) which suggests this is a line plot. Let me search for more visual descriptions to understand all the plots:Let me read more around Figure 4 to understand what types of plots it contains:Now let me look for visual descriptions and embedded images to understand the structure of figures better. Let me check if there are visual descriptions that mention line plots or graphs:Let me search for embedded images and their visual descriptions:Let me look at the visual descriptions of the image that corresponds to Figure 6:This visual description seems to be of a different image not related to Figure 6. Let me look for a chart or graph description by searching through the document more systematically. Let me read sections with image data to find charts:The visual descriptions I'm seeing are for photographs, not charts. Let me search for keywords related to charts or visualizations more carefully:Good! Let me read this section to find charts and visualizations:Good! I found the section with chart visual descriptions. These appear to be histograms/bar charts for Figure 4. Now let me search for descriptions that mention line graphs or line plots:Let me specifically look for \"line\" combined with visualization terms:Now let me search specifically for Figure 6's visualization descriptions by looking near the Figure 6 location more carefully. Let me look for the embedded images around Figure 6:Let me check for chart/plot related embedded images. Looking back at Figure 6, I saw it has three subfigures (a), (b), and (c). Let me search for embedded images that might show line plots by looking at descriptions with \"axis\":I found histograms/bar charts. Now let me look for any line-based visualizations. Let me search for \"CIDEr\" which was mentioned in Figure 6(c) about COCO caption evaluation:I can see Figure 6(c) shows data with labels like \"0-shot 4-shot 8-shot 16-shot\" and \"CIDEr\", which strongly suggests it's a line plot showing performance across different shot settings. Let me look for a visualization description that mentions this, or look for visual descriptions that discuss multiple data points connected:The text \"0-shot 4-shot 8-shot 16-shot\" followed by \"(c) COCO caption\" suggests Figure 6(c) is indeed a line plot or chart. However, the embedded image Im14.jpg description seems unrelated (clinical setting). Let me look at the data more carefully and count what we know so far. Let me check if there are visual descriptions specifically for charts with \"shot\":", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 191452, "error": null, "extra": {"thread_id": 1992, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_48224411a27b457b894b9bc78d679631", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 942, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "1"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:18Z", "latency_ms": 52514, "error": null, "raw_text_chars": 860}]}, "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll examine pages 5-10 to count the tables.\n\nPage 5: No tables visible in the text content shown.\n\nPage 6: Table 1 is present - \"Llama 2 family of models\" showing training data, parameters, context length, GQA, tokens, and LR for Llama 1 and Llama 2 models.\n\nPage 7: \n- Table 2: \"CO2 emissions during pretraining\" showing time, power consumption, and carbon emitted for different Llama 2 models.\n- Table 3: \"Overall performance on grouped academic benchmarks\" comparing MPT, Falcon, Llama 1, and Llama 2 models.\n\nPage 8:\n- Table 4: \"Comparison to closed-source models on academic benchmarks\" showing results for GPT-3.5, GPT-4, PaLM, PaLM-2-L, and Llama 2.\n- Table 5: \"SFT annotation\" showing examples of helpfulness and safety annotations.\n\nPage 9: No complete tables visible in the shown content.\n\nPage 10: No tables visible in the shown content.\n\nAnswer: 5", "citations": [], "input_tokens": 199213, "output_tokens": 280, "cost_micros": 601839, "latency_ms": 52468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 9:**\n- Table 5: SFT annotation examples\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nAnswer: 6", "citations": [], "input_tokens": 80762, "output_tokens": 144, "cost_micros": 244446, "latency_ms": 6391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count all the tables on pages 5-10.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Figure 5 (not a table)\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 8:**\n- Table 5: SFT annotation - example of helpfulness and safety annotation\n\n**Page 9:**\n- No tables (contains Figures 11 and discussion)\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nTotal count: 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 109911, "output_tokens": 181, "cost_micros": 332448, "latency_ms": 8235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables that appear in pages 5-10 of the document.\n\nPage 5: \n- Table 1: Llama 2 family of models (comparing parameters, context length, etc.)\n\nPage 6:\n- No tables\n\nPage 7:\n- Table 3: Overall performance on grouped academic benchmarks\n\nPage 8:\n- Table 4: Comparison to closed-source models on academic benchmarks\n- Table 5: SFT annotation example (shown at top of page)\n\nPage 9:\n- No tables (this page discusses supervised fine-tuning details)\n\nPage 10:\n- Table 6: Statistics of human preference data for reward modeling\n\nCounting all tables in pages 5-10:\n- Page 5: 1 table\n- Page 6: 0 tables\n- Page 7: 1 table\n- Page 8: 2 tables\n- Page 9: 0 tables\n- Page 10: 1 table\n\nTotal: 1 + 0 + 1 + 2 + 0 + 1 = 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 80961, "output_tokens": 265, "cost_micros": 246858, "latency_ms": 7983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\nTotal: 1 table\n\n**Page 6:**\n- Table 2: CO\u2082 emissions during pretraining\n- Figure 5: Training Loss for LLAMA 2 models (this is a figure, not a table)\nTotal: 1 table\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\nTotal: 1 table\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\nTotal: 1 table\n\n**Page 9:**\n- Table 5: SFT annotation examples\nTotal: 1 table\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\nTotal: 1 table\n\nTotal tables on pages 5-10: 1 + 1 + 1 + 1 + 1 + 1 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 105634, "output_tokens": 249, "cost_micros": 320637, "latency_ms": 8264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document that was flagged as most relevant (2307.09288v2.pdf.xml) and check pages 5-10 for tables.I need to search for page markers to identify pages 5-10. Let me search for page indicators in the document.Let me read more of the document to understand its structure and find page markers.Good! I can see page numbers are indicated with ``. Let me search for these page markers to find pages 5-10.Perfect! Now I know that:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo I need to read from line 1660 to line 2460 to cover pages 5-10. Let me search for table markers in this range.Great! I can see the content from pages 5-10. Let me continue reading to make sure I capture all tables in this range. I've found several tables so far. Let me count them systematically:\n\nFrom line 1660 to 2460 (pages 5-10), I've seen:\n1. Table 1 (line 1664) - LLAMA 2 family of models\n2. Table 2 (line 1862) - CO2 emissions during pretraining\n3. Table 3 (line 1959) - Overall performance on grouped academic benchmarks\n4. Table 4 (line 2131) - Comparison to closed-source models\n5. Table 5 (line 2209) - SFT annotation\n6. Table 6 (line 2336) - Statistics of human preference data\n\nLet me verify by reading the remaining portion up to line 2460 to make sure I haven't missed any tables:Perfect! Now I can see all the `
CoT Demo
` tags in the document. Based on the PageNumber markers I found earlier:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo pages 5-10 cover lines 1660 to 2460 (just before page 11).\n\nThe tables that fall within this range are:\n1. Line 1664: `
` - Table 1 (on page 5)\n2. Line 1862: `
` - Table 2 (on page 6)\n3. Line 1959: `
` - Table 3 (on page 7)\n4. Line 2131: `
` - Table 4 (on page 8)\n5. Line 2209: `
` - Table 5 (on page 8)\n6. Line 2336: `
` - Table 6 (on page 10)\n\nTables at lines 2479 and 2544 are on page 11 or later, so they don't count.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54500, "error": null, "extra": {"thread_id": 1993, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d18e0e208d4c44ad9ccab3815ed12b04", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 413, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:25Z", "latency_ms": 40452, "error": null, "raw_text_chars": 575}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 4, which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the bar chart showing \"Violation % (lower is safer)\":\n- Llama 2-70b-chat has the lowest violation percentage (approximately 0-5%)\n- Llama 2-13b-chat has the second lowest (approximately 0-5%)\n- Llama 2-34b-chat has the third lowest (approximately 5-10%)\n\nAnswer: Llama 2-70b-chat, Llama 2-13b-chat, Llama 2-34b-chat", "citations": [], "input_tokens": 199224, "output_tokens": 182, "cost_micros": 600402, "latency_ms": 40406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart in Figure 3, the models with the lowest violation percentages are:\n\n1. LLAMA 2-70b-chat (lowest violation %)\n2. ChatGPT 0301 (second lowest)\n3. LLAMA 2-34b-chat (third lowest)\n\nAnswer: Llama-2 70b-chat, ChatGPT 0301, Llama-2 34b-chat", "citations": [], "input_tokens": 80773, "output_tokens": 157, "cost_micros": 244674, "latency_ms": 6141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart, the three models with the lowest violation percentages are:\n1. Llama-2-70b-chat (approximately 0.5%)\n2. Llama-2-34b-chat (approximately 1%)\n3. Llama-2-13b-chat (approximately 1.5%)\n\nAnswer: Llama-2-70b-chat, Llama-2-34b-chat, Llama-2-13b-chat", "citations": [], "input_tokens": 109922, "output_tokens": 165, "cost_micros": 332241, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and close-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the figure, the models are ordered from left to right with their violation percentages. The three models with the lowest violation percentages are:\n\n1. Llama 2-Chat (70B) - appears to have the lowest violation percentage\n2. ChatGPT (0301) - appears to have the second lowest\n3. Falcon (40B-instruct) - appears to have the third lowest\n\nAnswer: Llama 2-Chat (70B), ChatGPT (0301), Falcon (40B-instruct)", "citations": [], "input_tokens": 80972, "output_tokens": 176, "cost_micros": 245556, "latency_ms": 7233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, falcon 40b instruct, llama 2 chat 70b", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:05Z", "latency_ms": 10343, "error": null, "raw_text_chars": 376}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I can see the violation percentages (lower is safer):\n\nThe top-3 safest models (lowest violation %) are:\n1. Llama-2 7b-chat: 3%\n2. Llama-2 13b-chat: 3%\n3. Llama-2 70b-chat: 4%\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 70b-chat", "citations": [], "input_tokens": 105645, "output_tokens": 151, "cost_micros": 319200, "latency_ms": 10343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 70b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the top-3 models.Let me search more specifically for Figure 3 content:Now let me read the area around line 1363 to see the actual figure data:Based on the document, Figure 3 shows safety violation percentages where \"lower is safer\". Let me look for the actual values to determine the top-3 (lowest violation %) models:The visual description indicates that the first 4 bars (dark blue) have the lowest values (approximately 3-7), which are the safest models. Based on the model order from lines 1391-1420, the models are listed in order:\n\n1. Llama-2 7b-chat\n2. Llama-2 13b-chat\n3. Llama-2 34b-chat\n4. Llama-2 70b-chat\n5. MPT 7b-chat\n6. Vicuna 13b-v1.1\n7. Vicuna 33b-v1.3\n8. Falcon 40b-instruct\n9. PaLM Bison\n10. ChatGPT 0301\n\nThe description states the first 4 bars (dark blue) have values approximately 3-7, which are the lowest (safest). So the top-3 safest models would be the first three models.\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 34b-chat", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48187, "error": null, "extra": {"thread_id": 1994, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8f47a07752394e8d87ac6ac816505c2a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 for QUAC (f1) results in the 0-shot setting:\n\nThe table shows:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 199228, "output_tokens": 210, "cost_micros": 600834, "latency_ms": 79592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the 0-shot QUAC results are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting for QUAC is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 80777, "output_tokens": 227, "cost_micros": 245736, "latency_ms": 7592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the 0-shot results for the QUAC dataset.\n\nIn the QUAC (f1) 0-shot column, the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot QUAC column is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 109926, "output_tokens": 271, "cost_micros": 333843, "latency_ms": 8594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 \"Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\", I need to find the QUAC 0-shot column.\n\nThe table shows QUAC results with 0-shot and 1-shot columns. For the 0-shot setting on QUAC:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nThe highest 0-shot score on QUAC is 44.1, achieved by Llama 1 33B.\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 80976, "output_tokens": 284, "cost_micros": 247188, "latency_ms": 8469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:10Z", "latency_ms": 29391, "error": null, "raw_text_chars": 525}]}, "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the QUAC (f1) 0-shot scores are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 105649, "output_tokens": 274, "cost_micros": 321057, "latency_ms": 29391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table 23 and identify the best performing model in the 0-shot setting on the QUAC dataset.Let me search for \"Table 23\" in the document.Now let me read the section containing Table 23 to see the complete table.Perfect! I can see Table 23 completely now. Looking at the QUAC (f1) 0-shot column (column 7 in the table), the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting on QUAC is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40921, "error": null, "extra": {"thread_id": 1995, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1765040c73804b7381cc9d57c302f53d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 202, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find Table 21 in the document and count how many slots (cells) are missing data.\n\nLooking through the document, I can find Table 21 on page 49. Let me examine it:\n\nTable 21 is titled \"Code generation results on Human-Eval and MBPP.\"\n\nThe table has the following structure:\n- Rows for different models: MPT (7B, 30B), Falcon (7B, 40B), Llama 1 (7B, 13B, 33B, 65B), and Llama 2 (7B, 13B, 34B, 70B)\n- Columns for: Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80)\n\nLet me count the missing entries (shown as \"-\" or blank):\n\nLooking at the table:\n- MPT 7B: has values for pass@1 under Human-Eval (18.3) and MBPP (22.6), but \"-\" for pass@100 and pass@80\n- MPT 30B: has values for pass@1 (25.0, 32.8), but \"-\" for pass@100 and pass@80\n- Falcon 7B: has values for pass@1 (0.0, 11.2), but \"-\" for pass@100 and pass@80\n- Falcon 40B: has values for pass@1 (0.6, 29.8), but \"-\" for pass@100 and pass@80\n- All Llama 1 and Llama 2 models have complete data (no missing slots)\n\nMissing slots:\n- MPT 7B: 2 missing (pass@100 for both benchmarks)\n- MPT 30B: 2 missing\n- Falcon 7B: 2 missing\n- Falcon 40B: 2 missing\n\nTotal: 8 missing slots\n\nAnswer: 8", "citations": [], "input_tokens": 199211, "output_tokens": 461, "cost_micros": 604548, "latency_ms": 75532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 in the document, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Rows: Different models (MPT 7B, Falcon 7B/40B, LLAMA 1 7B/13B/33B/65B, LLAMA 2 7B/13B/34B/70B)\n- Columns: Human-Eval pass@1, Human-Eval pass@100, MBPP pass@1, MBPP pass@80\n\nLet me count the missing entries (marked with \"-\"):\n\nMPT 7B: pass@100 for Human-Eval is missing (1 slot)\nMPT 30B: pass@100 for Human-Eval is missing (1 slot)\nFalcon 40B: pass@1 and pass@100 for Human-Eval are missing, pass@1 and pass@80 for MBPP are missing (4 slots)\n\nTotal missing slots: 1 + 1 + 4 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 80760, "output_tokens": 280, "cost_micros": 246480, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count the missing slots.\n\nLooking at Table 21, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model name, size, and then 4 metric columns (pass@1, pass@100 for Human-Eval, and pass@1, pass@80 for MBPP)\n- Row groups for: MPT (2 sizes), Falcon (2 sizes), LLAMA 1 (4 sizes), and LLAMA 2 (4 sizes)\n\nLet me count the cells systematically:\n\nFor MPT:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 30B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor Falcon:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 40B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor LLAMA 1 (4 sizes: 7B, 13B, 33B, 65B):\n- All sizes have all 4 metrics filled = 0 missing\n\nFor LLAMA 2 (4 sizes: 7B, 13B, 34B, 70B):\n- All sizes have all 4 metrics filled = 0 missing\n\nTotal missing slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 109909, "output_tokens": 429, "cost_micros": 336162, "latency_ms": 10592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model, Human-Eval (pass@1, pass@100), MBPP (pass@1, pass@80)\n- Row headers: MPT, Falcon, Llama 1, Llama 2\n\nLet me count the cells:\n- MPT row: 7B size is listed, then has values 18.3, -, 22.6, -\n- Falcon row: 7B size is listed, then has values 0.0, -, 11.2, -\n- Llama 1 row: 7B size is listed, then has values 15.8, 52.5, 22.0, 64.0\n- Llama 2 row: 7B size is listed, then has values 18.3, 60.2, 30.6, 69.0\n\nThe \"-\" symbols indicate missing data.\n\nCounting the dashes:\n- MPT: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Falcon: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Llama 1: 0 dashes (all values present)\n- Llama 2: 0 dashes (all values present)\n\nTotal missing slots: 2 + 2 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 80959, "output_tokens": 385, "cost_micros": 248652, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 to count the missing slots (empty cells).\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model | Size | Human-Eval pass@1 | Human-Eval pass@100 | MBPP pass@1 | MBPP pass@80\n- Rows for: MPT (7B, 30B), Falcon (7B, 40B), LLAMA 1 (7B, 13B, 33B, 65B), LLAMA 2 (7B, 13B, 34B, 70B)\n\nLet me count the cells:\n- Total rows with data: 12 (excluding header)\n- Total columns with data: 6 (including Model and Size)\n- So for the data columns (excluding Model column): 12 rows \u00d7 4 data columns = 48 potential data cells\n\nNow counting the filled cells in the data columns:\n- MPT 7B: 4 values (18.3, -, 22.6, -)\n- MPT 30B: 4 values (25.0, -, 32.8, -)\n- Falcon 7B: 4 values (0.0, -, 11.2, -)\n- Falcon 40B: 4 values (0.6, -, 29.8, -)\n- LLAMA 1 7B: 4 values (10.5, 36.5, 17.7, 56.2)\n- LLAMA 1 13B: 4 values (15.8, 52.5, 22.0, 64.0)\n- LLAMA 1 33B: 4 values (21.7, 70.7, 30.2, 73.4)\n- LLAMA 1 65B: 4 values (23.7, 79.3, 37.7, 76.8)\n- LLAMA 2 7B: 4 values (12.8, 45.6, 20.8, 62.", "citations": [], "input_tokens": 105632, "output_tokens": 512, "cost_micros": 324576, "latency_ms": 13625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Table 21 and count the missed slots.Let me search for Table 21 in the document:Now let me search for where Table 21 appears with more context:Let me read the area around line 7837 where Table 21 is located:Now I have Table 21. Let me analyze it carefully. The table shows code generation results with columns for Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80). I can see that some entries have \"-\" which indicates missing data. Let me count all the \"-\" entries:\n\nLooking at the table structure:\n- MPT 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- MPT 30B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 40B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- LLAMA 1 models (7B, 13B, 33B, 65B): All have values, no \"-\"\n- LLAMA 2 models (7B, 13B, 34B, 70B): All have values, no \"-\"\n\nTotal missed slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42437, "error": null, "extra": {"thread_id": 1996, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b8bd21d9aaba4efda0a40946ca2b4039", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine pages 49-50 to count the datasets reported.\n\nOn page 49 (Table 20), I can see results for several standard benchmarks including:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets on page 49.\n\nOn page 50 (Table 21), I can see code generation results on:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets on page 50.\n\nTotal datasets reported across pages 49-50: 10 + 2 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 199213, "output_tokens": 177, "cost_micros": 600294, "latency_ms": 43437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the datasets mentioned.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: NaturalQuestions and TriviaQA results\n- Table 23: SQUAD and QUAC results\n- Table 24: AGI Eval comparison (mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count all unique datasets mentioned across these tables on pages 49-50:\n\nFrom Table 20 (page 49):\n1. BoolQ\n2. PIQA\n3. SIQA\n4. HellaSwag\n5. WinoGrande\n6. ARC-e\n7. ARC-c\n8. OBQA\n9. CSQA\n10. MMLU\n\nFrom Table 21 (page 49):\n11. Human-Eval\n12. MBPP\n\nFrom Table 22 (page 50):\n13. NaturalQuestions\n14. TriviaQA\n\nFrom Table 23 (page 50):\n15. SQUAD\n16. QUAC\n\nFrom Table 24 (page 50):\n17. AGI Eval (which includes multiple subtasks: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nCounting the subtasks of AGI Eval separately:\n17. AQuA-RAT\n18. LogiQA\n19. LSAT-AR\n20. LSAT-LR\n21.", "citations": [], "input_tokens": 80762, "output_tokens": 512, "cost_micros": 249966, "latency_ms": 10782, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the number of datasets reported.\n\nLooking at page 49, I can see:\n- Table 20: \"Performance on standard benchmarks\" - this discusses various benchmarks but appears to be evaluation results rather than listing distinct datasets\n- Table 21: \"Code generation results on Human-Eval and MBPP\" - mentions 2 datasets: Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: \"NaturalQuestions\" and \"TriviaQA\" - 2 datasets\n- Table 23: \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\" - 2 datasets: SQUAD and QUAC\n- Table 24: \"Comparison to open source models on AGI Eval (English)\" - 1 dataset: AGI Eval (with multiple subtasks listed: AQUA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count the distinct datasets mentioned:\n1. Human-Eval\n2. MBPP\n3. NaturalQuestions\n4. TriviaQA\n5. SQUAD\n6. QUAC\n7. AGI Eval (this is one dataset with multiple subtasks)\n\nAdditionally, in Table 20, there are multiple benchmarks mentioned: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU - that's 10 more datasets.\n\nTotal distinct datasets across pages 49-50:\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10)\nFrom Table 21: Human-Eval, MBPP (2)\nFrom Table 22: NaturalQuestions, TriviaQA (2)\nFrom Table 23: SQUAD,", "citations": [], "input_tokens": 109911, "output_tokens": 512, "cost_micros": 337413, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "23", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 to count the datasets reported there.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n- Table 22: NaturalQuestions and TriviaQA Performance\n\nLooking at page 50, I can see:\n- Table 23: Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\n- Table 24: Comparison to Open Source Models on AGI Eval (English) - mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math\n\nNow let me count all unique datasets mentioned across pages 49-50:\n\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10 datasets)\n\nFrom Table 21: Human-Eval, MBPP (2 datasets)\n\nFrom Table 22: NaturalQuestions, TriviaQA (2 datasets)\n\nFrom Table 23: SQUAD, QUAC (2 datasets)\n\nFrom Table 24: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math (8 datasets, noting that SAT-en and SAT-en (w/o Psg.) are variants of the same dataset but listed separately)\n\nTotal unique datasets: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 80961, "output_tokens": 493, "cost_micros": 250278, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to carefully examine pages 49-50 to count the number of datasets reported.\n\nLooking at page 49 (Table 20), I can see:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets in Table 20.\n\nLooking at page 49 (Table 21), I can see:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets in Table 21.\n\nLooking at page 50 (Table 22), I can see:\n- NaturalQuestions\n- TriviaQA (Wiki)\n\nThat's 2 datasets in Table 22.\n\nLooking at page 50 (Table 23), I can see:\n- SQUAD\n- QUAC\n\nThat's 2 datasets in Table 23.\n\nLooking at page 50 (Table 24), I can see:\n- AQuA-RAT\n- LogiQA\n- LSAT-AR\n- LSAT-LR\n- LSAT-RC\n- SAT-en\n- SAT-en (w/o Psg.)\n- SAT-math\n\nThat's 8 datasets in Table 24.\n\nTotal datasets across pages 49-50: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 105634, "output_tokens": 355, "cost_micros": 322227, "latency_ms": 11062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the information on pages 49-50.Let me search for page numbers in the document to locate pages 49-50.Let me search for page markers more broadly:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35907, "error": null, "extra": {"thread_id": 1997, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db129915abb34a60aeaa6083e2f2fd47", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 109, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "49", "normalised_gold": "13"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:15Z", "latency_ms": 43610, "error": null, "raw_text_chars": 650}]}, "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 9 and count the emojis in the left and right subfigures.\n\nLooking through the document, I find Figure 9 on page 16 (document page 16). The figure shows \"Issues with multi-turn memory (left) can be improved with GAtt (right).\"\n\nLooking at the left subfigure:\n- It shows a conversation with text-only responses (no emojis visible)\n\nLooking at the right subfigure:\n- First response: 4 emojis (\ud83d\udc4b\ud83d\udc41\ufe0f\ud83d\udc44\ud83d\udc41\ufe0f)\n- Second response: 4 emojis (\u2708\ufe0f\u2708\ufe0f\ud83d\udeeb\ud83d\udcba)\n- Third response: 4 emojis (\ud83e\uddb4\ud83e\udd95\ud83e\uddb4\ud83d\udd25)\n- Fourth response: 2 emojis (\ud83c\uddee\ud83c\uddf9\ud83c\udf5d\ud83d\udc90)\n\nTotal in right subfigure: 4 + 4 + 4 + 2 = 14 emojis\n\nTotal in left subfigure: 0 emojis\n\nDifference: 14 - 0 = 14\n\nAnswer: 14", "citations": [], "input_tokens": 199223, "output_tokens": 267, "cost_micros": 601674, "latency_ms": 43578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which is titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure:\n- Shows a conversation about \"Always answer with emojis\"\n- The response \"Who are you?\" appears to have text but I need to count emojis\n- Looking at the left side, I can see there are some emojis in the responses\n\nRight subfigure:\n- Shows the same conversation structure with GAtt improvement\n- The response appears to have more emojis\n\nLet me count more carefully by examining the figure:\n\nLeft side conversation:\n- \"Who are you?\" response appears to have some emojis\n- \"How to go from Paris to NY?\" response has text with some emojis\n\nRight side conversation:\n- \"Who are you?\" response has emojis\n- \"How to go from Paris to NY?\" response has more emojis\n- There appears to be an additional question about Italy\n\nLooking at the visual representation in Figure 9, the right subfigure clearly shows more emoji usage throughout the conversation compared to the left subfigure. \n\nCounting the visible emojis:\n- Left: appears to have fewer emojis (approximately 2-3 visible)\n- Right: appears to have more emojis throughout (approximately 10-12 visible)\n\nThe difference appears to be approximately 8-9 emojis.\n\nAnswer: 8", "citations": [], "input_tokens": 80772, "output_tokens": 344, "cost_micros": 247476, "latency_ms": 10140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\n**Left subfigure (without GAtt):**\n- Shows a conversation with \"Who are you?\" followed by \"How to go from Paris to NY?\" and \"What caused the extinction of the dinosaurs?\"\n- I can see there are emoji circles (\ud83d\ude0a symbols) used in the conversation\n- Counting the emojis: I see 3 emoji symbols\n\n**Right subfigure (with GAtt):**\n- Shows the same conversation structure with \"Who are you?\" followed by \"How to go from Paris to NY?\" \n- Counting the emojis: I see 3 emoji symbols as well\n\nWait, let me recount more carefully by looking at the figure description and structure:\n\nLeft side: I count 3 emoji/circle symbols in the conversation flow\nRight side: I count 3 emoji/circle symbols in the conversation flow\n\nActually, looking more carefully at the figure, both sides appear to have the same number of emoji symbols (3 each) representing the conversation turns.\n\nThe difference would be: 3 - 3 = 0\n\nAnswer: 0", "citations": [], "input_tokens": 109921, "output_tokens": 294, "cost_micros": 334173, "latency_ms": 9984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 in the document, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure (without GAtt):\n- Shows a conversation about acting as Oscar Wilde\n- I can see emoji-like elements in the visualization\n\nRight subfigure (with GAtt):\n- Shows the same type of conversation\n- Also has emoji-like elements in the visualization\n\nLet me count more carefully. The figure appears to show attention visualization patterns with what look like emoji or icon representations in the conversation bubbles.\n\nFrom the image description and visual elements:\n- Left side: Shows conversation elements with some emoji representations\n- Right side: Shows conversation elements with emoji representations\n\nLooking at the actual figure more carefully, the right subfigure appears to have more emoji icons visible in the conversation compared to the left subfigure. \n\nCounting the visible emojis/icons in each:\n- Left: appears to have fewer emoji elements\n- Right: appears to have more emoji elements\n\nBased on the visual comparison, the right subfigure has approximately 2 more emojis than the left subfigure.\n\nAnswer: 2", "citations": [], "input_tokens": 80971, "output_tokens": 281, "cost_micros": 247128, "latency_ms": 10141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 (titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\"):\n\n**Left subfigure (Generic Preprompt):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcbb\u2328\ufe0f\" (3 emojis)\n- User: \"How to go from Paris to NY?\"\n- Assistant: Long text response with no emojis\nTotal emojis in left: 3\n\n**Right subfigure (Preprompt with Answer Template):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcac...\" (2 emojis shown, plus \"...\")\n- User: \"How to go from Paris to NY?\"\n- Assistant: \"\ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3\" (4 emojis)\n- User: \"What caused the extinction of the dinosaurs?\"\n- Assistant: \"\u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80\" (4 emojis)\n- User: \"Italy?\"\n- Assistant: \"\ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f\" (4 emojis)\n\nLet me recount the right side more carefully:\n- First response: \ud83e\udd16\ud83d\udcac (2 emojis, the \"...\" indicates continuation but not additional emojis)\n- Second response: \ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3 (4 emojis)\n- Third response: \u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80 (4 emojis)\n- Fourth response: \ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f (4 emojis)\nTotal emojis in right: 2 + 4 + 4 + 4 = 14\n\nDifference: 14 - 3 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 105644, "output_tokens": 462, "cost_micros": 323862, "latency_ms": 13312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "15"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and count the emojis in each subfigure.Let me search for Figure 9 in the document:Now let me read the section around line 3147 to see Figure 9:Let me continue reading to see the complete Figure 9 with emoji counts:I can see Figure 9 is described, but it mentions an embedded image. Let me look at the embedded image description more carefully. The text shows numbers like \"8\", \"0\" which might be emoji counts. Let me search for the actual image description or look for more context:The image description I saw was for a different figure (a scatter plot). Let me look back at the text content of Figure 9 more carefully. I can see there are conversations shown with numbers like \"8\" and \"0\". Let me search for the actual figure content more carefully:The text I'm seeing doesn't show the actual figure properly. Let me look for an embedded image associated with Figure 9. The page header says \"Always answer with emojis\" which is the instruction. Let me search for the actual visual description or OCR of the image:Let me go back and look more carefully at the figure. The text between lines 3146-3181 should contain the figure content. Let me look for an image file or better description:The visual description starting at line 3190 describes a scatter plot, which is NOT Figure 9. That appears to be a different figure (the embedded image X6.png). The actual Figure 9 content is in the text between lines 3147-3180. Let me look for an image file that might contain the actual Figure 9. Let me search for embedded images near Figure 9:I notice the page headers say \"Always answer with emojis\" twice (lines 3142-3143). This suggests the page has two columns - left and right subfigures. Looking at the figure text:\n\nLeft subfigure appears to show:\n- Line 3149: \"Who are you?\" \n- Line 3151: \"Who are you?\"\n- Line 3153: \"8\" (could indicate 8 emojis)\n- Line 3157: \"How to go from Paris to NY?\"\n- Line 3159: \"How to go from Paris to NY?\"\n- Line 3161: \"8\" (could indicate 8 emojis)\n- Line 3163: \"0\" (could indicate 0 emojis)\n- Line 3165-3168: Text response without emojis\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (could indicate 0 emojis)\n\nLet me search for an image description specifically for Figure 9. Let me look before line 3146:Let me look for an image file associated with Figure 9. Let me search for embedded images around this area:So there's an embedded image X6.png at line 3185, which is right after Figure 9 ends at line 3181. But the visual description for X6.png shows it's a scatter plot, not Figure 9. Let me look for an image BEFORE Figure 9:Let me search for image descriptions that might actually be Figure 9. Let me look for an OCR or description of dialogues with emojis:Great! I found relevant information. Let me look at lines 2729-2760 which describe emoji exchanges:Excellent! This is describing an image related to Figure 9. Looking at the OCR text at lines 2729-2737:\n- \"Always answer with emojis\" (the header/instruction)\n- \"8\" (appears after \"Who are you?\")\n- \"...\" \n- \"How to go from Paris to NY?\"\n\nAnd the visual description mentions:\n1. First exchange: emojis (thinking face, eyes, and speech bubble) - that's 3 emojis\n2. Second exchange: transportation-themed emojis (airplane, boat, landmark, car, globe) - that's 5 emojis\n3. Third exchange: dinosaur-themed emojis (dinosaur, calendar/document, ocean wave, volcano, fire) - that's 5 emojis\n4. Fourth exchange: Italy-related emojis (flag, food items, landmark) - that's at least 3 emojis\n\nBut wait, the OCR shows \"8\" which might mean 8 emojis. Let me look at lines 1795-1835 which also mention \"Always answer with emojis\":This describes a different version showing the LEFT subfigure (without GAtt) - it shows the model failing to follow the \"always answer with emojis\" instruction in the third exchange (text response instead of emojis).\n\nNow let me look more carefully at the actual Figure 9 structure. Looking back at the figure text I saw earlier, the caption says \"Issues with multi-turn memory (left) can be improved with GAtt (right).\" The page has TWO column headers \"Always answer with emojis\" (lines 3142-3143), suggesting left and right subfigures.\n\nLet me count the emojis more carefully. Looking at the embedded image X1.png descriptions:\n\n**LEFT subfigure (lines 1795-1835)**: Model FAILS to always use emojis\n- First exchange: 2 emojis (robot face and laptop)\n- Second exchange: (question only shown, no response visible in description)\n- Third exchange: TEXT response (not emojis) - this shows the failure!\n\n**RIGHT subfigure (lines 2726-2760)**: Model SUCCEEDS with emojis\n- First exchange: 3 emojis (thinking face, eyes, speech bubble)\n- Second exchange: 5 emojis (airplane, boat, landmark, car, globe)\n- Third exchange: 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n- Fourth exchange: 3+ emojis (flag, food items, landmark)\n\nLet me count the \"food items\" more carefully. It says \"flag, food items, landmark\" - \"food items\" is plural, so at least 2 food emojis. That would be at least 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis minimum for the fourth exchange.\n\nTotal for RIGHT: 3 + 5 + 5 + (at least 4) = at least 17 emojis\n\nBut wait, let me look at the OCR text again at line 2730 - it shows \"8\" right after \"Always answer with emojis\". This might indicate 8 emojis in a single response or might be part of the interface.\n\nLet me search for more specific information about counting emojis in each subfigure:The document doesn't use the term \"subfig\". Let me look more carefully at the actual textual representation of Figure 9 between lines 3146-3181. The numbers \"8\", \"0\", \"8\" might be emoji counts:I see it! The pattern shows:\n- Line 3149: \"Who are you?\" (question appears twice - probably left and right)\n- Line 3151: \"Who are you?\" (duplicate)\n- Line 3153: \"8\" (LEFT subfigure response)\n- Line 3157: \"How to go from Paris to NY?\" (appears twice)\n- Line 3159: \"How to go from Paris to NY?\" (duplicate)\n- Line 3161: \"8\" (RIGHT subfigure response)\n- Line 3163: \"0\" (LEFT subfigure response - no emojis, text instead)\n- Lines 3165-3169: Text response (LEFT fails to use emojis)\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (LEFT subfigure - no emojis)\n- Line 3177: \"Italy?\"\n- Line 3179: \"8\" (response)\n\nThis is confusing. Let me look for an actual image file description. Let me check if there's a better description by looking at different chunks:Let me try a different approach. Let me look for the actual visual description or image file that corresponds to Figure 9. Let me search around page 15 for embedded images:The image X6.png after Figure 9 is NOT Figure 9 - it's a scatter plot. The actual Figure 9 content must be represented in the text between lines 3146-3181. But this text representation is unclear.\n\nLet me look BEFORE Figure 9 to see if there's an image file that IS Figure 9. Looking at line 3139, it says \"PageNumber 15\" and then the two page headers \"Always answer with emojis\". This must be a two-column layout. Let me search backwards for an embedded image that might be Figure 9:So Figure 9 starts immediately after the page break at line 3140. There's no embedded image file BEFORE it. The figure content itself must be in the text, or the image description is missing.\n\nBased on what I've found, let me piece together what I know from the two image descriptions I found (X1.png from lines 1795 and 2726):\n\n**LEFT subfigure** (lines 1795-1835 - model WITHOUT GAtt):\n- First response: 2 emojis\n- Later responses: TEXT (no emojis) - shows failure\n\n**RIGHT subfigure** (lines 2726-2760 - model WITH GAtt):\n- First response: 3 emojis \n- Second response: 5 emojis\n- Third response: 5 emojis \n- Fourth response: \"flag, food items, landmark\"\n\nFor the fourth response, \"food items\" is plural, indicating at least 2 items. So minimum: 1 flag + 2 food + 1 landmark = 4 emojis.\n\nLet me count more carefully. Looking at the visual description at lines 2750-2753 again:\n1. \"thinking face, eyes, and speech bubble emojis\" - that's 3 emojis\n2. \"airplane, boat, landmark, car, globe\" - that's 5 emojis\n3. \"dinosaur, calendar/document, ocean wave, volcano, fire\" - that's 5 emojis\n4. \"flag, food items, landmark\" - the term \"food items\" suggests multiple food emojis\n\nActually, let me check if there's a more specific count in the OCR at line 2730:OK! The OCR text at line 2730 shows \"8\". This might mean 8 emojis total for the first response, OR it might be labeling something else. But the description clearly lists:\n1. 3 emojis (thinking face, eyes, speech bubble)\n2. 5 emojis (airplane, boat, landmark, car, globe) \n3. 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n4. At least 3+ emojis (flag, food items [plural = 2+], landmark)\n\nNow for the LEFT subfigure. Let me find its details:So the LEFT subfigure shows:\n- First exchange: 2 emojis (robot face and laptop) \n- Second exchange: (no response details given)\n- Third exchange: TEXT response (not emojis) - this is the FAILURE\n\nSo the LEFT has only 2 emojis total before it fails.\n\nThe RIGHT has: 3 + 5 + 5 + (at least 3) = at least 16 emojis\n\nBut the question asks specifically about Figure 9. Let me verify these two X1.png images at lines 1795 and 2726 are actually the left and right parts of Figure 9. They both have the same instruction \"Always answer with emojis\" at the top, which matches the page headers for Figure 9.\n\nActually, looking more carefully, let me check how many food items specifically. The text says \"flag, food items, landmark\". If \"food items\" means 2 items, then the fourth response would be: 1 + 2 + 1 = 4 emojis.\n\nSo RIGHT would be: 3 + 5 + 5 + 4 = 17 emojis\nLEFT has: 2 emojis\n\nDifference: 17 - 2 = 15\n\nWait, but I need to verify this is correct. Let me think about this differently. The description says \"food items\" (plural). The minimum plural is 2. But it could be more. Let me see if I can find more specific information.\n\nLooking at the specific items listed for Italy: \"flag, food items, landmark\". Given the context is Italy, common food emojis might be pizza and pasta (2 items), or there could be 3+ food items. Without more specific information, I'll assume \"food items\" means at least 2.\n\nActually, looking at the pattern of the other responses which are very specific:\n- Response 1: lists 3 specific emojis\n- Response 2: lists 5 specific emojis \n- Response 3: lists 5 specific emojis\n- Response 4: says \"flag, food items, landmark\"\n\nIf the describer was being consistent, and they specifically didn't count the food items individually but said \"food items\" plural, it likely means 2 food items (pizza and pasta would be typical for Italy).\n\nSo: 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis\n\nRIGHT subfigure total: 3 + 5 + 5 + 4 = 17 emojis\nLEFT subfigure total: 2 emojis\n\nAnswer: 17 - 2 = 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 179203, "error": null, "extra": {"thread_id": 1998, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_913336fbec024206afbfe0bdfdc22736", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1666, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "15"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find Figure 122 in the document and look for information about the color of OSCARb in the prompt.\n\nLooking at the document, I can find Figure 122 which is titled \"Self-Reflection to Improve Coding\" in Section 10.3.\n\nIn Figure 122, there are two prompts shown. The first prompt asks to \"Generate python code to draw similar curves\" and shows a reference image with a graph. The second prompt asks \"Can you double-check the code and align it better with the reference image?\"\n\nLooking at the graphs in Figure 122, I can see there are plots comparing \"OSCarB\" and \"MiniVLM (ours)\". In the visualizations shown, OSCarB appears to be represented in orange/red color in the legend and plot lines.\n\nAnswer: orange", "citations": [], "input_tokens": 117434, "output_tokens": 184, "cost_micros": 355062, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in Section 10.3 on Self-Reflection to Improve Coding, I can see the prompt includes a reference image with a comparison chart.\n\nIn the chart showing \"Comparison of OSCarB and MiniVLM\", OSCarB is represented by the orange/coral colored bars.\n\nAnswer: Orange", "citations": [], "input_tokens": 133651, "output_tokens": 77, "cost_micros": 402108, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see the prompt mentions \"OSCARb\" in the context of generating Python code to draw similar curves.\n\nIn the figure, there is a bar chart showing \"OSCARb\" and \"MiniVLM (ours)\" with bars for \"Base,\" \"Large,\" and \"Huge\" configurations. The bars for OSCARb appear to be orange/amber colored in the visualization.\n\nAnswer: orange", "citations": [], "input_tokens": 103918, "output_tokens": 116, "cost_micros": 313494, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see two side-by-side examples.\n\nIn the right example (the bar chart comparison), the prompt asks to \"Write python codes to generate similar figures.\" The reference image shows a bar chart comparing OSCAR_B and MiniVLM across different categories.\n\nLooking at the bar chart, OSCAR_B is represented by blue bars, while MiniVLM (ours) is represented by orange bars.\n\nAnswer: Blue", "citations": [], "input_tokens": 139293, "output_tokens": 120, "cost_micros": 419679, "latency_ms": 7781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 122 and identify the color of OSCARb in the prompt.Let me search for \"Figure 122\" in the document:Now let me search for where Figure 122 appears with more context:Let me read the section around line 32131 and 32550 to see Figure 122:I can see the figure content. Let me continue reading to see more details about the colors:Let me continue reading to see the color information for the bars:I need to look more carefully at the actual figure. Let me search for more specific information about the figure 122 visual description or color information. Let me read around line 32300-32350 more carefully where the bar chart appears:The image described starting at line 32358 (Image7.jpg) is showing a Golden Retriever dog, which is not the chart. Let me look at the figure that appears before that - the bar chart figure starting at line 32302. This shows the data but doesn't explicitly state colors. Let me search for any embedded image descriptions that might be the actual bar chart:Great! I found it. Let me read the section around line 19587 where it says \"Blue bars represent OSCarB\":However, this is describing a different figure. Let me check if this is Figure 122 or another figure. Let me look back at the context around line 32300 where the chart with OSCarB appears in the \"Prompt: Write python codes to generate similar figures\" section, which is part of Figure 122. Let me look for the visual description of the image that corresponds to the reference image in the prompt:Good! This shows that OSCarB is represented by blue bars. Now let me verify this is indeed related to Figure 122. Let me check if Figure 122's prompt section has this same image. Let me look around where the figure caption for Figure 122 is:This is showing a different chart (the left side of Figure 122 with CIDEr curves). Let me look at the right side which should be the bar chart. The section at line 32297-32354 shows the bar chart prompt. Let me look for embedded images near that area. Based on the structure, the embedded images have visual descriptions. Let me check what comes just before line 32297:I see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interrupting the flow. The actual bar chart reference image in the prompt should be described elsewhere. Let me look for where Figure 122 starts and what images are in it. Let me search for the image that appears right before the \"Prompt: Write python codes to generate similar figures\":Let me look at the first occurrence at line 16349 to see if it has a visual description:This looks like it's showing both charts mixed together in the OCR text. Let me look for the embedded image before this section to find the visual description:So this is Figure 47, not Figure 122. Let me go back to the Figure 122 area and look more carefully. The prompt section starting at line 32299 shows the reference image that contains the bar chart. Let me look for what image comes right before or is part of that prompt section. Let me search more systematically around the Figure 122 area to find embedded images:Perfect! So Figure 122 \"demonstrates the application of self-reflection to improve the results shown in Figure 47.\" This means Figure 122 is showing the same charts as Figure 47, but with self-reflection applied. Since Figure 47 had the bar chart with OSCarB and MiniVLM, and I found earlier at line 19587 that \"Blue bars represent OSCarB\", this should apply to Figure 122 as well since it's based on Figure 47.\n\nLet me verify by checking if there's a specific visual description for the reference image in the right-side prompt of Figure 122. Let me look for any embedded image that might be between lines 32286-32302:Let me look at Figure 47 more carefully. Let me search for where Figure 47 begins with an embedded image:Let me look for embedded images near Figure 47. Let me search backwards from line 16333 to find the embedded images that are part of Figure 47:Let me now search for where Figure 47 contains the bar chart reference image. Since Figure 47 shows both charts (the curve chart and the bar chart), let me look for embedded images in the Figure 47 section. Let me search for \"Embedded image\" near Figure 47:These embedded images are from Section 5, not Figure 47. Let me search more systematically. Let me look for embedded images that appear right before or in Figure 47 section. Let me check around line 15900-16350:Let me search for \"Embedded image\" directly:There are many embedded images. Based on what I've seen, the visual description at line 19587 clearly states \"Blue bars represent OSCarB\" for a bar chart showing \"Comparison of OSCarB and MiniVLM.\" This is the same chart that appears in both Figure 47 and Figure 122 (since Figure 122 is showing improvements to Figure 47 via self-reflection).\n\nLet me verify once more by looking at line 39070 which also mentioned OSCarB color:Interesting! This description says \"Red/coral bars: Represent OSCarB method\" while the earlier one at line 19587 said \"Blue bars represent OSCarB\". These might be describing different versions or different embedded images of the same chart. Let me check if these are in different contexts. Let me look at what figure this is part of (line 39070):Let me check what figure or section this is in:This is Image10.jpg. Let me look back to see what figure this is part of. Let me search backwards for \"Figure\" before line 38980:These are Instagram Reels images, not related to Figure 122. Let me search more directly. Let me look for what section/figure is near line 39000-39100. Let me search backwards for \"Figure\" or \"Sec\":These are all Instagram food images. Let me search for which figure or section contains line 39070. Let me look much earlier before these Instagram images:Still Instagram images. Let me just search for \"Figure 122\" to look for its caption and boundaries:So Figure 122's caption is at line 32550. The figure shows two parts - Left (curves) and Right (bar chart). The GPT-4V output for the right side starts at line 32552. This is within Figure 122. Now I need to find the reference image (the prompt input) for the right side, which should show the original bar chart with OSCarB colors. The prompt for the right side is at line 32299 \"Write python codes to generate similar figures.\" The reference image data is at lines 32302-32354. But this doesn't tell me colors directly since it's just OCR text.\n\nBased on my findings:\n1. Figure 122 is based on Figure 47 (as stated at line 32131-32132)\n2. The bar chart in the figures compares OSCarB and MiniVLM\n3. At line 19587, the visual description clearly states \"Blue bars represent OSCarB\"\n4. At line 39070, another visual description states \"Red/coral bars: Represent OSCarB\"\n\nThese seem to be describing different embedded images. Let me check if the one at 19587 is specifically part of Figure 47:This is Section 7.3, not Figure 47. Let me look for what this section is:This is Section 7.3. So the embedded image with \"Blue bars represent OSCarB\" is in Section 7.3, not in Figure 47. Let me search for what figures are in the Sec 10.3 Self-Reflection area where Figure 122 is. Let me look for embedded images right before or around the prompt at line 32299. Actually, the reference image for the prompt should be immediately before it. Let me look:The
starting at line 32302 is the reference image for the prompt \"Write python codes to generate similar figures.\" This figure contains the bar chart data (OSCARB, MiniVLM, etc.) shown as OCR text from lines 32304-32354. But this OCR text doesn't tell me the colors. The actual visual description of this embedded image should be before or after the figure content. Let me check line 32354 onwards:That's the Golden Retriever image (Image7.jpg), which is not the bar chart. So the bar chart figure (lines 32302-32354) doesn't have a separate \"Embedded image\" marker. This means the figure content IS the embedded image content, just displayed as the
OCR text.\n\nLet me look for Image4.jpg or other images that might be before line 32302:I can see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interspersed in the document but not part of Figure 122's chart. The actual chart images in Figure 122 are shown as
blocks without \"Embedded image\" labels.\n\nLooking at the document structure, I notice that Figure 122 references Figure 47. Since I know that:\n1. Figure 122 is improving upon Figure 47\n2. They show the same charts (left: curves, right: bar chart)\n3. The color of the bars should be consistent between them\n\nLet me look more carefully at Figure 47 to find which embedded image is the bar chart. Let me search around Figure 47's location (around line 16333):This is still in Section 4.5 Multilingual, before Figure 47. Let me search for images closer to Figure 47. Actually, let me take a different approach. Since Figure 47 appears around line 16333 and the caption says it shows examples, the actual embedded images for Figure 47 should have visual descriptions. But since Figure 47 shows generated output rather than original images, the
blocks ARE the actual content.\n\nGiven the conflicting information (line 19587 says \"Blue\" for OSCarB, line 39070 says \"Red/coral\" for OSCarB), let me check which one is actually related to Figure 122 or Figure 47. Let me look at the context more carefully. The document says Figure 122 improves Figure 47. The prompt in Figure 122 at line 32299 is \"Write python codes to generate similar figures\" and the reference image is at lines 32302-32354 showing the bar chart data.\n\nSince Figure 122's caption (line 32550) says \"Right: the percentage is added to align with the reference image\", the reference image (the prompt image) is the ORIGINAL one that needs improvement. This original should be from Figure 47.\n\nLet me look at the data itself. At line 32312, it says \"OSCARB\" (all caps), and at line 32553, in the GPT-4V output, it says \"OSCarB\" (mixed case). Based on the color descriptions I found, and given that there are multiple embedded images of this chart throughout the document, I need to determine which color appears in the specific prompt image in Figure 122.\n\nGiven the two conflicting descriptions and the difficulty in determining which applies to Figure 122's prompt image specifically, let me check if there's any pattern. The one at line 19587 explicitly lists the color scheme at lines 19514-19518:\n- \"Bright cyan/blue for first method\" \n- \"Orange-red for second method\"\n\nAnd it says at line 19587: \"Blue bars represent OSCarB\"\n\nThe one at line 39070 says:\n- \"Red/coral bars: Represent OSCarB method\"\n- \"Teal/cyan bars: Represent MiniVLM method\"\n\nThese are OPPOSITE! Let me check the legend text. At line 32312-32314 in Figure 122's prompt image, it shows:\n\"OSCARB\"\n\"MiniVLM (ours)\"\n\nAnd at line 32553-32561 in the GPT-4V output for Figure 122:\n\"OSCarB\"\n\"MiniVLM (ours)\"\n\nLet me check the earlier descriptions for legend position. At line 19505-19506: \"Legend in upper right distinguishes the two methods\"\n\nGiven that standard chart conventions typically list legend items in order, and OSCarB is listed first (before MiniVLM), OSCarB would typically be the first color in the color scheme.\n\nLooking at line 19515-19516:\n- \"Bright cyan/blue for first method\"\n- \"Orange-red for second method\"\n\nIf OSCarB is the first method (listed first in the legend), then it should be \"Bright cyan/blue\" which matches line 19587: \"Blue bars represent OSCarB\"\n\nAnswer: blue", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 238671, "error": null, "extra": {"thread_id": 1999, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e4d50e05962f4bf6af3a2d9a2bff8f0a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2074, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 113 to identify the music app shown in the computer screen.\n\nLooking at the document, Figure 113 is described in the section on \"Notification Understanding\" (Section 9.8, page 143).\n\nIn the image caption and description for Figure 113, I can see a computer desktop screenshot. Looking at the desktop icons visible in the image, I can see several application icons including:\n- YouTube\n- Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown on the desktop is labeled as \"Music\" in the icon.\n\nAnswer: Music", "citations": [], "input_tokens": 117435, "output_tokens": 134, "cost_micros": 354315, "latency_ms": 8593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in Section 9.8 (GUI Navigation - Notification Understanding), I can see the computer desktop screen.\n\nOn the desktop, there are several icons visible on the left side, including:\n- YouTube Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown is YouTube Music.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 133652, "output_tokens": 80, "cost_micros": 402156, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I can see a computer desktop screenshot with a notification that says \"Kevin Lin is calling you.\" \n\nThe figure shows several application icons on the desktop and in the taskbar. However, I cannot clearly identify a specific music app from the image description or details provided in the document about this figure.\n\nThe document states: \"The image on the computer screen is a desktop background of a bridge over a body of water. There are several icons on the desktop, including a YouTube icon, a Recycle Bin icon, and a Terminal icon. At the bottom of the screen, there is a taskbar with several application icons, such as Microsoft Edge and File Explorer.\"\n\nAnswer: Not clearly identifiable from the provided information", "citations": [], "input_tokens": 103919, "output_tokens": 160, "cost_micros": 314157, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "not clearly identifiable from provided information", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I need to examine the desktop icons shown in the computer screen image.\n\nThe figure shows a Windows desktop with several application icons on the left side. Among these icons, I can see \"YouTube Music\" listed as one of the desktop shortcuts.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 139294, "output_tokens": 67, "cost_micros": 418887, "latency_ms": 6266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 113 and identify the music app shown in the computer screen.Let me search for \"Figure 113\" in the document.Now let me read the section around line 29414 to see Figure 113 and its description.I can see Figure 113 is referenced, but I need to see the actual image content. Let me look a bit earlier to see the full figure with the image description.I need to look at the actual figure content for Figure 113. Let me search for where the figure starts by looking earlier.Perfect! I found Figure 113. Looking at the content around lines 29326-29350, I can see the computer screen showing desktop icons. Line 29330-29331 clearly shows:\n\n\"You Tube\nMusic\"\n\nThis appears to be \"YouTube Music\" split across two lines in the OCR text. This is the music app shown on the computer screen in Figure 113.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52515, "error": null, "extra": {"thread_id": 2000, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_26bb070ab7624a93b3c5c955dc0af2c0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 106921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and look for the price of the keyboard shown in the screenshot.\n\nLooking at the document, Figure 111 is described in the \"List of Figures\" section as:\n\"Figure 111: Section 9.8: online shopping.\"\n\nLet me search for Figure 111 in the actual content. It appears in Section 9.8 on \"Online Shopping (Ergonomic Keyboard)\".\n\nLooking at the prompt description for Figure 111:\n\"Prompt:\nImagine that you are a robot operating a phone. Like how humans operate the phone, you can move your finger to an icon, click an icon with your finger, scroll down a screen, or type some texts with the keyboard.\n\nYou are asked to shop for an ergonomic keyboard, your budget is between $50 and $100.\n\nPrevious action: \" My next move would be to move my finger to the \"Add to Cart\" button, which is located below the \"Buy New\" button and above the \"Ships from Amazon\" section. I would then click on the \"Add to Cart\" button to add the item to my shopping cart. \"\n\nYou have now completed the previous action as described above. Below is what you see on the phone screen. What will you do next?\"\n\nThe screenshot shows a cart with:\n\"Subtotal $4999\"\n\nand the product listed is:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540 ...\n$4999\"\n\nThe price shown is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 117431, "output_tokens": 353, "cost_micros": 357588, "latency_ms": 12750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "arm": "azure_premium_lc", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:56:47Z", "latency_ms": 24578, "error": null, "raw_text_chars": 640}]}, "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in the screenshot.\n\nLooking through the document, I can find Figure 111 which is part of Section 9.8 on GUI Navigation, specifically about Online Shopping for an Ergonomic Keyboard.\n\nIn Figure 111, the screenshot shows a shopping cart with a keyboard item. The text indicates:\n\n\"Subtotal $4999\"\n\nAnd in the cart details, it shows:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...\n$4999\"\n\nThe price shown is $49.99 (represented as $4999 in the format used in the screenshot, which appears to be missing the decimal point).\n\nAnswer: 49.99", "citations": [], "input_tokens": 133648, "output_tokens": 176, "cost_micros": 403584, "latency_ms": 24578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 111 in the document, which shows a phone screenshot of a shopping cart.\n\nThe screenshot displays:\n- A product: \"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\"\n- The price shown is: $49.99\n- There's a subtotal of $49.99\n- The item shows \"In Stock\" status\n\nAnswer: 49.99", "citations": [], "input_tokens": 103915, "output_tokens": 97, "cost_micros": 313200, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in that screenshot.\n\nLooking through the document for Figure 111...\n\nFound it on page 140:\n\nFigure 111 shows a smartphone screen displaying an Amazon shopping cart. The image shows:\n- A Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\n- Price: $49.99\n- The button shows \"Proceed to checkout (1 item)\"\n- Subtotal: $49.99\n\nThe price of the keyboard shown in the screenshot is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 139290, "output_tokens": 144, "cost_micros": 420030, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find Figure 111 and identify the keyboard price.Let me search for \"Figure 111\" in the document to locate it.Now let me search for where Figure 111 appears in the content with context.Let me read around line 29062 where Figure 111 is located to see the actual content and price information.Perfect! I found Figure 111 which shows a screenshot of shopping for an ergonomic keyboard. The price is clearly shown in the screenshot as $49.99 (displayed as \"$4999\" without the decimal in multiple places in lines 29088 and 29095).\n\nAnswer: 49.99", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52062, "error": null, "extra": {"thread_id": 2001, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd7392a113cf44358e67b4c74d3afe31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 172, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "arm": "azure_basic_lc", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:00:12Z", "latency_ms": 7967, "error": null, "raw_text_chars": 594}]}, "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and identify the battery percentage shown in that screenshot.\n\nLooking through the document for Figure 107, I can see it's in Section 9.8 on GUI Navigation, specifically related to online shopping.\n\nIn the image caption list, Figure 107 is described as \"Section 9.8: online shopping.\"\n\nLooking at the actual Figure 107 in the document, I can see it's a smartphone screenshot showing an Amazon shopping interface. At the top of the screenshot, there is a time display \"5:32\" and next to it shows \"75\" which represents the battery percentage.\n\nAnswer: 75", "citations": [], "input_tokens": 117429, "output_tokens": 143, "cost_micros": 354432, "latency_ms": 7967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and look for the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107...\n\nFound it in Section 9.8 GUI Navigation under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" in the top left corner, and the battery percentage \"76\" is displayed in the top right corner of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 133646, "output_tokens": 109, "cost_micros": 402573, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at Figure 107 to find the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107, I can see it's in the \"Sec. 9.8 GUI Navigation\" section under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" displayed at the top left, and the battery percentage \"76\" shown at the top right of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 103913, "output_tokens": 112, "cost_micros": 313419, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 107 in Section 9.8 (GUI Navigation - Online Shopping), I need to find the battery percentage shown in the screenshot.\n\nIn the top-right corner of the screenshot shown in Figure 107, the status bar displays:\n- Time: 5:27\n- Battery percentage: 76\n\nAnswer: 76", "citations": [], "input_tokens": 139288, "output_tokens": 76, "cost_micros": 419004, "latency_ms": 6592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 107 and identify the battery percentage shown in the screenshot.Let me search for \"Figure 107\" in the document to locate it quickly.Now let me search for the specific line with more context:Let me read around line 28504 to see Figure 107:I can see Figure 107 shows a phone screenshot. I can see \"5:27\" as the time at line 28507, and at line 28511 I see \"76\". Let me check if this is the battery percentage. Looking at the structure, it appears to show:\n- 5:27 (time)\n- 76 (likely battery percentage)\n\nLet me read a bit more to see if there's additional context or if the battery percentage is shown elsewhere:Based on the document content, Figure 107 shows a phone screenshot with the time \"5:27\" at the top, and the number \"76\" appears right after it. In typical phone screenshots, the battery percentage is displayed in the status bar at the top of the screen, usually near the time. The number 76 appears to be the battery percentage.\n\nAnswer: 76", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64703, "error": null, "extra": {"thread_id": 2002, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_281e7f398f814d8a9e02d3b21485af45", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 226, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 110562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Figure 101 to find information about the Politico website news cover.\n\nLooking at the document, Figure 101 is described in Section 9.8 \"GUI Navigation - Web Browsing (Today's News)\" on page 131.\n\nThe figure shows a Google search results page with news stories. The prompt asks GPT-4V about what happens after reading the first news article. Looking at the \"Top stories\" section in the image, I can see several news sources listed including:\n\n- **Politico**: \"State Department doubles down after Zelenskyy slams reluctance...\"\n- CNN: \"Catastrophic flooding swamped Vermont's capital...\"\n- Reuters: \"US says it no longer deems Donald Trump immune from E. Jean...\"\n- DCist: \"D.C. Council Approves Emergency Crime Bill\"\n- The New York Times: \"Hard Right Presses Culture War Fights on Defense Bill...\"\n- NPR: \"The Anthropocene began in 1950s, scientists say\"\n\nThe Politico headline mentions \"Zelenskyy,\" which refers to Volodymyr Zelenskyy, the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 117439, "output_tokens": 268, "cost_micros": 356337, "latency_ms": 11250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in Section 9.8 (Web Browsing - Today's News), I can see the Google search results page showing several news headlines.\n\nThe top news item from Politico shows: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThis refers to Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 133656, "output_tokens": 87, "cost_micros": 402273, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in the document, I can see a screenshot showing a news article from Politico. The article is titled \"State Department doubles down after Zelenskyy slams reluctance...\" \n\nThe article discusses Ukrainian President Volodymyr Zelenskyy and Ukraine's desire to join NATO. Zelenskyy is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 103923, "output_tokens": 85, "cost_micros": 313044, "latency_ms": 7108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101, I can see a Google search results page showing news articles. The figure description mentions \"Top stories\" and lists several news sources including Politico.\n\nThe first story listed under Politico is: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 139298, "output_tokens": 88, "cost_micros": 419214, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant (2309.17421v2.pdf.xml) to find Figure 101 and identify the information about the Politico website cover.Let me search for \"Figure 101\" in the document to find the specific location:Let me search for \"politico\" to find the relevant section:I can see mentions of Politico with information about Zelenskyy. Let me search more specifically for \"Figure 101\":Great! I found the reference to Figure 101 at line 27715. Let me read that section:Let me look earlier in the document to see the actual Figure 101 image content. I need to search backwards from line 27715 to find the visual description:Now I can see the context. Figure 101 shows a Google News page with various news articles. Looking at the content around lines 27625-27631, I can see:\n\n- Politico news source\n- Article title: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy (also spelled Zelensky in other parts of the document), who is the President of Ukraine. Let me verify this is correct by looking at the article content earlier:\n\nFrom lines 27419-27421 and 27445-27446, I can confirm:\n- \"State Department doubles down after Zelenskyy slams reluctance on Ukraine's entry to NATO\"\n- \"In a statement posted to Twitter on Tuesday, Ukrainian President Volodymyr Zelenskyy took issue with NATO's reluctance...\"\n\nSo the person on the cover of the news on the Politico website in Figure 101 is Zelenskyy (Volodymyr Zelenskyy), who is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84875, "error": null, "extra": {"thread_id": 2003, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6c36693d9bfb44369693c25b87e59141", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 305, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Figure 89 to identify the brand name of the coffee machine.\n\nLooking at the document, Figure 89 is in Section 9.7 \"Embodied Agent\" under \"Operating Machine\". The figure shows a coffee machine's operating panel with buttons and includes text that mentions operating menu instructions.\n\nIn the image shown in Figure 89, I can see a coffee machine operating panel. Looking at the visible text and branding on the machine, I can see \"Keurig\" appears to be the brand name based on the styling and layout typical of Keurig coffee makers.\n\nAnswer: Keurig", "citations": [], "input_tokens": 117434, "output_tokens": 139, "cost_micros": 354387, "latency_ms": 7641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine's operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 133651, "output_tokens": 61, "cost_micros": 401868, "latency_ms": 6500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 103918, "output_tokens": 60, "cost_micros": 312654, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7, which shows the coffee machine operating panel with button menus, I can see the brand name displayed on the machine.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 139293, "output_tokens": 47, "cost_micros": 418584, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Figure 89 and identify the coffee machine brand.Let me search for \"Figure 89\" in the document:Now let me search more specifically to find where Figure 89 appears:Let me read the section around line 25761 to see Figure 89 and its image:I can see Figure 89 references a coffee machine. Let me look a bit earlier to see the actual image content:Let me continue reading to see the menu and coffee machine images:Let me continue to find the actual coffee machine image with the brand visible:I see that line 25763 shows \"JoooDeee\" which appears to be OCR text from the coffee machine image. Let me look at the earlier Figure 88 section which also shows the coffee machine, as it might have clearer brand information:I can see the OCR text on line 25609 and 25763 shows \"JoooDeee\". Let me search for Figure 88 which is mentioned just before to see if there's more context about the brand:Let me look before line 25584 to see if there's an image with better OCR or visual description for Figure 88:Let me search more specifically around where Figure 88 might have its embedded image:Let me look earlier to find where Figure 88 actual image appears. Let me search backwards from where Figure 88 was mentioned:Let me search more systematically. Let me look for where the coffee machine images are actually embedded. Let me search around where both Figure 88 and 89 would appear together:Let me try a different approach and look for pages around 116-117 where Figures 88 and 89 are mentioned, searching for embedded coffee machine images. Let me search for \"Joo\" which appeared in the OCR text:Great! Let me look at line 34358 and chunk 155184 which has a better visual description with \"JoooDeee\":Perfect! I found it. According to the OCR text on line 34358, the coffee machine has \"JoooDeee\" visible on it. Looking at the visual description starting at line 34363, it describes the device as a water dispenser/cooler, but based on the context of the document discussing coffee machines and the OCR text showing \"JoooDeee\", this appears to be the brand name visible on the coffee machine in Figure 89.\n\nHowever, \"JoooDeee\" seems like it might be an OCR misreading. Let me check if there's any other reference that might clarify the brand. Looking at the OCR pattern \"JoooDeee\", this could potentially be \"Jura\" (a well-known coffee machine brand) misread by OCR, but I should report what the document actually says.\n\nBased on the document, the OCR text from the coffee machine image shows \"JoooDeee\" with \"DI\" below it.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 148609, "error": null, "extra": {"thread_id": 2004, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f9e89fe20d454f6c90a127387a939523", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 624, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 1, I need to identify the relation arrows (edges) in the knowledge graph and determine which ones do not point to specific leaf nodes.\n\nThe figure shows a knowledge graph centered around Artemisia Gentileschi (Q212657) and Orazio Gentileschi (Q367560). The relations shown include:\n\n- **child** (from Orazio to Artemisia)\n- **father** (from Artemisia to Orazio)\n- **citizen** (from both to Italy Q38)\n- **occupation** (pointing to \"Painter\")\n- **style** (from Artemisia to Baroque Q37853)\n- **movement** (from Artemisia to Baroque Q37853)\n- **place of birth** (from Artemisia to Rome Q220)\n- **member** (from Artemisia, appears to point to another entity)\n\nThe arrows that do NOT point to specific leaf nodes (i.e., they point to other entities that are themselves nodes in the graph rather than terminal values) are:\n\n- child (points to another person entity)\n- father (points to another person entity)\n\nAnswer: child, father", "citations": [], "input_tokens": 60197, "output_tokens": 266, "cost_micros": 184581, "latency_ms": 12796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify which relation arrows do not point to specific leaf nodes (entities with specific values or names).\n\nLet me examine each relation arrow from the entities shown:\n\nFrom Q367560 (Orazio Gentileschi):\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- movement \u2192 Q37853 (Baroque) - points to a class/type node\n- child \u2192 Artemisia Gentileschi Q212657 - points to a specific person\n\nFrom Artemisia Gentileschi Q212657:\n- father \u2192 Orazio Gentileschi Q367560 - points to a specific person\n- birth \u2192 [date shown] - points to specific value\n- place of birth \u2192 [location] - points to specific value\n- Citizen \u2192 Italy Q38 - points to a specific country\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- style \u2192 Q37853 (Baroque) - points to a class/type node\n- member \u2192 [organization] - appears to point to a specific entity\n\nThe relations that point to class/type nodes rather than specific leaf nodes are:\n- occupation (points to the class \"Painter\")\n- movement/style (points to the class \"Baroque\")\n\nAnswer: occupation, movement, style", "citations": [], "input_tokens": 22214, "output_tokens": 311, "cost_micros": 71307, "latency_ms": 7718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "movement, occupation, style", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the relation arrows (edges) in the knowledge graph that do not point to specific leaf nodes (literal values).\n\nIn the knowledge graph shown in Figure 1, I can see the following relationships:\n\n- \"Painter\" (Q1028181) - this is an entity node, not a leaf\n- \"Baroque\" (Q37853) - this is an entity node, not a leaf\n- \"Italy\" (Q38) - this is an entity node, not a leaf\n- \"Rome\" (Q212657) - this is an entity node, not a leaf\n- Orazio Gentileschi (Q367560) - this is an entity node, not a leaf\n- Artemisia Gentileschi - the central entity\n\nThe relations that point to these non-leaf entity nodes (rather than to literal values like dates or strings) are:\n\n- occupation (pointing to Painter)\n- style (pointing to Baroque)\n- movement (pointing to Baroque)\n- citizen (pointing to Italy)\n- place of birth (pointing to Rome)\n- father (pointing to Orazio Gentileschi)\n- child (pointing to Artemisia Gentileschi)\n- member (appears to be a relation as well)\n\nAnswer: occupation, style, movement, citizen, place of birth, father, child, member", "citations": [], "input_tokens": 25539, "output_tokens": 310, "cost_micros": 81267, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "child, citizen, father, member, movement, occupation, place of birth, style", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows in the Knowledge Graph that do not point to leaf nodes (specific values).\n\nIn the Knowledge Graph shown, the relations are labeled on the arrows connecting entities. Let me trace each relation:\n\n- \"occupation\" \u2192 points to \"Painter\" (Q37853) - this is a leaf node\n- \"movement\" \u2192 points to \"Baroque\" (Q1028181) - this is a leaf node\n- \"style\" \u2192 appears to connect nodes but the endpoint is not clearly a specific leaf value\n- \"place of birth\" \u2192 points to a specific location\n- \"child\" \u2192 points to \"Artemisia\" (another person entity, not a leaf value)\n- \"father\" \u2192 points to \"Orazio\" (another person entity, not a leaf value)\n- \"member\" \u2192 connects to citizenship information\n- \"citizen\" \u2192 points to \"Italy\" (a leaf node)\n\nThe relations that point to other entities rather than specific attribute values (leaf nodes) are:\n\nAnswer: child, father", "citations": [], "input_tokens": 17550, "output_tokens": 231, "cost_micros": 56115, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows that do not point to specific leaf nodes (entity or property nodes).\n\nFrom the Knowledge Graph Context cloud, I can see the following relationships:\n\n- E1 (OrazioGentileschi) -- occupation --> P1 (Painter) - points to leaf node P1\n- E1 -- movement --> P2 (Baroque) - points to leaf node P2\n- E1 -- birth --> E1 - points back to itself, not a leaf node\n- E1 -- child --> E2 - points to E2, which has further connections\n- E2 (ArtemisiaGentileschi) -- father --> E1 - points to E1, which has further connections\n- E2 -- occupation --> P1 - points to leaf node P1\n- E2 -- style --> P2 - points to leaf node P2\n- E2 -- place of birth --> E3 - points to E3, which has further connections\n- E2 -- member --> E2 - points back to itself, not a leaf node\n- E2 -- Citizen --> E3 - points to E3, which has further connections\n- E1 -- Citizen --> E3 - points to E3, which has further connections\n\nThe relations that do not point to specific leaf nodes are those pointing to entities that have their own outgoing connections or pointing back to themselves:\n\nAnswer: birth, child, father, place of birth, member, Citizen", "citations": [], "input_tokens": 23598, "output_tokens": 341, "cost_micros": 75909, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "birth, child, citizen, father, member, place of birth", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify which relation arrows do not point to specific leaf nodes.Let me search for \"Figure 1\" in the document to locate it:Let me read more to see the complete Figure 1 description:I can see Figure 1 shows a knowledge graph. Let me look more carefully at the structure. The text shows entities and relations. Let me search for more context about the graph structure:Based on the text extracted from Figure 1, I can see the knowledge graph structure. The entities and relations listed are:\n\n- Q1028181 (Painter)\n- Q37853 (Baroque)\n- Q367560 (Orazio Gentileschi)\n- Q212657 (with \"Artemisia Gentileschi Citizen\" below it)\n- Q38 (Italy)\n\nRelations shown:\n- occupation\n- style\n- movement\n- occupation (appears twice)\n- child\n- birth\n- place of birth\n- father\n- member\n\nLooking at the structure, the relations that appear to connect between entities but may not point to specific leaf nodes would be those that connect to higher-level entities or categories. From the layout, I need to identify which relation arrows don't terminate at leaf nodes.\n\nLet me look for any figures or images that might show the actual graph structure more", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59797, "error": null, "extra": {"thread_id": 2005, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c3fdd69258514ce3917c73b31fe40d3e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 249, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me look for any figures or images that might show actual graph structure more", "normalised_gold": "birth, member, movement, place of birth"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to locate Figure 5 in the document and examine the lines shown in it.\n\nLooking through the document, I can find Figure 5 on page 8. The figure is titled \"Retrieval Analysis\" and shows citation evaluation (Micro) of generated texts using knowledge graphs with retrieval accuracy 100 (gold), 80, 60, 40, and 20.\n\nThe figure contains four lines:\n- A yellow line (precision)\n- A green line (recall)\n- A blue line (F1_Score)\n- A red line (correctness)\n\nLooking at the graph, I need to identify which line does not intersect with any other line.\n\nThe red line (correctness) starts at the highest point and remains consistently above all other lines throughout the graph. It descends from left to right but never crosses or intersects with any of the other three lines (yellow, green, or blue).\n\nAnswer: red", "citations": [], "input_tokens": 60196, "output_tokens": 207, "cost_micros": 183693, "latency_ms": 12141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, which shows \"Retrieval Analysis\" with multiple lines plotted against Retrieval Accuracy on the x-axis:\n\nThe lines shown are:\n- precision (yellow)\n- recall (green)\n- F1_Score (appears to be another color)\n- correctness (red/orange)\n\nThe correctness line (red/orange colored line at the top of the graph) maintains a consistently high value around 80-90 and does not intersect with any of the other lines throughout the plot.\n\nAnswer: red (or orange/correctness line)", "citations": [], "input_tokens": 22213, "output_tokens": 134, "cost_micros": 68649, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "red or orange correctness line", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 titled \"Retrieval Analysis\", I need to identify which line does not intersect with any other line.\n\nThe figure shows four lines representing different metrics (precision, recall, F1_Score, and correctness) plotted against retrieval accuracy from gold (100) to 20.\n\nExamining the lines:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines \n- The blue line (F1_Score) intersects with other lines\n- The red line (correctness) stays at the top of the graph and does not intersect with any of the other three lines\n\nAnswer: red", "citations": [], "input_tokens": 25538, "output_tokens": 150, "cost_micros": 78864, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I need to identify the line that has no intersection with any other line.\n\nFigure 5 shows citation evaluation metrics (Micro) with retrieval accuracy from 100 (gold) down to 20. There are four lines in the graph representing:\n- Correctness (blue)\n- Precision (yellow) \n- Recall (green)\n- F1 (red)\n\nExamining the lines, the blue line (Correctness) remains at the top of the graph and does not intersect with any of the other three lines throughout the entire range of retrieval accuracy values.\n\nAnswer: Blue", "citations": [], "input_tokens": 17549, "output_tokens": 144, "cost_micros": 54807, "latency_ms": 5610, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I can see a chart showing citation evaluation metrics (precision, recall, F1_score, and correctness) plotted against retrieval accuracy levels (20, 40, 60, 80, and gold/100).\n\nExamining the lines in the chart:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines\n- The blue line (F1_score) intersects with other lines\n- The red line (correctness) runs above all other lines and does not intersect with any of them\n\nAnswer: red", "citations": [], "input_tokens": 23597, "output_tokens": 138, "cost_micros": 72861, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 5 and identify the line with no intersections.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25967, "error": null, "extra": {"thread_id": 2006, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a9d66758d1df48679fe6def6615062f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 40, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document to find figure 5 and identify line with no intersections", "normalised_gold": "red"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many tables in the document include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1 (page 3) - Shows an example data entry, not evaluation metrics\n2. Table 2 (page 3) - Human Evaluation on dataset quality - no F1\n3. Table 3 (page 7) - Citation Quality evaluation - shows \"F1.\" column (both Micro and Macro)\n4. Table 4 (page 7) - Text quality evaluation - no F1\n5. Table 5 (page 7) - Citation quality with knowledge removed - shows \"F1.\" column\n6. Table 6 (page 8) - Human evaluation alignment - no F1\n7. Table 7 (page 9) - Comparison of General and Specific questions - shows \"F1.\" column\n8. Table 8 (page 15) - Human annotation instructions - no F1\n9. Tables 9-17 (pages 16-21) - Prompts and instructions - no F1\n10. Table 18 (page 22) - Evolutionary question generation example - no F1\n11. Tables 19-20 (pages 23-24) - Example outputs - no F1\n\nAdditionally, I can see:\n- Figure 4 (page 8) shows \"F1_Score\" in a graph\n- Figure 5 (page 8) shows \"F1_Score\" in a graph\n\nBut the question asks specifically about tables, not figures.\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 60181, "output_tokens": 381, "cost_micros": 186258, "latency_ms": 25483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all tables in the document that include \"F1\" as a metric.\n\nLet me go through the tables systematically:\n\n1. Table 1 (Example data entry) - No F1 metric\n2. Table 2 (Human Evaluation on BioKaLMA dataset) - No F1 metric\n3. Table 3 (Citation Quality) - Yes, has \"F1.\" columns (both Micro and Macro)\n4. Table 4 (Text quality evaluation) - No F1 metric\n5. Table 5 (Citation quality with removed knowledge) - Yes, has \"F1.\" column\n6. Table 6 (Human Evaluation on alignment) - No F1 metric\n7. Table 7 (Comparison of General and Specific questions) - Yes, has \"F1.\" column\n\nNow let me check if there are more tables in the appendices:\n\n8. Table 8 (Human Evaluation Instructions) - No F1 metric\n9. Table 9 (Instruction for evolutionary question construction) - No F1 metric\n10. Table 10 (Instruction for round 2-5) - No F1 metric\n11. Table 11 (General question generation) - No F1 metric\n12. Table 12 (Specific question generation) - No F1 metric\n13. Table 13 (Answer generation instruction) - No F1 metric\n14. Table 14 (GPT-EVAL Coherence) - No F1 metric\n15. Table 15 (GPT-EVAL Consistency) - No F1 metric\n16. Table 16 (GPT-EVAL Fluency) - No F1 metric\n17. Table 17 (GPT-EVAL Relevance) - No F1 metric\n18. Table 18 (Evolutionary question construction example) - No F1 metric\n19. Table 19 (Example from ChatGPT) - No F1 metric\n20. Table 20 (Example from GPT-4) - No F1 metric\n\nTables with F1 metric: Table 3, Table 5, and Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 22198, "output_tokens": 502, "cost_micros": 74124, "latency_ms": 9109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1: Shows an example data entry - no F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - no F1 metric\n3. Table 3: Citation Quality evaluation - includes \"F1.\" column (both Micro and Macro F1)\n4. Table 4: Evaluation on generated text quality - no F1 metric\n5. Table 5: Citation quality evaluation with knowledge removed - includes \"F1.\" column\n6. Table 6: Human Evaluation on text-citation alignment - no F1 metric\n7. Table 7: Comparison of evaluation results on General and Specific question setting - includes \"F1.\" column\n8. Table 8: Instruction for human annotators - not a data table with metrics\n9. Tables 9-17: These are prompt/instruction tables - no F1 metrics\n10. Table 18: Example of generated questions - no F1 metric\n11. Tables 19-20: Examples of attributed answers - no F1 metrics\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 25523, "output_tokens": 289, "cost_micros": 80904, "latency_ms": 8578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for tables that include \"F1\" as a metric.\n\nLet me identify all tables in the document:\n\n1. Table 1: Shows an example data entry in BioKaLMA - does not contain F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - contains metrics: Authenticity, Relevance, Naturalness, Significance - no F1\n3. Table 3: Citation Quality evaluation - contains columns including \"F1.\" in both Micro and Macro sections - **YES, contains F1**\n4. Table 5: Citation quality evaluation for generated texts - contains columns: Corr., Prec., Rec., F1. - **YES, contains F1**\n5. Table 6: Result of Human Evaluation on text-citation alignment - contains Alignment and Human Avg. - no F1\n6. Table 7: Comparison between general and specific questions - contains columns including F1. - **YES, contains F1**\n7. Table 8: Instruction for human annotators - no metrics table\n8. Table 9: Not clearly visible as a numbered table in the content shown\n9. Table 10: Instruction and demonstration - not a metrics table\n10. Table 11: Instruction and demonstration - not a metrics table\n11. Table 12: Instruction and demonstration - not a metrics table\n12. Table 13: Instruction for answer generation - not a metrics table\n13. Tables 14-17: Evaluation criteria descriptions - not metrics tables\n14. Table 18: Example of generated questions - not a metrics table\n15. Table 19: Stephen Crane biographical information - not a metrics table with F1\n\nCounting tables with F1 metric:\n- Table 3: YES\n- Table 5: YES\n- Table 7: YES\n\nAnswer: 3", "citations": [], "input_tokens": 17534, "output_tokens": 420, "cost_micros": 58902, "latency_ms": 11342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. **Table 1**: Shows an example data entry - no F1 metric\n2. **Table 2**: Human Evaluation metrics (Authenticity, Relevance, Naturalness, Significance) - no F1\n3. **Table 3**: Citation Quality evaluation - includes \"Micro F1.\" and \"Macro F1.\" columns - **YES**\n4. **Table 4**: Text quality evaluation (Coh., Con., Flu., Rel.) - no F1\n5. **Table 5**: Citation quality with removed knowledge - includes \"F1.\" column - **YES**\n6. **Figure 4**: Shows precision, recall, and F1_Score for [NA] - **YES** (this is labeled as a figure but formatted as a table)\n7. **Figure 5**: Citation evaluation with retrieval accuracy - includes \"F1_score\" column - **YES** (also labeled as figure but formatted as a table)\n8. **Table 6**: Human evaluation on alignment - no F1\n9. **Table 7**: Comparison of General and Specific questions - includes \"Citation Eval. F1.\" column - **YES**\n10. **Table 8**: Human annotation instructions - no F1\n11. Tables 9-17: Various prompts and instructions - no F1 metrics\n12. **Table 18**: Example of evolutionary question construction - no F1\n13. **Table 19**: Example output from ChatGPT - no F1\n14. **Table 20**: Example output from GPT-4 - no F1\n\nTables/Figures with F1 metric:\n- Table 3\n- Table 5\n- Figure 4 (table format)\n- Figure 5 (table format)\n- Table 7\n\nAnswer: 5", "citations": [], "input_tokens": 23582, "output_tokens": 429, "cost_micros": 77181, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned (2310.05634v2.pdf.xml) to find all tables that include \"F1\" as a metric.Let me continue reading the document to search for all tables:Now let me search for \"F1\" to find which tables contain this metric:Let me read the specific sections containing the tables to identify which ones include F1 as a metric:Now let me check all the other tables to see if they contain F1. Let me search more systematically:Let me also check Table 6:Now let me check the tables in the appendix to ensure I haven't missed any:Now let me scan through all the remaining content to ensure I have checked all tables. Let me list out what I've found so far:\n\nTables with F1:\n1. Table 3 - Has F1 (both Micro and Macro F1)\n2. Table 5 - Has F1 \n3. Table 7 - Has F1\n\nTables without F1:\n1. Table 1 - Does not have F1 (has Authenticity, Relevance, Naturalness, Significance)\n2. Table 2 - This appears to be just a caption reference, same as Table 1\n3. Table 4 - Does not have F1 (has Coh., Con., Flu., Rel.)\n4. Table 6 - Does not have F1 (has Alignment and Human Avg.)\n\nLet me continue checking the rest of the tables (8-20) mentioned in the document:Based on my thorough examination of the document, I have identified all tables and checked which ones include \"F1\" as a metric. Let me verify my findings:\n\nTables with F1 as a metric:\n1. **Table 3**: Citation Quality evaluation - includes both Micro F1 and Macro F1 columns\n2. **Table 5**: Citation quality evaluation for generated texts using a KG with N pieces of knowledge removed - includes F1 column\n3. **Table 7**: Comparison of evaluation results on General and Specific question setting - includes F1 in the Citation Evaluation columns\n\nTables without F1 as a metric:\n- Table 1: Example data entry (no F1)\n- Table 2: Human Evaluation on BioKaLMA dataset (no F1)\n- Table 4: Evaluation on generated text quality (no F1, only Coh., Con., Flu., Rel.)\n- Table 6: Result of Human Evaluation on text-citation alignment (no F1, only Alignment and Human Avg.)\n- Tables 8-20: These are instruction tables, prompt demonstrations, and examples (no F1 metrics)\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 116094, "error": null, "extra": {"thread_id": 2007, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d5e6c0fe45c9431aaf087f3d70bed847", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 545, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the paper, which shows the citation quality evaluation results for different models:\n\nThe alignment scores for ChatGPT at different temperatures are:\n- ChatGPT (0.1): 85.9\n- ChatGPT (0.5): 84.5\n- ChatGPT (0.9): 84.1\n\nThe highest alignment score is at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 60189, "output_tokens": 103, "cost_micros": 182112, "latency_ms": 22187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nFrom Table 3 (Citation Quality evaluation), the alignment scores for ChatGPT are:\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 22206, "output_tokens": 129, "cost_micros": 68553, "latency_ms": 5421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from the paper.\n\nLooking at Table 3 in the paper, which shows \"Citation Quality OpenAI models and LLaMA family models\" including alignment scores:\n\n- ChatGPT (0.1): Alignment = 85.9(2.5)\n- ChatGPT (0.5): Alignment = 84.5(1.1)\n- ChatGPT (0.9): Alignment = 84.1(0.5)\n\nThe highest alignment score among the ChatGPT models is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 25531, "output_tokens": 159, "cost_micros": 78978, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which presents the citation quality evaluation results including alignment scores for different models:\n\nFor ChatGPT at different temperatures:\n- ChatGPT (0.1): Alignment = 85.9\n- ChatGPT (0.5): Alignment = 84.5\n- ChatGPT (0.9): Alignment = 84.1\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 17542, "output_tokens": 125, "cost_micros": 54501, "latency_ms": 5592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nLooking at Table 3 (Citation Quality evaluation), I can see the alignment scores for different ChatGPT temperatures:\n\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 for ChatGPT at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 23590, "output_tokens": 139, "cost_micros": 72855, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about temperature and ChatGPT alignment scores.Let me search for temperature settings and ChatGPT results more specifically:Now let me read the specific section where temperature experiments with ChatGPT are discussed:Perfect! I found the table with alignment scores for ChatGPT at different temperatures. From Table 3, I can see:\n\n- ChatGPT (0.1): Alignment score = 85.9(2.5)\n- ChatGPT (0.5): Alignment score = 84.5(1.1)\n- ChatGPT (0.9): Alignment score = 84.1(0.5)\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78952, "error": null, "extra": {"thread_id": 2008, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_826b46b364ee4f52853639bf7b854cb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Section A and subsections A.1, A.2, A.3), I need to compare the length of descriptions for each step:\n\n**A.1 Person Selection**: Approximately 150 words - describes selecting name pairs from the biographical database and filtering criteria.\n\n**A.2 Name Disambiguation**: Approximately 200 words - describes using SPARQL queries to map names to WikiData entities, handling duplicate names, and extracting one-hop sub-graphs.\n\n**A.3 Evolutionary Question Generation**: Approximately 450-500 words - provides extensive detail about the iterative process, knowledge injection, specificity and coherence scoring, the formula for scoring, and implementation details across five iterations.\n\nThe Evolutionary Question Generation step clearly contains the most detailed explanation, including mathematical formulas, specific algorithms for knowledge selection, and detailed descriptions of the iterative process.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 60191, "output_tokens": 204, "cost_micros": 183633, "latency_ms": 21312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Appendix A), I need to compare the lengths of descriptions for each step:\n\n1. **Person Selection (A.1)**: Describes selecting name pairs from biographical database - relatively brief, about 1 paragraph\n\n2. **Name Disambiguation (A.2)**: Describes mapping names to WikiData entities using SPARQL queries - moderate length, about 1-2 paragraphs\n\n3. **Evolutionary Question Generation (A.3)**: Describes the iterative process of extending paragraphs, selecting knowledge, calculating scores with formulas, and generating questions - significantly longer, spanning multiple paragraphs with detailed explanations of specificity scores, coherence scores, the scoring formula, iteration process, and implementation details\n\nThe Evolutionary Question Generation step clearly contains the most extensive description with the most detail about the methodology.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 22208, "output_tokens": 193, "cost_micros": 69519, "latency_ms": 6781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "To determine which step takes the most words to describe in the dataset construction process, I need to examine the three main steps described in Section 2.2 and detailed in Appendix A:\n\n1. **Person Selection** (Section A.1): This section describes filtering and selecting people from the biographical database. It's relatively brief.\n\n2. **Name Disambiguation** (Section A.2): This section explains mapping names to WikiData entities using SPARQL queries and extracting one-hop sub-graphs. It's moderately detailed.\n\n3. **Evolutionary Question Generation** (Section A.3): This section provides extensive detail about the iterative process of extending paragraphs, knowledge selection algorithms, scoring mechanisms (specificity and coherence scores), and the multiple rounds of generation with specific templates and prompts.\n\nThe Evolutionary Question Generation section is significantly longer and more detailed than the other two steps, including mathematical formulas, multiple iteration explanations, and extensive procedural details.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 25533, "output_tokens": 217, "cost_micros": 79854, "latency_ms": 7764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at section 2.2 Dataset Construction and Appendix A Dataset Construction, the three main steps are:\n\n1. Person Selection\n2. Name Disambiguation \n3. Evolutionary Question Generation\n\nComparing the word counts in Appendix A where these steps are described in detail:\n\n- A.1 Person Selection: approximately 150 words\n- A.2 Name Disambiguation: approximately 180 words\n- A.3 Evolutionary Question Generation: approximately 130 words (before the subsections)\n\nHowever, the Evolutionary Question Generation section has significantly more content when including all the detailed prompt tables (Tables 9-12) and examples that follow it, making it the most extensively described step overall.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 17544, "output_tokens": 165, "cost_micros": 55107, "latency_ms": 5703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the dataset construction section (2.2), I need to compare the length of descriptions for each of the three steps:\n\n1. **Person Selection** (A.1): Approximately 150 words\n2. **Name Disambiguation** (A.2): Approximately 200 words \n3. **Evolutionary Question Generation** (A.3): Approximately 450+ words (includes detailed explanation of the iterative process, knowledge selection algorithm with formula, scoring mechanisms, etc.)\n\nThe Evolutionary Question Generation step is described in significantly more detail than the other two steps, taking up the most space in the paper.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 23592, "output_tokens": 143, "cost_micros": 72921, "latency_ms": 5844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about dataset construction steps and compare their descriptions.Let me search for \"dataset construction\" in the document:Let me look at the detailed dataset construction section:Let me read the Dataset Construction section starting at line 506:Good! I can see the three steps mentioned at line 521-523. Now let me read the Appendix A which has the detailed dataset construction process:Perfect! I can now see the three steps of dataset construction in detail. Let me count the words in each section to determine which takes the most words to describe.\n\nLet me read each section more carefully to count:Now let me see where the Evolutionary Question Generation section ends:Perfect! Now I have all three sections. Let me analyze the word count for each step:\n\n**A.1 Person Selection** (lines 2089-2110): Approximately 22 lines of content\n**A.2 Name Disambiguation** (lines 2113-2145): Approximately 33 lines of content \n**A.3 Evolutionary Question Generation** (lines 2148-2222): Approximately 75 lines of content\n\nBy visual inspection and line count, the \"Evolutionary Question Generation\" section is significantly longer than the other two steps. It contains detailed explanations about:\n- The evolutionary approach\n- Iteration process\n- Knowledge annotation\n- Specificity and coherence scoring\n- Formula for scoring\n- Implementation details\n\nThis is clearly the step that takes the most words to describe.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78750, "error": null, "extra": {"thread_id": 2009, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b0d2d82267f4e5eab9a849c3924465e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:37Z", "latency_ms": 20843, "error": null, "raw_text_chars": 345}]}, "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 2, the task formulation section discusses the \"Setting of Conscious Incompetence.\"\n\nThe relevant text states: \"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 60205, "output_tokens": 80, "cost_micros": 181815, "latency_ms": 20828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document's definition of the \"Conscious Incompetence\" setting in Section 2.1:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 22222, "output_tokens": 71, "cost_micros": 67731, "latency_ms": 4952, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\nAnswer: It can be partially verified by the knowledge graph G", "citations": [], "input_tokens": 25547, "output_tokens": 40, "cost_micros": 77241, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "it can be partially verified by knowledge graph g", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of when a sentence can map to both [NA] and knowledge citations.\n\nIn Section 2.1 \"Task Formulation,\" under \"Setting of Conscious Incompetence,\" the document states:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can.\"\n\nHowever, this is incomplete. Looking at the full context, the document explains that [NA] is used when a sentence \"includes some knowledge to be verified, but the knowledge is absent in the knowledge graph G.\"\n\nThe key insight comes from the demonstration in Table 13, which shows sentences containing both specific knowledge citations from the graph AND [NA] citations. This occurs when a sentence contains both:\n1. Knowledge that IS present in the knowledge graph (cited with specific knowledge)\n2. Knowledge that IS NOT present in the knowledge graph (cited with [NA])\n\nAnswer: When it contains both knowledge present in and absent from the knowledge graph", "citations": [], "input_tokens": 17558, "output_tokens": 214, "cost_micros": 55884, "latency_ms": 7110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2727272727272727, "method": "str_norm", "normalised_pred": "when it contains both knowledge present in and absent from knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, in the \"Setting of Conscious Incompetence\" section (2.1), a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph $G$.\"\n\nAnswer: When it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 23606, "output_tokens": 91, "cost_micros": 72183, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the author's definition of conscious incompetence and when a sentence can map to both [NA] and a list of sub-graph knowledge.Let me search for \"conscious incompetence\" in the document:Now let me read the section around line 485 and 514 where \"conscious incompetence\" is defined:Perfect! I found the answer. According to the document, in the section on \"Setting of Conscious Incompetence\", a sentence can map to both [NA] and a list of sub-graph knowledge when it can be partially verified by the knowledge graph G.\n\nAnswer: when it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34875, "error": null, "extra": {"thread_id": 2010, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6035a4bba19d42d88826ef02286ce3c9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure of question-guided reasoning and count the numbers displayed in blue color.\n\nLooking at Figure 1 on page 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims,\" I can see:\n\n- Q1: What is the greatest depth of the Black Sea?\n- A1: Black sea has a maximum depth of **2,212** meters.\n- Q2: How far can sunlight penetrate water?\n- A2: Sunlight does not penetrate water below **1,000** meters.\n- At the bottom: \"2,212 is greater than 1,000. Therefore, the claim is\"\n\nIn the figure, I can see numbers that appear to be highlighted or emphasized:\n- 2,212 (appears twice - once in A1 and once in the reasoning)\n- 1,000 (appears twice - once in A2 and once in the reasoning)\n\nLooking at the visual representation in the figure, the numbers that are displayed in blue/highlighted color are:\n- 2,212 (in A1)\n- 1,000 (in A2)\n- 2,212 (in the final reasoning line)\n- 1,000 (in the final reasoning line)\n\nSo there are 4 instances of numbers shown in blue color.\n\nAnswer: 4", "citations": [], "input_tokens": 27152, "output_tokens": 309, "cost_micros": 86091, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n1. The claim about the Black Sea\n2. Q1 asking about the greatest depth of the Black Sea\n3. A1 stating \"Black sea has a maximum depth of 2,212 meters\" - the number **2,212** appears in blue\n4. Additional text showing \"Average depth\" with what appears to be another number\n5. Q2 asking about how far sunlight can penetrate water\n6. A2 stating \"Sunlight does not penetrate water below 1,000 meters\" - the number **1,000** appears in blue\n7. At the bottom, there's reasoning that states \"2,212 is greater than 1,000\" - both **2,212** and **1,000** appear in blue here as well\n\nLooking carefully at the figure, I can identify the following blue-colored numbers:\n- 2,212 (appears multiple times)\n- 1,000 (appears multiple times)\n\nHowever, the question asks for how many numbers with blue color are mentioned, not how many times they appear. The distinct blue numbers are:\n1. 2,212\n2. 1,000\n\nAnswer: 2", "citations": [], "input_tokens": 11640, "output_tokens": 300, "cost_micros": 39420, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n- The claim about sunlight and the Black Sea\n- Q1 asking about the greatest depth of the Black Sea\n- A1 stating \"Black sea has a maximum depth of **2,212 meters**\" (2,212 appears to be in blue)\n- Q2 asking about how far sunlight can penetrate water\n- A2 stating \"Sunlight does not penetrate water below **1,000 meters**\" (1,000 appears to be in blue)\n- At the bottom, there's text saying \"2,212 is greater than 1,000\" where both numbers appear to be in blue\n\nSo the blue-colored numbers mentioned are:\n1. 2,212 (in A1)\n2. 1,000 (in A2)\n3. 2,212 (in the comparison at bottom)\n4. 1,000 (in the comparison at bottom)\n\nHowever, if we count unique numbers, there are 2 distinct numbers (2,212 and 1,000). But if we count each mention, there are 4 total mentions of numbers in blue.\n\nLooking more carefully at the figure, the numbers that appear to be highlighted/emphasized in blue are:\n- 2,212 (appears twice)\n- 1,000 (appears twice)\n\nCounting total mentions: 4\n\nAnswer: 4", "citations": [], "input_tokens": 12756, "output_tokens": 349, "cost_micros": 43503, "latency_ms": 8500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure that shows question-guided reasoning and count the blue-colored numbers in it.\n\nLooking at the document, Figure 1 is mentioned in the text as showing an example of question-guided reasoning for the claim \"Sunlight can reach the deepest part of the Black Sea.\" The text states: \"This reasoning process can be formulated as question-guided reasoning, where the verification of the claim is guided by asking and answering a series of relevant questions, as shown in Figure 1.\"\n\nHowever, Figure 1 itself is not shown in the provided document content. \n\nThe document does show Figure 4, which displays the QACHECK user interface with an example about \"Lars Onsager won the Nobel prize when he was 30 years old.\" In this figure, I can see:\n- \"Reasoning depth: 0\" and \"Reasoning depth: 1\" labels\n- \"1968\" (the year Lars Onsager won the Nobel Prize)\n- \"1903\" (the year Lars Onsager was born)\n- \"65\" (mentioned in the final prediction: \"He was 65 when he won the Nobel prize\")\n\nLooking at the visible figures in the document, Figure 4 shows the question-guided reasoning process example. The numbers that appear to be highlighted or emphasized in the context of the reasoning are: 1968, 1903, and 65.\n\nWithout being able to see the actual colors in Figure 1 (which is referenced but not displayed), and based on Figure 4 which shows a complete example of question-guided reasoning, I can count 3 distinct numbers that would typically be highlighted in such a demonstration.\n\nAnswer: 3", "citations": [], "input_tokens": 8625, "output_tokens": 374, "cost_micros": 31485, "latency_ms": 9280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nLooking at Figure 1, I can see:\n- The claim about sunlight reaching the deepest part of the Black Sea\n- Step 1 with Q1 and A1, where A1 mentions \"2,212 meters\" \n- Step 2 with Q2 and A2, where A2 mentions \"1,000 meters\"\n- The conclusion states \"2,212 is greater than 1,000\"\n\nThe numbers mentioned are:\n1. 2,212 (meters - maximum depth of Black Sea)\n2. 1,000 (meters - how far sunlight penetrates)\n\nBoth numbers appear to be in the same color as the rest of the text in the boxes. Looking at the figure description and the actual content, I need to identify which numbers are specifically in blue color.\n\nFrom the document's Figure 1, examining the text carefully, the numbers that appear are 2,212 and 1,000. The question asks specifically about numbers with \"blue color.\"\n\nBased on the visual representation in Figure 1, I can identify 2 numbers: 2,212 and 1,000.\n\nAnswer: 2", "citations": [], "input_tokens": 12525, "output_tokens": 288, "cost_micros": 41895, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the example figure related to \"question-guided reasoning\" and count the blue-colored numbers.Let me search for \"question-guided reasoning\" in the document to locate the relevant section.Now let me read the section around line 217 where Figure 1 is mentioned, which shows \"An example of question-guided reasoning\":Perfect! I can see Figure 1 which shows the question-guided reasoning example. Let me continue reading to see the complete figure description and identify all blue-colored numbers:Based on the document content I've read, I can see Figure 1 which shows the question-guided reasoning example. The figure contains:\n\n1. The text mentions \"2,212 m\" (Black Sea depth)\n2. The text mentions \"2,212 meters\" in the answer A1\n3. The text mentions \"1,000 meters\" in the answer A2\n4. The visual description mentions \"Blue accent colors for interactive elements (icons, links)\"\n\nLet me look more carefully at the OCR text section which lists the specific numbers visible in the figure:\n\nFrom lines 288-298, the OCR text shows:\n- 2,212 m (appears multiple times)\n- 1,253 m\n- 7,257 ft\n- 547,000 km\u00b3\n- 131,200 cu mi\n- 10+\n\nAnd from the main figure text (lines 219-270):\n- 2,212 m (Black Sea depth)\n- 1,000 meters (sunlight penetration)\n- 200 meters (mentioned in the evidence text)\n\nThe key information states: \"Blue accent colors for interactive elements (icons, links)\" which suggests that numbers appearing in blue would be those that are interactive or emphasized.\n\nLooking at the structure of Figure 1, the numbers that appear to be in blue color based on the context are:\n1. **2,212** (the answer to Q1 about Black Sea depth)\n2. **1,000** (the answer to Q2 about sunlight penetration)\n\nThese are the two key numerical answers that appear in the question-guided reasoning process.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45156, "error": null, "extra": {"thread_id": 2011, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ee68d3622e54a41be76708c8f52b00e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 300, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:57Z", "latency_ms": 14639, "error": null, "raw_text_chars": 1219}]}, "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to identify which modules use Large Language Models (LLMs) as their backbone.\n\nFrom Section 3, the QACHECK system has 5 modules:\n1. **Claim Verifier** - Section 3.1 states it's \"based on InstructGPT\" (an LLM)\n2. **Question Generator** - Section 3.2 states \"Similar to the claim verifier, we also leverage InstructGPT\" (an LLM)\n3. **Question Answering Model** - Section 3.3 describes three implementations, with the default being \"GPT Reciter-Reader\" which uses InstructGPT (an LLM). However, this module also has non-LLM options (Retriever-Reader, FLAN-T5).\n4. **QA Validator** - Section 3.4 states \"We again implement the validator by prompting InstructGPT\" (an LLM)\n5. **Reasoner** - Section 3.5 provides \"two different implementations: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\"\n\nCounting the modules that use LLM backbones:\n- Claim Verifier: Yes (InstructGPT)\n- Question Generator: Yes (InstructGPT)\n- Question Answering Model: Yes (uses GPT Reciter-Reader by default)\n- QA Validator: Yes (InstructGPT)\n- Reasoner: Yes (InstructGPT is one of the two implementations)\n\nAll 5 modules have LLM implementations, with some offering alternatives.\n\nAnswer: 5", "citations": [], "input_tokens": 27149, "output_tokens": 403, "cost_micros": 87492, "latency_ms": 14639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system are implemented using Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in Section 3:\n\n1. **Claim Verifier (Section 3.1)**: \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator (Section 3.2)**: \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Model (Section 3.3)**: This module has three different implementations:\n - Retriever-Reader: Uses BM25 retriever and RoBERTa-large reader (not primarily LLM-based)\n - FLAN-T5: This is an LLM\n - GPT Reciter-Reader: Uses InstructGPT (LLM)\n \n The text states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default implementation uses LLM.\n\n4. **QA Validator (Section 3.4)**: \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner (Section 3.5)**: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nCounting the modules that are implemented with LLM backbone:\n1. Claim Verifier - Yes (InstructGPT)\n2. Question Generator - Yes (InstructGPT)\n3. Question Answering Model - Yes (default is GPT Reciter-Reader with InstructGPT)\n4. QA Validator - Yes (InstructGPT)\n5. Reasoner - Yes (FLAN-T5", "citations": [], "input_tokens": 11637, "output_tokens": 512, "cost_micros": 42591, "latency_ms": 10953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): Uses InstructGPT with in-context learning\n - LLM-based \u2713\n\n2. **Question Generator** (Section 3.2): Uses InstructGPT for in-context learning\n - LLM-based \u2713\n\n3. **Question Answering Model** (Section 3.3): Has three implementations:\n - Retriever-Reader: Uses BM25 + RoBERTa-large fine-tuned on SQUAD\n - FLAN-T5: Uses FLAN-T5 model\n - GPT Reciter-Reader: Uses InstructGPT\n - Since the system uses one of these at a time, and the default is GPT Reciter-Reader (mentioned in the text), this module uses LLM \u2713\n\n4. **QA Validator** (Section 3.4): Uses InstructGPT with demonstrations\n - LLM-based \u2713\n\n5. **Reasoner** (Section 3.5): Has two implementations:\n - FLAN-T5 (end-to-end QA model)\n - InstructGPT\n - Both options use LLMs \u2713\n\nAll five modules are implemented with Large Language Models as their backbone (either InstructGPT or FLAN-T5, both of which are LLMs).\n\nAnswer: 5", "citations": [], "input_tokens": 12753, "output_tokens": 375, "cost_micros": 43884, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Module** (Section 3.3): This module has three different implementations:\n - Retriever-Reader: Uses RoBERTa-large (which is a language model)\n - FLAN-T5: \"we also incorporate the FLAN-T5 model\" - This is an LLM\n - GPT Reciter-Reader: Uses InstructGPT - This is an LLM\n \n The default implementation uses GPT Reciter-Reader (LLM-based).\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nAll five modules are implemented with Large Language Models as their backbone (either as the primary or available implementation).\n\nAnswer: 5", "citations": [], "input_tokens": 8622, "output_tokens": 425, "cost_micros": 32241, "latency_ms": 10062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - Uses LLM \u2713\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - Uses LLM \u2713\n\n3. **Question Answering Model** (Section 3.3): This module has three different implementations:\n - Retriever-Reader: Uses RoBERTa-large fine-tuned on SQuAD (this is a reader model, but the main approach is retrieval-based)\n - FLAN-T5: This is an LLM \u2713\n - GPT Reciter-Reader: Uses InstructGPT as the reciter \u2713\n \n The document states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default uses LLM.\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - Uses LLM \u2713\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs \u2713\n\nCounting the modules that use LLMs:\n1. Claim Verifier - LLM\n2. Question Generator - LLM\n3. Question Answering Model - LLM (default implementation)\n4. QA Validator - LLM\n5. Reasoner - LLM\n\nAll 5 modules use LLMs in their implementation (either exclusively or as the default/primary option).\n\nAnswer: 5", "citations": [], "input_tokens": 12522, "output_tokens": 503, "cost_micros": 45111, "latency_ms": 11405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find information about modules implemented with Large Language Models backbone.Let me search for information about modules implemented with LLM backbone in the document.Now let me search for information about which modules use LLM backbone:Let me read more of the document to find details about the system architecture and which modules use Large Language Models:Now let me search for information specifically about which modules are implemented with Large Language Models:Based on the document, I can now identify which modules are implemented with Large Language Models (specifically InstructGPT). Let me verify this information:\n\nFrom the document, I can see that the QACHECK system has **five modules**:\n1. **Claim Verifier** - implemented with InstructGPT (Large Language Model)\n2. **Question Generator** - implemented with InstructGPT (Large Language Model)\n3. **Question Answering Model** - has three implementations, one of which (GPT Reciter-Reader) uses InstructGPT (Large Language Model)\n4. **QA Validator** - implemented with InstructGPT (Large Language Model)\n5. **Reasoner** - has two implementations, one of which uses InstructGPT (Large Language Model)\n\nHowever, I need to count how many modules are implemented with LLM backbone. Looking at the text more carefully:\n\n- **Claim Verifier**: Uses InstructGPT (LLM) \u2713\n- **Question Generator**: Uses InstructGPT (LLM) \u2713\n- **Question Answering Model**: Has 3 options (Retriever-Reader, FLAN-T5, GPT Reciter-Reader), but the QA module itself is not purely LLM-based in all implementations\n- **QA Validator**: Uses InstructGPT (LLM) \u2713\n- **Reasoner**: Has 2 implementations (FLAN-T5 and InstructGPT), one uses LLM \u2713\n\nLet me re-read to get the precise count of modules that ARE implemented with LLM backbone (not just have an option for it):\n\nFrom the text:\n- Claim Verifier: \"We build the claim verifier based on InstructGPT\" - **LLM**\n- Question Generator: \"we also leverage InstructGPT for in-context learning\" - **LLM**\n- QA Validator: \"We again implement the validator by prompting InstructGPT\" - **LLM**\n- Reasoner: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - has LLM option but also non-LLM option\n\nThe Question Answering Model has multiple implementations, and the document states the system uses \"GPT Reciter-Reader as the default implementation\" which uses InstructGPT.\n\nSo counting modules that are implemented with LLM backbone: Claim Verifier (1), Question Generator (2), QA Validator (3), and considering the default implementations, the Reasoner could be 4.\n\nLet me check if there's a clearer statement in the document about this specific question.Let me read the sections more carefully to understand which modules specifically use LLM backbone:\n\nBased on my reading of the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT\" - Uses LLM \u2713\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning\" - Uses LLM \u2713\n3. **Question Answering Model** (Section 3.3): Multiple implementations, NOT all use LLM\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT\" - Uses LLM \u2713\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Has LLM option\n\nThe modules that are definitively implemented with LLM (InstructGPT) backbone are:\n1. Claim Verifier\n2. Question Generator \n3. QA Validator\n\nThat's **3 modules** that are implemented with Large Language Models backbone.\n\nHowever, if we consider that the Reasoner also has an InstructGPT implementation and FLAN-T5 is also a Large Language Model, it could be 4. Let me verify if FLAN-T5 counts as an LLM.\n\nFLAN-T5 is described in the document as \"a Seq2Seq model pre-trained on more than 1.8K tasks with instruction tuning\" - this is indeed a Large Language Model.\n\nSo if the question is asking about modules that ARE implemented (not optionally, but actually) with LLM backbone:\n- Claim Verifier: InstructGPT (LLM) \u2713\n- Question Generator: InstructGPT (LLM) \u2713\n- QA Validator: InstructGPT (LLM) \u2713\n- Reasoner: Has two implementations, both are LLMs (FLAN-T5 and InstructGPT) \u2713\n\nThat would be **4 modules**.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 100172, "error": null, "extra": {"thread_id": 2012, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5a2a7a1ab0194a9ba5958368d2c54734", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 686, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 5 and identify the final step shown in it.\n\nLooking at page 5, I can see Figure 4, which shows a screenshot of the QACHECK user interface with numbered annotations.\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) is clearly labeled as \"The final prediction result with rationale\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 27168, "output_tokens": 141, "cost_micros": 83619, "latency_ms": 10530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 5, which shows the QACHECK user interface screenshot, the final step (labeled as \"4\") shows:\n\n\"The final prediction result with rationale\"\n\nThis section displays:\n- \"Prediction with rationale:\"\n- The reasoning explanation\n- \"Finish\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 11656, "output_tokens": 80, "cost_micros": 36168, "latency_ms": 4530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 (the screenshot at the top of page 5), I can see the interface is annotated with 4 numbered sections:\n\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe fourth and final step shows \"Prediction with rationale:\" followed by the reasoning and final answer.\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 12772, "output_tokens": 111, "cost_micros": 39981, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figure at the top of page 5 (Figure 4), I can see the system interface showing the fact-checking process for the claim \"Lars Onsager won the Nobel prize when he was 30 years old.\"\n\nThe final step shown in the figure is labeled \"The final prediction result with rationale\" which displays:\n\n\"Lars Onsager won the Nobel prize in 1968. Lars Onsager was born in 1903. He was 65 when he won the Nobel prize. Therefore, the final answer is: False.\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 8641, "output_tokens": 135, "cost_micros": 27948, "latency_ms": 5422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 5, I need to identify the final step in the architecture diagram.\n\nThe flow shows:\n1. Claim \u2192 Claim Verifier\n2. Question Generator \u2192 QA Model \u2192 Validator \u2192 Relevant Context (in a loop)\n3. Claim Verifier \u2192 Reasoner\n4. Reasoner \u2192 Label\n\nThe final step in the process is when the Reasoner outputs the Label.\n\nAnswer: Label", "citations": [], "input_tokens": 12541, "output_tokens": 104, "cost_micros": 39183, "latency_ms": 4155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "label", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 5 and identify the final step.Let me search for page 5 content in the document:Let me search for page markers and figures in the document:Let me read around line 819 where Figure 4 is mentioned, as this might be on page 5:Perfect! I can see Figure 4 which appears to be on page 5 (after the PageBreak marker at line 815). Looking at the figure, I can see it shows a step-by-step process. Let me examine the final step more carefully:\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) shows: \"Prediction with rationale:\" followed by the text and ending with \"**Finish**\".\n\nLooking at the structure, the final step in the figure is marked as \"**Finish**\" at line 888.\n\nAnswer: Finish", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33953, "error": null, "extra": {"thread_id": 2013, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca5f20bedd764740aef245cbc4ae41c8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 195, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finish", "normalised_gold": "4 final prediction result with rationale"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 2 and Figure 3 on page 4:\n\nIn Figure 2 (the system architecture), the \"Reasoner\" module is shown in a blue rectangle.\n\nIn Figure 3 (showing the three QA model implementations), the \"Reader\" component appears in orange/coral rectangles in parts (a) and (c).\n\nHowever, the question asks about a model rectangle that appears in BOTH the QA model and Reasoner modules. Looking at the figures more carefully:\n\n- In Figure 3(b), the \"FLAN-T5\" box appears in yellow\n- In Figure 3(c), the \"Reader\" boxes appear in orange\n- The \"Reasoner\" in the main architecture uses similar model components\n\nThe \"Reader\" model component appears in an orange/coral colored rectangle in both the QA module implementations and is used in the reasoning process.\n\nAnswer: Orange", "citations": [], "input_tokens": 27172, "output_tokens": 206, "cost_micros": 84606, "latency_ms": 10108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 that shows the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 4, which shows \"The architecture of our QACHECK system,\" I can see the system architecture with various modules including:\n- Claim Verifier\n- Question Generator\n- QA Model\n- Validator\n- Reasoner\n\nThe figure shows these modules connected with arrows. However, this figure does not clearly show colored rectangles that would indicate a specific model appearing in both the QA Model and Reasoner modules.\n\nLooking more carefully at the document, Figure 3 on page 5 shows \"Illustrations of the three different implementations of the Question Answering module in QACHECK\" with three subfigures (a), (b), and (c). In subfigure (a) \"Retriever-Reader\" and subfigure (c) \"GPT Reciter-Reader\", there is a \"Reader\" component shown.\n\nAccording to the text in Section 3.5, the Reasoner \"is a special question-answering model\" and the text mentions that both the QA module (Figure 3) and potentially the Reasoner use a \"Reader\" component.\n\nHowever, the document is in grayscale/black and white, so I cannot determine colors from the PDF provided.\n\nAnswer: Cannot determine - document appears to be in grayscale", "citations": [], "input_tokens": 11660, "output_tokens": 312, "cost_micros": 39660, "latency_ms": 9907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine document appears to be in grayscale", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 4, I need to identify the rectangles that appear in both the \"QA Model\" and \"Reasoner\" modules.\n\nIn the architecture diagram (Figure 2), the QA Model outputs \"(Q, A)\" pairs, and the Reasoner outputs \"Label\". \n\nThe component that connects both modules is the \"Wikipedia Corpus\" which is shown as a cylinder/database shape that provides evidence to the QA Model, and the accumulated context from Q&A pairs feeds into the Reasoner.\n\nHowever, looking more carefully at the diagram structure, both the QA Model and Reasoner are shown as rectangular boxes in the flowchart. The rectangles representing these modules themselves appear to be gray/neutral colored boxes in the system architecture.\n\nBut examining the actual visual elements more carefully, there isn't a colored rectangle that appears in both specific modules as separate components. The modules themselves are represented as boxes in the flow diagram.\n\nAnswer: gray", "citations": [], "input_tokens": 12776, "output_tokens": 212, "cost_micros": 41508, "latency_ms": 7203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 to identify the color of the model rectangle that appears in both the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 3 (which shows the system architecture), I can see the general architecture diagram. However, the question asks specifically about page 4.\n\nOn page 4, I can see Figure 3, which shows three different QA model implementations:\n(a) Retriever-Reader\n(b) FLAN-T5\n(c) GPT Reciter-Reader\n\nLooking at the diagram, I need to identify which model appears in both the QA module and the Reasoner module. According to the text in section 3.5, the Reasoner has two implementations: \"1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model.\"\n\nIn Figure 3, I can see rectangles representing different models. The FLAN-T5 model is shown in Figure 3(b), and this same model is mentioned as being used in the Reasoner module.\n\nLooking at the visual representation in Figure 3(b), the FLAN-T5 rectangle appears to be blue in color.\n\nAnswer: blue", "citations": [], "input_tokens": 8645, "output_tokens": 287, "cost_micros": 30240, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Figure 2 on page 4, which shows \"The architecture of our QACHECK system.\"\n\nLooking at the architecture diagram, I can see several modules represented as rectangles. The question asks about a rectangle that appears in both the QA Model and Reasoner modules.\n\nIn the diagram, I can see:\n- A \"QA Model\" box in the loop section\n- A \"Reasoner\" box after the loop\n- Both of these modules appear to have the same visual representation\n\nHowever, looking more carefully at the figure description and the actual diagram components, the modules that represent model components (like the Question Answering module and Reasoner) are shown as rectangles in the flowchart.\n\nThe document shows these as standard flowchart boxes, but I cannot definitively determine a specific color from the description provided. Looking at typical diagram conventions and the figure as presented, these model components appear to be represented in standard boxes.\n\nWithout being able to see the actual colors in the PDF image clearly described in the text, I cannot make a definitive statement about the specific color.\n\nAnswer: Cannot be determined from the provided text", "citations": [], "input_tokens": 12545, "output_tokens": 247, "cost_micros": 41340, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided text", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the figure on page 4 and identify the color of the model rectangle that appears in both the QA model and Reasoner module.Let me search for page 4 content and figures in the document:Let me search for figure-related content and read more of the document to find page 4:Let me read the section containing Figure 3 which appears to show different implementations of the QA module:This is Figure 3. Let me continue reading to find more figures and look for page markers:Let me search for page numbers or section breaks to locate page 4:Let me read around the fourth PageBreak (line 815) which should be around page 4:This is Figure 4, which shows a user interface. Let me continue reading to see if there are more visual elements described:Now I need to go back and look at Figure 2, which shows the architecture of the QACHECK system. That figure should show both the QA model and Reasoner module. Let me read around line 416 where Figure 2 is mentioned:I see Figure 2 is described, but I need to look at more details about the visual representation. Let me search for information about colors in the figure or read earlier to see the full figure description:The embedded image description in Figure 2 only describes a yellow/golden amber circular icon with a question mark. But the question asks about a model rectangle that appears in both QA model and Reasoner modules. Let me search for more complete visual descriptions or look for other figures. Let me check if there's more detailed architecture visualization:Let me read that chunk:Let me read that section:This doesn't seem to be the right figure. Let me search more specifically for information about the architecture diagram or look for mentions of colors in model components:Let me search for \"green\" or other colors that might appear in the architecture diagram:The green icons appear to be information icons. Let me look at the text structure of Figure 2 more carefully. The figure shows \"QA Model\" and \"Reasoner\" as separate boxes. Let me search for \"orange\" or \"red\" colors:Let me try searching for \"model\" and \"reasoner\" together or look at the complete Figure 2 text more carefully:Let me look at a different approach. The question mentions \"page 4\", so let me count the PageBreaks more carefully. Let me look between the 3rd and 4th PageBreak which should be page 4:So Figure 3 is on page 4 (after the 3rd PageBreak at line 648). Figure 3 shows the three different implementations of the Question Answering module. The visual description mentions a green circular icon with \"i\". But this doesn't show both QA model and Reasoner module together.\n\nLet me look for if there are more images or visual descriptions in Figure 3:I see in Figure 3 there are three parts (a), (b), and (c) showing different QA implementations. Each shows \"Reader\" as a component, and there's a green circular icon. But I need to understand the question better - it asks about a \"model rectangle\" that appears in both QA model and Reasoner module.\n\nLet me re-read Figure 2 which shows the overall architecture, and look for visual elements that might represent both components. Let me search for mentions of \"Reader\" since that appears in both:I notice that \"Reader\" appears in Figure 3 as a component. In the figure, the description says there's a green circular icon with \"i\". The \"Reader\" component in the Retriever-Reader, FLAN-T5, and GPT Reciter-Reader architectures likely uses the same visual element. But the question asks about something that appears in both \"QA model\" and \"Reasoner module\".\n\nWait - let me re-read the architecture description. The Reasoner is described as \"a special question-answering model\". So both might use the same underlying reader model. Let me look at where the reader model is used in Figure 3. The green \"i\" icon might be the answer indicator. But the question asks about a \"model rectangle\" not an icon.\n\nLet me think about this differently. Perhaps the document has better visual descriptions elsewhere. Let me search for all embedded images:There are multiple embedded images. The question specifically mentions Figure on page 4. Based on the PageBreaks, page 4 would be between line 648 and 815. Let me look more carefully at what figures or diagrams might show architectural components with rectangles:\n\nLooking back at Figure 3 (lines 651-707), it shows three sub-figures (a), (b), and (c) representing different QA implementations. Each has components like \"Retriever\", \"Reader\", \"FLAN-T5\", \"InstructGPT\". These might be shown as rectangles in the original figure.\n\nIn Figure 3, all three QA implementations show a \"Reader\" component. And the text mentions that the Reasoner module (section 3.5, lines 919-933) is \"a special question-answering model\" that can be implemented using \"FLAN-T5\" or \"InstructGPT model\".\n\nSo FLAN-T5 appears in both:\n- In Figure 3(b) as the QA model implementation \n- In the Reasoner as one of the two implementation options\n\nLet me check if there's a visual description that mentions FLAN-T5 as a colored rectangle. Let me search for \"FLAN\" in visual descriptions:Perfect! So FLAN-T5 is used in BOTH:\n1. Figure 3(b) - as a QA Model implementation\n2. Section 3.5 (Reasoner) - as one of the two reasoner implementations\n\nNow I need to find the visual description showing what color the FLAN-T5 rectangle is. Let me look more carefully at the embedded images around Figure 3. However, the visual description at line 719 only describes the green \"i\" icon, not the model boxes themselves.\n\nLet me check if there are more visual descriptions or embedded images in the Figure 3 section. The document might have multiple embedded images for the same figure. Let me read more around that area:These are descriptions of other images. Let me look at the specific section where Im3.png first appears (around line 711) and see if there are additional visual descriptions I missed:Still showing the green \"i\" icon. Let me look at Im5.jpg which mentions GPT3 - this might be describing the model boxes:Im5.jpg shows a GPT3 logo with purple and pink/magenta colors. But I need to find descriptions of the model rectangles/boxes in Figure 3. Let me search for descriptions that mention \"rectangle\" or \"box\" with colors:Purple appears in the GPT3 logo. Since the question asks about a component that appears in BOTH QA Model and Reasoner, and we know FLAN-T5 is used in both, let me look for visual descriptions that might show FLAN-T5 box. Let me search for \"yellow\" since that was mentioned earlier:Yellow appears in question mark icons. Let me try a different approach. Let me look at all the visual descriptions more systematically. Let me check descriptions that mention model names or rectangular shapes directly. Let me search for visual descriptions that mention boxes or rectangles with specific colors:Not helpful. Let me think differently. The embedded images are specific parts of the figure. Maybe there are multiple embedded images within Figure 3 that I haven't found yet. Let me look at the embedded images that appear chronologically around Figure 3 content. Figure 3 is around lines 651-720. Let me check what other images might be part of it:\n\nActually, wait - let me reconsider. Looking at the OCR text, Im3.png shows \"i\", Im5.jpg shows \"GPT3\". These are components within the figures. Let me look for images that might show \"FLAN-T5\" or \"FLAN\" text:FLANonly appears as text, not in embedded image OCR. Let me look for embedded images with OCR text that might contain model names. Let me check if there are images showing \"T5\" or looking at what other embedded images show:All I'm finding are green \"i\" icons. Let me try a completely different approach. Perhaps I need to look at the actual figure diagram structure more carefully. In Figure 3, there are three sub-diagrams showing different QA implementations. Each has boxes/rectangles for components. Looking at the text structure:\n\nFigure 3(a): Retriever-Reader has \"Retriever\" and \"Reader\" boxes\nFigure 3(b): FLAN-T5 has \"FLAN-T5\" box \nFigure 3(c): GPT Reciter-Reader has \"InstructGPT\" and \"Reader\" boxes\n\nThe common element between Figure 3 and the Reasoner would be the model itself. Since both QA module and Reasoner can use FLAN-T5, I should look for a visual description of the FLAN-T5 box/rectangle.\n\nLet me search through all the visual descriptions more systematically for anything mentioning model boxes or FLAN. Actually, let me look for embedded images that have model-related OCR text. Let me look for embedded images around the areas discussing models:\n\nWait, I should look for the actual image file names and descriptions that might show text boxes or model components. Let me search for descriptions that mention \"text\" and colors:Turquoise/cyan blue! Let me read that chunk:This turquoise/cyan blue button shows \"Finish\" - that's from Figure 4 (the UI screenshot). Let me look systematically for all visual descriptions that mention rectangular shapes or boxes. Actually, let me search for specific model component descriptions. Let me look for \"RoBERTa\" which is mentioned as the Reader model:RoBERTa is used in the Reader model but there's no visual description mentioning it. Let me try to systematically go through the embedded images and look for any that might show architectural component boxes. Let me look at the images that haven't been described yet. Let me check Im2.png more thoroughly since that's Figure 2:These are still just the icons. Let me try a completely different approach. Maybe the XML parser extracted images separately from the diagram. Let me look at the beginning of the document to see if there are more detailed figure descriptions I'm missing:This is Figure 1. The visual content shows search icons and question marks. Let me consider: maybe the XML extraction treats architectural diagram components differently. Perhaps the model boxes themselves don't have detailed color descriptions, or the color information is embedded in a way I haven't found yet.\n\nLet me reconsider what the question is actually asking. It says \"the model rectangle in the figure of page 4 that appears both in QA model and Reasoner module\". If Figure 3 is on page 4, and Figure 3 shows three different QA implementations, then the component that would also appear in the Reasoner would be the UNDERLYING MODEL.\n\nLooking back at the text:\n- Section 3.3 (QA module) describes three implementations, one being FLAN-T5 (line 675-686)\n- Section 3.5 (Reasoner) says it can be implemented using \"the end-to-end QA model based on FLAN-T5\" (line 931-932)\n\nSo FLAN-T5 is the model that appears in both. Now I need to find what color the FLAN-T5 rectangle is shown in Figure 3. Since the visual descriptions I've found so far only show icons (green \"i\", yellow \"?\", orange \"?\"), maybe the model boxes themselves are not described with specific colors in the extracted XML, or I need to look elsewhere.\n\nLet me try to find if there's any systematic description of the figure architecture. Maybe there's a legend or color scheme description:Turquoise appears as a \"Finish\" button and footer element. But wait, let me read chunk 155824 which mentions a layout with multiple colored sections:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 328655, "error": null, "extra": {"thread_id": 2014, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4736292b32d6461e98620f394ccee4da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2072, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "let me try to find if there s any systematic description of figure architecture maybe there s legend or color scheme description turquoise appears as finish button and footer element but wait let me read chunk 155824 which mentions layout with multiple colored sections", "normalised_gold": "yellow"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompt instruction examples shown in the main body of the paper (not in the appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction example:\n - \"Claim = CLAIM / We already know the following: CONTEXT / Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instruction examples:\n - Initial question: \"Claim = CLAIM / To verify the above claim, we can first ask a simple question:\"\n - Follow-up question: \"Claim = CLAIM / We already know the following: CONTEXT / To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction example:\n - \"Claim = CLAIM / We already know the following: CONTEXT / Now we further know: NEW QA PAIR / Does the QA pair have additional knowledge useful for verifying the claim?\"\n\n4. **Section 3.5 (Reasoner)** - The text mentions prompts are in \"Appendix A.4\" but doesn't show the full prompt in the main body.\n\nTotal prompts shown in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 27159, "output_tokens": 335, "cost_micros": 86502, "latency_ms": 9625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper.\n\nLooking through the document, I can find the following prompt instructions in the main body (not in the Appendix):\n\n1. **Claim Verifier prompt** (Section 3.1):\n```\nClaim = CLAIM We already know the following:\nCONTEXT\nCan we know whether the claim is true or false now? Yes or no?\n```\n\n2. **Question Generator - Initial question prompt** (Section 3.2):\n```\nClaim = CLAIM\nTo verify the above claim, we can first ask a simple question:\n```\n\n3. **Question Generator - Follow-up question prompt** (Section 3.2):\n```\nClaim = CLAIM\nWe already know the following: CONTEXT\nTo verify the claim, what is the next question we need to know the answer to?\n```\n\n4. **QA Validator prompt** (Section 3.4):\n```\nClaim = CLAIM\nWe already know the following:\nCONTEXT\nNow we further know: NEW QA PAIR\nDoes the QA pair have additional knowledge useful for verifying the claim?\n```\n\nThat gives us 4 prompt instruction examples in the main body of the paper.\n\nAnswer: 4", "citations": [], "input_tokens": 11647, "output_tokens": 289, "cost_micros": 39276, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompts/instruction examples that are explicitly shown in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction:\n ```\n Claim = CLAIM\n We already know the following:\n CONTEXT\n Can we know whether the claim is true or false now? Yes or no?\n ```\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instructions:\n - For initial question generation:\n ```\n Claim = CLAIM\n To verify the above claim, we can first ask a simple question:\n ```\n \n - For follow-up questions:\n ```\n Claim = CLAIM\n We already know the following:\n CONTEXT\n To verify the claim, what is the next question we need to know the answer to?\n ```\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction:\n ```\n Claim = CLAIM\n We already know the following:\n CONTEXT\n Now we further know:\n NEW QA PAIR\n Does the QA pair have additional knowledge useful for verifying the claim?\n ```\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12763, "output_tokens": 336, "cost_micros": 43329, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (excluding the Appendix).\n\nLet me go through the main body of the paper systematically:\n\n1. **Section 3.1 (Claim Verifier)**: The text mentions \"We prompt InstructGPT with ten distinct in-context examples as detailed in Appendix A.1\" - but no examples are shown in the main body, only referenced.\n\n2. **Section 3.2 (Question Generator)**: Two prompts are shown:\n - Initial question generation prompt (1 prompt)\n - Follow-up question generation prompt (1 prompt)\n\n3. **Section 3.3 (Question Answering Model)**: No complete prompt examples are shown in the main body.\n\n4. **Section 3.4 (QA Validator)**: One prompt instruction is shown for the validator module (1 prompt)\n\n5. **Section 3.5 (Reasoner)**: The text mentions \"the InstructGPT model with the prompts given in Appendix A.4\" - but no examples are shown in the main body.\n\nCounting the prompts actually displayed in the main body:\n- Section 3.2: 2 prompts (initial question generation + follow-up question generation)\n- Section 3.4: 1 prompt (QA validator)\n\nTotal: 3 prompts in the main body\n\nAnswer: 3", "citations": [], "input_tokens": 8632, "output_tokens": 331, "cost_micros": 30861, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - 1 prompt instruction:\n - \"Claim = CLAIM... Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - 2 prompt instructions:\n - Initial question generation: \"Claim = CLAIM... To verify the above claim, we can first ask a simple question:\"\n - Follow-up questions: \"Claim = CLAIM... We already know the following: CONTEXT... To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - 1 prompt instruction:\n - \"Claim = CLAIM... We already know the following: CONTEXT... Now we further know: NEW QA PAIR... Does the QA pair have additional knowledge useful for verifying the claim?\"\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12532, "output_tokens": 279, "cost_micros": 41781, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about instruction examples in the main body of the paper.Let me continue reading to find information about instruction examples in the main body of the paper.Let me read the Appendix section to find the prompts and instruction examples.Perfect! I can now see the prompts section clearly. Let me count the instruction examples mentioned. Looking at the document, I can see:\n\n1. **A.1 Prompts for Claim Verifier** - Shows \"<10 demonstrations in total>\"\n2. **A.2 Prompts for Question Generation** (initial question) - Shows \"<10 demonstrations in total>\"\n3. **A.2 Prompts for follow-up question generation** - Shows \"<10 demonstrations in total>\"\n4. **A.3 Prompts for Validator** - Shows \"<10 demonstrations in total>\"\n5. **A.4 Prompts for Reasoner** - Shows \"<10 demonstrations in total>\"\n\nHowever, the question asks about \"instruction examples listed in the main body of the paper.\" Let me check if there's more specific information in the main body about how many examples are actually shown.Let me search more specifically for mentions of how many examples are shown in the main body.Great! I found it. Let me read line 582 more carefully. The main body states: \"We prompt InstructGPT with **ten distinct in-context examples** as detailed in Appendix A.1\"\n\nLet me verify this is the answer by checking if there are other mentions of specific numbers in the main body.Perfect! I found another mention in line 790: \"with a suite of **ten demonstrations** shown in Appendix A.3\"\n\nLet me search for all instances where numbers of examples/demonstrations are mentioned in the main body.Based on my reading of the document, I found that in the main body of the paper, there are **two explicit mentions** of the number of instruction examples/demonstrations:\n\n1. Line 582: \"We prompt InstructGPT with **ten distinct in-context examples**\" (for the Claim Verifier)\n2. Line 790: \"with a suite of **ten demonstrations**\" (for the Validator)\n\nHowever, the question asks specifically \"How many prompts instruction examples are listed in the main body of the paper?\" - it asks for the COUNT/NUMBER that is mentioned, not how many times it's mentioned.\n\nAccording to the main body:\n- 10 examples for Claim Verifier (mentioned on line 582)\n- 10 demonstrations for Validator (mentioned on line 790)\n\nSince both mention \"10\" and the question asks for the number in float format and wants a single integer answer, the answer is:\n\n**10**\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77358, "error": null, "extra": {"thread_id": 2015, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_302989d449c9437a85f8635948950b78", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 438, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "4"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance scores for ProgramFC and QACheck (the proposed system) in the HOVER dataset from Table 1.\n\nFrom Table 1 on page 6:\n\n**HOVER dataset:**\n- ProgramFC scores: 54.27 (2-hop), 54.18 (3-hop), 52.88 (4-hop)\n- QACheck scores: 55.67 (2-hop), 54.67 (3-hop), 52.35 (4-hop)\n\nNow I'll calculate the performance gap for each:\n- 2-hop: 55.67 - 54.27 = 1.40\n- 3-hop: 54.67 - 54.18 = 0.49\n- 4-hop: 52.35 - 52.88 = -0.53\n\nAverage performance gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.453333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 27174, "output_tokens": 268, "cost_micros": 85542, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nLooking at Table 1 in the document, I can find the F1 scores for both systems on HOVER:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nTo calculate the average performance gap across all three HOVER subsets:\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 11662, "output_tokens": 275, "cost_micros": 39111, "latency_ms": 5983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12778, "output_tokens": 247, "cost_micros": 42039, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1 in the document, I can see the performance results:\n\nFor HOVER dataset:\n- ProgramFC: 54.27\n- QACheck: 55.67\n\nFor 2-hop:\n- ProgramFC: 54.18\n- QACheck: 54.67\n\nFor 3-hop:\n- ProgramFC: 52.88\n- QACheck: 52.35\n\nFor 4-hop:\n- ProgramFC: 59.66\n- QACheck: 59.47\n\nThe HOVER dataset has three subsets (2-hop, 3-hop, 4-hop) based on reasoning depth.\n\nTo calculate the average performance gap:\n- 2-hop gap: 54.67 - 54.18 = 0.49\n- 3-hop gap: 52.35 - 52.88 = -0.53\n- 4-hop gap: 59.47 - 59.66 = -0.19\n\nAverage gap = (0.49 + (-0.53) + (-0.19)) / 3 = -0.23 / 3 = -0.0767\n\nRounded to 2 decimal places: -0.08\n\nAnswer: -0.08", "citations": [], "input_tokens": 8647, "output_tokens": 319, "cost_micros": 30726, "latency_ms": 6530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-0.08", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck in the HOVER dataset from Table 1.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12547, "output_tokens": 251, "cost_micros": 41406, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about ProgramFC performance and the proposed system on the HOVER dataset.Let me search for tables or sections containing performance comparisons on the HOVER dataset.Let me read more of the document to find the tables with performance results.Let me search for tables with results and performance metrics.Perfect! I found Table 1 with the performance results. Let me extract the exact values:\n\nFrom Table 1, for the HOVER dataset:\n- **ProgramFC**: \n - 2-hop: 54.27\n - 3-hop: 54.18\n - 4-hop: 52.88\n\n- **QACheck (proposed system)**:\n - 2-hop: 55.67\n - 3-hop: 54.67\n - 4-hop: 52.35\n\nNow I need to calculate the average performance gap between QACheck and ProgramFC:\n\nFor each hop level:\n- 2-hop gap: 55.67 - 54.27 = 1.40\n- 3-hop gap: 54.67 - 54.18 = 0.49\n- 4-hop gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51640, "error": null, "extra": {"thread_id": 2016, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b7dbcb3c5c4d4293862871c529bf39fc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 231, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify the main tasks the authors conducted experiments on by examining the document.\n\nFrom the abstract and introduction (page 1), the authors state:\n\"More in detail, we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 PRELIMINARIES (page 2), the document explicitly describes two main tasks:\n- Section 2.1: Event Relation Extraction\n- Section 2.2: Deductive Reasoning\n\nThese are consistently mentioned throughout the paper as the primary experimental tasks:\n- The pilot study (Section 3.1.1) uses \"MAVEN-ERE and ProofWriter\" \n- Main experiments (Section 4.1) evaluate on \"MAVEN-ERE, Causal-TimeBank, and ProofWriter\"\n- MAVEN-ERE and Causal-TimeBank are event relation extraction datasets\n- ProofWriter is a deductive reasoning dataset\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 65638, "output_tokens": 233, "cost_micros": 200409, "latency_ms": 34842, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks that the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This is extensively discussed throughout the paper, with experiments on datasets like MAVEN-ERE and Causal-TimeBank. The task involves identifying coreference, temporal, causal, and subevent relations between events.\n\n2. **Deductive Reasoning** - This is the second main task, with experiments conducted on the ProofWriter dataset. The task involves logical reasoning to prove, disprove, or determine if statements are unknown based on given facts and rules.\n\nThese two tasks are explicitly mentioned in the abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 28196, "output_tokens": 189, "cost_micros": 87423, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks where experiments were conducted.\n\nFrom the Abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 (Preliminaries): \"In this section, we first introduce two tasks that this paper mainly explores.\" followed by Section 2.1 \"Event Relation Extraction\" and Section 2.2 \"Deductive Reasoning\"\n\nFrom the experimental setup sections, both tasks are consistently used throughout the paper for evaluation (Tables 1, 2, 3, and various figures reference MAVEN-ERE/Causal-TimeBank for ERE and ProofWriter for deductive reasoning).\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 34236, "output_tokens": 175, "cost_micros": 105333, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for the main experimental tasks:\n\nFrom the Introduction and throughout the paper, the authors clearly state they conduct investigations on two main tasks:\n\n1. **Event Relation Extraction (ERE)** - mentioned extensively, including datasets MAVEN-ERE and Causal-TimeBank\n2. **Deductive Reasoning** - mentioned alongside ERE, including the ProofWriter dataset\n\nThis is confirmed in multiple places:\n- Section 2 \"PRELIMINARIES\" has subsections 2.1 \"Event Relation Extraction\" and 2.2 \"Deductive Reasoning\"\n- Section 3.1.1 states: \"We conduct a manual evaluation on MAVEN-ERE and ProofWriter\"\n- The abstract mentions \"event relation extraction and deductive reasoning\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 26309, "output_tokens": 193, "cost_micros": 81822, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This task aims to identify relations (Coreference, Temporal, Causal, and Subevent) between two events in text. The authors used datasets like MAVEN-ERE and Causal-TimeBank for this task.\n\n2. **Deductive Reasoning** - This task involves beginning with known facts and rules, then iteratively making new inferences. The authors used the ProofWriter dataset for this task.\n\nThese two tasks are explicitly mentioned in Section 2 (PRELIMINARIES) where the authors state: \"In this section, we first introduce two tasks that this paper mainly explores.\" The section then proceeds with subsections 2.1 Event Relation Extraction and 2.2 Deductive Reasoning.\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 34535, "output_tokens": 211, "cost_micros": 106770, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify the two main tasks the author conducted experiments on.Let me continue reading to find the specific tasks mentioned:Based on the document, I can see that the author evaluated LLMs in two practical scenarios:\n\n1. Event relation extraction (lines 473)\n2. Deductive reasoning tasks (lines 480-481)\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57891, "error": null, "extra": {"thread_id": 2017, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fc47d9b3789248779051669f324d1694", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 114, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about the number of possible combinations of relationships between two events.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the task involves identifying four types of relations between two events:\n\n1. **Coreference relations**: 2 possible labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 possible labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 possible labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 possible labels (NO_SUBEVENT, SUBEVENT)\n\nThe total number of combinations would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, let me verify this with Figure 1, which shows an example calculation. In Figure 1, it states:\n\n\"Logical Inconsistency (LI) = Number of Conflicts (1) / Number of Combinations (6) = 1/6 \u2248 16.7%\"\n\nThe text explains: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis is referring to pairwise combinations between the 4 relation types, not the total number of possible relationship combinations.\n\nFor the total number of possible combinations of relationships, the calculation is:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nAnswer: 84", "citations": [], "input_tokens": 65637, "output_tokens": 393, "cost_micros": 202806, "latency_ms": 38250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the paper defines 4 types of relations between two events:\n\n1. **Coreference relations**: 2 labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 labels (NO_SUBEVENT, SUBEVENT)\n\nThe document also explicitly mentions in the logical inconsistency calculation (Figure 1 and Section 2.1):\n\n\"Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis refers to combinations between the 4 relation types (choosing 2 from 4 types to check for conflicts), not the total possible relationship combinations.\n\nHowever, looking more carefully at the document, particularly in Figure 1, it states:\n\"Number of Combinations (+ O) = 6\"\n\nThis is the number of pairwise combinations when checking for conflicts between the 4 different relation types: C(4,2) = 4!/(2!\u00d72!) = 6.\n\nBut the question asks about \"combinations of relationships between two events,\" which would be the total number of possible relationship combinations (one label from each of the 4 types):\n\nTotal combinations = 2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, re-reading the context in Figure 1 more carefully, the \"6\" refers to checking consistency between pairs of the 4 relation types, not the total possible labelings.\n\nLooking at the question again and the context in the paper where they discuss logical inconsistency, the relevant number appears to be **6** - the number of combinations when checking pairs of relation types for conflicts", "citations": [], "input_tokens": 28195, "output_tokens": 512, "cost_micros": 92265, "latency_ms": 12843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper states that for each pair of events, there are 4 types of relations to determine:\n\n1. **Coreference relations**: {NO_COREFERENCE, COREFERENCE} - 2 options\n2. **Temporal relations**: {NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON} - 7 options\n3. **Causal relations**: {NO_CAUSAL, PRECONDITION, CAUSE} - 3 options\n4. **Subevent relations**: {NO_SUBEVENT, SUBEVENT} - 2 options\n\nThe paper also provides a specific formula in Figure 1 for calculating the \"total number of combinations\" when computing logical inconsistency:\n\nIn the example, it states: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis formula 4*(4-1)/2 = 6 represents the number of pairs of relation types (combinations of 2 from 4 types), which is used to check for conflicts between different relation types.\n\nHowever, if the question asks about the total possible combinations of relationship assignments (not pairs for conflict checking), it would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84 possible combinations\n\nBut based on the context in Figure 1 where they explicitly calculate \"total number of combinations\" as 6 using the formula 4*(4-1)/2, the paper is referring to the number of pairs of relation types that need to be checked for logical consistency.\n\nAnswer: 6", "citations": [], "input_tokens": 34235, "output_tokens": 428, "cost_micros": 109125, "latency_ms": 11375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper.\n\nFrom Section 2.1 (Event Relation Extraction), the paper identifies four types of relations between two events:\n\n1. **Coreference relations**: 2 options\n - NO_COREFERENCE\n - COREFERENCE\n\n2. **Temporal relations**: 7 options\n - NO_TEMPORAL\n - BEFORE\n - OVERLAP\n - CONTAINS\n - SIMULTANEOUS\n - ENDS-ON\n - BEGINS-ON\n\n3. **Causal relations**: 3 options\n - NO_CAUSAL\n - PRECONDITION\n - CAUSE\n\n4. **Subevent relations**: 2 options\n - NO_SUBEVENT\n - SUBEVENT\n\nThe paper states in Section 2.1: \"We formulate ERE as a multi-classification problem, determining one label (relation) for each of these four relation types.\"\n\nSince we need to determine one label for each of the four relation types, and these are independent choices, the total number of possible combinations is:\n\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nThis is also confirmed in Section 2.1 where it mentions calculating logical inconsistency as \"the ratio of the number of conflicts to the total number of combinations (i.e., all combinations of each two relations).\"\n\nAnswer: 84", "citations": [], "input_tokens": 26308, "output_tokens": 335, "cost_micros": 83949, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper discusses the Logical Inconsistency (LI) metric. In the example shown in Figure 1, there's a specific calculation:\n\n\"Among these, \"SIMULTANEOUS\" and \"CAUSE\" are identified as conflicting with each other based on the logical constraints we have defined, resulting in a single conflict. Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: $4 * (4 - 1) / 2 = 6$.\"\n\nThis calculation shows that with 4 types of relations (Coreference, Temporal, Causal, and Subevent), the number of combinations between pairs of these relations is 6.\n\nThis is confirmed in the definition of Logical Inconsistency in Section 2.1:\n\"logical inconsistency is calculated as the ratio of the number of conflicts (i.e., the answers that conflict with the given logical constraints) to the total number of combinations (i.e., all combinations of each two relations).\"\n\nThe example explicitly states: \"Hence, the logical inconsistency in this example is computed as LI = 1/6 (or approximately 16.7%).\"\n\nThe denominator 6 represents the total number of possible combinations of relationships between two events.\n\nAnswer: 6", "citations": [], "input_tokens": 34534, "output_tokens": 346, "cost_micros": 108792, "latency_ms": 10032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the definition of relationships between two events.Let me search for more specific terms related to event relationships:Perfect! I found the answer. Let me read that specific chunk to get the complete information:Based on the document, I can see that for each pair of events, there are 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36141, "error": null, "extra": {"thread_id": 2018, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_45c25fd9bb164100939e531a52487f98", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 140, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows \"Vicuna-13B and Llama2-13B's performance on MAVEN-ERE and Causal-TimeBank after pre-training on LLM-LR\":\n\nFor Vicuna-13B-PT on MAVEN-ERE, the results are:\n- vanilla ICL: 15.3% Micro-F1\n- vanilla ICL w. CoT: 15.8% Micro-F1\n- CoT w. logical constraints: 18.0% Micro-F1\n- w. all logical constraints: 16.3% Micro-F1\n- w. retrieved logical constraints: 16.1% Micro-F1\n- w. post-processing: 11.0% Micro-F1\n\nThe best performance is 18.0% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 65644, "output_tokens": 212, "cost_micros": 200112, "latency_ms": 35562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B after pre-training (Vicuna-13B-PT) on MAVEN-ERE:\n\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest Micro-F1 score is 18.0%.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 28202, "output_tokens": 149, "cost_micros": 86841, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, I need to find the best performance of Vicuna-13B-PT on MAVEN-ERE.\n\nThe table shows the following Micro-F1 scores for Vicuna-13B-PT on MAVEN-ERE:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34242, "output_tokens": 168, "cost_micros": 105246, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, which shows the performance of Vicuna-13B-PT (pre-trained) on MAVEN-ERE:\n\nThe results show:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest micro-F1 score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 26315, "output_tokens": 151, "cost_micros": 81210, "latency_ms": 4937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B and Llama2-13B after pre-training on LLM-LR:\n\nFor Vicuna-13B on MAVEN-ERE, the Micro-F1 scores are:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest performance is 18.0% with CoT w. logical constraints.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34541, "output_tokens": 175, "cost_micros": 106248, "latency_ms": 5140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about Vicuna-13B's performance on MAVEN-ERE.Let me search for information about MAVEN-ERE and Vicuna-13B performance:Let me read the table that shows the performance results:Perfect! I found the table showing Vicuna-13B's performance on MAVEN-ERE. Looking at the table, I can see three different approaches for Vicuna (Vicuna-13B):\n\n1. vanilla ICL: 13.8% Micro-F1\n2. vanilla ICL w. CoT: 11.6% Micro-F1\n3. CoT w. logical constraints: 14.9% Micro-F1\n\nThe best performance was with \"CoT w. logical constraints\" at 14.9% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38983, "error": null, "extra": {"thread_id": 2019, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b286a9a39b954fac8755dc6ce4c285c7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 166, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for information about the Sociology subject...\n\nFrom Figure 3 on page 3, I can see that Sociology is listed under \"Humanities & Social Sci. (9%)\" with \"Sociology (287, 2.48%)\".\n\nFrom Table 8 on page 18 (Humanities & Social Science results), I can see that Sociology has 252 samples in the test set.\n\nAdding the validation and test samples:\n- Validation set appears to have a portion of the 947 total H&SS samples\n- Test set for Sociology: 252 samples\n\nLooking more carefully at the statistics in Table 1 on page 4, the total is 11,550 questions with a Dev:Validation:Test split of 150:900:10500.\n\nFrom the detailed breakdown in Figure 3, Sociology shows (287, 2.48%), which represents 287 total samples across all splits.\n\nAnswer: 287", "citations": [], "input_tokens": 259120, "output_tokens": 216, "cost_micros": 1559580, "latency_ms": 77812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in MMMU that belong to the sociology subject.\n\nLooking at the document, I can find this information in Figure 3, which shows the breakdown of questions by disciplines, subjects, and subfields.\n\nIn Figure 3, under \"Humanities & Social Sci. (9%)\", I can see:\n\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Sociology (287, 2.48%)\n- Psychology (340, 2.94%)\n\nThe number in parentheses after \"Sociology\" shows 287 questions, which represents 2.48% of the total.\n\nAnswer: 287", "citations": [], "input_tokens": 79424, "output_tokens": 155, "cost_micros": 240597, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in the MMMU dataset that belong to the sociology subject.\n\nLooking through the document, I can find information about the distribution of samples across subjects in Figure 3 and Table 10.\n\nFrom Figure 3, which shows \"MMMU contains 11.5K multimodal questions covering six broad disciplines, 30 subjects, and 183 subfields,\" I can see the breakdown of subjects with their sample counts.\n\nUnder \"Humanities & Social Sci. (9%)\", I can see:\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- Sociology (287, 2.48%)\n\nThe number shown for Sociology is 287 samples.\n\nAnswer: 287", "citations": [], "input_tokens": 109175, "output_tokens": 183, "cost_micros": 330270, "latency_ms": 8484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the number of samples in the Sociology subject.\n\nIn Figure 3 on page 3, there is a breakdown table showing the distribution of samples across different subjects. Under \"Humanities & Social Sci. (9%)\", I can see:\n\nv Sociology (287, 2.48%)\n\nThe number 287 represents the number of samples in the Sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 83138, "output_tokens": 100, "cost_micros": 250914, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:12Z", "latency_ms": 6562, "error": null, "raw_text_chars": 406}]}, "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the sociology subject in MMMU.\n\nIn Figure 3, which shows the distribution of subjects across disciplines, I can see:\n\nUnder \"Humanities & Social Sci. (9%)\":\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- **Sociology (287, 2.48%)**\n\nThe number 287 represents the number of samples in the sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 103232, "output_tokens": 123, "cost_micros": 311541, "latency_ms": 6562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about MMMU and sociology samples in the provided documents.Let me search for information about sociology and sample counts in the document:I found the information! Let me read the specific section with the table that shows the sample counts:Perfect! I found the answer. According to line 1572, Sociology has 287 samples in MMMU, which represents 2.48% of the dataset.\n\nAnswer: 287", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 63671, "error": null, "extra": {"thread_id": 2020, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6e9ac9023c84b0284ac8c1e7870f526", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 141, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and compare it to the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: According to Figure 3 on page 3, Public Health has **544 samples** (shown as \"Public Health (544, 4.7%)\").\n\n2. **Public Health samples showcased**: I need to count the case study figures related to Public Health from Table 10 on page 21. According to this table, Public Health has the following case study figures:\n - Correct Case: 63\n - Lack of Knowledge: 65\n - Textual Understanding Error: 64\n\n This gives us **3 showcased samples** from Public Health.\n\nThe ratio would be: 3/544 = 0.0055147...\n\nRounding to a reasonable number of decimal places: 0.0055\n\nAnswer: 0.0055", "citations": [], "input_tokens": 259140, "output_tokens": 214, "cost_micros": 1559655, "latency_ms": 83546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the ratio to the total Public Health samples.\n\nFrom Table 7 (Health & Medicine results), I can see that Public Health has 509 samples in the test set.\n\nNow I need to count how many Public Health samples are showcased in the paper (Appendix B - Case Study).\n\nLooking through the Case Study figures list and the actual figures:\n\nPublic Health samples shown:\n- Figure 63: Public Health 1 (Epidemiology) - Correct Case\n- Figure 64: Public Health 2 (Epidemiology) - Textual Understanding Error\n- Figure 65: Public Health 3 (Epidemiology) - Lack of Knowledge\n\nSo there are 3 Public Health samples showcased.\n\nThe ratio is: 3 / 509 = 0.005892...\n\nRounding to a reasonable number of decimal places:\n\nAnswer: 0.0059", "citations": [], "input_tokens": 79444, "output_tokens": 220, "cost_micros": 241632, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the percentage of Public Health samples that are showcased in the paper.\n\nFrom the document, I can find:\n\n1. Total number of Public Health samples in the test set:\n - From Table 7, Public Health has 509 samples in the test set\n\n2. Number of Public Health samples showcased in the paper:\n - I need to count the figures in Appendix B that are from Public Health\n - From the List of Case Study Figures (pages 20-21):\n - Figure 63: Public Health 1: Correct Case\n - Figure 64: Public Health 2: Textual Understanding Error\n - Figure 65: Public Health 3: Lack of Knowledge\n \n So there are 3 Public Health samples showcased.\n\n3. Calculate the percentage:\n - Percentage = (3 / 509) \u00d7 100 = 0.589...\n\nWait, let me verify the total number. Looking at Table 1 (page 4), Public Health has 544 samples total. But the question asks about samples \"in this paper\", which could mean the test set.\n\nFrom Table 7 on page 17, Public Health (test set) = 509 samples.\n\nThe ratio would be: 3/509 = 0.00589...\n\nIf we want this as a percentage: 0.589%\n\nBut the question asks for \"a ratio\" as a decimal number, so:\n\n3/509 = 0.005893\n\nAnswer: 0.0059", "citations": [], "input_tokens": 109195, "output_tokens": 347, "cost_micros": 332790, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the percentage.\n\nFrom the document, I can find information about Public Health samples:\n\n1. First, let me find the total number of Public Health samples in the dataset.\nFrom Table 1 (Key statistics), I can see the total questions is 11,550.\n\nLooking at Figure 3 or the breakdown tables, I need to find Public Health information:\n- From Table 7 (Health & Medicine results), I can see Public Health is listed with 509 samples in the test set.\n- The validation set has 150 samples total for Health & Medicine.\n\nActually, looking more carefully at the tables:\n- Table 7 shows Health & Medicine breakdown with Public Health having 509 samples in the test set\n- There's also validation data\n\nLet me look at the case study section (Appendix B):\nFrom Table 10 (Table index of case study figures by subjects), I can count the Public Health cases:\n- Public Health 1: Correct Case (Figure 63)\n- Public Health 2: Textual Understanding Error (Figure 64)\n- Public Health 3: Lack of Knowledge (Figure 65)\n\nSo there are 3 Public Health cases showcased in the paper.\n\nNow I need to find the total number of Public Health samples:\nFrom Table 7, the Public Health row shows 509 samples in the test set.\n\nThe ratio would be: 3/509 = 0.00589...\n\nWait, let me verify this is asking for the percentage of showcased samples. \n\n3/509 \u00d7 100 = 0.589%\n\nAs a ratio (not percentage): 3/509 = 0.00589\n\nAnswer: 0.00589", "citations": [], "input_tokens": 83158, "output_tokens": 399, "cost_micros": 255459, "latency_ms": 11937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.00589", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and divide by the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: Looking at Figure 3, Public Health has 544 samples (4.7% of total).\n\n2. **Public Health samples showcased in the paper**: Looking at Table 10 (Case Study Figures by subjects), I can find the Public Health figures:\n - Figure 63: Public Health 1: Correct Case\n - Figure 64: Public Health 2: Textual Understanding Error\n - Figure 65: Public Health 3: Lack of Knowledge\n\nSo there are 3 Public Health case study figures shown in the paper.\n\nThe ratio is: 3 / 544 = 0.00551...\n\nAnswer: 0.0055", "citations": [], "input_tokens": 103252, "output_tokens": 191, "cost_micros": 312621, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about 'Public Health' samples and calculate the percentage.Let me search for tables and figures that might contain information about Public Health samples:Now let me look at Figure 3 and tables that show the breakdown of subjects:Perfect! I found the information. Let me read more to make sure I have all the details:Perfect! I found the information in the table. From line 1505, I can see:\n\n\"Public Health (544, 4.7%)\"\n\nThis indicates that:\n- Public Health has 544 samples\n- This represents 4.7% of all samples in the dataset\n\nThe answer to the question \"Among all samples belonging to 'Public Health' subject, how many percentage samples showcased in this paper?\" is 4.7%.\n\nSince the question asks for a ratio (percentage) and wants a single decimal number, the answer is:\n\nAnswer: 4.7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53796, "error": null, "extra": {"thread_id": 2021, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8dc9bfdb2b5b47d48d7a6fa4dc0574a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "4.7", "normalised_gold": "0.55"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:19Z", "latency_ms": 62062, "error": null, "raw_text_chars": 760}]}, "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Economics-related error cases that fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\nFrom the Case Study section (pages 20-21), I can see Table 10 which shows the index of case study figures by subjects with associated error categories.\n\nLooking at the Economics row in Table 10:\n- Economics has: Correct Case (22), Perceptual Error (23, 24)\n\nThis indicates that there are 2 Economics-related error cases in the Perceptual Error category (Figures 23 and 24).\n\nLet me verify by checking the actual figures:\n- Figure 23 (page 37): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n- Figure 24 (page 38): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n\nAnswer: 2", "citations": [], "input_tokens": 259127, "output_tokens": 212, "cost_micros": 1559532, "latency_ms": 62014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics-related figures with error categories.\n\nLooking through the case study figures in Appendix B:\n\n1. Figure 22 - Economics: Econometrics - This is a CORRECT case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n This case shows GPT-4V making an error in correctly identifying numbers in the table.\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n This case shows GPT-4V failing to correctly interpret the intersection point in the graph.\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n\nFrom Table 10, I can see:\n- Economics has entries for figures 22, 23, 24\n- Figure 22 is marked as \"Correct Case\"\n- Figure 23 is marked as \"Perceptual Error\"\n- Figure 24 is marked as \"Perceptual Error\"\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 79431, "output_tokens": 298, "cost_micros": 242763, "latency_ms": 8734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\n1. **Figure 23** - Economics: Macroeconomics - Error Category: Perceptual Error\n - GPT-4V incorrectly attributes numbers to the correct objects for production\n\n2. **Figure 24** - Economics: Macroeconomics - Error Category: Perceptual Error\n - GPT-4V fails to interpret the image correctly regarding the equilibrium interest rate\n\nLooking through the List of Case Study Figures table (Table 10), I can see:\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nSo there are **2** Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 109182, "output_tokens": 227, "cost_micros": 330951, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the case study section and count how many fall into the \"Perceptual Error\" category.\n\nLooking through the case study figures, I'll identify Economics-related cases:\n\n1. Figure 22 - Economics: Econometrics - This is a correct case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n\nNow let me verify these are all the Economics cases by checking the Table of Contents in the case study section (Table 10):\n\nFrom Table 10:\n- Economics 1: Correct Case (Figure 22/36)\n- Economics 2: Perceptual Error (Figure 23/37)\n- Economics 3: Perceptual Error (Figure 24/38)\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 83145, "output_tokens": 237, "cost_micros": 252990, "latency_ms": 7797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in Appendix B (Case Study) that are categorized as \"Perceptual Error\".\n\nLet me search through the Case Study section for Economics cases:\n\n1. **Figure 22** - Economics: Econometrics - This is a **correct case**, not an error case.\n\n2. **Figure 23** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\n3. **Figure 24** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nThat's all the Economics cases listed.\n\nTherefore, there are 2 Economics-related error cases that fall into the Error Category of Perceptual Error.\n\nAnswer: 2", "citations": [], "input_tokens": 103239, "output_tokens": 242, "cost_micros": 313347, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about Economics-related error cases and Perceptual Error categories in the provided documents.Let me search for content related to \"Economics\" and \"Perceptual Error\" in this document.Let me search for tables or error categories in the document:Now let me look for tables with error categories and subject areas. Let me search more broadly:Let me look at the specific area around line 7247 where there's a question about stocks (Economics-related):Let me search for a table that shows the distribution of error categories by subject area:Let me search for tables that might show error distribution:Let me look for error analysis tables. Let me search around the beginning of the document for methodology or error analysis sections:Let me search for \"Figure\" to find error analysis figures/tables:Great! Let me read the section with Figure 6 which shows error distribution:Good! I can see Figure 6 shows the overall error distribution, but I need to find a breakdown by subject area. Let me look for tables that show error categories by discipline:I can see there's a table showing different subjects and error cases. Let me read around line 5808 where there's an Accounting entry:Perfect! I can see the list of case studies. I can see:\n- Economics 1: Correct Case\n- Economics 2: Perceptual Error\n- Economics 3: Perceptual Error\n\nSo there are 2 Economics cases with Perceptual Error. Let me verify this is the complete list by checking if there are more Economics cases:Perfect! I found Table 10 which shows the case study figures by subjects with associated error categories. Looking at the Economics row:\n\n- Subject:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94360, "error": null, "extra": {"thread_id": 2022, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ce7c6d6aee6641609ca2f09db6ff3175", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 531, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the subfields for the Energy & Power subject in the MMMU dataset.\n\nLooking at the document, I can find this information in Table 11 on page 111, which lists \"Subfields of each subject.\"\n\nUnder the \"Tech & Engineering\" discipline, I can see the \"Energy & Power\" subject with its subfields listed.\n\nFrom Table 11:\nEnergy & Power: Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 259135, "output_tokens": 118, "cost_micros": 1557465, "latency_ms": 60233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists subfields of each subject:\n\nFor Energy & Power under the Tech & Engineering discipline, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 79439, "output_tokens": 52, "cost_micros": 239097, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists all subfields by subject:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 109190, "output_tokens": 35, "cost_micros": 328095, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_basic_lc", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:21Z", "latency_ms": 5093, "error": null, "raw_text_chars": 174}]}, "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists the subfields of each subject:\n\nFor Energy & Power, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 83153, "output_tokens": 47, "cost_micros": 250164, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document, which lists \"Subfields of each subject,\" I can find the subfields for Energy & Power.\n\nAccording to the table:\n\n**Energy & Power:** Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 103247, "output_tokens": 73, "cost_micros": 310836, "latency_ms": 4766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the subfields in the Energy & Power subject.I found two subfields mentioned for Energy and Power. Let me search for more information to ensure I have a complete list.Let me search more specifically for all Energy and Power subfield mentions:Based on the document, I found two subfields explicitly mentioned for Energy and Power:\n1. Fluid Mechanics (Figure 89)\n2. Thermodynamics (Figure 90)\n\nLet me verify if there are any other subfields by looking at tables or lists that might contain more comprehensive information:Perfect! I found the information. In line 1545, it shows the subfields for Energy Power:\n\n\"Fluid Mechanics, Heat Transfer ...\"\n\nLet me search for more complete information about all the subfields:Excellent! I found the complete list. Let me verify this is in a table showing all subfields:Perfect! I found the complete list of subfields for Energy & Power in the table. The subfields are:\n- Thermodynamics\n- Heat Transfer\n- Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 66234, "error": null, "extra": {"thread_id": 2023, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db9765522b274b60ae516c6ef2df0d73", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 314, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU benchmark.\n\nLooking at the detailed subject breakdown tables in the appendix:\n\nFrom Table 9 (Tech & Engineering results on page 19):\n- Computer Science (Comp. Sci.) column shows GPT-4V(ision) (Playground) achieved **57.1%**\n\nFrom Table 6 (Science results on page 16):\n- Math column shows GPT-4V(ision) (Playground) achieved **45.0%**\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 259141, "output_tokens": 158, "cost_micros": 1558401, "latency_ms": 61328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can find Computer Science:\n- Computer Science (371): 57.1%\n\nLooking at Table 6 (Science results), I can find Math:\n- Math (505): 45.0%\n\nThe difference is: 57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 79445, "output_tokens": 114, "cost_micros": 240045, "latency_ms": 5546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU dataset.\n\nLooking through the tables in the document, I need to find the breakdown by subject for GPT-4V.\n\nFrom Table 6 (Science results), I can see:\n- Math: GPT-4V(ision) (Playground) achieves 45.0%\n\nFrom Table 9 (Tech & Engineering results), I can see:\n- Computer Science (Comp. Sci.): GPT-4V(ision) (Playground) achieves 57.1%\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 109196, "output_tokens": 167, "cost_micros": 330093, "latency_ms": 7889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science (CS) and Math subjects in the MMMU dataset.\n\nLooking through the document for GPT-4V results by subject:\n\nIn Table 2 (page 6), I can see overall results, but I need to find the breakdown by specific subjects.\n\nLooking at the appendix sections for detailed subject breakdowns:\n\nFrom Table 9 (page 19) - Tech & Engineering results:\n- Computer Science is listed under Tech & Engineering\n- GPT-4V(ision) (Playground) shows: Comp. Sci. = 57.1%\n\nFrom Table 6 (page 16) - Science results:\n- Math is listed under Science\n- GPT-4V(ision) (Playground) shows: Math = 45.0%\n\nLet me verify these numbers are correct by checking the table again:\n\nTable 6 (Science results):\n- GPT-4V(ision) (Playground): Math column shows 45.0\n\nTable 9 (Tech & Engineering results):\n- GPT-4V(ision) (Playground): Comp. Sci. column shows 57.1\n\nThe performance difference is:\n57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 83159, "output_tokens": 300, "cost_micros": 253977, "latency_ms": 9016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can see Computer Science is listed with GPT-4V achieving **57.1%**.\n\nLooking at Table 6 (Science results), I can see Math is listed with GPT-4V achieving **45.0%**.\n\nThe performance difference is: 57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 103253, "output_tokens": 119, "cost_micros": 311544, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find GPT-4V's performance on CS and Math subjects in the MMMU dataset.Let me search for performance tables or results sections that would contain GPT-4V's performance by subject.Let me search for tables with performance metrics:Let me look for a results table that shows performance by subject. Let me read around line 5873 and 5978 where I saw Computer Science and Math mentioned:This appears to be a table of figure references. Let me search for actual performance data tables. Let me look for \"accuracy\" or percentage data:Let me read the beginning of the paper to find the main results table:Let me search for a table with actual performance numbers. Let me look for \"GPT-4V\" with numbers:Let me look for Table 2 which should contain the main results broken down by subject:Good! I found Table 2. Now I need to find the breakdown by individual subjects. The table shows broad disciplines but I need specific subjects like Computer Science and Math. Let me search for another table with subject-level breakdown:Table 9 should have Tech & Engineering which includes Computer Science. Let me read that table:Perfect! I found GPT-4V's Computer Science performance at 57.1% (line 5254). Now I need to find Math performance. Let me check Table 6 for Science subjects:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 132796, "error": null, "extra": {"thread_id": 2024, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b9c01f114dd641199d8ac115ae26b4c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 506, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-4.0", "normalised_gold": "12.1"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:26Z", "latency_ms": 64485, "error": null, "raw_text_chars": 1513}]}, "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 1 in the document and count the different icons shown as different image types.\n\nLooking at page 1, Figure 1 is titled \"Overview of the MMMU dataset\" and shows four main sections:\n\n1. **Comprehensive Disciplines** - Shows icons/images representing different disciplines (Engineering, Science, Art & Design, Business, Humanities & Social Sci., Medicine)\n\n2. **Heterogeneous Image Types** - Shows various image type icons including:\n - Diagrams\n - Tables\n - Plots and Charts\n - Photographs\n - Chemical Structures\n - Paintings\n - Medical Images\n - Sheet Music\n - Geometric\n - Pathology images\n - Microscopic Images\n - Comics\n\n3. **Interleaved Text and Images** - Shows a medical question example with 3 MRI images\n\n4. **Expert-level Skills Test** - Shows a diagram with three components: Perception, Knowledge, and Reasoning\n\nThe question asks specifically about \"different icon are shown as different image types\" in Figure 1. Looking at the \"Heterogeneous Image Types\" section, I can count the distinct image type icons shown.\n\nCounting the image types listed in the \"Heterogeneous Image Types\" section:\nDiagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, and there's an ellipsis (...) suggesting more types.\n\nBased on what's explicitly visible and labeled in Figure 1, I can count approximately 12 distinct image type representations shown.\n\nAnswer: 12", "citations": [], "input_tokens": 259123, "output_tokens": 378, "cost_micros": 1563243, "latency_ms": 64406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays various categories:\n\n1. **Comprehensive Disciplines** - showing a pie chart with percentages for different disciplines (Engineering 26%, Art & Design 11%, Business 14%, Medicine 17%, Science 23%, Humanities & Social Sci. 9%)\n\n2. **Heterogeneous Image Types** - listing various types: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\n\n3. **Interleaved Text and Images** - showing an example with 3 medical images\n\n4. **Expert-level Skills Test** - showing three categories:\n - Perception (with an icon)\n - Knowledge (with an icon)\n - Reasoning (with an icon)\n\nThe question asks about \"different icons shown as different image types\" in Figure 1. Looking at the heterogeneous image types section, I can count the distinct image type categories mentioned (not just listed as text, but representing different types):\n\nFrom the visible list and description: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics (and the ellipsis suggests more).\n\nThat's 12 explicitly mentioned types, but the question asks about icons shown. The figure shows examples and visual representations rather than just listing them.\n\nLooking more carefully at Figure 1, I need to count the actual different image type examples/icons displayed, not just mentioned in text.\n\nAnswer: 12", "citations": [], "input_tokens": 79427, "output_tokens": 403, "cost_micros": 244326, "latency_ms": 11984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which is titled \"Overview of the MMMU dataset,\" I can see it displays four main sections:\n\n1. **Comprehensive Disciplines** - showing a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - showing various image type icons\n3. **Interleaved Text and Images** - showing a sample question with images\n4. **Expert-level Skills Test** - showing three overlapping circles for Perception, Knowledge, and Reasoning\n\nIn the \"Heterogeneous Image Types\" section, I can count the following distinct icons representing different image types:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Chemical (structures)\n5. Photos/Photographs\n6. Paintings\n7. Geometric (shapes)\n8. Music (sheet music)\n9. Medical (images)\n\nLet me recount more carefully by examining the visual elements shown in that section. The figure shows various small icons/symbols representing different image types in a grid-like arrangement.\n\nCounting the distinct image type icons shown: 9 different icons are displayed representing different image types (Diagrams, Tables, Plots and Charts, Chemical Structures, Photographs, Paintings, Geometric shapes, Music/Sheet Music, and Medical Images).\n\nAnswer: 9", "citations": [], "input_tokens": 109178, "output_tokens": 314, "cost_micros": 332244, "latency_ms": 10828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to look at Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays four main characteristics:\n\n1. **Comprehensive Disciplines** - showing 6 disciplines with percentages:\n - Engineering (26%)\n - Art & Design (11%)\n - Business (14%)\n - Science (23%)\n - Humanities & Social Sci. (9%)\n - Medicine (17%)\n\n2. **Heterogeneous Image Types** - listing various types like: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Music, Geometric, Pathology images, Microscopic Images, Comics, \u2026\n\n3. **Interleaved Text and Images** - showing an example question with images\n\n4. **Expert-level Skills Test** - listing various types of reasoning and knowledge\n\nThe figure shows icons/visual representations for these categories, but the question asks specifically about \"different icon shown as different image types.\"\n\nLooking at the \"Heterogeneous Image Types\" section in Figure 1, I can count the distinct image type categories mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nAnd there's an ellipsis (\u2026) indicating more types.\n\nHowever, the question asks about icons shown in Figure 1. Looking at the visual representation, the heterogeneous image types section appears to show example icons or representations of these different types.\n\nBased on the visible list in the \"Heterogeneous Image Types\" section of Figure 1, I can count 12 distinct image type categories explicitly listed before the ellipsis.\n\nAnswer: 12", "citations": [], "input_tokens": 83141, "output_tokens": 438, "cost_micros": 255993, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine Figure 1 to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see four main sections:\n\n1. **Comprehensive Disciplines** - Shows a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - This section lists various image types\n3. **Interleaved Text and Images** - Shows an example question with images\n4. **Expert-level Skills Test** - Shows a diagram with arrows\n\nIn the \"Heterogeneous Image Types\" section, I can see a list of different image types mentioned:\n- Diagrams\n- Tables\n- Plots and Charts\n- Photographs\n- Chemical Structures\n- Paintings\n- Medical Images\n- Sheet Music\n- Geometric\n- Pathology images\n- Microscopic Images\n- Comics\n- (and the list continues with \"...\")\n\nHowever, the question asks about \"icons shown as different image types.\" Looking at the figure more carefully, I don't see actual icons representing each image type - rather, I see text labels listing the image types.\n\nLet me reconsider - perhaps the question is asking about visual representations or examples shown. In Figure 1, I can see:\n- A pie chart (1 type of visualization)\n- Example images in the \"Interleaved Text and Images\" section (3 medical scan images)\n- A flow diagram in the \"Expert-level Skills Test\" section\n\nBut this doesn't seem to match what the question is asking either.\n\nRe-reading the question: \"How many different icon are shown as different image types in Figure 1?\"\n\nLooking at the figure description more carefully, in the \"Heterogeneous Image Types\" section, there appears to be a list of image types. The text mentions: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nCounting the explicitly listed items before the \"...\": 12 different image types are mentioned.\n\nAnswer: 12", "citations": [], "input_tokens": 103235, "output_tokens": 475, "cost_micros": 316830, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and count the different icons shown as different image types.Let me search for \"Figure 1\" in the document:Now let me read the beginning of the document content to find Figure 1:Good, I can see Figure 1 information. Let me continue reading to see the complete description of the heterogeneous image types shown in Figure 1:Perfect! I can see the \"Heterogeneous Image Types\" section in Figure 1. The document lists the following image types:\n\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThe text shows \"...\" after Comics, but I need to verify if there are icons shown for each type. Let me search for more information about Figure 1 to see if there's a visual description that tells us how many icons are actually displayed:Looking back at the text from Figure 1, I can see it lists these image types under \"Heterogeneous Image Types\":\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nLet me count the distinct types mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts (this appears to be one category)\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nHowever, I need to verify if the figure actually shows icons for each. Let me look for embedded images or visual descriptions around Figure 1 to see if there are actual icon images:I see that Im1.png is an embedded image showing a hierarchical flowchart. That's not the heterogeneous image types. Let me search more carefully through the document. Let me look for more embedded images around Figure 1:Let me look more carefully at the beginning of the document. Im1.png appeared right after the figure caption for Figure 1. Let me look at Figure 1 more systematically. Let me check if there are multiple images that are part of Figure 1:I see. The Figure 1 section ends at line 1160 with `
`, and then there are embedded images. The text shows , , within the figure. Let me search more systematically. The figure lists:\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nBut I need to see if there are icons displayed. Let me search for where the actual icons might be described. Let me look for \"icon\" in the document:Perfect! I found it. At line 1265, there's a reference to a camera icon. Let me read that section more carefully since it's right after Figure 1:Good! So now I can see there are embedded images that are icons representing different image types. Let me systematically go through and look for all the icons that are part of Figure 1. The document structure shows Im11.png is a camera icon and Im12.png is a geometric icon. Let me look back at the text of Figure 1 to understand the layout better. Looking at the document, it seems the figure starts at line 1097 and the text lists image types. Let me search for more icon descriptions nearby:I see that after the icon descriptions (Im11.png - camera, Im12.png - geometric), the document moves to Figure 3 (a table). So the icons are between Figure 1 and Figure 3. Let me go back and look more carefully at the images embedded right after Figure 1 ends. Let me look for all embedded images between line 1160 (where Figure 1 ends) and line 1455 (where Figure 3 starts):I see Im13.png is a germ/pathogen icon. That's another icon in the series. Let me systematically look at all embedded images between Figure 1 ending and where we transition to other figures. Let me search systematically from line 1160 to 1860:\n\nBased on what I've seen:\n- Im1.png (line 1164): Flowchart diagram\n- Im11.png (line 1256): Camera icon (Photographs)\n- Im12.png (line 1428): Geometric icon\n- Im13.png (line 1863): Germ/pathogen icon\n\nLet me continue looking for more icons. Let me search for the next embedded images:Good! Im14.png is a microscope icon. Let me continue to find more icons. Let me look at the next embedded images. Let me search more systematically. Based on what the abstract says: \"comprising 30 highly heterogeneous image types\" - but Figure 1 specifically lists these types. Let me look at the section with the image types more carefully. I need to look back at line 2501 which says \"Music\" and \"Medical\" - these might be labels for icons:Ah! Figure 5 shows different types of images: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical. That's 9 types. But this is Figure 5, not Figure 1. Let me go back and look very carefully at what exactly is in Figure 1. Let me re-read the text in Figure 1:\n\nFrom lines 1134-1138, the text explicitly states under \"Heterogeneous Image Types\":\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nSo let me count these carefully:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nBut the question asks about \"different icons shown as different image types in Figure 1\". I need to look back at Figure 1 itself more carefully. Let me check if there are actually icons displayed IN Figure 1, not just listed. Let me re-examine the structure. The figure tag starts at 1097 and ends at 1160. Let me look at what images are referenced within that figure:I see! Within the figure (lines 1097-1160), there are references to ``, ``, and `` (lines 1113-1115 as part of a question, and again at lines 1154-1158). But the key section is \"Heterogeneous Image Types\" which lists the types. However, the question asks about icons shown as different image types. \n\nThe text lists image types but doesn't specify how many icons are actually displayed visually. Based on the embedded images that come right after Figure 1:\n- Im1.png (flowchart - but this appears to be an example diagram, not an icon representing a type)\n- Im11.png (camera icon - represents Photographs)\n- Im12.png (geometric shapes icon - represents Geometric)\n- Im13.png (germ icon - could represent Medical/Pathology/Microscopic)\n- Im14.png (microscope icon - represents Microscopic/Medical)\n\nBut I need to find ALL the icons that are specifically showing different image types in Figure 1. Let me look more systematically at what comes after Figure 1 but before Figure 2 (which starts at line 1289). All embedded images between line 1160 and line 1289 might be icons for Figure 1.\n\nFrom my reading:\n- Line 1164: Im1.png (flowchart diagram - this might be part of the figure illustration)\n- Line 1256: Im11.png (camera icon)\n- Line 1428: Im12.png (geometric shapes)\n\nLet me check if there are other embedded images I missed before Figure 2:\n\nLooking at the embedded image list I got earlier, between lines 1160 and 1289 I have:\n- Im1.png at line 1164\n- Im11.png at line 1256\n- Im12.png at line 1428 (but line 1289 is Figure 2, so this is after Figure 2 starts)\n\nWait, let me recheck. Figure 2 caption is at line 1289. So Im12.png at line 1428 is after Figure 2. Let me look more carefully:So between Figure 1 (ending at line 1160) and Figure 2 (starting at line 1288), there are only 2 embedded images:\n- Im1.png at line 1164\n- Im11.png at line 1256\n\nBut actually, looking at the visual descriptions, Im1.png is described as a \"Hierarchical flowchart/organizational diagram\" which seems to be an example diagram that's PART of Figure 1, not an icon representing an image type.\n\nIm11.png is clearly described as a \"Stylized camera icon\" which represents the Photographs image type.\n\nLet me think about this differently. Maybe the icons are all embedded within the actual Figure 1 structure, but the XML parsing has extracted them as separate images. Looking at the text in Figure 1, it lists types under \"Heterogeneous Image Types\". Perhaps in the original PDF, there are visual icons shown alongside or above these text labels, and those icons have been extracted as separate embedded images.\n\nGiven the timeframe and what I can see, let me search for a clearer pattern. Looking at Figure 5 (line 2475) which shows \"Performance of models on different types of images\" and lists: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical - that's 9 types.\n\nBut I need to answer specifically about Figure 1. Let me think about the structure: Figure 1 has a section called \"Heterogeneous Image Types\" that lists multiple types. The question asks how many different icons are shown. \n\nLooking at the abstract again (line 1212): \"comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures.\"\n\nBut in Figure 1's \"Heterogeneous Image Types\" section, it explicitly lists: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nIf I count unique types (treating \"Plots and Charts\" as one type):\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThat's 12 distinct types listed (plus \"...\" suggesting more).\n\nHowever, Figure 5 later in the paper shows icons for 9 types. Perhaps Figure 1 shows icons for these 9 main types as well. Let me verify Figure 5 shows exactly 9 by re-reading:\n\nFrom line 2487-2503 in Figure 5:\n1. Diagrams\n2. Tables\n3. Charts\n4. Chemical\n5. Photos\n6. Paintings\n7. Geometric\n8. Music\n9. Medical\n\nThat's exactly 9 types in Figure 5.\n\nGiven the typical structure of such figures and the pattern I see, my best answer is that Figure 1 shows icons for **9** different image types.\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 192280, "error": null, "extra": {"thread_id": 2025, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f360703be6ba4537aef7760373d79d2f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1273, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:29Z", "latency_ms": 75843, "error": null, "raw_text_chars": 831}]}, "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts that appear in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Figure 4: A comparison chart showing benchmarks plotted on a 2D graph with \"Breadth (Knowledge)\" on x-axis and \"Depth (Reasoning)\" on y-axis\n- A table comparing different datasets\nTotal: 1 chart\n\n**Page 6:**\n- Table 2: Results table (not a chart)\nTotal: 0 charts\n\n**Page 7:**\n- Figure 5: Bar chart showing \"Performance of models on different types of images\"\n- Table 3: Results table (not a chart)\nTotal: 1 chart\n\n**Page 8:**\n- Figure 6: Pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\nTotal: 1 chart\n\n**Page 9:**\n- No charts, only text\nTotal: 0 charts\n\n**Page 10:**\n- No charts, only text (references section)\nTotal: 0 charts\n\nTotal charts in pages 5-10: 1 + 0 + 1 + 1 + 0 + 0 = 3\n\nAnswer: 3", "citations": [], "input_tokens": 259120, "output_tokens": 289, "cost_micros": 1561222, "latency_ms": 75796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts included in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Contains text and references but no charts visible\n\n**Page 6:**\n- Table 2: \"Overall results of different models on the MMMU validation and test set\" - This is a table, not a chart\n\n**Page 7:**\n- Figure 5: \"Performance of models on different types of images\" - This is a bar chart\n- Table 3: \"Result decomposition across question difficulty levels\" - This is a table, not a chart\n\n**Page 8:**\n- Figure 6: \"Error distribution over 150 annotated GPT-4V errors\" - This is a pie chart\n- Figure 7: Shows an example case with images and text - Not a chart\n\n**Page 9:**\n- Contains text discussion, no charts\n\n**Page 10:**\n- Contains text and references, no charts\n\nCounting the charts:\n- Page 7: Figure 5 (bar chart) = 1 chart\n- Page 8: Figure 6 (pie chart) = 1 chart\n\nTotal charts in pages 5-10: 2\n\nAnswer: 2", "citations": [], "input_tokens": 79424, "output_tokens": 276, "cost_micros": 242412, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll carefully count all charts in Pages 5-10.\n\n**Page 5:**\n- Figure 2: This shows sample MMMU examples in a table format, not a chart\n- No charts on this page\n\n**Page 6:**\n- Table 2: This is a table showing model results, not a chart\n- No charts on this page\n\n**Page 7:**\n- Figure 5: This is a bar chart showing \"Performance of models on different types of images\"\n- Table 3: This is a table, not a chart\n- Count: 1 chart\n\n**Page 8:**\n- Figure 6: This is a pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\n- Figure 7: This shows an example with illustrations but is not a chart\n- Count: 1 chart\n\n**Page 9:**\n- No figures or charts on this page (only references text)\n\n**Page 10:**\n- No figures or charts on this page (only references text)\n\nTotal charts in Pages 5-10: 1 (Figure 5) + 1 (Figure 6) = 2\n\nAnswer: 2", "citations": [], "input_tokens": 109175, "output_tokens": 264, "cost_micros": 331485, "latency_ms": 9110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts (plots, graphs, tables, diagrams) that appear in pages 5-10 of the document.\n\nLet me go through pages 5-10:\n\n**Page 5:**\n- Table 2: Overall results of different models (this is a large table)\n- Count: 1\n\n**Page 6:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a bar chart)\n- Count: 2\n\n**Page 7:**\n- Figure 6: Error analysis pie chart showing distribution of error types\n- Figure 7: Example error case with text and an image (not a chart)\n- Count: 1\n\n**Page 8:**\n- Continuation of references, no charts\n\n**Page 9:**\n- Continuation of references, no charts\n\n**Page 10:**\n- References continue, no charts\n\nTotal charts in pages 5-10:\n- Page 5: 1 (Table 2)\n- Page 6: 2 (Table 3, Figure 5)\n- Page 7: 1 (Figure 6)\n- Pages 8-10: 0\n\nTotal: 1 + 2 + 1 + 0 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 300, "cost_micros": 253914, "latency_ms": 8781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count all charts appearing in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Table 1: Key statistics of the MMMU benchmark\n\n**Page 6:**\n- Table 2: Overall results of different models on the MMMU validation and test set\n\n**Page 7:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a table/chart)\n\n**Page 8:**\n- Figure 6: Error distribution over 150 annotated GPT-4V errors (this is a table/chart)\n- Figure 7: A sample error case (this is an example, not a chart)\n\n**Page 9:**\n- No charts (this page discusses conclusion and references begin)\n\n**Page 10:**\n- No charts (continues references section)\n\nLet me recount more carefully by looking at what constitutes a \"chart\":\n\nPages 5-10:\n- Page 5: Table 1 (1 chart)\n- Page 6: Table 2 (1 chart)\n- Page 7: Table 3 (1 chart), Figure 5 which shows a table of performance data (1 chart)\n- Page 8: Figure 6 showing error distribution as a table (1 chart), Figure 7 is an example case study, not a chart\n- Pages 9-10: References, no charts\n\nTotal charts: 5\n\nAnswer: 5", "citations": [], "input_tokens": 103232, "output_tokens": 346, "cost_micros": 314886, "latency_ms": 10516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll read the document to count the charts in pages 5-10.Let me search for page markers to identify pages 5-10:Let me read through the document to find page markers:I can see page markers. Let me continue searching for pages 5-10:Perfect! Now I know the line numbers for pages 5-10. Let me read those pages to count the charts:Now let me search for charts/figures systematically in pages 5-10. Let me look for figure markers:Now let me carefully count the charts in pages 5-10. Let me search for \"Figure\" markers in this range:\n\nBased on my reading of pages 5-10 (lines 2071-3184), I can identify the following charts:\n\n**Page 5** (starts at line 2071):\n- No charts (only text and Table 2)\n\n**Page 6** (starts at line 2462):\n- **Figure 5**: \"Performance of models on different types of images\" - this is a bar chart showing performance metrics\n\n**Page 7** (starts at line 2687):\n- **Figure 6**: \"Error distribution over 150 annotated GPT-4V errors\" - this is a pie chart showing error percentages\n- **Figure 7**: Shows a perceptual error example (not a chart, but a diagram/illustration)\n\n**Page 8** (starts at line 2912):\n- No charts (only text starting with Conclusion section)\n\nLet me continue reading pages 9 and 10:**Page 9** (starts at line 3045): References section - no charts\n\n**Page 10** (starts at line 3184): References section - no charts\n\nNow let me verify my count by carefully reviewing what I found:\n\n**Summary of charts in Pages 5-10:**\n\n- **Page 5**: 0 charts (contains Table 2, which is a table, not a chart)\n- **Page 6**: 1 chart (Figure 5 - bar chart)\n- **Page 7**: 1 chart (Figure 6 - pie chart) + Figure 7 (which is an illustration/example, not a chart)\n- **Page 8**: 0 charts (text only)\n- **Page 9**: 0 charts (references)\n- **Page 10**: 0 charts (references)\n\nTotal charts in pages 5-10: **2 charts**\n\n(Figure 5 is a bar chart and Figure 6 is a pie chart. Figure 7 is not a chart but an illustrative example showing a perceptual error case.)\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79750, "error": null, "extra": {"thread_id": 2026, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8497190a9f7e4c40b3088d1ce4cbc759", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 453, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across two dimensions - Breadth (Knowledge) and Depth (Reasoning).\n\nThe figure shows various datasets plotted on these two axes:\n- VQA, GQA, VisWiz (basic breadth and depth)\n- TextVQA, MMBench, SEED (moderate breadth)\n- MM-Vet, ScienceQA (moderate breadth)\n- OKVQA (moderate breadth)\n- MMMU (positioned highest on both breadth and depth)\n\nAccording to the figure and the accompanying table, MMMU covers:\n- 11.5K questions\n- 30 Types of images\n- 6 disciplines, 30 subjects, and 183 subfields\n\nThis is explicitly described in the caption as excelling \"in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nAnswer: MMMU", "citations": [], "input_tokens": 259139, "output_tokens": 228, "cost_micros": 1559964, "latency_ms": 60530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the paper, I need to examine which dataset covers the broadest range of disciplines and knowledge areas.\n\nFrom the figure, the datasets compared are:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nThe figure shows that MMMU covers:\n- 6 disciplines\n- 30 subjects\n- 183 subfields\n- 11.5K questions\n\nIn comparison, other datasets are more limited in scope. For example, ScienceQA covers specific subjects but is noted as being at elementary to middle school level. The other datasets (VQA, GQA, etc.) focus on specific task types rather than broad disciplinary coverage.\n\nThe paper explicitly states that MMMU \"covers 30 subjects across 6 disciplines\" and emphasizes its \"breadth\" across multiple disciplines including Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.\n\nAnswer: MMMU", "citations": [], "input_tokens": 79443, "output_tokens": 256, "cost_micros": 242169, "latency_ms": 8655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across breadth (knowledge) and depth (reasoning) dimensions:\n\nThe figure shows various datasets plotted, with MMMU positioned to excel in both breadth and depth. Among the nine different datasets shown (VQA, GQA, VizWiz, TextVQA, OKVQA, SEED, MMBench, MM-Vet, ScienceQA, and MMMU), MMMU is positioned highest on the breadth axis.\n\nAnswer: MMMU", "citations": [], "input_tokens": 109194, "output_tokens": 132, "cost_micros": 329562, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset covers the most breadth of knowledge.\n\nThe figure shows a comparison of different datasets along two axes:\n- Breadth (Knowledge) - horizontal axis\n- Depth (Reasoning) - vertical axis\n\nFrom the figure, the datasets shown include:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nLooking at the horizontal positioning (breadth), MMMU is positioned furthest to the right, indicating it has the greatest breadth of knowledge coverage among the datasets shown. This is also supported by the text which states MMMU covers \"30 Types\" of images and spans across 6 disciplines, 30 subjects, and 183 subfields.\n\nAnswer: MMMU", "citations": [], "input_tokens": 83157, "output_tokens": 210, "cost_micros": 252621, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset represents the most breadth (knowledge) on the x-axis.\n\nThe scatter plot shows various datasets plotted with \"Breadth (Knowledge)\" on the x-axis and \"Depth (Reasoning)\" on the y-axis. MMMU is shown as a star at the highest position for both breadth and depth.\n\nAmong the datasets shown in Figure 4, MMMU is positioned furthest to the right on the Breadth axis, indicating it has the most breadth of knowledge coverage.\n\nAnswer: MMMU", "citations": [], "input_tokens": 103251, "output_tokens": 132, "cost_micros": 311733, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which dataset reflects the most breadth of knowledge.Let me search for Figure 4 in the document:Good! I found the caption for Figure 4. Now let me read the actual content around Figure 4 to see the details about which dataset reflects the most breadth of knowledge:Now let me look at the actual Figure 4 to see the visual comparison. Let me search for more context about breadth of knowledge in the figure:Let me read the section that shows the actual Figure 4 visualization:Perfect! I can see Figure 4 shows a scatter plot with \"Breadth (Knowledge)\" on one axis and \"Depth (Reasoning)\" on the other axis. The figure shows the following datasets plotted:\n- MMMU\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- MMBench\n- SEED\n- MM-Vet\n- ScienceQA\n- OKVQA\n\nFrom the figure structure (lines 1834-1858), I can see \"Breadth (Knowledge)\" is labeled as one of the axes. The caption states \"MMMU excels in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nLooking at the text in chunk 156205 (lines 2022-2029), it explicitly states: \"From the breadth perspective, the prior benchmarks are heavily focused on daily knowledge and common sense. The covered image format is also limited. Our benchmark aims to cover college-level knowledge with 30 image formats...\"\n\nThis indicates that **MMMU** reflects the most breadth of knowledge among the nine datasets shown in Figure 4.\n\nAnswer: MMMU", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57452, "error": null, "extra": {"thread_id": 2027, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e1415f7fe2384e1ba85595cd09d85b89", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 282, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:30Z", "latency_ms": 77217, "error": null, "raw_text_chars": 404}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 on page 8 of the document, which shows the \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe error categories and their percentages are:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 259144, "output_tokens": 133, "cost_micros": 1557856, "latency_ms": 77171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I need to look at Figure 6, which shows the error distribution over 150 annotated GPT-4V errors.\n\nFrom Figure 6, I can see the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 79448, "output_tokens": 174, "cost_micros": 240954, "latency_ms": 5937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "azure_premium_lc", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:45Z", "latency_ms": 7750, "error": null, "raw_text_chars": 387}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe pie chart shows the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 109199, "output_tokens": 125, "cost_micros": 329472, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 in the document, which shows the error analysis of 150 GPT-4V error cases:\n\nThe distribution shows:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding Error: 6%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 83162, "output_tokens": 122, "cost_micros": 251316, "latency_ms": 5827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 which shows the error distribution over 150 annotated GPT-4V errors:\n\n1. Perceptual Error: 35%\n2. Lack of Knowledge: 29%\n3. Reasoning Error: 26%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 103256, "output_tokens": 77, "cost_micros": 310923, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and identify the top-3 error types over 150 annotated GPT-4V errors.Let me search for Figure 6 in the document:Now let me read the section around line 2692 where Figure 6 is located:Perfect! I found Figure 6 which shows the error distribution over 150 annotated GPT-4V errors. Looking at the data:\n\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33125, "error": null, "extra": {"thread_id": 2028, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c246c6154fa7469892ccf49cfee50145", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 158, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:53Z", "latency_ms": 59125, "error": null, "raw_text_chars": 830}]}, "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 for error types mentioned in the case studies.\n\nFrom page 95 (Figure 80 - Architecture and Engineering: Surveying and Mapping):\n- This is a correct case, no error type mentioned\n\nFrom page 96 (Figure 82 - Architecture and Engineering: Civil Engineering):\n- Error Category: Reasoning Error\n\nFrom page 97 (Figure 83 - Computer Science: Data Structure and Algorithm):\n- This is a correct case, no error type mentioned\n\nFrom page 98 (Figure 84 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error, Lack of Knowledge\n\nFrom page 99 (Figure 85 - Computer Science: Operating System):\n- Error Category: Perceptual Error\n\nFrom page 100 (Figure 86 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 259136, "output_tokens": 226, "cost_micros": 1559901, "latency_ms": 59077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 (Figures 82-87) to identify all error types mentioned in the case studies.\n\nLooking at each figure in the specified range:\n\n**Figure 82** (Architecture and Engineering: Civil Engineering) - Page 96\n- Error Category: Reasoning Error\n\n**Figure 83** (Computer Science: Data Structure and Algorithm) - Page 97\n- This is a correct case, no error category\n\n**Figure 84** (Computer Science: Compiler Principle) - Page 98\n- Error Category: Perceptual Error, Lack of Knowledge\n\n**Figure 85** (Computer Science: Operating System) - Page 99\n- Error Category: Perceptual Error\n\n**Figure 86** (Computer Science: Compiler Principle) - Page 100\n- Error Category: Perceptual Error\n\n**Figure 87** (Electronics: Signal Processing) - Page 101\n- This is a correct case, no error category\n\nSo the error types mentioned in pages 95-100 are:\n- Reasoning Error\n- Perceptual Error\n- Lack of Knowledge\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 79440, "output_tokens": 265, "cost_micros": 242295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 of the document to identify all error types mentioned in the case studies.\n\nLooking through the figures on these pages:\n\n**Page 95 (Figure 81):** Architecture and Engineering - Correct case (no error type)\n\n**Page 96 (Figure 82):** Architecture and Engineering - **Reasoning Error**\n\n**Page 97 (Figure 83):** Computer Science - Correct case (no error type)\n\n**Page 98 (Figure 84):** Computer Science - **Perceptual Error, Lack of Knowledge**\n\n**Page 99 (Figure 85):** Computer Science - **Perceptual Error**\n\n**Page 100 (Figure 86):** Computer Science - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 109191, "output_tokens": 192, "cost_micros": 330453, "latency_ms": 8281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 of the document for error types mentioned in case studies.\n\nLooking at the case studies in this range:\n\nPage 95 (Figure 81 - Architecture and Engineering): No error type specified (correct case)\n\nPage 96 (Figure 82 - Architecture and Engineering): **Reasoning Error**\n\nPage 97 (Figure 83 - Computer Science): No error type specified (correct case)\n\nPage 98 (Figure 84 - Computer Science): **Perceptual Error, Lack of Knowledge**\n\nPage 99 (Figure 85 - Computer Science): **Perceptual Error**\n\nPage 100 (Figure 86 - Computer Science): **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 83154, "output_tokens": 184, "cost_micros": 252222, "latency_ms": 6655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "Looking through pages 95-100 of the document (Figures 82-86 in the case studies):\n\nFigure 82 (Architecture and Engineering: Civil Engineering) - **Reasoning Error**\n\nFigure 83 (Computer Science: Data Structure and Algorithm) - No error type mentioned (this is a correct case)\n\nFigure 84 (Computer Science: Compiler Principle) - **Perceptual Error, Lack of Knowledge**\n\nFigure 85 (Computer Science: Operating System) - **Perceptual Error**\n\nFigure 86 (Computer Science: Compiler Principle) - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 103248, "output_tokens": 158, "cost_micros": 312114, "latency_ms": 11889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll read the document to find the error types mentioned in the case studies on pages 95-100.Let me search for page numbers to locate pages 95-100:Let me search for \"case stud\" to find the case studies section:Let me read around line 5765 where Table 10 mentions case study figures with error categories:Good! I found Table 10 which shows the error categories. The columns are: Correct Case, Perception, Lack of Knowledge, Reasoning, and Other. Let me continue reading to see the complete table:Good! I found that the error categories are: Perception, Lack of Knowledge, Reasoning, and Other (plus \"Correct Case\" which is not an error). Now I need to find the actual case study pages 95-100. Let me search for figure 95 or case 95:Let me search for the case studies section more specifically. Let me look for the pattern of case numbers 95-100:Figure 96 is something different. Let me check the structure - from the earlier search, I saw \"90 Energy and Power 2: Reasoning Error 104\" and \"95 Mechanical Engineering 3: Re\". Let me search for these patterns directly:Good! So these are Figure numbers 90-95. Let me read that section where it lists figures 90-100:I can see figures 90-95 here. Now I need to find what comes after figure 95. Let me search earlier in the document to see the complete list:This appears to be a table of contents or index. I need to understand better - these numbers (95-100) might refer to page numbers or figure numbers. Let me look at this list more carefully. I can see:\n\n- Page 95: Figure 81 Architecture and Engineering 1: Correct Case\n- Page 96: Figure 83 Computer Science 1: Correct Case \n- Page 97: ?\n- Page 98: Figure 84 Computer Science 2: Perceptual Error, Lack of Knowledge\n- Page 99: Figure 85 Computer Science 3: Perceptual Error\n- Page 100: Figure 86 Computer Science 4: Perceptual Error\n\nBut I need to verify this interpretation. Let me search for where these actual figures appear in the document:Perfect! Now let me read from page 95 to page 100. Let me find line numbers around page 95:Good, I'm at page 95. Now let me continue reading through pages 95-100:Good! I'm now at page 97. Let me continue reading to get to page 100:Good! I'm at page 99. Let me continue to page 100:Good! Now let me continue to see the rest of page 100:Perfect! Now I have reviewed pages 95-100. Let me compile all the error types I found in the case studies on these pages:\n\nFrom the case studies I read on pages 95-100:\n\n**Page 95:**\n- Figure 81: Correct Case (not an error)\n\n**Page 96:**\n- Figure 82: Reasoning Error\n\n**Page 97:**\n- Figure 83: Correct Case (not an error)\n\n**Page 98:**\n- Figure 84: Perceptual Error, Lack of Knowledge\n\n**Page 99:**\n- Figure 85: Perceptual Error\n\n**Page 100:**\n- Figure 86: Perceptual Error\n- Figure 87: Correct Case (not an error)\n\nNow let me verify by looking back at the table of contents I saw earlier to ensure I have the right mapping. Let me check once more:Perfect! This confirms my interpretation. The table shows:\n- Figure 81 is on pages 94-95\n- Figure 82 is on page 96 (spanning from before)\n- Figure 83 is on pages 96-97\n- Figure 84 is on page 98\n- Figure 85 is on page 99\n- Figure 86 is on page 100\n- Figure 87 is on page 101 (starting at page 100)\n\nSo the error types mentioned in case studies on pages 95-100 are:\n\n1. **Reasoning Error** (Figure 82 - Architecture and Engineering)\n2. **Perceptual Error** (Figure 84, 85, 86 - Computer Science)\n3. **Lack of Knowledge** (Figure 84 - Computer Science)\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 114937, "error": null, "extra": {"thread_id": 2029, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77fb85ac92354e66b7280742dc24bcdb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 755, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to examine pages 65-70 to count the successful cases (marked with a checkmark \u2713).\n\n**Page 65** (Figure 51): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 66** (Figure 52): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 67** (Figure 53): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 68** (Figure 54): Clinical Medicine: Pathology - **Error case** \u2717 (Perceptual Error)\n\n**Page 69** (Figure 55): Clinical Medicine: Heart Disease - **Error case** \u2717 (Lack of Knowledge)\n\n**Page 70** (Figure 56): Diagnostics and Laboratory Medicine: Radiology - **Correct case** \u2713\n\nCounting the successful cases (those with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 259120, "output_tokens": 295, "cost_micros": 1561358, "latency_ms": 64484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful (correct) cases in pages 65-70 of the document.\n\nLet me go through each figure in this range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine: Pathology - \"A sample **error** case\" \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine: Heart Disease - \"A sample **error** case\" \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Laboratory Medicine: Radiology - \"A sample **correct** case\" \u2713\n\nCounting the correct cases: Pages 65, 66, 67, and 70 each have one correct case.\n\nTotal successful cases = 4\n\nAnswer: 4", "citations": [], "input_tokens": 79424, "output_tokens": 257, "cost_micros": 242127, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (marked with \u2713 or \"V\") in Pages 65-70.\n\nLet me go through each figure in this page range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine 1: Correct Case \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine 2: Correct Case \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine 3: Correct Case \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine 4: Perceptual Error \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine 5: Lack of Knowledge \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Lab Medicine 1: Correct Case \u2713\n\nCounting the successful cases (marked with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 109175, "output_tokens": 280, "cost_micros": 331725, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases in Pages 65-70 of the document.\n\nLet me go through each page:\n\n**Page 65 (Clinical Medicine 1):**\n- Figure 51: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 66 (Clinical Medicine 2):**\n- Figure 52: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 67 (Clinical Medicine 3):**\n- Figure 53: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 68 (Clinical Medicine 4):**\n- Figure 54: \"A sample error case of Clinical Medicine (subfield: Pathology). Error category: Perceptual Error\"\n- This is an ERROR case \u2717\n\n**Page 69 (Clinical Medicine 5):**\n- Figure 55: \"A sample error case of Clinical Medicine (subfield: Heart Disease). Error category: Lack of Knowledge\"\n- This is an ERROR case \u2717\n\n**Page 70 (Diagnostics and Laboratory Medicine 1):**\n- Figure 56: \"A sample correct case of Diagnostics and Laboratory Medicine (subfield: Radiology)\"\n- This is a CORRECT case \u2713\n\nTotal successful (correct) cases in pages 65-70: 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 348, "cost_micros": 254634, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:48Z", "latency_ms": 11110, "error": null, "raw_text_chars": 1296}]}, "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (correct predictions by GPT-4V) in Pages 65-70.\n\nLet me go through each figure in this range:\n\n**Page 65 - Figure 51 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (E) Familial polyposis coli\n- Ground Truth: (E) Familial polyposis coli\n- **SUCCESS**\n\n**Page 66 - Figure 52 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (D) Ruptured berry aneurysm\n- Ground Truth: (D) Ruptured berry aneurysm\n- **SUCCESS**\n\n**Page 67 - Figure 53 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 68 - Figure 54 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 69 - Figure 55 (Clinical Medicine: Heart Disease)**\n- GPT-4V answered (B) Mitral regurgitation\n- Ground Truth: (C) Aortic value regurgitation\n- Error Category: Lack of Knowledge\n- **FAILURE**\n\n**Page 70 - Figure 56 (Diagnostics and Laboratory Medicine: Radiology)**\n- GPT-4V correctly identified (C) Dandy-Walker syndrome\n- Ground Truth: (C) Dandy-Walker syndrome\n- **SUCCESS**\n\nTotal successful cases: 3\n\nAnswer: 3", "citations": [], "input_tokens": 103232, "output_tokens": 424, "cost_micros": 316056, "latency_ms": 11110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list and look at pages 65-70 to find information about successful cases.I need to search for content related to pages 65-70. Let me search for page markers in the document.Let me search for page numbers in a different format:Let me search for the word \"successful\" or \"cases\" to understand the document structure:Let me search for \"cases\" or look for page numbers differently. Let me read more of the document to understand its structure:This is not helpful. Let me search for the actual page numbers in the document. PDF documents often have page numbers embedded. Let me try different patterns:I can see there's a \"Case Study\" section. Let me look for the appendix section that might have pages 65-70. Let me read the entire document more systematically. First, let me check how long the document is:Great! I found a list of case studies with page numbers. I can see this is a table mapping case numbers to their descriptions and page numbers. Let me continue reading to see pages 65-70:Perfect! I found the information. Looking at pages 65-70, I can see:\n\n- Page 65: Case 51 - Clinical Medicine 1: **Correct Case**\n- Page 66: Case 52 - Clinical Medicine 2: **Correct Case**\n- Page 67: Case 53 - Clinical Medicine 3: **Correct Case**\n- Page 68: Case 54 - Clinical Medicine 4: Perceptual Error\n- Page 69: Case 55 - Clinical Medicine 5: Lack of Knowledge\n- Page 70: Case 56 - Diagnostics and Lab Medicine 1: **Correct Case**\n\nCounting the \"Correct Case\" entries in pages 65-70:\n1. Page 65: Clinical Medicine 1: Correct Case\n2. Page 66: Clinical Medicine 2: Correct Case\n3. Page 67: Clinical Medicine 3: Correct Case\n4. Page 70: Diagnostics and Lab Medicine 1: Correct Case\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54718, "error": null, "extra": {"thread_id": 2030, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77d82c91b9624d83aeec575d8461194f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 347, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many key steps the authors divided causal inference into.\n\nLooking through the document, I can see in Section 3.2 \"Generating the Explanations\" on page 6, there is a discussion of step-by-step explanations and subskills:\n\n\"Apart from the question-answer pairs, we also generate the step-by-step explanations. Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis clearly states that they identified **six subskills** for causal inference.\n\nAdditionally, in Figure 1 on page 2, I can see these six steps are illustrated in the example.\n\nIn Section 4 \"Our CAUSALCOT Model\" on page 7, it also mentions \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: \u2460 identifying the causal graph structure; \u2461 determining the causal query type; \u2462 formulating the query symbolically precisely; and \u2463 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: \u2464 correctly deducing the estimand using causal inference techniques; and finally \u2465 evaluating the estimand to answer the question.\"\n\nThis confirms the six steps again.\n\nAnswer: 6", "citations": [], "input_tokens": 75261, "output_tokens": 410, "cost_micros": 231933, "latency_ms": 16531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Figure 1, there is a section titled \"Correct steps to lead to the ground-truth answer:\" which shows 6 numbered steps:\n\n1) Parse the causal graph\n2) Classify the query type\n3) Formulate the query to its symbolic form\n4) Collect the available data\n5) Derive the estimand using causal inference\n6) Solve for the estimand by plugging in the relevant data\n\nThis is also confirmed in Section 3.2 where they discuss generating explanations: \"We identify the following six subskills: 1 causal graph extraction; 2 correct query type interpretation; 3 symbolic formalization of the query; 4 semantic parsing to compile the available data; 5 estimand derivation; and 6 arithmetic calculation to solve the estimand\"\n\nAdditionally, in Section 4 describing their CAUSALCOT model, they state: \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps... Then... the formal solution: 5 correctly deducing the estimand... and finally 6 evaluating the estimand\"\n\nAnswer: 6", "citations": [], "input_tokens": 31749, "output_tokens": 291, "cost_micros": 99612, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Section 4, \"Our CAUSALCOT Model,\" the authors describe their approach based on the causal inference engine. On page 7, they explicitly state:\n\n\"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nThis clearly describes 6 steps:\n1. Identifying the causal graph structure\n2. Determining the causal query type\n3. Formulating the query symbolically precisely\n4. Extracting relevant data from the prompt\n5. Correctly deducing the estimand using causal inference techniques\n6. Evaluating the estimand to answer the question\n\nThis is also shown in Figure 1 and Figure 4, where 6 steps are illustrated.\n\nAnswer: 6", "citations": [], "input_tokens": 38818, "output_tokens": 294, "cost_micros": 120864, "latency_ms": 8828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the key steps of causal inference.\n\nIn Section 3.2 \"Generating the Explanations,\" the document states:\n\n\"Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis is also reflected in Figure 4 which shows the CAUSALCOT model with 6 steps, and in Section 4 where they describe their prompting strategy that \"breaks down a causal reasoning problem into multiple symbolically-grounded, simpler steps.\"\n\nAnswer: 6", "citations": [], "input_tokens": 33630, "output_tokens": 227, "cost_micros": 104295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can identify the key steps that the authors divided causal inference into.\n\nThe figure clearly shows 6 numbered steps:\n\n1. Parse the causal graph (Causal Relation Extraction)\n2. Classify the query type (Causal Question Classification)\n3. Formulate the query to its symbolic form (Formalization)\n4. Collect the available data (Semantic Parsing)\n5. Derive the estimand using causal inference (Formal Causal Inference)\n6. Solve for the estimand by plugging in the relevant data (Arithmetics)\n\nThis is further confirmed in Section 3.2 where the explanation generation is described, and in Section 4 where the CAUSALCOT model is introduced, which follows these same 6 steps.\n\nAnswer: 6", "citations": [], "input_tokens": 39832, "output_tokens": 211, "cost_micros": 122661, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the provided document to find information about the key steps the author divided causal inference into.Let me search for information about how the author divided the process or steps:Perfect! I found the answer. The text clearly states that the author divided causal inference into 6 steps. Let me verify this by reading a bit more context:Perfect! The document clearly shows that the author divided causal inference into **6 steps**:\n\n1. Extract the causal graph\n2. Determine the query type\n3. Formalize the query\n4. Gather all relevant data\n5. Deduce the estimand using causal inference\n6. Calculate the estimand\n\nThe text explicitly states: \"first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then... 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44937, "error": null, "extra": {"thread_id": 2031, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b14a0eb01624709bf2cbb4b451f998a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about subskills related to collecting available data...\n\nIn Figure 1 on page 2, I can see the step-by-step reasoning process for answering a causal inference question. Step 4 is labeled \"Collect the available data\" and it has an associated subskill label.\n\nThe subskill associated with Step 4 \"Collect the available data\" is **Semantic Parsing**.\n\nThis is also confirmed in Section 3.2 where it mentions the six subskills:\n- \u2460 causal graph extraction\n- \u2461 correct query type interpretation\n- \u2462 symbolic formalization of the query\n- \u2463 semantic parsing to compile the available data\n- \u2464 estimand derivation\n- \u2465 arithmetic calculation\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 75267, "output_tokens": 185, "cost_micros": 228576, "latency_ms": 32921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the step-by-step process for answering a causal inference question, Step 4 is labeled as \"Collect the available data:\" and the corresponding subskill is clearly marked as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 31755, "output_tokens": 68, "cost_micros": 96285, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation for solving a causal inference question:\n\nStep 4 is labeled \"Collect the available data\" and the subskill required for this step is identified as \"Semantic Parsing\" (shown in the colored box in the figure).\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 38824, "output_tokens": 79, "cost_micros": 117657, "latency_ms": 5264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation, and Section 3.2 which discusses \"Generating the Explanations,\" I can identify the subskills mentioned.\n\nStep 4 in Figure 1 is labeled \"Collect the available data\" and the document describes this as requiring \"semantic parsing to compile the available data\" in Section 3.2.\n\nAnswer: semantic parsing", "citations": [], "input_tokens": 33636, "output_tokens": 96, "cost_micros": 102348, "latency_ms": 5125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can see that Step 4 is labeled \"Collect the available data\" and the subskill associated with it is \"Semantic Parsing.\"\n\nThis is also confirmed in Section A.8 (Explanation Template) where Step \u2463 states: \"Collect all the available data: The available data are: '$d$'.\"\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 39838, "output_tokens": 109, "cost_micros": 121149, "latency_ms": 5516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about what subskill is needed to collect the available data.Let me search for more specific terms related to data collection:Perfect! I found the answer. Let me read that section more carefully to get the complete context:Based on the document, I can now provide the answer. The document clearly shows that step 4 \"Collect the available data\" has the subskill listed as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32687, "error": null, "extra": {"thread_id": 2032, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_46f68e1513d341d0b0ee21cfa0dae2f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 137, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the rungs of the Ladder of Causation...\n\nFrom Section 2.1 \"The Ladder of Causation\" on page 3:\n\nThe document describes three distinct rungs:\n- Rung 1 (\"seeing\"): Statistical associations\n- Rung 2 (\"doing\"): Interventions\n- Rung 3 (\"imagining\"): Counterfactuals\n\nRung 2 is specifically described as dealing with interventions and the do-operator.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 75267, "output_tokens": 131, "cost_micros": 227766, "latency_ms": 31077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, Section 2.1 \"The Ladder of Causation\" describes the three rungs:\n\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nThe document states that Rung 2 \"enables us to formalize the concept of actively intervening in the world.\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 31755, "output_tokens": 111, "cost_micros": 96930, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the second rung of the ladder of causation...\n\nFrom Section 2.1 \"The Ladder of Causation\":\n\n\"Rung 2 (\"doing\"). This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"If I take an aspirin now, will my headache subside?\"). Interventions can be formalized using the do-operator [24] and Causal Bayesian Networks [67]...\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 38824, "output_tokens": 126, "cost_micros": 118362, "latency_ms": 6250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 which describes \"The Ladder of Causation\":\n\nThe three rungs are:\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nRung 2 is explicitly described as dealing with interventions, using the do-operator to formalize the concept of actively intervening in the world.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 33636, "output_tokens": 122, "cost_micros": 102738, "latency_ms": 5062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 \"The Ladder of Causation\":\n\n**Rung 2 (\"doing\").** This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"*If I take an aspirin now, will my headache subside?*\"). Interventions can be formalized using the *do-operator* [24] and *Causal Bayesian Networks* [67] to represent, for example, the distribution over $Y$ when intervening on $X$ to set its value to $x$ as $P(Y = y|do(X = x))$.\n\nAnswer: Intervention or \"doing\"", "citations": [], "input_tokens": 39838, "output_tokens": 159, "cost_micros": 121899, "latency_ms": 8094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about the second ladder of causation.Let me search for \"ladder of causation\" in the document:Let me search for \"second rung\" or information about the rungs:Good! I found a reference at line 553. Let me read that section:Perfect! I found the answer. The document clearly states that the three rungs of the Ladder of Causation are:\n- Rung 1: associational\n- Rung 2: interventional\n- Rung 3: counterfactual\n\nAnswer: interventional", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43718, "error": null, "extra": {"thread_id": 2033, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_7af68403347145c59b6ebb40237510ed", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 215, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.0, "method": "str_norm", "normalised_pred": "interventional", "normalised_gold": "intervention"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the chain-type causal graph and count its directed edges.\n\nLooking through the document, I can find the causal graph structures in Figure 6 on page 19, which shows \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\"\n\nIn Figure 6, I can see a \"Chain\" graph structure. The Chain graph shows:\n- X \u2192 (middle node) \u2192 Y\n\nThis represents a chain with three nodes connected by two directed edges:\n1. One edge from X to the middle node\n2. One edge from the middle node to Y\n\nTherefore, a chain-type causal graph has 2 directed edges.\n\nAnswer: 2", "citations": [], "input_tokens": 75271, "output_tokens": 156, "cost_micros": 228153, "latency_ms": 30437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\", I can see the \"Chain\" graph structure.\n\nThe Chain graph shows: X \u2192 M \u2192 Y\n\nThis is a simple chain structure where:\n- X has an arrow to M (1 edge)\n- M has an arrow to Y (1 edge)\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 31759, "output_tokens": 133, "cost_micros": 97272, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, there is a table titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\" which shows:\n\nFor the \"Chain\" causal graph:\n- CGTE Alias: Chain\n- Treatment-Effect Pair: X \u2192 Y\n\nThe diagram shows a simple chain structure: X \u2192 Z \u2192 Y\n\nThis is a chain with 3 nodes (X, Z, Y) connected sequentially.\n\nCounting the directed edges:\n1. X \u2192 Z (one edge)\n2. Z \u2192 Y (one edge)\n\nTotal: 2 directed edges\n\nAnswer: 2", "citations": [], "input_tokens": 38828, "output_tokens": 165, "cost_micros": 118959, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 18, there is a \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\" The table shows:\n\n| Causal Graph | CGTE Alias | Treatment-Effect Pair |\n| ------------ | ----------- | --------------------- |\n| Chain | Chain | X Y |\n\nThe visual representation shows: X \u2192 M \u2192 Y\n\nThis indicates a chain structure where X points to M (a mediator), and M points to Y.\n\nTherefore, there are 2 directed edges in a chain-type causal graph: one from X to M, and one from M to Y.\n\nAnswer: 2", "citations": [], "input_tokens": 33640, "output_tokens": 174, "cost_micros": 103530, "latency_ms": 6796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of a chain-type causal graph.\n\nIn Section A.3 \"Collection of Causal Graphs,\" there is a table (Figure 6) that lists all causal graph types with their structures.\n\nFor the \"Chain\" graph, the table shows:\n- Causal Graph Alias: Chain\n- CGTE Alias: Chain \n- Treatment-Effect Pair: X \u2192 Z \u2192 Y (X and Y are treatment-effect pair)\n\nThe notation X \u2192 Z \u2192 Y indicates:\n- One edge from X to Z\n- One edge from Z to Y\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 39842, "output_tokens": 164, "cost_micros": 121986, "latency_ms": 7343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the definition of a chain-type causal graph and count the directed edges.Let me search for \"chain-type\" or \"causal graph\" in the document:Let me search for \"causal graph\" to find the definition:Let me read the section around line 2387 where the causal graph definition appears:Now let me search for \"chain\" with \"graph\" or look for specific graph types. Let me check Figure 6 which shows all the causal graphs:Good! I can see \"Chain\" is listed as one of the causal graphs at line 2101-2104. Now let me look for the actual visualization or definition of the Chain graph. Let me check if there's a figure showing these graphs:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48766, "error": null, "extra": {"thread_id": 2034, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca8eb7f650264585a1c5b7287fc4aa99", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 247, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2387", "normalised_gold": "2"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 on page 8, which shows \"Performance of all models on our CLADDER dataset v1.5\":\n\nThe \"Overall Acc.\" column shows:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe highest overall accuracy is achieved by CAUSALCOT with 70.40%.\n\nLooking at the document, CAUSALCOT is described as \"GPT-4 + CAUSALCOT\" in the context, which means it's GPT-4 enhanced with their causal chain-of-thought prompting strategy.\n\nThe best performing model is CAUSALCOT (which uses GPT-4 as the base model) with an accuracy of 70.40%.\n\nAnswer: 70", "citations": [], "input_tokens": 75259, "output_tokens": 336, "cost_micros": 230817, "latency_ms": 24141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in Table 2 (on page 8), I need to find which model achieves the highest overall accuracy on the CLADDER dataset v1.5.\n\nFrom Table 2:\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- **+ CAUSALCOT: 70.40%**\n\nThe model with the highest overall accuracy is **CAUSALCOT** (which is GPT-4 with the CausalCoT prompting strategy) at 70.40%.\n\nAnswer: 4", "citations": [], "input_tokens": 31747, "output_tokens": 263, "cost_micros": 99186, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in the document:\n\nFrom Table 2 (CLADDER v1.5 - the main/current version):\n- GPT-4 + CAUSALCOT achieves 70.40% accuracy, which is the highest performance among all models tested\n\nFrom Table 6 (CLADDER v1.0 - earlier version):\n- GPT-4 + CAUSALCOT achieves 66.64% accuracy, which is also the highest\n\nThe document clearly states on page 3: \"our CAUSALCOT achieves an accuracy of 70.40%, which substantially improves the performance of vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nAnd on page 8: \"And amongst all, our CAUSALCOT achieves the highest performance of 70.40%\"\n\nCAUSALCOT is described as a chain-of-thought prompting strategy used with GPT-4, so the best performing model is GPT-4 with CAUSALCOT.\n\nSince GPT-4 is mentioned to have approximately 1T (1 trillion) parameters, and CAUSALCOT is a prompting strategy applied to GPT-4:\n\nAnswer: 1000000000000", "citations": [], "input_tokens": 38816, "output_tokens": 288, "cost_micros": 120768, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1000000000000", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset according to the document.\n\nLooking at Table 2 (page 8), which shows \"Performance of all models on our CLADDER dataset v1.5\", I can see the overall accuracy for each model:\n\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- + CAUSALCOT: 70.40%\n\nThe model with the highest overall accuracy is \"GPT-4 + CAUSALCOT\" with 70.40%.\n\nThe text also states: \"our CAUSALCOT achieves the highest performance of 70.40%, which is substantially better than the vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nSince CAUSALCOT is based on GPT-4 (as stated in the document: \"We build our CAUSALCOT prompting strategy using GPT-4\"), the best performing model is GPT-4 when using the CAUSALCOT prompting strategy.\n\nThe overall accuracy is 70.40%, which rounds to 70%.\n\nAnswer: 70", "citations": [], "input_tokens": 33628, "output_tokens": 390, "cost_micros": 106734, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 (Performance of all models on CLADDER dataset v1.5), which shows the main results:\n\nThe models and their Overall Accuracy are:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe model with the highest overall accuracy is \"+ CAUSALCOT\" at 70.40%.\n\nCAUSALCOT is described in the paper as a chain-of-thought prompting strategy built on top of GPT-4, so it's GPT-4 enhanced with their CAUSALCOT method.\n\nThe accuracy is 70.40%, which as a percentage would be approximately 70 (rounding to the nearest integer).\n\nAnswer: 70", "citations": [], "input_tokens": 39830, "output_tokens": 325, "cost_micros": 124365, "latency_ms": 8687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about model performance on Cladder.Let me search for \"Cladder\" in the document to find the relevant information:Now let me search for tables or results sections that might contain performance metrics:Perfect! I found Table 2 which shows the performance of all models on the CLADDER dataset. Let me read that section:Perfect! I can see Table 2 which shows the performance of all models on the CLADDER dataset. Looking at the \"Overall Acc.\" column:\n\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- +CAUSALCOT: 70.40\n\nThe model that performs the best on Cladder is \"+CAUSALCOT\" with an overall accuracy of 70.40.\n\nAnswer: 70", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36610, "error": null, "extra": {"thread_id": 2035, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e78cd64845f341ad917f431928ce6d64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 165, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor (human) is above/smarter than the student (model).\n\nThe second subfigure shows \"Superalignment\" where the supervisor (human) is below/weaker than the student (superhuman model).\n\nThe difference is the relative capability levels are reversed - in Traditional ML the supervisor is stronger, while in Superalignment the student is stronger.\n\nAnswer: Supervisor is stronger than student (vs. student stronger than supervisor)", "citations": [], "input_tokens": 121916, "output_tokens": 121, "cost_micros": 367563, "latency_ms": 32577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe figure shows three scenarios comparing Traditional ML, Superalignment, and \"Our Analogy.\"\n\nIn the first subfigure (Traditional ML), the supervisor (human) is above the human level line, while the student (model) is below the human level line.\n\nIn the second subfigure (Superalignment), the supervisor (human) is at the human level line, while the student (model) is far above the human level line.\n\nThe key difference is that in Traditional ML, the supervisor is stronger than/above the student, whereas in Superalignment, the student is much stronger than the supervisor.\n\nAnswer: Supervisor is stronger than student (versus student stronger than supervisor)", "citations": [], "input_tokens": 45459, "output_tokens": 162, "cost_micros": 138807, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student versus student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the **supervisor is stronger than the student** (supervisor is above the human level line, student is below it).\n\nThe second subfigure shows \"Superalignment\" where the **supervisor is weaker than the student** (supervisor is at human level, student is above it - superhuman).\n\nAnswer: Supervisor is stronger than student (vs weaker than student)", "citations": [], "input_tokens": 49232, "output_tokens": 103, "cost_micros": 149241, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.25, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs weaker than student", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor is at human level and the student is below human level.\n\nThe second subfigure shows \"Superalignment\" where the supervisor is at human level and the student is above human level (superhuman).\n\nThe key difference is the student's capability relative to human level.\n\nAnswer: Student is weaker than human vs student is stronger than human", "citations": [], "input_tokens": 47601, "output_tokens": 99, "cost_micros": 144288, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2222222222222222, "method": "str_norm", "normalised_pred": "student is weaker than human vs student is stronger than human", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to compare the first subfigure (Traditional ML) with the second subfigure (Superalignment).\n\nIn the first subfigure (Traditional ML):\n- Supervisor: Human\n- Student: Robot\n- The human is stronger/smarter than the robot\n\nIn the second subfigure (Superalignment):\n- Supervisor: Human \n- Student: Superhuman Robot\n- The human is weaker/less capable than the superhuman robot\n\nThe key difference is the relative capability between supervisor and student.\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 89555, "output_tokens": 133, "cost_micros": 270660, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and analyze the difference between the first and second subfigures regarding supervisor-student relationships.Let me search for Figure 1 in the document:Now let me read the actual Figure 1 caption and description more carefully:Based on the figure caption and structure, I can see that Figure 1 has three subfigures:\n1. Traditional ML\n2. Superalignment \n3. Our Analogy\n\nThe question asks about the difference between the first subfigure (Traditional ML) and the second subfigure (Superalignment) regarding the supervisor-student relationship.\n\nFrom the caption: \"Traditional ML focuses on the setting where humans supervise models that are weaker than humans. For the ultimate superalignment problem, humans will have to supervise models much smarter than them.\"\n\nThe key difference is about the relative capability/intelligence level between supervisor and student:\n- In Traditional ML (first subfigure): Supervisor is stronger/smarter than student\n- In Superalignment (second subfigure): Supervisor is weaker than student (students are \"much smarter\" than supervisors)\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43967, "error": null, "extra": {"thread_id": 2036, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e51022c429054652ae2e24b4002aba31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the figures from Page 3 to Page 10 in this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 2: Shows weak-to-strong generalization results with three panels (NLP Tasks, Chess Puzzles, ChatGPT Reward Modeling)\n\n**Page 4:**\n- No figures on this page (Related Work section)\n\n**Page 5:**\n- No figures on this page (Related Work continued)\n\n**Page 6:**\n- No figures on this page (Methodology section)\n\n**Page 7:**\n- No figures on this page (Main Results section continues)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization with naive finetuning (6 panels labeled a-f)\n\n**Page 9:**\n- Figure 4: Shows bootstrapping results on chess puzzles (2 panels labeled a-b)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss (2 panels labeled a-b)\n\nTotal figures from Page 3 to Page 10:\n- Figure 2 (Page 3)\n- Figure 3 (Page 8)\n- Figure 4 (Page 9)\n- Figure 5 (Page 10)\n\nAnswer: 4", "citations": [], "input_tokens": 121899, "output_tokens": 301, "cost_micros": 370212, "latency_ms": 35593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all the figures that appear on pages 3 through 10 of this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 1: An illustration of methodology (Traditional ML, Superalignment, Our Analogy)\n- Figure 2: Shows test accuracy on representative NLP task, Chess Puzzles, and ChatGPT Reward Modeling\n\n**Page 4:**\n- No figures visible on this page (contains related work section)\n\n**Page 5:**\n- No figures visible on this page (continues related work section)\n\n**Page 6:**\n- No figures visible on this page (continues related work section)\n\n**Page 7:**\n- No figures visible on this page (contains methodology section)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization results with multiple panels (a,b,c,d,e,f)\n- Figure 4: Shows bootstrapping results on chess puzzles\n\n**Page 9:**\n- No figures visible on this page (continues main results section)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss\n- Figure 6: Shows auxiliary loss improving generalization across datasets\n\nCounting the figures from pages 3-10:\n- Page 3: 2 figures (Figure 1, Figure 2)\n- Page 8: 2 figures (Figure 3, Figure 4)\n- Page 10: 2 figures (Figure 5, Figure 6)\n\nTotal: 6 figures\n\nAnswer: 6", "citations": [], "input_tokens": 45442, "output_tokens": 356, "cost_micros": 141666, "latency_ms": 8421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from Page 3 to Page 10 in this document.\n\n**Page 3:**\n- Figure 2: \"An illustration of our methodology...\" showing weak-to-strong generalization results\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- Figure 3: \"Promising weak-to-strong generalization...\" showing test accuracy panels (a,b,c,d,e,f)\n\n**Page 8:**\n- None\n\n**Page 9:**\n- Figure 4: \"Bootstrapping improves weak-to-strong generalization...\" showing panels (a,b)\n\n**Page 10:**\n- Figure 5: \"Substantially improved generalization...\" showing panels (a,b)\n- Figure 6: \"Simple auxiliary loss improves generalization...\" showing multiple test accuracy plots\n\nLet me count:\n- Page 3: 1 figure (Figure 2)\n- Page 7: 1 figure (Figure 3)\n- Page 9: 1 figure (Figure 4)\n- Page 10: 2 figures (Figure 5 and Figure 6)\n\nTotal: 1 + 1 + 1 + 2 = 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 49215, "output_tokens": 303, "cost_micros": 152190, "latency_ms": 7608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from page 3 to page 10 of this paper.\n\nLet me go through the document systematically:\n\n**Page 3:** Figure 1 (An illustration of methodology showing Traditional ML, Superalignment, and Our Analogy)\n\n**Page 4:** Figure 2 (Strong models trained with weak supervision generalize beyond their supervisor - showing 3 subplots for NLP Task, Chess, and ChatGPT Reward Modeling)\n\n**Page 5-6:** No figures visible in the main text sections shown\n\n**Page 7:** Figure 3 (mentioned in the text - showing test accuracy and performance gap recovered for NLP tasks, chess puzzles, and ChatGPT reward modeling - this appears to be on page 8 based on the layout)\n\n**Page 8:** Figure 3 appears here (6 subplots - a,b,c showing test accuracy and d,e,f showing performance gap recovered)\n\n**Page 9:** Figure 4 (Bootstrapping improves weak-to-strong generalization on chess puzzles - 2 subplots)\n\n**Page 10:** Figure 5 (Substantially improved generalization on NLP datasets with auxiliary loss - 2 subplots)\n\nCounting the figures:\n- Page 3: Figure 1\n- Page 4: Figure 2 \n- Page 8: Figure 3\n- Page 9: Figure 4\n- Page 10: Figure 5\n\nTotal figures from page 3 to page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 47584, "output_tokens": 358, "cost_micros": 148122, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count all figures from Page 3 to Page 10.\n\n**Page 3:**\n- Figure 2: Strong models trained with weak supervision generalize beyond their supervisor (bar chart)\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- None\n\n**Page 8:**\n- Figure 3: Promising weak-to-strong generalization with naive finetuning (grid of heatmaps/tables - appears as panels a, b, c, d, e, f)\n\n**Page 9:**\n- Figure 4: Bootstrapping improves weak-to-strong generalization on chess puzzles (2 panels: a and b)\n\n**Page 10:**\n- Figure 5: Substantially improved generalization on NLP datasets with a simple auxiliary loss (2 panels: a and b)\n\nTotal count:\n- Figure 2: 1\n- Figure 3: 1 (though it has 6 panels, it's one figure)\n- Figure 4: 1\n- Figure 5: 1\n\nAnswer: 4", "citations": [], "input_tokens": 89538, "output_tokens": 261, "cost_micros": 272529, "latency_ms": 9000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count the figures from Page 3 to Page 10.Now I need to identify which figures appear between Page 3 and Page 10. Let me read the relevant sections more carefully:Now let me check what's on page 11 to confirm the boundary:Based on my analysis, let me count the figures that appear from Page 3 to Page 10:\n\n- **Page 3**: No figures (only text - Related Work section starts)\n- **Page 4**: No figures (Related Work section continues) \n- **Page 5**: 1 figure (the PGR illustration figure showing weak performance, weak-to-strong performance, strong ceiling performance)\n- **Page 6**: No figures (text only)\n- **Page 7**: 1 figure (Figure 3 - showing weak-to-strong generalization results across NLP, Chess, and ChatGPT RM)\n- **Page 8**: 1 figure (Figure 4 - Bootstrapping improves weak-to-strong generalization on chess puzzles)\n- **Page 9**: 1 figure (Figure 5 - NLP with auxiliary loss)\n- **Page 10**: 1 figure (Figure 6 - Simple auxiliary loss improves generalization across most datasets)\n\nTotal figures from Page 3 to Page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55516, "error": null, "extra": {"thread_id": 2037, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ad05d8ff7ccf4712bbe93b3462e811bc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 271, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to locate Figure 3 in the document and count the number of subfigures it contains.\n\nLooking at the document, Figure 3 appears on page 8. The figure is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure contains:\n- Panel (a): NLP Tasks - test accuracy plot\n- Panel (b): Chess Puzzles - test accuracy plot \n- Panel (c): ChatGPT Reward Modeling - test accuracy plot\n- Panel (d): NLP Tasks - performance gap recovered plot\n- Panel (e): Chess Puzzles - performance gap recovered plot\n- Panel (f): ChatGPT Reward Modeling - performance gap recovered plot\n\nThe figure has 6 labeled panels/subfigures (a, b, c, d, e, f).\n\nAnswer: 6", "citations": [], "input_tokens": 121893, "output_tokens": 212, "cost_micros": 368859, "latency_ms": 54641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 on page 8. The caption reads:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe caption explicitly mentions panels (a,b,c) and (d,e,f), which indicates there are 6 subfigures total in Figure 3.\n\nLooking at the actual figure layout, I can see:\n- (a) NLP Tasks\n- (b) Chess Puzzles \n- (c) ChatGPT Reward Modeling\n- (d) NLP Tasks (PGR)\n- (e) Chess Puzzles (PGR)\n- (f) ChatGPT Reward Modeling (PGR)\n\nAnswer: 6", "citations": [], "input_tokens": 45436, "output_tokens": 322, "cost_micros": 141138, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 which is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure caption describes panels (a,b,c) and (d,e,f):\n- (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task\n- (d,e,f) Same as panels a,b,c but for performance gap recovered\n\nSo Figure 3 has 6 subfigures total: (a), (b), (c), (d), (e), and (f).\n\nAnswer: 6", "citations": [], "input_tokens": 49209, "output_tokens": 201, "cost_micros": 150642, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can see Figure 3 is described as:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe description explicitly mentions:\n- (a), (b), (c) - three subfigures showing test accuracy\n- (d), (e), (f) - three subfigures showing performance gap recovered\n\nThis gives us a total of 6 subfigures in Figure 3.\n\nAnswer: 6", "citations": [], "input_tokens": 47578, "output_tokens": 255, "cost_micros": 146559, "latency_ms": 8389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking through the document for Figure 3...\n\nFound it! Figure 3 is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure consists of:\n- (a) NLP Tasks - showing test accuracy\n- (b) Chess Puzzles - showing test accuracy \n- (c) ChatGPT Reward Modeling - showing test accuracy\n- (d) Performance Gap Recovered (%) - NLP Tasks\n- (e) Performance Gap Recovered (%) - Chess Puzzles\n- (f) Performance Gap Recovered (%) - ChatGPT Reward Modeling\n\nSo Figure 3 has 6 subfigures labeled (a) through (f).\n\nAnswer: 6", "citations": [], "input_tokens": 89532, "output_tokens": 204, "cost_micros": 271656, "latency_ms": 8046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count its subfigures.Let me search for Figure 3 in the document:Now let me search for where Figure 3 appears in the document:Good! I found Figure 3. Let me read the complete caption around line 975:Perfect! I can see from the caption that Figure 3 has subfigures labeled (a), (b), (c), (d), (e), and (f). Let me continue reading to confirm all subfigures:Perfect! I can now clearly see all the subfigures in Figure 3. Based on the caption and the content, Figure 3 contains:\n\n- (a) NLP Tasks - test accuracy\n- (b) Chess Puzzles - test accuracy \n- (c) ChatGPT Reward Modeling - test accuracy\n- (d) NLP Tasks - performance gap recovered\n- (e) Chess Puzzles - performance gap recovered\n- (f) ChatGPT Reward Modeling - performance gap recovered\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40671, "error": null, "extra": {"thread_id": 2038, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_131c7eb21d53472097ae34a0c3413951", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 191, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all sections that discuss the experiment setup in this paper about weak-to-strong generalization.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the general methodology and setup for weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the three main task settings: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information:\n - A.1: NLP TASKS - Details about data preprocessing, models, training hyperparameters, weak labels, and evaluation\n - A.2: CHESS PUZZLES - Details about data preprocessing, training hyperparameters, weak labels, and evaluation\n - A.3: CHATGPT REWARD MODELING - Details about data preprocessing, models, training hyperparameters, and weak labels\n - A.4: AUXILIARY CONFIDENCE LOSS - Details about the confidence loss method\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Describes additional experimental setups:\n - C.1: CHESS PUZZLES\n - C.2: NLP TASKS: DIFFICULTY THRESHOLDING\n - C.3: GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes additional experimental settings:\n - D.1: SELF-SUPERVISED VISION MODELS\n - D.2: LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Describes synthetic experiments:\n - E.1: SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n - E.2: DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n - E.3: MAKING IMITATION TRIVIAL\n\nAnswer: Section 3 (Methodology), Section 4.1 (Tasks), Appendix A (Further Experimental Details), Appendix A.1 (NLP Tasks), Appendix A.2 (Chess Puzzles), Appendix A.3 (ChatGP", "citations": [], "input_tokens": 121905, "output_tokens": 512, "cost_micros": 373395, "latency_ms": 49562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgp, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup in this document.\n\nLet me go through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the overall methodology and experimental setup for weak-to-strong learning.\n\n2. **Section 4.1 TASKS** - This describes the tasks used in experiments (NLP benchmarks, Chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix is dedicated to experimental details.\n\n4. **Appendix A.1 NLP TASKS** - Details about NLP task setup including data preprocessing, models, training hyperparameters, weak labels, and evaluation.\n\n5. **Appendix A.2 CHESS PUZZLES** - Details about chess puzzle setup including data preprocessing, training hyperparameters, weak labels, and evaluation.\n\n6. **Appendix A.3 CHATGPT REWARD MODELING** - Details about reward modeling setup including data preprocessing, models, training hyperparameters, weak labels.\n\n7. **Appendix A.4 AUXILIARY CONFIDENCE LOSS** - Detailed description of the auxiliary confidence loss method.\n\n8. **Appendix C EASY-TO-HARD GENERALIZATION** - Discusses experimental setup for easy-to-hard generalization experiments.\n\n9. **Appendix C.1 CHESS PUZZLES** - Setup for easy-to-hard generalization on chess.\n\n10. **Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING** - Setup for difficulty-based experiments on NLP.\n\n11. **Appendix C.3 GPT-4 PREDICTED DIFFICULTY** - Setup using GPT-4 predicted difficulty.\n\n12. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - Discusses alternative experimental settings.\n\n13. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Setup for vision model experiments.\n\n14. **Appendix D.2 LINEAR PROBING** - Setup for linear probing experiments.\n\n15. **Appendix E THE EFFECTS OF WEAK LABEL STRUCTURE** - Discusses experimental setup for studying weak label structure.\n\n16. **", "citations": [], "input_tokens": 45448, "output_tokens": 512, "cost_micros": 144024, "latency_ms": 10405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "16", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup from the document.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the overall methodology and setup for the weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the specific tasks used in experiments (NLP benchmarks, chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix section provides detailed experimental setup information, including:\n - A.1 NLP TASKS\n - A.2 CHESS PUZZLES\n - A.3 CHATGPT REWARD MODELING\n - A.4 AUXILIARY CONFIDENCE LOSS\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Contains experimental setup for easy-to-hard generalization experiments, including:\n - C.1 CHESS PUZZLES\n - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n - C.3 GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes setup for additional experiments:\n - D.1 SELF-SUPERVISED VISION MODELS\n - D.2 LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Contains experimental setup for studying weak label structures:\n - E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n - E.2 DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n - E.3 MAKING IMITATION TRIVIAL\n\nAnswer: Section 3, Section 4.1, Appendix A, Appendix A.1, Appendix A.2, Appendix A.3, Appendix A.4, Appendix C.1, Appendix C.2, Appendix C.3, Appendix D.1, Appendix D.2, Appendix E.1, Appendix E.2, Appendix E.3", "citations": [], "input_tokens": 49221, "output_tokens": 499, "cost_micros": 155148, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2105263157894737, "method": "list_set", "normalised_pred": "appendix, appendix 1, appendix 2, appendix 3, appendix 4, appendix c 1, appendix c 2, appendix c 3, appendix d 1, appendix d 2, appendix e 1, appendix e 2, appendix e 3, section 3, section 4 1", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experimental setup from the document.\n\nLet me scan through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the core experimental methodology for weak-to-strong learning, including creating weak supervisors, training strong students, and training with ground truth labels.\n\n2. **Section 4.1 TASKS** - This describes the specific tasks used in experiments: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information, including:\n - A.1 NLP TASKS (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n - A.2 CHESS PUZZLES (data preprocessing, training hyperparameters, weak labels, evaluation)\n - A.3 CHATGPT REWARD MODELING (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n - A.4 AUXILIARY CONFIDENCE LOSS (detailed description of the method)\n\n4. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - This discusses additional experimental settings:\n - D.1 SELF-SUPERVISED VISION MODELS\n - D.2 LINEAR PROBING\n\n5. **Appendix C** mentions experimental setup for easy-to-hard generalization experiments.\n\n6. **Appendix E** discusses experimental setup for synthetic experiments on weak label structure.\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix D OTHER WEAK-TO-STRONG SETTINGS, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING", "citations": [], "input_tokens": 47590, "output_tokens": 470, "cost_micros": 149820, "latency_ms": 10702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix d other weak to strong settings, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for sections that discuss experiment setup:\n\n1. **Section 3 METHODOLOGY** - Describes the general weak-to-strong learning setup\n2. **Section 4.1 TASKS** - Describes the three task types (NLP, Chess, Reward Modeling)\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - Contains detailed experimental setup information with subsections:\n - A.1 NLP TASKS\n - A.2 CHESS PUZZLES\n - A.3 CHATGPT REWARD MODELING\n - A.4 AUXILIARY CONFIDENCE LOSS\n4. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Describes ImageNet experiment setup\n5. **Appendix D.2 LINEAR PROBING** - Describes linear probing experiment setup\n6. **Appendix C EASY-TO-HARD GENERALIZATION** - Describes setup for easy-to-hard experiments with subsections:\n - C.1 CHESS PUZZLES\n - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n - C.3 GPT-4 PREDICTED DIFFICULTY\n7. **Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY** - Describes synthetic experiment setup\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix C EASY-TO-HARD GENERALIZATION, Appendix C.1 CHESS PUZZLES, Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING, Appendix C.3 GPT-4 PREDICTED DIFFICULTY, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING, Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 89544, "output_tokens": 499, "cost_micros": 276117, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix c 1 chess puzzles, appendix c 2 nlp tasks difficulty thresholding, appendix c 3 gpt 4 predicted difficulty, appendix c easy to hard generalization, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix e 1 synthetic experiments on simulation difficulty, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to identify all sections that discuss the experiment setup.Let me continue reading to see the complete document structure and identify all sections related to experiment setup.Now let me search specifically for \"experiment\" or \"setup\" to find sections discussing experimental setup:Now let me search more systematically for sections that discuss experimental setup:Based on my reading of the document, I can now identify all the sections that discuss experiment setup. Let me compile the complete list:Now I have a comprehensive view of the document. Let me identify all sections that discuss experiment setup:\n\nAnswer: 3 METHODOLOGY, 4.1 TASKS, A FURTHER EXPERIMENTAL DETAILS, A.1 NLP TASKS, A.2 CHESS PUZZLES, A.3 CHATGPT REWARD MODELING, A.4 AUXILIARY CONFIDENCE LOSS, E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69733, "error": null, "extra": {"thread_id": 2039, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bfc57bfcbbd44c6786a0cd5058f61c15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "1 nlp tasks, 2 chess puzzles, 3 chatgpt reward modeling, 3 methodology, 4 1 tasks, 4 auxiliary confidence loss, e 1 synthetic experiments on simulation difficulty, further experimental details", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the citation information for the paper by Long Ouyang published in 2022.\n\nLooking through the references section, I can find:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730\u201327744, 2022.\n\nHowever, the document does not contain information about how many times this paper has been cited. The document is the paper itself (\"Weak-to-Strong Generalization\") which cites the Ouyang et al. 2022 paper, but it does not provide citation counts for the references it lists.\n\nI cannot determine the number of citations for the Ouyang et al. 2022 paper from this document alone.\n\nAnswer: Cannot be determined from the provided document", "citations": [], "input_tokens": 121904, "output_tokens": 231, "cost_micros": 369177, "latency_ms": 39750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations of the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find references to \"Ouyang et al., 2022\" in the references section:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times this paper is cited throughout the document. Let me search for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Christiano et al., 2017; Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 45447, "output_tokens": 423, "cost_micros": 142686, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations to Long Ouyang 2022 in the references and throughout the document.\n\nLooking at the references section, I can find:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times \"Ouyang et al., 2022\" or similar citations appear in the document.\n\nSearching through the document:\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nLet me verify by searching more carefully through the text for all instances of \"Ouyang\":\n\n- Introduction section: 1 citation\n- Related Work section: 1 citation \n- Tasks section: 1 citation\n- ChatGPT reward modeling subsection: 1 citation\n- Models subsection (A.3): 1 citation\n- Alignment plan section: 1 citation\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 49220, "output_tokens": 405, "cost_micros": 153735, "latency_ms": 8905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations of Ouyang et al., 2022 or Long Ouyang, 2022.\n\nLooking through the document, I can find the following citations to \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"(Stiennon et al., 2020; Ouyang et al., 2022)\"\n6. Page 47: \"(h - tiano et al., 2017; Ouyang et al., 2022)\"\n\nLet me count all instances:\n1. Page 1 - 1 citation\n2. Page 2 - 1 citation\n3. Page 5 - 1 citation\n4. Page 7 - 1 citation\n5. Page 32 - 1 citation\n6. Page 47 - 1 citation\n\nTotal: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 47589, "output_tokens": 417, "cost_micros": 149022, "latency_ms": 7516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations for the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find the reference:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. *Advances in Neural Information Processing Systems*, 35: 27730\u201327744, 2022.\n\nNow I need to count how many times this paper (Ouyang et al., 2022) is cited in the document.\n\nSearching through the document for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nAnswer: 6", "citations": [], "input_tokens": 89543, "output_tokens": 289, "cost_micros": 272964, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about Long Ouyang's 2022 paper and its citation count.Let me search for \"Long Ouyang\" and \"2022\" in this document, and look for citation information.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25655, "error": null, "extra": {"thread_id": 2040, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d3a50787f8a940c590bfec1b692a8eb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 70, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2022", "normalised_gold": "6"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\n- Subfigure (a) shows that for ground truth labels, **finetuning** (solid lines) performs best across all model sizes, reaching approximately 95% accuracy for the largest models.\n\n- Subfigure (b) shows weak-to-strong performance with weak labels, where **fewshot weak labels (n=5)** (the method shown with crosses/\u00d7) performs best for the largest student models.\n\n- Subfigure (c) directly compares methods and shows that **finetune aux. loss** (solid line with triangles) achieves the highest performance, reaching approximately 90% accuracy for the largest models.\n\nOverall, across the figure, **finetune aux. loss** appears to be the best method when considering weak-to-strong learning performance.\n\nAnswer: finetune aux. loss", "citations": [], "input_tokens": 121910, "output_tokens": 207, "cost_micros": 368835, "latency_ms": 39203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "finetune aux loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods:\n\n- Subfigure (a) compares zero-shot, 5-shot ground truth, and finetune ground truth\n- Subfigure (b) compares zero-shot, 5-shot ground truth, and 5-shot with weak labels \n- Subfigure (c) compares 5-shot with weak labels, naive finetuning, and finetuning with auxiliary confidence loss\n\nAcross all three subfigures, examining the performance curves for the largest strong student models (rightmost side of each plot), the method that achieves the highest test accuracy is **finetuning with the auxiliary confidence loss** (shown with triangles in subfigure c).\n\nAnswer: Finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 45453, "output_tokens": 193, "cost_micros": 139254, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods across model sizes:\n\nThe figure compares:\n- Zero-shot\n- Few-shot with ground truth (n=5)\n- Finetune with ground truth\n- Few-shot with weak labels (n=5)\n- Finetune (baseline)\n- Finetune with auxiliary loss\n\nFrom the plots, particularly subfigure (c) which directly compares weak-to-strong performance, the method that consistently achieves the highest test accuracy across strong student compute sizes is the finetuning with auxiliary confidence loss (shown with solid line and triangles).\n\nAnswer: finetune with aux. loss", "citations": [], "input_tokens": 49226, "output_tokens": 163, "cost_micros": 150123, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "finetune with aux loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which contains three subfigures (a), (b), and (c), I need to identify the six methods shown and determine which performs best.\n\nFrom the figure:\n- Subfigure (a) shows: zero-shot, 5-shot with ground truth (g.t.), and finetuning with ground truth (g.t.)\n- Subfigure (b) shows: zero-shot, 5-shot with ground truth, and 5-shot with weak labels\n- Subfigure (c) shows: 5-shot prompting (weak-to-strong), naive finetuning, and finetuning with auxiliary confidence loss\n\nThe six methods are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetuning with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary confidence loss\n\nLooking at subfigure (c), which directly compares three weak-to-strong methods, the finetuning with auxiliary confidence loss (shown with solid line and triangles) achieves the highest performance, especially for larger model sizes.\n\nAmong all methods shown across the three subfigures, finetuning with ground truth labels would naturally perform best, but among the weak-to-strong methods (which is the focus of subfigure c), the auxiliary confidence loss performs best.\n\nAnswer: finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 47595, "output_tokens": 339, "cost_micros": 147870, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\nIn subfigure (a), comparing zero-shot, 5-shot with ground truth, and finetuning with ground truth, **finetuning with ground truth** achieves the highest test accuracy.\n\nIn subfigure (b), which shows 5-shot prompting with weak labels from different weak supervisors, the best performing is **5-shot with weak labels from the largest weak supervisor (0.1)**.\n\nIn subfigure (c), which compares 5-shot weak labels, naive finetuning, and finetuning with auxiliary loss, **finetuning with auxiliary loss** achieves the highest performance.\n\nOverall, across all methods shown in Figure 9, the best performing method is:\n\nAnswer: finetuning with auxiliary loss", "citations": [], "input_tokens": 89549, "output_tokens": 194, "cost_micros": 271557, "latency_ms": 8141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and identify which method performs best among the six shown.Let me search for Figure 9 in the document:Let me read the section containing Figure 9 to see the actual figure and understand which methods are being compared:Perfect! Now I can see Figure 9 with three subfigures (a), (b), and (c). Let me read a bit more to understand the comparison better:Based on my reading of the document, Figure 9 contains three subfigures (a, b, c) that compare six different methods:\n\nFrom the figure caption and description:\n- **Panel (a)**: zero-shot, 5-shot with ground truth, finetune with ground truth\n- **Panel (b)**: 5-shot with weak labels (multiple supervisor sizes), zero-shot, 5-shot with ground truth\n- **Panel (c)**: 5-shot weak-to-strong prompting, naive finetuning (weak-to-strong), and **finetuning with the confidence loss (auxiliary loss)**\n\nThe text at line 2078-2079 states: \"However, **weak-to-strong finetuning with the confidence loss still generally outperforms** weak-to-strong few-shot prompting.\"\n\nAnd the figure caption at line 1942-1944 states: \"Few-shot weak-to-strong performance becomes competitive with or outperforms finetuning for the largest strong students, though **finetuning with the confidence loss does better**.\"\n\nThe six methods being compared across the three subfigures are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetune with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary/confidence loss\n\nAnswer: Finetuning with the confidence loss (auxiliary loss)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47672, "error": null, "extra": {"thread_id": 2041, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4655e497891e45598d32be191f19ac1d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with confidence loss auxiliary loss", "normalised_gold": "finetune g t"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:52Z", "latency_ms": 23593, "error": null, "raw_text_chars": 1091}]}, "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the figure with a tree shape and identify which branch has the least leaves.\n\nLooking through the document, I can see Figure 1 on page 2, which shows a \"Technology tree of RAG research\" with a tree-like structure.\n\nExamining this tree diagram, I can see it has a trunk labeled \"Retrieval-Augmented Generation\" and branches extending upward. The main branches appear to be:\n\n1. A left branch (colored in teal/green) showing the evolution from pre-training through fine-tuning stages\n2. A middle branch (colored in orange) labeled \"Instructlens\" \n3. A right branch (colored in blue) showing the inference stage with multiple methods\n\nLooking at the number of \"leaves\" (individual methods/papers) on each branch:\n- The left branch (Pre-training/Fine-tuning) has several leaves\n- The middle branch (Instructlens) appears to have the fewest leaves - it's relatively short with minimal branching\n- The right branch (Inference) has many leaves with extensive branching\n\nThe branch labeled \"Instructlens\" in the middle appears to have the least number of leaves.\n\nAnswer: Instructlens", "citations": [], "input_tokens": 64633, "output_tokens": 269, "cost_micros": 197934, "latency_ms": 23577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "instructlens", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves.\n\nLooking at Figure 1 (the technology tree of RAG research), I can see it's organized by year (2020, 2023, 2024) and has three main branches at the top labeled:\n- \"Augmentation Stage\"\n- \"Fine-tuning\" \n- \"Pre-training\"\n\nLet me count the leaves (methods/papers) in each branch:\n\n**Pre-training branch:**\n- 2020: REALM (implied from context)\n- 2022: Retro, CoG, SANTA, TIGER\n- 2023-2024: RAVEN, Retro++, InstructRetro\n\nThis appears to have around 7-8 leaves.\n\n**Fine-tuning branch:**\nThis has many methods spread across 2023-2024, including PROMPTAGATOR, LM-Indexer, various methods - appears to have 15+ leaves.\n\n**Inference branch:**\nThis has the most methods, with numerous entries across 2023-2024 - appears to have 30+ leaves.\n\nLooking more carefully at the tree structure, the \"Pre-training\" branch (on the left side of the tree) has the fewest methods/leaves compared to the other branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 33376, "output_tokens": 305, "cost_micros": 104703, "latency_ms": 9515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 (the Technology tree of RAG research), I need to count the number of methods/papers shown in each branch:\n\n- **Pre-training branch** (bottom): Contains methods like TIGER, SANTA, Atlas, RAVEN, RETRO++, INSTRUCTRETRO, CoG, EAR, KALMV - approximately 9-10 items\n\n- **Fine-tuning branch** (middle): Contains methods like G-Retriever, RADA, CT-RAG, HyKGE, FILCO, InstructRetro, RA-DIT, Self-RAG, FLARE, and many others - this is the largest branch with approximately 25+ items\n\n- **Inference branch** (top): Contains methods like RAPTOR, UniMS-RAG, CRAG, BGM, IAG, BEQ, ARM-RAG, and several others - approximately 15-20 items\n\nThe Pre-training branch clearly has the fewest methods/leaves compared to the other two branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 40683, "output_tokens": 242, "cost_micros": 125679, "latency_ms": 8514, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the figure in the document that has a tree shape and identify which branch has the least leaves.\n\nLooking at the document, I can see Figure 1, which is described as a \"Technology tree of RAG research.\" This figure shows three main stages: Pre-training, Fine-tuning, and Inference, with various methods listed under each branch.\n\nLet me count the items in each branch:\n\n**Pre-training branch:**\n- CoG, RAVEN, Atlas, RAG, RETRO++, INSTRUCTRETRO, TIGER, SANTA (and a few more visible items)\n- This appears to have around 8-10 items\n\n**Fine-tuning branch:**\n- UniMS-RAG, Self-RAG, MK-ToD, QLM-Doc-ranking, SKR, SANTA, IRCOT, TIGER, Self-Mem, SCM4LLMs (and more)\n- This appears to have the most items, around 15-20+\n\n**Inference branch:**\n- CRAG, CT-RAG, EARCoN, CREA-ICL, PRCA, BEQ, ARM-RAG, PROMPTAGATOR, ZemiR-GQA (and many more)\n- This also has many items\n\nLooking at the figure more carefully, the **Pre-training** branch appears to have the fewest items/leaves compared to the Fine-tuning and Inference branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 30919, "output_tokens": 336, "cost_micros": 97797, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves (items).\n\nLooking at Figure 1 titled \"Technology Tree of RAG research,\" I can see it's organized into three main columns representing different stages:\n- Pre-training\n- Fine-tuning \n- Inference\n\nLet me count the items in each branch/column:\n\n**Pre-training column:** Contains items like:\n- G-Retriever, CRAG, IAG, FILCO, ARM-RAG, RADA, RAPTOR, BGM, HyKGE, DRAGON-AI, PaperQA, CREA-ICL\n- PRCA, Token-Elimination, KALMV, ITRG\n- 1-PAGER, FABULA, SKR, ToC, QLM-Doc-ranking, Recomp\n- KGP, LLM-R, ITER-RETGEN, COQ, LLM-IE, ICRALM, DSP\n- KnowledGPT, IRCOT, PGRA, PKG, SCM4LLMs, Filter-Reranker, RePLUG\n- CoG, TIGER, Retro++\n- GenRead\n- (empty row)\n- RECITE\n\n**Fine-tuning column:** Contains items like:\n- UniMS-RAG, CT-RAG, BEQ, CoN, EAR\n- RAST, Self-RAG, LM-Indexer, RA-DIT, Dual-Feedback-ToD, MK-ToD, RAG_Robust, Retrieve-and-Sample\n- InstructRetro\n- RAVEN\n- SANTA, RRR, Self-Mem, UPRISE, R-GQA, Zemi\n- SUGRE, AAR, FLARE\n- PROMPTAGATOR, RAG-e2e\n- Atlas\n- RAG\n\n**Inference column:** This appears to be the largest with many items distributed across multiple years.\n\nComparing the three branches, the **Fine-tuning** column appears to have fewer distinct method names than the other two columns, but I need to count more carefully", "citations": [], "input_tokens": 37501, "output_tokens": 512, "cost_micros": 120183, "latency_ms": 18250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "comparing three branches fine tuning column appears to have fewer distinct method names than other two columns but i need to count more carefully", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find a figure with a tree shape and identify the branch with the least leaves.Let me search for references to figures with tree shapes in the document:Let me read through the document to find figures. I'll start by looking at the actual content:The document mentions a \"technology tree\" in Figure 1. Let me look for Figure 1:Let me search for where figures are actually displayed in the document:Good! There are embedded images. Let me read around Im1.png which should be Figure 1 (the technology tree):This looks like it's the OCR text from the tree figure. Let me continue reading to see all the branches:Perfect! I found the tree-shaped figure. It's a tree diagram with three main branches:\n1. Fine-tuning (green, left branch)\n2. Pre-training (orange/gold, middle branch) \n3. Inference (blue-green, right branch)\n\nLet me continue reading the visual description to see which branch has the least leaves (papers/items):Perfect! The visual description clearly states:\n\n- **Fine-tuning branch**: Most extensive with numerous sub-branches and approaches\n- **Pre-training branch**: Narrowest with fewer approaches\n- **Inference branch**: Dense with many recent (2024) developments\n\nThe Pre-training branch is described as the \"narrowest with fewer approaches,\" meaning it has the least leaves.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62969, "error": null, "extra": {"thread_id": 2042, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bc29640067d14f5eb3f9f7d53e8522d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 357, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 on page 4 of the document, which shows \"Comparison between the three paradigms of RAG,\" I can see three main sections representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/shapes used:\n\n1. User icon (person silhouette)\n2. Query icon (document/text box)\n3. Documents icon (stacked documents)\n4. Indexing box\n5. Retrieval box\n6. Prompt box (document icon)\n7. Frozen LLM (snowflake icon with text)\n8. Output box\n9. Pre-Retrieval box (with sub-elements like \"Query Rewriting\", \"Query Expansion\", \"Query Enhancement\")\n10. Post-Retrieval box (with sub-elements like \"Re-rank\", \"Memory\", \"Fusion\")\n11. Various module boxes in the Modular RAG section (Routing, Search, Predict, Retrieve, Rewrite, RAG, Rerank, Read, Demonstrate, Fusion, Memory)\n12. Different pattern sections showing Rewrite, Retrieve, Read combinations\n\nCounting the distinct icon types (not individual instances):\n- User icon (person)\n- Query icon\n- Documents icon\n- Standard rectangular boxes (for processes)\n- Snowflake icon (for Frozen LLM)\n- Document/page icon (for Prompt)\n\nLooking more carefully at the distinct visual elements:\n1. Person/user icon\n2. Text/query box\n3. Documents stack icon\n4. Process boxes (rectangles)\n5. Snowflake (frozen LLM)\n6. Document icon\n\nAnswer: 6", "citations": [], "input_tokens": 64616, "output_tokens": 404, "cost_micros": 199908, "latency_ms": 15077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see:\n\n1. **User Query icon** - represented by a person/user symbol\n2. **Documents icon** - represented by stacked document/paper symbols\n3. **Indexing icon** - appears to show database/storage symbols\n4. **Retrieval icon** - shown with magnifying glass or search symbols\n5. **Read icon** - shown as a document being read\n6. **Predict icon** - shown with a specific symbol\n7. **Output icon** - shown at the end of processes\n8. **Prompt icon** - shown in the middle paradigm\n9. **Frozen LLM icon** - represented by a brain or model symbol\n10. **Rerank icon** - shown in Advanced RAG\n11. **Rewrite icon** - shown in Modular RAG\n12. **Demonstrate icon** - shown in Modular RAG\n13. **Routing icon** - shown in Modular RAG\n14. **Search icon** - shown in Modular RAG\n15. **Fusion icon** - shown in Modular RAG\n16. **Memory icon** - shown in Modular RAG\n\nLet me recount more carefully by looking at the actual distinct visual icons in the figure:\n\nFrom the figure, I can identify these distinct icon types:\n- User/person icon\n- Document/paper stack icon\n- Database/storage icon\n- Magnifying glass (search/retrieval)\n- Brain/model icon (for LLM)\n- Arrow/flow icons\n- Various process boxes with different labels\n\nCounting the truly distinct visual icons (not just labeled boxes):\n1. User icon\n2. Document icon\n3. Database/indexing icon\n4. Magnifying glass/search icon\n5. Brain/LLM icon\n6. Output/result icon\n\nAnswer: 6", "citations": [], "input_tokens": 33359, "output_tokens": 449, "cost_micros": 106812, "latency_ms": 10735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 titled \"Comparison between the three paradigms of RAG,\" I can see three diagrams showing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify each distinct icon type used across all three paradigms:\n\n1. **User** - person icon (shown in all three)\n2. **Query** - document/text icon (shown in all three)\n3. **Documents** - stack of documents icon (shown in all three)\n4. **Indexing** - cylindrical database icon (shown in Naive and Advanced RAG)\n5. **Retrieval** - magnifying glass/search icon (shown in all three)\n6. **Rewrite** - circular arrow icon (shown in Advanced RAG)\n7. **Rerank** - list with arrows icon (shown in Advanced RAG)\n8. **Prompt** - text/document icon (shown in all three)\n9. **Frozen LLM** - brain/processing icon (shown in all three)\n10. **Read** - book/reading icon (shown in all three)\n11. **Output** - result/output icon (shown in all three)\n12. **Modules (R)** - box with R (shown in Modular RAG)\n13. **Search** - magnifying glass (shown in Modular RAG)\n14. **Routing** - directional arrow icon (shown in Modular RAG)\n15. **Predict** - crystal ball/prediction icon (shown in Modular RAG)\n16. **Demonstrate** - pointing hand icon (shown in Modular RAG)\n17. **Memory** - storage icon (shown in Modular RAG)\n18. **Fusion** - merge icon (shown in Modular RAG)\n19. **Summary** - document summary icon (shown in Advanced RAG)\n20. **Judge** - decision diamond (shown in Modular RAG)\n21. **Generate** - generation icon (shown in Modular RAG)\n\nLet me recount more carefully by looking at the actual distinct icon shapes:\n\n1. User (person)\n2. Query (document)\n3. Documents (stack)", "citations": [], "input_tokens": 40666, "output_tokens": 512, "cost_micros": 129678, "latency_ms": 10766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three sections (Left, Middle, Right) representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/symbols used in the figure:\n\n1. \"User Query\" box (rectangular box with text)\n2. \"Indexing\" box\n3. \"Retrieval\" box\n4. \"Rerank\" box\n5. \"Rewrite\" box\n6. \"Fusion\" box\n7. \"Summary\" box\n8. \"Demonstrate\" box\n9. \"Read\" box\n10. \"Search\" box\n11. \"Retrieve\" box\n12. \"Memory\" box\n13. \"Predict\" box\n14. \"Routing\" box\n15. \"Documents\" box\n16. \"RAG\" box\n17. \"Output\" box\n18. \"Response\" box\n19. \"Judge\" box\n20. \"Generate\" box\n21. \"Transformation/Decomposition\" box\n22. \"Iterate\" box\n23. Arrow symbols (connecting the boxes)\n24. \"Max Times/Threshold\" decision point\n25. \"Max Depth (Tree)/Threshold\" decision point\n26. \"Generate Special Token/Threshold\" decision point\n27. \"N Times\" indicator\n\nLooking more carefully at the visual elements, the distinct icon types are:\n- Rectangular boxes (for processes/modules)\n- Arrows (for flow direction)\n- Diamond or decision point shapes (for conditional logic)\n\nCounting the distinct visual icon types (not the number of instances, but types):\n1. Rectangular box/module icon\n2. Arrow icon\n3. Diamond/decision point icon\n\nAnswer: 3", "citations": [], "input_tokens": 30902, "output_tokens": 415, "cost_micros": 98931, "latency_ms": 10655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 in the document to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three main diagrams:\n\n1. **Naive RAG (Left)**: Shows a flowchart with:\n - User Query\n - Documents\n - Indexing\n - Retrieval\n - Prompt\n - Frozen LLM\n - Output\n\n2. **Advanced RAG (Middle)**: Shows:\n - User Query\n - Documents\n - Indexing\n - Pre-Retrieval (with sub-components: Query Routing, Query Rewriting, Query Expansion)\n - Retrieval\n - Post-Retrieval (with sub-components: Rerank, Summary, Fusion)\n - Prompt\n - Frozen LLM\n - Output\n\n3. **Modular RAG (Right)**: Shows:\n - Multiple modules (Routing, Search, Predict, Rewrite, RAG, Rerank, Retrieve, Read, Demonstrate, Fusion, Memory)\n - Various patterns including Naive RAG, Advanced RAG, DSP, and ITER-RETGEN\n\nLooking at the visual representation, the distinct icon shapes/types I can identify are:\n1. Rectangular boxes (for most components)\n2. Rounded rectangles/boxes (for grouping/subgraphs)\n3. Arrows/lines (for connections)\n\nHowever, if we're counting distinct visual elements or node types based on their representation in the figure, the components are primarily represented as boxes/rectangles with text labels. The figure uses a consistent box/rectangle style for representing different components.\n\nBased on the mermaid diagram structure shown, all components are represented using similar rectangular node shapes, with the main distinction being in the grouping (subgraphs) and connections rather than distinct icon types.\n\nAnswer: 1", "citations": [], "input_tokens": 37484, "output_tokens": 444, "cost_micros": 119112, "latency_ms": 12312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count the distinct icons used.Let me search for \"Figure 3\" in the document:Now let me read around line 788 to find Figure 3:Let me continue reading to find Figure 3:I see Figure 2 but need to continue searching for Figure 3. Let me search more:Perfect! I found Figure 3. Let me continue reading to see the complete visual description:Perfect! I have found Figure 3 and its visual description. Let me carefully count the distinct icons mentioned in the visual description:\n\nFrom the visual description of Figure 3, I can identify the following distinct icons:\n\n1. **User/Query icon** - mentioned in Naive RAG and Advanced RAG\n2. **Documents icon** - mentioned in Naive RAG and Advanced RAG (stacked papers/documents)\n3. **Snowflake icon** - mentioned in Naive RAG and Advanced RAG (representing Frozen LLM)\n4. **Document icon** - mentioned in Naive RAG (at the Prompt stage)\n5. **Shuffle/rerank icon** - mentioned in Advanced RAG Post-Retrieval\n6. **Waveform/summary icon** - mentioned in Advanced RAG Post-Retrieval\n7. **Layers/fusion icon** - mentioned in Advanced RAG Post-Retrieval\n\nLet me recount more carefully by looking at what the visual description explicitly states:\n- User/Query icons (top of diagrams)\n- Documents icons (top of diagrams)\n- Document icon (at Prompt stage)\n- Snowflake icon (representing Frozen LLM)\n- Shuffle/rerank icon (in Post-Retrieval)\n- Waveform/summary icon (in Post-Retrieval)\n- Layers/fusion icon (in Post-Retrieval)\n\nThat gives us 7 distinct icons total.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51812, "error": null, "extra": {"thread_id": 2043, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c31c1d3df3774ac882e3f8a22f227910", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 283, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "9"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that has a retrieval granularity of \"Phrase\" in Table I.\n\nLooking at Table I (Summary of RAG methods) on page 6, I can see the columns: Method, Retrieval Source, Retrieval Data Type, Retrieval Granularity, Augmentation Stage, and Retrieval process.\n\nScanning the \"Retrieval Granularity\" column, I find one entry with \"Phrase\":\n- CoG [29] - which has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of reference [29]. Looking at the references section starting on page 17, reference [29] is:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 64633, "output_tokens": 220, "cost_micros": 197199, "latency_ms": 34000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that uses \"phrase\" as its retrieval granularity.\n\nLooking at Table I \"SUMMARY OF RAG METHODS\", I can find the retrieval granularity column. Let me search for entries with \"Phrase\" as the retrieval granularity.\n\nFrom Table I, I can see:\n- CoG [29] has:\n - Retrieval Source: Wikipedia\n - Retrieval Data Type: Text\n - Retrieval Granularity: Phrase\n - Augmentation Stage: Pre-training\n - Retrieval process: Iterative\n\nNow I need to find the full title of the CoG paper. Looking at the references section:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 33376, "output_tokens": 222, "cost_micros": 103458, "latency_ms": 7266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for methods with \"Phrase\" retrieval granularity in Table I...\n\nFrom Table I (SUMMARY OF RAG METHODS), I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nLooking at the references section for reference [29]:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 40683, "output_tokens": 133, "cost_micros": 124044, "latency_ms": 8407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with phrase-level retrieval granularity.\n\nLooking at Table I (Summary of Retrieval Source and Retrieval Granularity Used in RAG Methods), I can find methods with \"Phrase\" as their retrieval granularity.\n\nFrom the table, I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of the CoG paper from the references section.\n\nLooking at the references, reference [29] is:\n\"T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\"\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 30919, "output_tokens": 187, "cost_micros": 95562, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with retrieval granularity of \"Phrase\".\n\nLooking at Table I (Summary of RAG Methods), I can search for entries where \"Retrieval Granularity\" is \"Phrase\".\n\nScanning through Table I, I find:\n- CoG [29] has retrieval granularity: Phrase\n\nNow I need to find the full title of paper [29] from the references section.\n\nLooking at reference [29]:\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in *The Eleventh International Conference on Learning Representations*, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 37501, "output_tokens": 174, "cost_micros": 115113, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about retrieval granularity and phrase-level methods in the provided documents.Let me continue reading to find the title and look for information about retrieval granularity:Good! I found the paper title: \"Retrieval-Augmented Generation for Large Language Models: A Survey\". Now I need to search for information about retrieval granularity and specifically which method has \"phrase\" as its retrieval granularity. Let me search for tables or sections that discuss retrieval granularity:Perfect! I found a table that mentions \"Phrase\" as a retrieval granularity. Let me read that section to find the complete information:Great! I can see that \"CoG [29]\" has \"Phrase\" as its retrieval granularity. Now I need to find the full title of the CoG paper. Let me look for the reference [29]:Perfect! I found reference [29]. Let me read that section to get the full title:I can see reference [29] starts at line 4090. Let me read a bit more to get the complete title:Perfect! I found the full title of reference [29], which is the paper that proposes the method (CoG) with phrase retrieval granularity.\n\nThe full title is: **\"Copy is all you need\"**\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52827, "error": null, "extra": {"thread_id": 2044, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_27dfaf3e8bfa47e1b55fb4feceaed85a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 297, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through Table II to find datasets that have exactly three methods listed.\n\nLooking at Table II (SUMMARY OF RAG METHODS), I need to count the methods for each dataset:\n\n- WebQ (Web Questions): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - 3 methods \u2713\n- MS MARCO: [4], [40], [52] - 3 methods \u2713\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum(QM): [60], [123] - 2 methods\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB [128],MMCU_Medical [129]: [81] - 1 method\n- QuALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n- GraphQA: [84] - 1 method\n- Amazon(Toys,Sport,Beauty): [39], [40] - 2 methods\n- T-REx [141],ZsRE [142]: [27], [51] - 2 methods\n- HellaSwag: [20], [66] - 2 methods\n- CoT Reasoning: [27] - 1 method\n- CSQA: [55] - 1 method\n- StrategyQA: [14], [24], [48], [51], [55], [58] - 6 methods\n- XSum: [17] - 1 method\n- VioLens: [19] - 1 method\n- TREC: [33] - 1 method\n- SST-2: [20], [33], [38] - 3 methods \u2713\n- CodeSearchNet: [76] -", "citations": [], "input_tokens": 64631, "output_tokens": 512, "cost_micros": 201573, "latency_ms": 17125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "codesearchnet 76", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): [3], [4], [22], [27], [40], [43], [54], [62], [71], [112] [20], [44], [72] [13], [30], [34], [45], [50], [64] - more than 3\n- TriviaQA (TQA): [4], [27], [59], [62], [112] [22], [25], [43], [44], [71], [72] [20], [23], [30], [32], [45], [69], [112] - more than 3\n- SQUAD: [3], [4], [13], [30], [50], [68] - 6 methods\n- Web Questions (WebQ): [4], [40], [52] - exactly 3 methods\n- PopQA: [4], [40], [52] - exactly 3 methods\n- MS MARCO: [117] - only 1 reference\n\n**QA - Multi-hop:**\n- HotpotQA: [7], [14], [22], [27], [59], [62], [69], [71], [91] - 9 methods\n- 2WikiMultiHopQA: [14], [51], [61], [91] - 4 methods\n- MuSiQue: [120] - only 1 reference\n\n**QA - Long-form:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB: [", "citations": [], "input_tokens": 33374, "output_tokens": 512, "cost_micros": 107802, "latency_ms": 11546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "cmb", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): More than 3 methods\n- TriviaQA (TQA): More than 3 methods\n- SQUAD: More than 3 methods\n- Web Questions (WebQ): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - **exactly 3 methods**\n- MS MARCO: [4], [40], [52] - **exactly 3 methods**\n\n**QA - Multi-hop:**\n- HotpotQA: More than 3 methods\n- 2WikiMultiHopQA: [14], [24], [48], [59], [61], [91] - 6 methods\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n\n**QA - Long-form QA:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain QA:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB, MMCU_Medical: [81] - 1 method\n\n**QA - Multi-Choice QA:**\n- QUALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): [13], [27], [34], [42] - 4 methods\n- KBP: [74],", "citations": [], "input_tokens": 40681, "output_tokens": 512, "cost_micros": 129723, "latency_ms": 12062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "kbp 74", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Table II and find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n1. Natural Question (NQ) - has 10 methods listed\n2. TriviaQA (TQA) - has 5 methods listed\n3. SQuAD - has 7 methods listed\n4. Web Questions (WebQ) - has 6 methods listed\n5. PopQA - has 3 methods: [7], [25], [67] \u2713\n6. MS MARCO - has 3 methods: [4], [40], [52] \u2713\n7. HotpotQA - has 8 methods listed\n8. 2WikiMultiHopQA - has 6 methods listed\n9. MuSiQue - has 4 methods listed\n10. ELI5 - has 5 methods listed\n11. NarrativeQA (NQA) - has 4 methods listed\n12. ASQA - has 2 methods listed\n13. QMSum (QM) - has 2 methods listed\n14. Qasper - has 2 methods listed\n15. COVID-QA - has 2 methods listed\n16. CMB, MMCU Medical - has 1 method listed\n17. QuALITY - has 2 methods listed\n18. ARC - has 2 methods listed\n19. CommonsenseQA - has 2 methods listed\n20. GraphQA - has 1 method listed\n21. Wizard of Wikipedia (WoW) - has 4 methods listed\n22. KBP - has 2 methods listed\n23. DuleMon - has 1 method listed\n24. CamRest - has 2 methods listed\n25. Amazon (Toys, Sport, Beauty) - has 2 methods listed\n26. WikiEvent - has 4 methods listed\n27. RAMS - has 2 methods listed\n28. T-REx, ZsRE - has 2 methods listed\n29. HellaSwag - has 2 methods listed\n30. CoT Reasoning - has 1 method listed", "citations": [], "input_tokens": 30917, "output_tokens": 512, "cost_micros": 100431, "latency_ms": 11141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "30 cot reasoning has 1 method listed", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to find datasets in Table II that have exactly three methods listed.\n\nLet me go through Table II systematically and count the number of methods for each dataset:\n\n**QA - Single-hop:**\n- Natural Question (NQ): 12 methods\n- TriviaQA (TQA): 12 methods\n- SQuAD: 7 methods\n- Web Questions (WebQ): 6 methods\n- PopQA: 3 methods \u2713\n- MS MARCO: 3 methods \u2713\n\n**QA - Multi-hop:**\n- HotpotQA: 15 methods\n- 2WikiMultiHopQA: 6 methods\n- MuSiQue: 4 methods\n\n**QA - Long-form QA:**\n- ELI5: 5 methods\n- NarrativeQA (NQA): 4 methods\n- ASQA: 2 methods\n- QMSum (QM): 2 methods\n\n**QA - Domain QA:**\n- Qasper: 2 methods\n- COVID-QA: 2 methods\n- CMB, MMCU_Medical: 1 method\n\n**QA - Multi-Choice QA:**\n- QuALITY: 2 methods\n- ARC: 2 methods\n- CommonsenseQA: 2 methods\n\n**QA - Graph QA:**\n- GraphQA: 1 method\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): 4 methods\n- KBP: 2 methods\n- DuleMon: 1 method\n- CamRest: 2 methods\n- Amazon (Toys, Sport, Beauty): 2 methods\n\n**IE:**\n- WikiEvent: 4 methods\n- RAMS: 2 methods\n- T-REx, ZsRE: 2 methods\n\n**Reasoning:**\n- HellaSwag: 2 methods\n- CoT Reasoning: 1 method\n- CSQA: 1 method\n\n**Others:**\n- MMLU: 7 methods\n- WikiText-103: 4 methods\n-", "citations": [], "input_tokens": 37499, "output_tokens": 512, "cost_micros": 120177, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table II and identify datasets with exactly three methods.Let me search for \"TABLE II\" in the document:Now let me read the section containing TABLE II:Now I can see Table II. Let me analyze it carefully to find datasets with exactly three methods. I need to count the number of method references for each dataset. Let me go through the table systematically:\n\nLooking at the table structure, I can see:\n- PopQA [116]: [7], [25], [67] - exactly 3 methods\n- MS MARCO [117]: [4], [40], [52] - exactly 3 methods\n- PopQA has methods: [7], [25], [67] - that's 3 methods\n- MS MARCO has methods: [4], [40], [52] - that's 3 methods\n- SST-2 [156]: [20], [33], [38] - exactly 3 methods\n\nLet me verify by checking each row more carefully:\n\n1. PopQA [116]: [7], [25], [67] = 3 methods\n2. MS MARCO [117]: [4], [40], [52] = 3 methods \n3. QMSum(QM) [125]: [60], [123] = 2 methods\n4. Qasper [126]: [60], [63] = 2 methods\n5. COVID-QA [127]: [35], [46] = 2 methods\n6. QUALITY [130]: [60], [63] = 2 methods\n7. ARC [131]: [25], [67] = 2 methods\n8. CommonsenseQA [132]: [58], [66] = 2 methods\n9. GraphQA [84]: [84] = 1 method\n10. KBP [134]: [74], [135] = 2 methods\n11. DuleMon [136]: [74] = 1 method\n12. CamRest [137]: [78], [79] = 2 methods\n13. Amazon(Toys,Sport,Beauty) [138]: [39], [40] = 2 methods\n14. RAMS [140]: [36], [37] = 2 methods\n15. T-REX [141],ZsRE [142]: [27], [51] = 2 methods\n16. HellaSwag [143]: [20], [66] = 2 methods\n17. CoT Reasoning [144]: [27] = 1 method\n18. CSQA [145]: [55] = 1 method\n19. PubHealth [150]: [25], [67] = 2 methods\n20. Biography [151]: [67] = 1 method\n21. WikiASP [152]: [24] = 1 method\n22. XSum [153]: [17] = 1 method\n23. VioLens [154]: [19] = 1 method\n24. TREC [155]: [33] = 1 method\n25. SST-2 [156]: [20], [33], [38] = 3 methods\n26. CodeSearchNet [157]: [76] = 1 method\n27. NoMIRACL [56]: [56] = 1 method\n28. GSM8K [158]: [73] = 1 method\n29. JRC-Acquis [159]: [17] = 1 method\n\nAnswer: PopQA, MS MARCO, SST-2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42953, "error": null, "extra": {"thread_id": 2045, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ea697b93518746f587b9ed16f4f922df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 257, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "ms marco, popqa, sst 2", "normalised_gold": "ms marco, popqa, sst 2"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 10 where Section V \"AUGMENTATION PROCESS IN RAG\" begins, I can see the subsections listed:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 64634, "output_tokens": 83, "cost_micros": 195147, "latency_ms": 20108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the section titled \"AUGMENTATION PROCESS IN RAG\" and identify its subsections.\n\nFrom Section V (AUGMENTATION PROCESS IN RAG), I can see the following structure:\n\nV. AUGMENTATION PROCESS IN RAG\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nThese are the three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 33377, "output_tokens": 127, "cost_micros": 102036, "latency_ms": 4500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document, I can identify the subsections by examining the structure.\n\nThe section starts on page 10 and includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval \nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 40684, "output_tokens": 95, "cost_micros": 123477, "latency_ms": 4765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for the section \"AUGMENTATION PROCESS IN RAG\" (Section V), I can identify its subsections:\n\nFrom the document structure, Section V contains:\n- A. Iterative Retrieval\n- B. Recursive Retrieval \n- C. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 30920, "output_tokens": 91, "cost_micros": 94125, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document:\n\nThe section includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 37502, "output_tokens": 76, "cost_micros": 113646, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the \"AUGMENTATION PROCESS IN RAG\" section and identify its subsections.Let me search for the \"AUGMENTATION PROCESS IN RAG\" section in the document.Now let me read the section around line 2636 to see the subsections.Let me continue reading to see all subsections under \"AUGMENTATION PROCESS IN RAG\".Perfect! I can now see the structure of the \"AUGMENTATION PROCESS IN RAG\" section. It has three subsections:\n\n1. A. Iterative Retrieval (line 2646)\n2. B. Recursive Retrieval (line 2827)\n3. C. Adaptive Retrieval (line 2858)\n\nThe next section \"VI. TASK AND EVALUATION\" starts at line 2908, confirming that these are the only three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34782, "error": null, "extra": {"thread_id": 2046, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dfd2aaa1a1dd45f1bdec0523c2899c57", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 177, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I need to search through the document for information about methods that integrate knowledge into white-box models via directive fine-tuning.\n\nLooking through the document, I found relevant information on page 10 (document page 9-10) in section III. RETRIEVAL, subsection E. Adapter:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text explicitly states that PKG (which likely stands for \"Parametric Knowledge Guiding\" based on reference [75] mentioned in the text) introduces this method.\n\nAnswer: PKG", "citations": [], "input_tokens": 64627, "output_tokens": 158, "cost_micros": 196251, "latency_ms": 20969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 33370, "output_tokens": 94, "cost_micros": 101520, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a method that integrates knowledge into white-box models via directive fine-tuning.\n\nIn Section III.E (Adapter), I can find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text clearly states that PKG introduces this method for integrating knowledge into white-box models via directive fine-tuning.\n\nAnswer: PKG", "citations": [], "input_tokens": 40677, "output_tokens": 130, "cost_micros": 123981, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nThe document discusses RA-DIT (Retrieval-Augmented Dual Instruction Tuning) in reference [27], which is described as \"retrieval-augmented dual instruction tuning.\" This method involves fine-tuning both the retriever and generator components.\n\nIn Section IV.B on LLM Fine-tuning, the document states: \"Fine-tuning of LLM can also be coordinated with fine-tuning of the retriever to align preferences. A typical approach, such as RA-DIT [27], aligns the scoring functions between Retriever and Generator using KL divergence.\"\n\nThis method specifically focuses on instruction tuning (a form of directive fine-tuning) to integrate retrieval-augmented knowledge into language models.\n\nAnswer: RA-DIT", "citations": [], "input_tokens": 30913, "output_tokens": 200, "cost_micros": 95739, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ra dit", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about integrating knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 37495, "output_tokens": 95, "cost_micros": 113910, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about which method integrates knowledge into white-box models via directive fine-tuning.Let me search for content about \"directive fine-tuning\" and \"white-box models\":Let me read more of the document to find tables or sections discussing methods:Let me search for tables that compare methods:Let me read around the tables to find information about methods:Let me search for \"Table I\" to find the table that might contain method comparisons:This table shows methods but doesn't mention white-box or directive fine-tuning. Let me search more specifically:Let me search for a combination of terms:Let me look for \"fine-tuning\" methods and check around line 2053 which mentions modular RAG integration with fine-tuning:Let me look for sections on fine-tuning methods:Found it! Let me read around line 2526:Perfect! I found the answer. The text states \"PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\" Let me verify this is PKG by reading a bit more context:Perfect! The answer is clear. The document states: \"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\"\n\nAnswer: PKG", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 72530, "error": null, "extra": {"thread_id": 2047, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9ee1ee526a9645f5ba543d1cda3a3a6d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 4 to identify which nodes RAPTOR retrieved for both questions about the Cinderella story.\n\nFrom the document, Figure 4 shows the querying process for two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nLooking at Figure 4 on page 7, I can see:\n- Nodes highlighted indicate RAPTOR's selections\n- The figure shows a tree structure with nodes numbered 0-15 at the leaf layer, nodes 16-22 at an intermediate layer, and nodes 23-26 at the root layer\n\nFor Question 1 (central theme), RAPTOR retrieved nodes that are highlighted in one color.\nFor Question 2 (happy ending), RAPTOR retrieved nodes that are highlighted in another color.\n\nThe nodes that overlap (retrieved for BOTH questions) would be those highlighted with both colors.\n\nFrom the visual representation in Figure 4, I can see that certain nodes appear to be selected for both queries. Looking at the highlighted patterns, the nodes that appear to be retrieved for both questions are nodes 18 and 19 (visible in the intermediate layer where both highlighting patterns overlap).\n\nAnswer: 18, 19", "citations": [], "input_tokens": 58460, "output_tokens": 279, "cost_micros": 179565, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "18, 19", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are highlighted (retrieved by RAPTOR) for both questions about Cinderella.\n\nThe figure shows two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nAccording to the figure caption: \"Highlighted nodes indicate RAPTOR's selections, while arrows point to DPR's leaf nodes.\"\n\nExamining the visual representation in Figure 4, I can see the tree structure with nodes numbered 0-26. For nodes to be retrieved for both questions, they must be highlighted for both question 1 and question 2.\n\nLooking at the overlap between the two retrievals shown in the figure, the nodes that appear to be selected for both questions are those at the higher levels of the tree that contain summary information relevant to both the central theme and the happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved for both questions appear to be from the upper layers of the tree.\n\nAnswer: 23, 24, 25, 26", "citations": [], "input_tokens": 22903, "output_tokens": 241, "cost_micros": 72324, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "23, 24, 25, 26", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4, I need to identify which nodes are retrieved by RAPTOR for both questions about Cinderella.\n\nThe legend indicates:\n- Nodes retrieved for Question 1 (central theme) are shown in one color\n- Nodes retrieved for Question 2 (happy ending) are shown in another color\n- Nodes retrieved for BOTH questions would be those highlighted with both colors\n\nFrom the figure, examining the tree structure and the highlighted nodes, the nodes that appear to be retrieved for both questions are the nodes that contain overlapping/comprehensive information about the story.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions are:\n\nAnswer: 16, 17, 18", "citations": [], "input_tokens": 27206, "output_tokens": 161, "cost_micros": 84033, "latency_ms": 5530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "list_set", "normalised_pred": "16, 17, 18", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes RAPTOR retrieves for both Question 1 (\"What is the central theme of the story?\") and Question 2 (\"How did Cinderella find a happy ending?\").\n\nFrom the figure description and the illustration:\n- For Question 1, RAPTOR retrieves certain highlighted nodes\n- For Question 2, RAPTOR retrieves certain highlighted nodes\n\nThe nodes that appear to be retrieved for BOTH questions (nodes that would be highlighted for both questions) are the nodes that contain information relevant to both the central theme and Cinderella's happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions appear to be nodes 9 and 10 (which are the upper-level summary nodes that contain broader thematic information relevant to both questions).\n\nAnswer: Node 9, Node 10", "citations": [], "input_tokens": 23495, "output_tokens": 203, "cost_micros": 73530, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "node 10, node 9", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are retrieved by RAPTOR for both questions.\n\nThe legend indicates:\n- Orange nodes/arrows: RAPTOR retrieved for Question 1 (\"What is the central theme of the story?\")\n- Purple nodes/arrows: RAPTOR retrieved for Question 2 (\"How did Cinderella find a happy ending?\")\n\nFrom the tree diagram, I can see:\n- Question 1 (orange): Node 25 and Node 19 are highlighted\n- Question 2 (purple): Node 17 and Node 20 are highlighted\n\nLooking for nodes that appear in both colors (retrieved for both questions), I need to check if any nodes have both orange and purple highlighting. However, from the visual representation, each highlighted node appears to be associated with only one question - the nodes are distinct between the two questions.\n\nUpon careful examination of Figure 4, there don't appear to be any nodes that are highlighted in both colors, meaning no nodes are retrieved by RAPTOR for both questions simultaneously.\n\nAnswer: None", "citations": [], "input_tokens": 26136, "output_tokens": 239, "cost_micros": 81993, "latency_ms": 7280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which nodes are retrieved by RAPTOR for both questions.Let me search for Figure 4 in the document:Now let me read the specific section containing Figure 4 from the target document:Now let me look at the actual figure content. I need to read more lines to see the figure structure:I can see the figure caption clearly states the two questions. Now I need to look at the actual tree diagram to see which nodes are highlighted for both questions. Let me check if there's more detail in the figure or if I need to examine it more carefully. Let me search for more context about the nodes:Let me look at the image content more carefully. Let me read around line 863 where the OCR text shows the nodes:Perfect! Now I can see the key information. Looking at the visual description:\n\n- **Orange/peach borders** (RAPTOR for Question 1): Nodes 15, 11, 18\n- **Purple/magenta borders** (RAPTOR for Question 2): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe nodes that appear in BOTH lists (retrieved by RAPTOR for both questions) is: **Node 11**\n\nHowever, let me also check the arrows to be thorough:Based on the visual description of Figure 4:\n\n**Question 1** (orange/peach): Nodes 15, 11, 18\n**Question 2** (purple/magenta): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe node that appears in both lists is **11**.\n\nAdditionally, looking at the arrows (which point to DPR's retrievals), nodes 3 and 11 have both orange and purple arrows, but the question asks specifically about which nodes are retrieved by RAPTOR (indicated by the border colors), not DPR (indicated by the arrows).\n\nAnswer: 11", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61625, "error": null, "extra": {"thread_id": 2048, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_99be096db80e4817854f19cccddee98b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 378, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "11", "normalised_gold": "16, 19, 25"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 6 of the document, I can identify the axes of the graph.\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis is labeled \"F1\" (which represents the F1 score metric).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 58457, "output_tokens": 109, "cost_micros": 177006, "latency_ms": 33343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which is titled \"Comparison of querying methods\":\n\nThe horizontal axis is labeled \"Context Length\" (measuring from 500 to 2500).\n\nThe vertical axis is labeled \"F1\" (measuring from approximately 40 to 55).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 22900, "output_tokens": 74, "cost_micros": 69810, "latency_ms": 4109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis shows the performance metric (appears to be accuracy or F1 score percentage based on the scale showing values like 45, 80, etc.).\n\nAnswer: Context Length, Performance Score (F1/Accuracy)", "citations": [], "input_tokens": 27203, "output_tokens": 116, "cost_micros": 83349, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance score f1 accuracy", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, I need to identify the horizontal and vertical axes.\n\nFrom the figure caption and the graph itself:\n- The horizontal axis shows \"Context Length\" (measured in tokens, ranging from 500 to 2500)\n- The vertical axis shows the performance metric (which appears to be accuracy or F1 score, ranging from approximately 50 to 58)\n\nAnswer: Context Length, Performance metric (accuracy/F1 score)", "citations": [], "input_tokens": 23492, "output_tokens": 105, "cost_micros": 72051, "latency_ms": 5092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance metric accuracy f1 score", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods\" with results on 20 stories from the QASPER dataset.\n\nFrom the table in Figure 3:\n- The horizontal axis shows \"Context Length\" (measured in tokens: 500, 1000, 1250, 1500, 1750, 2000, 2500)\n- The vertical axis shows \"F1\" (performance metric)\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 26133, "output_tokens": 113, "cost_micros": 80094, "latency_ms": 4717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify its axes.Let me search for \"Figure 3\" in the document.Now let me search for the figure content with context:Let me read the section around line 786 to see the full figure details:Let me continue reading to see the complete axis information:This appears to be describing a different figure. Let me look more carefully at the figure data around line 826:Based on the figure caption and the data I see, I can see \"Context Length\" is mentioned at line 826. However, I need to find what the vertical axis represents. Let me search around the figure more carefully. The numbers like 45, 5, 7, 3, 4, 80 seem to be data values. Let me look for axis labels by reading the embedded image description or OCR text more carefully:Let me search for any text that might indicate what metric is being measured (the vertical axis). Let me look more broadly around Figure 3:I can see \"Context Length\" appears at line 826, which is likely the horizontal axis. The text mentions \"Figure 3 shows the performance\" but I need to find what specific performance metric. Let me search for what metric is being used in QASPER:Great! I found it. Let me read around line 712 to confirm this is for Figure 3:Perfect! I found the information. Looking at lines 693, 704, 711-712:\n- Line 693: \"F1\"\n- Line 704: \"Context Length\"\n- Line 711: \"X-axis shows context length\"\n- Line 712: \"Y-axis shows F1 score\"\n\nSo for Figure 3:\n- Horizontal axis (X-axis) = Context Length\n- Vertical axis (Y-axis) = F1\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 67266, "error": null, "extra": {"thread_id": 2049, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3509043a1828411e846352ac4e585d70", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 401, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines in each algorithm.\n\nLooking at Appendix F on page 18, I can see:\n\n**Algorithm 1: Tree Traversal Algorithm**\nLet me count the lines of code (excluding comments and function declarations):\n- Line 1: Scurrent \u2190 tree.layer[0]\n- Line 2: for layer in range(tree.num layers) do\n- Line 3: topk \u2190 []\n- Line 4: for node in Scurrent do\n- Line 5: score \u2190 dot product(query, node)\n- Line 6: top k.append((node, score))\n- Line 7: end for\n- Line 8: Slayer \u2190 sorted(top k)[:k].nodes\n- Line 9: Scurrent \u2190 Slayer\n- Line 10: end for\n- Line 11: return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\n\nThat's approximately 11 lines of code.\n\n**Algorithm 2: Collapsed Tree Algorithm** (page 19)\nLet me count the lines:\n- Line 1: tree \u2190 flatten(tree)\n- Line 2: top nodes \u2190 []\n- Line 3: for node in tree do\n- Line 4: top nodes.append((node, dot product(query, node))\n- Line 5: end for\n- Line 6: top nodes \u2190 sorted(top nodes)\n- Line 7: result \u2190 []\n- Line 8: total tokens \u2190 0\n- Line 9: for node in top nodes do\n- Line 10: if total tokens + node.token size < max tokens then\n- Line 11: result.append(node)\n- Line 12: end if\n- Line 13: total tokens \u2190 total tokens + node.token size\n- Line 14: end for\n- Line 15: return result\n\nThat's approximately 15 lines of code.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 58465, "output_tokens": 483, "cost_micros": 182640, "latency_ms": 36671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode section in Appendix F:\n\n**Algorithm 1: Tree Traversal Algorithm**\n- Lines: function declaration, initialization (Scurrent), for loop, topk initialization, nested for loop, score calculation, top_k.append, end for, Slayer assignment, Scurrent assignment, end for, return statement, end function\n- Total: **13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm**\n- Lines: function declaration, tree flatten, top_nodes initialization, for loop, top_nodes.append, end for, top_nodes sort, result initialization, total_tokens initialization, for loop, if statement, result.append, end if, total_tokens update, end for, return statement, end function\n- Total: **17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 22908, "output_tokens": 184, "cost_micros": 71484, "latency_ms": 5922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "arm": "azure_premium_lc", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:59Z", "latency_ms": 7156, "error": null, "raw_text_chars": 752}]}, "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Appendix F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 Tree Traversal Algorithm:**\n- Lines: function declaration, initialization of Scurrent, for layer loop, initialization of topk, for node loop, score calculation, append to top_k, end for, Slayer assignment, Scurrent update, end for, return statement, end function\n- Total: 13 lines\n\n**Algorithm 2 Collapsed Tree Algorithm:**\n- Lines: function declaration, flatten tree, initialization of top_nodes, for node loop, append with dot product, end for, sort top_nodes, result initialization, total_tokens initialization, for node loop, if condition, result append, end if, total_tokens update, end for, return statement, end function\n- Total: 16 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 27211, "output_tokens": 190, "cost_micros": 84483, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Appendix F:\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines 1-13 (including function definition, loops, operations, and end statements)\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines 1-17 (including function definition, loops, operations, and end statements)\n\nCounting all lines including the function declarations, loop structures, operations, and end statements, Algorithm 2 (Collapsed Tree Algorithm) has more lines.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 23500, "output_tokens": 124, "cost_micros": 72360, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Section F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines include: function declaration, variable initialization, for loop with nested for loop, operations inside loops, and return statement\n- Counting all lines: approximately 13 lines\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines include: function declaration, tree flattening, initialization, for loop for scoring, sorting, result initialization, token counter, for loop with conditional, and return statement\n- Counting all lines: approximately 15 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 26141, "output_tokens": 143, "cost_micros": 80568, "latency_ms": 6157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines for each algorithm.Let me search for the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section:Now let me search for more specific context around pseudocode:Let me read the section with the algorithms:Perfect! I can see both algorithms. Let me count the lines:\n\n**Algorithm 1: Tree Traversal Algorithm** (lines 2344-2356):\n- Line 2344: function TRAVERSETREE(tree, query, k)\n- Line 2345: Scurrent + tree.layer[0]\n- Line 2346: for layer in range(tree.num_layers) do\n- Line 2347: topk + []\n- Line 2348: for node in Scurrent do\n- Line 2349: score + dot_product(query, node)\n- Line 2350: top_k.append((node, score))\n- Line 2351: end for\n- Line 2352: Slayer + sorted(top_k)[:k].nodes\n- Line 2353: Scurrent + Slayer\n- Line 2354: end for\n- Line 2355: return So U S1 U S2 U . . . USk\n- Line 2356: end function\n\n**Total: 13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm** (lines 2375-2391):\n- Line 2375: function COLLAPSEDTREE(tree, query, k, max_tokens)\n- Line 2376: tree \u2039- flatten(tree)\n- Line 2377: top_nodes <- []\n- Line 2378: for node in tree do\n- Line 2379: top_nodes.append((node, dot_product(query, node))\n- Line 2380: end for\n- Line 2381: top_nodes \u2039- sorted(top_nodes)\n- Line 2382: result +- []\n- Line 2383: total_tokens <- 0\n- Line 2384: for node in top nodes do\n- Line 2385: if total_tokens + node.token_size < max_tokens then\n- Line 2386: result.append(node)\n- Line 2387: end if\n- Line 2388: total_tokens \u2039- total_tokens + node.token_size\n- Line 2389: end for\n- Line 2390: return result\n- Line 2391: end function\n\n**Total: 17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33921, "error": null, "extra": {"thread_id": 2050, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8432e22fe2074cf88b3f3fbd3ebd2c32", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1 on page 2, which shows the \"Formation of one tree layer\" process:\n\nThe figure shows the clustering step (labeled \"1. Clustering\") where text chunks numbered 1-5 are grouped. The diagram illustrates how nodes are clustered together, with some nodes appearing to be part of multiple clusters during the soft clustering process.\n\nIn the clustering visualization, I can see that:\n- The nodes are represented as numbered boxes (1, 2, 3, 4, 5)\n- Some nodes appear in multiple cluster groupings\n- Looking at the color coding in the diagram, nodes that belong to multiple clusters appear to be shown with a pink/red outline or highlighting\n\nExamining the \"Contents of a node\" box on the right side and the clustering diagram in the middle, nodes that appear in multiple clusters are highlighted in pink/red color.\n\nAnswer: pink, red", "citations": [], "input_tokens": 58465, "output_tokens": 221, "cost_micros": 178710, "latency_ms": 37828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink, red", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Tree construction process\" and \"Formation of one tree layer\":\n\nThe figure shows:\n- Text chunks numbered 1-5 at the bottom (leaf layer)\n- A clustering process in the middle showing how these chunks are grouped\n- The tree structure on the right\n\nIn the \"Formation of one tree layer\" section, I can see:\n- The clustering diagram shows nodes being grouped together\n- Looking at the clustering visualization, I need to identify which numbered nodes appear in multiple clusters\n\nExamining the clustering section carefully:\n- Node 1 appears to be in one cluster\n- Node 2 appears in multiple positions\n- Node 3 appears in multiple positions \n- Node 4 appears to be in one cluster\n- Node 5 appears in multiple positions\n\nLooking at the colors used in the figure:\n- The nodes that appear in multiple clusters appear to be colored in shades that look like they could be blue, green, or orange/yellow tones in the clustering diagram.\n\nHowever, upon closer examination of Figure 1, the nodes shown in the \"1. Clustering\" section use different colors. The nodes that appear in overlapping or multiple cluster regions appear to use colors like blue and green based on the grayscale representation in the figure.\n\nLooking more carefully at the specific nodes (2, 3, 5) that appear in the clustering overlap, they appear to use blue and green colors.\n\nAnswer: blue, green", "citations": [], "input_tokens": 22908, "output_tokens": 347, "cost_micros": 73929, "latency_ms": 10734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "blue, green", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify nodes that appear in multiple clusters during the clustering step.\n\nIn the \"Formation of one tree layer\" section of Figure 1, I can see the clustering process where nodes 1-5 are being grouped. \n\nExamining the clusters:\n- One cluster contains nodes 2 and 3\n- Another cluster contains nodes 4 and 5\n\nLooking at the visual representation, the nodes that appear in the clusters are shown with different colors. The nodes that would appear in multiple clusters (if any existed) would need to be identified by their color.\n\nHowever, from the figure, each node (1-5) appears to belong to only one cluster. Node 3 appears in the cluster with node 2, and node 5 appears in the cluster with node 4. None of the nodes are shown appearing in more than one cluster simultaneously.\n\nUpon careful examination of the figure, there don't appear to be any nodes shown in multiple clusters at the same time - the soft clustering concept is mentioned in the text but not visually demonstrated in this particular figure with overlapping memberships.\n\nAnswer: None", "citations": [], "input_tokens": 27211, "output_tokens": 246, "cost_micros": 85323, "latency_ms": 7733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 carefully to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Formation of one tree layer\" section:\n\nThe figure shows a clustering process where text chunks (numbered 1-5 at the bottom) are being clustered. \n\nIn the clustering visualization, I can see:\n- Text chunks numbered 1, 2, 3, 4, 5 at the bottom\n- The chunks are shown being grouped into clusters\n- After clustering (step 1), chunks 3 and 5 appear to be in multiple clusters based on the overlapping circles/groupings shown\n\nLooking at the visual representation, the nodes that appear in more than one cluster are shown in a yellow/gold color, while nodes that appear in only one cluster are shown in blue.\n\nSpecifically, nodes 3 and 5 appear in overlapping cluster regions and are colored yellow, indicating they belong to multiple clusters (soft clustering).\n\nAnswer: yellow", "citations": [], "input_tokens": 23500, "output_tokens": 224, "cost_micros": 73860, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "yellow", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to examine the tree structure diagram to identify nodes that appear in multiple clusters.\n\nFrom the diagram showing \"RAPTOR Tree\" and \"Formation of one tree layer\":\n\nThe diagram shows:\n- Text chunks labeled 1, 2, 3, 4, 5 at the bottom (leaf nodes)\n- These are clustered into groups\n- Summary nodes 6, 7, 8 are created from clusters\n- Higher level nodes 9, 10 are created from further clustering\n\nExamining the tree structure:\n- Node 6 is connected to nodes 1 and 2\n- Node 7 is connected to node 3\n- Node 8 is connected to nodes 4 and 5\n- Node 9 is connected to nodes 6 and 7\n- Node 10 is connected to node 8\n\nLooking at the clustering process description, the figure shows standard hierarchical clustering where each node belongs to one cluster at each level. The nodes are represented in the diagram without specific color coding mentioned in the text, and the diagram appears to be a standard tree structure where nodes don't appear in multiple clusters simultaneously.\n\nHowever, based on the visual representation in the document, the nodes appear to be shown in a neutral/default color (likely gray or black outlines) in the standard tree diagram format.\n\nAnswer: The figure does not show nodes in multiple clusters with distinct colors", "citations": [], "input_tokens": 26141, "output_tokens": 317, "cost_micros": 83178, "latency_ms": 9719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure does not show nodes in multiple clusters with distinct colors", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify the colors of nodes that appear in more than one cluster.Let me continue reading to see the full description of Figure 1:Based on my reading of the document, I can see the description of Figure 1. Let me look more carefully at the visual description section that describes the tree construction process and the colors of nodes. Looking at lines 426-456, I found the relevant information:\n\nIn the visual description of Figure 1:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nThe description states that in the tree traversal diagram:\n- \"Purple/lavender nodes at leaf positions\"\n- \"Pink nodes appear at internal/decision points\"\n\nAnd in the Part B (Collapsed Tree Retrieval) section, it mentions:\n- \"Mix of purple and pink boxes in linear sequence\"\n\nFrom the description of the tree formation process in lines 305-385, it shows nodes being clustered together, and the color coding explanation in lines 447-453 clearly indicates:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nSince the question asks about nodes that appear in more than one cluster, and based on the visual description which shows that pink nodes appear at internal/decision points while purple/lavender nodes are at leaf positions, the nodes that would appear in multiple clusters (as parent nodes summarizing multiple child clusters) would be the **pink** nodes.\n\nHowever, let me verify this interpretation by checking if there's more specific information about which colored nodes appear in multiple clusters in the demonstration.\n\nLooking at the figure caption (lines 302-303): \"Nodes clustered together are siblings; a parent node contains the text summary of that cluster.\"\n\nThis indicates that parent nodes (which would be pink based on the color coding) are the ones that span multiple clusters by summarizing them.\n\nAnswer: pink", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36328, "error": null, "extra": {"thread_id": 2051, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_50d7b2d924b34b7ab5e0bc22ccd0b3d6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 237, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink", "normalised_gold": "green, yellow"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the clustering algorithm used in this paper and what challenges it faces.\n\nFrom page 3-4, I can find the relevant information about the clustering algorithm:\n\nThe paper states: \"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nRegarding the challenge, it states: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces (Aggarwal et al., 2001).\"\n\nTo address this challenge, the paper mentions: \"To mitigate this, we employ Uniform Manifold Approximation and Projection (UMAP), a manifold learning technique for dimensionality reduction (McInnes et al., 2018).\"\n\nAnswer: Gaussian Mixture Models (GMMs), high dimensionality of vector embeddings", "citations": [], "input_tokens": 58463, "output_tokens": 208, "cost_micros": 178509, "latency_ms": 33342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "gaussian mixture models gmms, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll search through the document for information about the clustering algorithm and its challenges.\n\nLooking at Section 3 \"METHODS\", specifically the \"Clustering Algorithm\" subsection:\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nAnd regarding the challenge:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 22906, "output_tokens": 134, "cost_micros": 70728, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", the document states:\n\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nIt then discusses the challenge:\n\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 27209, "output_tokens": 120, "cost_micros": 83427, "latency_ms": 5312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", I can find the relevant information:\n\nThe clustering algorithm is based on **Gaussian Mixture Models (GMMs)**.\n\nThe challenge presented to it is stated as: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces.\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 23498, "output_tokens": 114, "cost_micros": 72204, "latency_ms": 4781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the clustering algorithm and what presents a challenge to it.\n\nFrom Section 3 \"METHODS\" under \"Clustering Algorithm\":\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nThen it mentions:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 26139, "output_tokens": 134, "cost_micros": 80427, "latency_ms": 6639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the clustering algorithm, what model it's based on, and what presents a challenge to it.Let me search for information about the clustering algorithm and its base model:Perfect! I found the information about the clustering algorithm. Let me verify by reading a bit more to ensure I have the complete answer:Based on the document, I have found the answer to the question:\n\n1. The clustering algorithm is based on **Gaussian Mixture Models (GMMs)**\n2. The challenge to it is **the high dimensionality of vector embeddings** (as stated: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\")\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34000, "error": null, "extra": {"thread_id": 2052, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6459e8a5fde745a3b9337a10119c8000", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 161, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the pseudocode for the tree traversal method in the appendix and identify step 5.\n\nLooking at Appendix F, I can see Algorithm 1 which is the Tree Traversal Algorithm.\n\nLet me examine the steps in the algorithm:\n\n```\nfunction TRAVERSETREE(tree, query, k)\n Scurrent \u2190 tree.layer[0]\n for layer in range(tree.num layers) do\n topk \u2190 []\n for node in Scurrent do\n score \u2190 dot product(query, node)\n top k.append((node, score))\n end for\n Slayer \u2190 sorted(top k)[:k].nodes\n Scurrent \u2190 Slayer\n end for\n return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\nend function\n```\n\nCounting the steps:\n1. `Scurrent \u2190 tree.layer[0]`\n2. `for layer in range(tree.num layers) do`\n3. `topk \u2190 []`\n4. `for node in Scurrent do`\n5. `score \u2190 dot product(query, node)`\n\nStep 5 is: `score \u2190 dot product(query, node)`\n\nAnswer: score \u2190 dot product(query, node)", "citations": [], "input_tokens": 58458, "output_tokens": 318, "cost_micros": 180144, "latency_ms": 33764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.15384615384615383, "method": "str_norm", "normalised_pred": "score \u2190 dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method steps in Section 3 of the document, step 5 states:\n\"5. Continue this process recursively for d layers, producing sets S1, S2, . .. , Sd.\"\n\nNow checking Algorithm 1 (Tree Traversal Algorithm) in Appendix F, the line that corresponds to this step is:\n\n```\nfor layer in range(tree.num_layers) do\n```\n\nThis is the loop that continues the process recursively through the layers.\n\nAnswer: for layer in range(tree.num_layers) do", "citations": [], "input_tokens": 22901, "output_tokens": 129, "cost_micros": 70638, "latency_ms": 5359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, in Section 3 under \"Querying\", step 5 of the tree traversal method states:\n\n\"5. Continue this process recursively for d layers, producing sets S1, S2, ... , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. Step 5 corresponds to the for loop that iterates through layers:\n\n```\nfor layer in range(tree.num_layers) do\n topk + []\n for node in Scurrent do\n score + dot_product(query, node)\n top_k.append((node, score))\n end for\n Slayer + sorted(top_k)[:k].nodes\n Scurrent + Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 27204, "output_tokens": 200, "cost_micros": 84612, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (Querying section), step 5 states:\n\n\"5. Continue this process recursively for d layers, producing sets S\u2081, S\u2082, . . . , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. The code that corresponds to step 5 is the for loop that iterates through the layers:\n\n```\nfor layer in range(tree.num_layers) do\n top\u2096 \u2190 []\n for node in Scurrent do\n score \u2190 dot_product(query, node)\n top_k.append((node, score))\n end for\n Slayer \u2190 sorted(top_k)[:k].nodes\n Scurrent \u2190 Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 23493, "output_tokens": 212, "cost_micros": 73659, "latency_ms": 7062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (page 4-5), step 5 states:\n\n\"5. Continue this process recursively for $d$ layers, producing sets $S_1, S_2, \\dots, S_d$.\"\n\nNow looking at Algorithm 1 (Tree Traversal Algorithm) in Appendix F (page 18), the pseudo code line that corresponds to this step is:\n\n```\nS_current \u2190 S_layer\n```\n\nThis line updates the current set of nodes to continue the process to the next layer.\n\nAnswer: S_current \u2190 S_layer", "citations": [], "input_tokens": 26134, "output_tokens": 141, "cost_micros": 80517, "latency_ms": 5858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "s current \u2190 s layer", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the pseudocode in the appendix related to step 5 of the tree traversal method.Let me search for \"tree traversal\" and look at the appendix section to find the pseudocode.Now let me find the actual appendix section with pseudocode. Let me search for \"step 5\" or look at the end of the document where appendices typically are.Let me continue looking for the appendix sections. Let me search further in the document.Good, I found Appendix A. Let me continue searching for Appendix F which was mentioned to contain the pseudocode for both methods.Perfect! Found Appendix F. Let me read that section:Perfect! I found Algorithm 1 for Tree Traversal. Now let me look closely at the steps. The pseudocode shows:\n\n1. Line 2345: `Scurrent + tree.layer[0]` (Initialize current set)\n2. Line 2346: `for layer in range(tree.num_layers) do` (Loop through layers)\n3. Line 2347: `topk + []` (Initialize topk list)\n4. Line 2348: `for node in Scurrent do` (Loop through nodes)\n5. Line 2349: `score + dot_product(query, node)` (Calculate score)\n\nStep 5 corresponds to line 2349 which calculates the score using dot_product.\n\nAnswer: score + dot_product(query, node)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 60780, "error": null, "extra": {"thread_id": 2053, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cbc9bd5486c544c293802a0ecf0d5bc7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 329, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "score dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 18671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify which figures in the paper contain line plots.\n\nLet me go through the figures mentioned in the document:\n\n**Figure 1**: Shows the Chameleon architecture with image tokenizer, mixed-modal auto-regressive LM, and image de-tokenizer. This is a diagram, not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation. This is an example output, not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon. This is an example output, not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs. This is an example output, not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"output norm and training loss curves\" - this includes line plots\n- Figure 5b: \"An ablation with Chameleon-7B with and without QK-Norm\" - shows training loss, which is a line plot\n- Figure 5c: \"An ablation with Chameleon-7B with and without dropout\" - shows training loss, which is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Curves for 600k steps for Chameleon-7B and Chameleon-34B\" - this is a line plot\n- Figure 6b: Shows \"Training loss curve with image generation disabled\" - this is a line plot\n- Figure 6c: Shows training loss curves with and without norm-reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories. This appears to be text/table format, not line plots.\n\n**Figure 8**: Shows task categories and examples of prompts. This is not a line plot.\n\n**Figure 9**: Contains:\n- Figure 9a: Shows \"prompt task fulfillment rates\" as a horizontal bar chart\n- Figure 9b: Shows comparison results as a horizontal bar chart\n\n**Figure 10**: Shows \"inter-annotator agreement\" as a horizontal bar chart.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 25078, "output_tokens": 479, "cost_micros": 82419, "latency_ms": 11155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram of the Chameleon architecture with image and text tokenization flow - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"Output Norm\" vs \"Step\" - this is a line plot\n- Figure 5b: Shows \"Training Loss\" vs \"Step\" with and without QK-Norm - this is a line plot\n- Figure 5c: Shows \"Training Loss\" vs \"Step\" with and without dropout - this is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Loss\" vs \"Step\" for 7B and 34B models - this is a line plot\n- Figure 6b: Shows \"Training Loss\" vs \"Step\" for 7B without image generation - this is a line plot\n- Figure 6c: Shows \"Training Loss\" vs \"Step\" with and without norm reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts - not a line plot.\n\n**Figure 9**: Contains two subfigures:\n- Figure 9a: Shows a bar chart of task fulfillment rates - not a line plot\n- Figure 9b: Shows a horizontal bar chart comparing Chameleon vs baselines - not a line plot\n\n**Figure 10**: Shows a horizontal bar chart of inter-annotator agreement - not a line plot.\n\nAnswer: Figure 5a, Figure 5b, Figure 5c, Figure 6a, Figure 6b, Figure 6c", "citations": [], "input_tokens": 34447, "output_tokens": 457, "cost_micros": 110196, "latency_ms": 10469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure 5a, figure 5b, figure 5c, figure 6a, figure 6b, figure 6c", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the document that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows the Chameleon architecture diagram with tokens - not a line plot.\n\n**Figure 2**: Sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Sample generation from Chameleon with images - not a line plot.\n\n**Figure 4**: Sample Chameleon outputs with recipe - not a line plot.\n\n**Figure 5**: This figure has three subfigures:\n- (a) Shows \"Uncontrolled growth of output norms\" with what appears to be line plots showing training progress\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - appears to be line plots\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - appears to be line plots\n\n**Figure 6**: This figure has parts showing training curves:\n- (a) \"Training loss curve with image generation disabled\" - line plot\n- (b) Shows training for Chameleon-34B - line plots\n\nThe caption mentions \"Training loss curves for Chameleon models under various settings.\"\n\n**Figure 7**: Example alignment data - not line plots.\n\n**Figure 8**: Task categories and examples of prompts - not line plots.\n\n**Figure 9**: The text mentions Figure 9a and 9b for task fulfillment and win rates - likely bar charts or similar, need to check the context. The text describes percentages but doesn't clearly indicate these are line plots.\n\n**Figure 10**: \"The inter-annotator agreement on the questions in the absolute evaluation\" - context suggests this might be a bar chart or similar visualization.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 26087, "output_tokens": 410, "cost_micros": 84411, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram with flowcharts for pre-training and generation processes - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: This figure has three parts:\n- (a) Shows \"Output norm\" over \"Step\" - this is a line plot\n- (b) Shows training loss over steps with and without QK-Norm - this is a line plot\n- (c) Shows training loss over steps with and without dropout - this is a line plot\n\n**Figure 6**: This figure has three parts:\n- (a) Shows \"Training Curves\" with loss over steps - this is a line plot\n- (b) Shows \"Training loss curve\" - this is a line plot\n- (c) Shows training loss with different settings - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts in a table - not a line plot.\n\n**Figure 9**: Shows performance comparison tables - not line plots, these are tables.\n\n**Figure 10**: Shows inter-annotator agreement in a table format - not a line plot.\n\nSo the figures containing line plots are: Figure 5 (parts a, b, c) and Figure 6 (parts a, b, c).\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 32138, "output_tokens": 382, "cost_micros": 102144, "latency_ms": 8875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify which figures contain line plots.Let me search for more specific information about each figure and read the sections containing figure descriptions:Now let me look at Figure 5 more carefully and check other figures:Let me look at the beginning of Figure 5 to see the first part:Good! I can see Figure 5 has multiple line plots. Now let me check other figures. Let me search for more figure captions and descriptions:Let me look for Figure 7 and Figure 8:Let me continue reading Figure 8 and check if there are more figures:I can see Figure 9a is a bar chart. Let me continue examining more figures. Let me search for all figures systematically by searching for specific figure numbers:Now let me look at the actual content of each figure more carefully. Let me check Figure 1:Figure 1 appears to be a diagram/schematic, not a line plot. Let me check Figure 2 and 3:Figures 2 and 3 appear to be examples of text and image generation, not line plots. Let me check Figure 4:Figure 4 is also a sample output, not a line plot. Now let me look more carefully at Figure 10 which I saw earlier has a bar chart format:Figure 10 is a bar chart showing counts for different agreement levels. Let me check if there are any other figures I might have missed by searching for more figure references:Figure 8 shows task categories and examples (text-based). Now let me systematically review what I found:\n\nBased on my reading of the document:\n\n- **Figure 1**: Diagram/schematic showing the Chameleon architecture\n- **Figure 2**: Sample text and image generation (not a line plot)\n- **Figure 3**: Sample generation (not a line plot) \n- **Figure 4**: Sample outputs (not a line plot)\n- **Figure 5**: Contains THREE line plots:\n - (a) Output norm vs Step (line plot)\n - (b) Training Loss vs Step with/without QK-Norm (line plot)\n - (c) Training Loss vs Step with/without dropout (line plot)\n- **Figure 6**: Contains THREE line plots:\n - (a) Training Loss vs Step for 7B and 34B models (line plot)\n - (b) Training Loss vs Step without image generation (line plot)\n - (c) Training Loss vs Step with/without norm reordering (line plot)\n- **Figure 7**: Example alignment data for different categories (not a line plot)\n- **Figure 8**: Task categories and examples of prompts (not a line plot)\n- **Figure 9**: Contains bar charts showing performance comparison (not line plots)\n- **Figure 10**: Bar chart showing inter-annotator agreement (not a line plot)\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 104391, "error": null, "extra": {"thread_id": 2054, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3ce67a1a47334e71b23dc25fd26d8b74", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 654, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 25075, "output_tokens": 41, "cost_micros": 75840, "latency_ms": 3905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 34444, "output_tokens": 38, "cost_micros": 103902, "latency_ms": 3641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 26084, "output_tokens": 41, "cost_micros": 78867, "latency_ms": 3483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 and its caption, I can see that the figure shows Chameleon's architecture with different colored elements representing different types of tokens.\n\nThe caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 32135, "output_tokens": 62, "cost_micros": 97335, "latency_ms": 4108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Fig 1 and determine what the green squares denote.Let me search for \"Fig 1\" in the document to locate the figure and its caption.Let me search for \"Figure 1\" instead:Good, I found Figure 1. Now let me read the section around line 354 to see the figure and find information about green squares:Perfect! I found the answer. In line 355, the figure caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 31469, "error": null, "extra": {"thread_id": 2055, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_242072fc7b9a40f1ba1d0b58497f99ea", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 150, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in chat example figures, excluding the Appendix.\n\nLet me go through the document systematically:\n\n**Figure 2** (page 3): Sample interleaved image and text generation from Chameleon\n- Shows 3 images (Keel-Billed Toucan, Puffin, Golden Pheasant)\n\n**Figure 3** (page 4): Sample generation from Chameleon\n- Shows 2 images (the STOP sign input image and the generated chameleon image)\n\n**Figure 4** (page 5): Sample Chameleon outputs\n- Shows 2 images (bananas input image and the banana bread output image)\n\n**Figure 7** (page 10): Example alignment data for different categories\n- This shows examples of different data types:\n - (a) Text - no images\n - (b) Code - no images\n - (c) Image Generation - 1 image shown\n - (d) Visual Chat - 1 image shown (flower arrangement)\n - (e) Interleaved Text/Image Generation - 2 images shown (appears to be related to Gyumri/Armenian content)\n\n**Figure 8** (page 11): Task categories and examples of prompts\n- Shows 4 small example images in the prompt examples\n\nNow let me check the Appendix section (starts at page 23):\n- The Appendix section A \"Samples\" contains additional examples, but I should exclude these.\n\nTotal count (excluding Appendix):\n- Figure 2: 3 images\n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 4 images (1 + 1 + 2)\n- Figure 8: 4 images\n\nTotal: 3 + 2 + 2 + 4 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 25074, "output_tokens": 446, "cost_micros": 81912, "latency_ms": 11907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows 3 bird images (Keel-Billed Toucan, Puffin, Golden Pheasant)\nTotal: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows 1 stop sign image in the prompt\n- Shows 1 chameleon image in the response\nTotal: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows 1 banana image in the prompt\n- Shows 1 banana bread image in the response\nTotal: 2 images\n\nThe Appendix starts at page 23 with \"A Samples\". The figures after that are in the Appendix and should be excluded.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 34443, "output_tokens": 263, "cost_micros": 107274, "latency_ms": 7530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures/images used in the chat example figures in the main body of the document (excluding the Appendix).\n\nLet me go through the figures that show chat/conversation examples:\n\n1. **Figure 1**: Shows a recipe example with bananas image and banana bread image = 2 images\n\n2. **Figure 2**: \"Sample interleaved image and text generation from Chameleon\" - This shows a response about quirky-looking birds with 3 bird images (Keel-Billed Toucan, Puffin, and Golden Pheasant) = 3 images\n\n3. **Figure 3**: \"Sample generation from Chameleon\" - Shows a stop sign image and a chameleon image = 2 images\n\n4. **Figure 4**: \"Sample Chameleon outputs\" - Shows bananas image and banana bread image = 2 images\n\n5. **Figure 7**: \"Example alignment data for different categories\" - This shows multiple examples including:\n - Poem (no image)\n - Renaissance Square (1 image)\n - Code example (no image)\n - Gyumri text (no image)\n - Flower arrangement (1 image)\n = 2 images\n\n6. **Figure 8**: \"Task categories and examples of prompts\" - Shows 4 images in the examples (pastry, pony, construction site, and one more)\n\nWait, let me recount Figure 8 more carefully. Looking at the attributions at the bottom: \"Seguin (2010); Agriflanders (2009); Tuszy\u0144ski (2015); Sokolov (2022)\" - this indicates 4 images.\n\nTotal count:\n- Figure 1: 2 images\n- Figure 2: 3 images \n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 2 images\n- Figure 8: 4 images\n\nTotal = 2 + 3 + 2 + 2 + 2 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 26083, "output_tokens": 489, "cost_micros": 85584, "latency_ms": 11483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows a conversation about quirky birds\n- Contains 3 images: Keel-Billed Toucan, Puffin, and Golden Pheasant\n- Count: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows a conversation about a stop sign and chameleon camouflage\n- Contains 2 images: stop sign in wooded area, and a reddish-brown chameleon on tree branch\n- Count: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows a conversation about baking with bananas\n- Contains 2 images: bowl of sliced bananas, and a loaf of banana bread\n- Count: 2 images\n\nThe Appendix section starts at \"Appendix\" on page 23. The example in Section A (page 24) with the Mountain Cur dog and the pothos plant are in the Appendix, so I should not count those.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 32134, "output_tokens": 325, "cost_micros": 101277, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find chat example figures and count the pictures in them, excluding any in the Appendix.Let me search for figures more specifically:Now let me look for chat examples and appendix sections. Let me read through the document systematically:Let me read the figures mentioned (Figures 2-4) which appear to be the chat example figures, and check where the Appendix starts:Now let me check Figure 4:Let me go back and read Figure 2 more carefully to count the images:Now let me look at the beginning of Figure 2 to see all images:I see there's a mislabeled image at the beginning. Let me count more carefully. Looking at Figure 2, I can see references to:\n- Im3.png (Toucan)\n- Im4.png (Puffin) \n- Im5.png (Golden Pheasant mentioned with marker)\n\nLet me check Figure 3 now:Let me look for where Figure 3 starts:I need to see if there's another image in Figure 3. Let me check after line 747:Good! So I see Im7.png is a chameleon image. Let me check if this is part of Figure 3. Looking at the page break, Im7.png appears after the section break at line 756 where \"2.1 Tokenization\" starts. Let me verify where Figure 3 ends and Figure 4 starts:Perfect! Now I can see Figure 4 ends at line 922. Let me look for more images in Figure 4. The prompt at line 822 mentions an tag. Let me check if there's an image for that:Now let me verify where the Appendix starts to ensure I don't count figures from there:Good! The Appendix starts at line 3372. Now let me systematically count the images in Figures 2, 3, and 4.\n\nLet me go back and look at the full scope of each figure more carefully:\n\n**Figure 2** (lines 471-620):\n- Starts with prompt at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Im5.png - Golden Pheasant (line 641)\n- Figure 2 caption at line 619\n\nWait, Im5.png at line 641 seems to be after the Figure 2 caption. Let me re-check:I see! There's a STOP sign figure at line 633-637, and then Im5.png (the Golden Pheasant) at line 641. The STOP sign appears to be the beginning of Figure 3's prompt. Let me trace this more carefully:So the STOP sign at line 633-637 with Im5.png description is actually part of Figure 3's input (the in the prompt at line 675 refers to it). Let me reorganize:\n\n**Figure 2** (chat example about birds):\n- Prompt starts at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Golden Pheasant text mentions at line 614, but the actual image appears to be missing from Figure 2 content\n- Figure 2 caption at line 619-620\n\nWait, I need to look more carefully at the structure. The embedded images seem to be labeled differently. Let me look for all embedded images in the main body before the Appendix:Now let me determine which images belong to Figures 2, 3, and 4 by checking the line ranges:\n\n- Figure 2 caption: line 619\n- Figure 3 caption: line 701 \n- Figure 4 caption: line 922\n- Appendix starts: line 3372\n\nImages before Appendix:\n- Image4.jpg (line 407) - before Figure 2\n- Image5.jpg (line 480) - This is the mislabeled banana bread image, but checking context it's BEFORE the birds prompt starts at line 471\n- Im3.png (line 543) - in Figure 2 range\n- Im4.png (line 579) - in Figure 2 range\n- Im5.png (line 641) - between Figure 2 (ends 620) and Figure 3 (ends 701)\n- Im6.png (line 711) - between Figure 3 (ends 701) and section 2.1 starts\n- Im7.png (line 777) - after section 2.1 starts (line 756)\n- Im8.png (line 876) - in Figure 4 range (Figure 4 caption at 922)\n- Im9.png (line 991) - after Figure 4 ends\n\nLet me read around line 480 to verify Image5.jpg:This Image5.jpg (banana bread) is clearly mislabeled/misplaced content. The prompt at line 471 asks for birds, but Image5.jpg shows banana bread. This seems to be a document processing error. However, it appears WITHIN the Figure 2 example (after the prompt, before the response text starts at line 518).\n\nLet me check where Figure 2 actually starts and what the structure is:So Figure 2 starts at line 471. Let me now carefully count all images that appear in the three chat example figures (2, 3, 4) before the Appendix:\n\nLooking at my grep results and the line ranges:\n\n**Figure 2** (lines 471-620):\n- Image5.jpg (line 480) - appears after the prompt but seems misplaced\n- Im3.png (line 543) - Toucan\n- Im4.png (line 579) - Puffin\n\nHowever, Image5.jpg shows banana bread which doesn't match the birds prompt. Let me check if there's a third bird image I'm missing. The text mentions three birds (Toucan, Puffin, Golden Pheasant) and has markers. Let me look for where the third bird image might be:\n\nLooking at my earlier reading, Im5.png (line 641) shows a bird (Golden Pheasant based on the description at lines 649-668). The caption for Figure 2 is at line 619-620, so Im5.png at line 641 is AFTER the Figure 2 caption.\n\nLet me check if Im5.png is actually part of Figure 3:I see! Im5.png (the Golden Pheasant/colorful bird) appears at line 641, but the prompt for Figure 3 starts at line 675 which references \"\" - and that is referring to the STOP sign that appeared earlier. So Im5.png is actually the third bird from Figure 2, even though it appears after the Figure 2 caption.\n\nLooking back at the structure more carefully, the figure captions seem to come before all the images are shown. Let me look at where the STOP sign figure starts:\n\nFrom line 633-637, there's a figure with \"STOP\" as OCR text. This must be the input image for Figure 3.\n\nLet me now systematically list the images:\n\n**Before Figure 2 caption (line 619):**\n- Image5.jpg (line 480) - banana bread (misplaced/error)\n- Im3.png (line 543) - Toucan \n- Im4.png (line 579) - Puffin\n\n**After Figure 2 caption but before Figure 3 caption (line 701):**\n- Im5.png (line 641) - Golden Pheasant (part of Figure 2 content)\n\n**Around Figure 3:**\n- STOP sign figure (lines 633-637, but before Im5.png structurally in the document)\n- Im6.png (line 711) - STOP sign (after Figure 3 caption)\n\nWait, I need to look at this more systematically. Let me check what the STOP sign figure at line 633 is:The figure at 633-637 has OCR text \"STOP\" but the embedded image Im5.png (line 641) has \"NO_CONTENT_HERE\" as OCR and shows a bird. This is confusing. Let me look at Im6.png which should be the STOP sign:Yes! Im6.png is the STOP sign. So the document structure has the figure tags and embedded images separated. The figure at line 633 with \"STOP\" corresponds to Im6.png (the actual embedded image file) at line 711.\n\nLet me now map this out properly by looking at the figure captions and the embedded images:\n\n**Figure 2** (caption at line 619): Chat about birds\n**Figure 3** (caption at line 701): Chat about chameleon camouflage \n**Figure 4** (caption at line 922): Chat about baking with bananas\n\nNow let me find which embedded images belong to each:So Im6.png appears RIGHT AFTER the Figure 3 caption. Now let me check Figure 4:So Im8.png (bananas) appears before the Figure 4 caption at line 922, and there's an marker at line 920 suggesting another image.\n\nNow let me look at Im7.png and Im9.png to see which figure they belong to:\n\nIm7.png is at line 777. Let me check the context:Im7.png (chameleon) is at line 777, which is AFTER section 2.1 starts at line 756 and after Figure 3 caption at line 701. But looking at the context, Im7.png shows a chameleon which matches Figure 3's theme. Let me check if this is part of the Figure 3 example by looking backwards:\n\nThe Figure 3 response at line 747 has an marker, and the text before talks about generating a chameleon image. So Im7.png is part of Figure 3's output!\n\nNow let me check Im9.png:Im9.png shows a loaf of baked good (banana bread final result). This is at line 991, which is after Figure 4 caption at line 922. The marker at line 920 in Figure 4 refers to this final dish image. So Im9.png is part of Figure 4.\n\nNow let me go back and check Im5.png more carefully. At line 641, the bird image appears after Figure 2 caption but before Figure 3 content. Looking at Figure 2's text, the Golden Pheasant is mentioned with at line 614. So Im5.png (Golden Pheasant) is part of Figure 2.\n\nLet me now do a final count:\n\n**Figure 2** (Chat about birds):\n- Im3.png - Toucan\n- Im4.png - Puffin \n- Im5.png - Golden Pheasant\nTotal: 3 images\n\n**Figure 3** (Chat about chameleon camouflage):\n- Im6.png - STOP sign (input)\n- Im7.png - Chameleon (generated output)\nTotal: 2 images\n\n**Figure 4** (Chat about baking with bananas):\n- Im8.png - Bananas (input)\n- Im9.png - Banana bread (generated output)\nTotal: 2 images\n\n**Total images in chat example figures (Figures 2-4), excluding Appendix: 3 + 2 + 2 = 7**\n\nWait, but I should double-check if Image5.jpg (the banana bread at line 480) is actually meant to be part of Figure 2 or if it's a document error. Let me look at the context again:Image5.jpg (banana bread) appears right after the birds prompt but before the response starts. This appears to be a document processing error or misplacement - it doesn't match the context at all. The response starting at line 518 talks about birds, not banana bread.\n\nGiven that this is clearly an error in the document, I should not count Image5.jpg as part of the chat example figures. The actual chat examples are:\n\n**Figure 2**: 3 bird images (Im3.png, Im4.png, Im5.png)\n**Figure 3**: 2 images (Im6.png STOP sign, Im7.png chameleon) \n**Figure 4**: 2 images (Im8.png bananas, Im9.png banana bread)\n\n**Total: 7 images**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 178546, "error": null, "extra": {"thread_id": 2056, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d55f4fd667ac403b901ea3294619b198", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1600, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 17687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find subplots that show obvious loss spikes in the document.\n\nLet me examine the figures that show training loss curves:\n\n1. **Figure 5**: Contains 3 subplots:\n - (a) Shows output norm with clear divergence/spike\n - (b) Shows training loss comparison with and without QK-Norm - one curve diverges (spikes)\n - (c) Shows training loss with and without dropout - appears stable\n\n2. **Figure 6**: Contains 3 subplots:\n - (a) Shows training loss curves for 7B and 34B models - appears stable without obvious spikes\n - (b) Shows training loss curve with image generation disabled - appears stable without obvious spikes\n - (c) Shows training loss with and without norm reordering - shows clear divergence/spike in one curve\n\nLet me count the subplots with obvious loss spikes:\n- Figure 5(a): Shows norm growth leading to divergence (1)\n- Figure 5(b): Shows loss divergence/spike (1)\n- Figure 6(c): Shows loss divergence/spike (1)\n\nTotal: 3 subplots show obvious loss spikes or divergences.\n\nAnswer: 3", "citations": [], "input_tokens": 25071, "output_tokens": 284, "cost_micros": 79473, "latency_ms": 8016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss curves with obvious spikes.\n\nLooking through the document:\n\n**Figure 5** (page 6-7):\n- (a) Shows \"Output norm\" vs Step - not a loss curve with spikes\n- (b) Shows \"Training Loss\" with and without QK-Norm - shows relatively smooth curves, one diverges but no obvious spikes\n- (c) Shows \"Training Loss\" with and without dropout - shows smooth curves\n\n**Figure 6** (page 7):\n- (a) Shows \"Training Loss\" for 7B and 34B models over 600k steps - smooth curves, no obvious spikes\n- (b) Shows \"Training Loss\" for 7B without image generation - smooth curve, no obvious spikes\n- (c) Shows \"Training Loss\" with and without norm reordering - one curve shows a clear spike/divergence around 8k-10k steps\n\nFrom Figure 6c, I can see one subplot that clearly shows an obvious loss spike (the curve without norm reordering that spikes dramatically).\n\nLooking more carefully at Figure 5b, the curve \"w/o QK-norm\" shows a sharp spike/divergence near the end.\n\nSo counting subplots with obvious loss spikes:\n- Figure 5b: 1 subplot (shows divergence/spike)\n- Figure 6c: 1 subplot (shows clear spike)\n\nAnswer: 2", "citations": [], "input_tokens": 34440, "output_tokens": 324, "cost_micros": 108180, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures in the document that show training loss curves with obvious loss spikes.\n\nLooking through the document:\n\n**Figure 5**: \"Output norm and training loss curves for Chameleon models under various settings\"\n- (a) Shows uncontrolled growth of output norms\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - this shows training loss with a divergence/spike\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - this shows training loss with divergences/spikes\n\n**Figure 6**: \"Training loss curves for Chameleon models under various settings\"\n- (a) Shows \"Training loss curve with image generation disabled does not suffer from instability issues\" - smooth curves, no spikes\n- (b) Shows \"For Chameleon-34B, using dropout does not fix divergences, both with and without reordering\" - this shows loss spikes/divergences\n\nCounting subplots with obvious loss spikes:\n- Figure 5(b): 1 subplot with loss spike\n- Figure 5(c): 1 subplot with loss spike \n- Figure 6(b): 1 subplot with loss spikes\n\nTotal: 3 subplots\n\nAnswer: 3", "citations": [], "input_tokens": 26080, "output_tokens": 301, "cost_micros": 82755, "latency_ms": 9171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss spikes in the document.\n\nLooking through the document for figures showing training loss or related metrics:\n\n**Figure 5: \"Output norm and training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows output norms with curves that spike/diverge\n- (b) Shows training loss with and without QK-Norm - one curve diverges/spikes\n- (c) Shows training loss with and without dropout - one curve appears to spike\n\n**Figure 6: \"Training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows smooth training curves for 7B and 34B models - no obvious spikes\n- (b) Shows training loss without image generation - smooth curve, no spikes\n- (c) Shows training with/without norm reordering where one diverges - this shows a spike\n\nSo counting subplots with obvious loss spikes:\n- Figure 5(a): 1 (shows divergence/spikes in output norms)\n- Figure 5(b): 1 (shows divergence in loss)\n- Figure 5(c): 1 (shows divergence in loss)\n- Figure 6(c): 1 (shows divergence)\n\nTotal: 4 subplots show obvious spikes/divergences\n\nAnswer: 4", "citations": [], "input_tokens": 32131, "output_tokens": 324, "cost_micros": 101253, "latency_ms": 9125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about subplots related to obvious loss spikes.Let me search for content related to \"loss spikes\" in the document:Let me search for \"subplot\":Let me read through the document more systematically to look for figures and their descriptions:Now let me look at Figure 5 and Figure 6 which seem to show training loss curves. Let me read those sections:Now let me look more closely at Figure 6 which appears to have subplots. Let me search for any reference to \"loss spikies\" or similar terms:Let me look at Figure 5 more carefully, as it shows \"Output norm and training loss curves for Chameleon models under various settings\":I can see Figure 5 has three subplots (a, b, c). Now let me check the figure caption more carefully and look at the actual charts. The caption says \"Figure 5 Output norm and training loss curves for Chameleon models under various settings.\" Let me read the text more carefully to understand what \"obvious loss spikies\" might refer to. Let me search for variations:Let me look at the entire document more systematically. Let me check around Figure 5 and 6 descriptions:Let me look more carefully at the whole document. Maybe \"spikies\" is a colloquial term in the figure or caption. Let me check the full text around Figures 5 and 6:So Figure 5 has subplots (a), (b), and (c). Let me check Figure 6:So Figure 6 also has subplots (a), (b), and (c). Now let me look at the actual chart data more carefully. The question asks about \"obvious loss spikies\" which likely refers to spikes in the loss curves. Let me look at the loss curve data more closely. Let me search the document for any terms that might be misspelled or colloquial:Based on my reading of the document, I can see that:\n\n**Figure 5** has three subplots:\n- (a) Output norms plot showing growth over training steps\n- (b) Training loss with and without QK-Norm\n- (c) Training loss with and without dropout\n\n**Figure 6** has three subplots:\n- (a) Training curves for 600k steps for Chameleon-7B and Chameleon-34B\n- (b) Training loss curve with image generation disabled\n- (c) For Chameleon-34B, using dropout comparison with and without norm-reordering\n\nLooking at the descriptions and the actual chart data in the text, the question asks about \"obvious loss spikies\" (spikes). From examining the text descriptions:\n\n- Figure 5(a) shows \"Uncontrolled growth of output norms\" - this is about norms, not loss spikes\n- Figure 5(b) shows training loss curves with the one \"w/o QK-norm\" showing a divergence\n- Figure 5(c) shows training loss curves with and without dropout - the \"w/o dropout\" curve appears to have issues\n- Figure 6(a) shows smooth training curves for both 7B and 34B models\n- Figure 6(b) shows a smooth training loss curve without image generation\n- Figure 6(c) shows curves where \"w/o norm reordering\" appears to diverge/spike\n\nLooking at the subplot captions that mention instability or divergences, which would correspond to loss spikes:\n- Figure 5(b): \"w/o QK-norm\" diverges\n- Figure 5(c): \"w/o dropout\" has spikes\n- Figure 6(c): \"w/o norm reordering\" has divergences/spikes\n\nThat would be 3 subplots showing obvious loss spikes.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 88405, "error": null, "extra": {"thread_id": 2057, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d21eabfb0c2b44d69030354b180154e2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 635, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 15734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a \"Summary of core architecture and optimization decisions made in Chameleon in contrast to LLaMa-1 and LLaMa-2\", I can see that:\n\n- LLaMa-1 7B has an \"\u00d7\" (cross mark) in the Qknorm column\n- LLaMa-1 33B has an \"\u00d7\" (cross mark) in the Qknorm column\n\nThis indicates that LLaMa-1 does NOT apply QKnorm.\n\nAnswer: no", "citations": [], "input_tokens": 25087, "output_tokens": 130, "cost_micros": 77211, "latency_ms": 5343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which compares core architecture and optimization decisions across models:\n\nFor LLaMa-1 models (both 7B and 33B), the Qknorm column shows \"\u00d7\" (which indicates no).\n\nAnswer: no", "citations": [], "input_tokens": 34456, "output_tokens": 63, "cost_micros": 104313, "latency_ms": 4297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which summarizes the core architecture and optimization decisions:\n\nFor LLaMa-1 models (both 7B and 33B), the \"Qknorm\" column shows \"\u00d7\", which indicates that QK-Norm was not applied.\n\nAnswer: No", "citations": [], "input_tokens": 26096, "output_tokens": 71, "cost_micros": 79353, "latency_ms": 4531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a comparison of core architecture and optimization decisions:\n\nFor LLaMa-1 (both 7B and 33B models), the \"Qknorm\" column shows \"\u00d7\", which indicates that query-key normalization (QK-Norm) was **not** applied.\n\nAnswer: No", "citations": [], "input_tokens": 32147, "output_tokens": 81, "cost_micros": 97656, "latency_ms": 4671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about whether llama-1 applies qknorm.Let me search for \"qknorm\" and \"llama\" in the document to find relevant information.Let me read the table area that contains information about qknorm and llama-1:Perfect! I found the table that shows whether llama-1 applies qknorm. In the table, for both LLaMa-1 models (7B and 33B), the Qknorm column shows \"\u00d7 \u2612\" which indicates \"no\".\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 30905, "error": null, "extra": {"thread_id": 2058, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2c3221de2294418da7d44837e6c67391", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 146, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm\", I can see the training loss curves.\n\nThe curve labeled \"w/o QK-norm\" (without QK-norm) shows a divergence/spike in the loss after approximately 20% of training progress, which appears to occur around step 20,000-25,000 based on the x-axis scale shown in the figure.\n\nAnswer: Around step 20,000-25,000", "citations": [], "input_tokens": 25078, "output_tokens": 122, "cost_micros": 77064, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around step 20 000 25 000", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for both conditions.\n\nThe curve labeled \"w/o QK-norm\" (without QK-Norm) shows the training progressing normally until it experiences a sudden spike and divergence.\n\nAnswer: Around 125k steps", "citations": [], "input_tokens": 34447, "output_tokens": 89, "cost_micros": 104676, "latency_ms": 5452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 125k steps", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves comparing the two conditions.\n\nThe figure shows that the model without QK-Norm diverges (loss spikes) after approximately 20% of training progress.\n\nAnswer: After approximately 20% of training", "citations": [], "input_tokens": 26087, "output_tokens": 87, "cost_micros": 79566, "latency_ms": 5046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "after approximately 20 of training", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for models with and without QK-norm.\n\nThe graph shows that the model without QK-norm or dropout (red line) diverges after approximately 20k-30k steps, where the loss spikes dramatically upward.\n\nAnswer: Around 20k-30k steps", "citations": [], "input_tokens": 32138, "output_tokens": 100, "cost_micros": 97914, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 20k 30k steps", "normalised_gold": "150k"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about loss spikes when without QK-norm.Let me search for \"QK-norm\" in the document to find the relevant information.Now let me read the specific sections that mention loss spikes and QK-norm. Let me look at the chart/figure around lines 1028-1076 which seems to show training loss curves.I can see a figure showing training loss with and without QK-norm. The x-axis shows steps from 0k to 175k (approximately). Let me look more carefully at the surrounding text to understand where the spike occurs. Let me check the text before and after this figure.Let me read more to find the caption or description of this figure:Good! Now I can see that figure (b) shows training loss with and without QK-Norm. Let me look back at the chart more carefully to understand where the spike happens. Let me check the text around lines 938-1000 to see if there's more description:Now I need to look at the actual description of figure 5b more carefully. Let me read around the text that discusses the training loss spike:Perfect! I found the answer. The text clearly states:\n\n\"In Figure 5b, we show training loss curves for Chameleon-7B with and without QK-Norm, and the latter diverges after approximately 20% of a training epoch.\"\n\nLet me look at the figure again to verify the step count. Looking at the figure data from earlier, the x-axis goes from 0k to approximately 175k steps. The text mentions \"after approximately 20% of a training epoch\" for the divergence/spike.\n\nHowever, let me look more carefully at what specific step number might correspond to this. Let me check if there's more specific information:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 73108, "error": null, "extra": {"thread_id": 2059, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c22c13a6350947eda6562103bdc5468c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 343, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "however let me look more carefully at what specific step number might correspond to this let me check if there s more specific information", "normalised_gold": "150k"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I need to find who verified and signed the complaint by looking at the verification section of the document.\n\nLooking at page 16 of the document, I can see the \"VERIFICATION\" section which states:\n\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury of the laws of the United States of America that the facts contained therein are true to the best of my knowledge and belief, based upon knowledge possessed by me and/or on information received from other law enforcement agents.\"\n\nThis is followed by a signature and \"Special Agent Marc Silski, Federal Bureau of Investigation\" with the date \"September 6, 2018\".\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 25011, "output_tokens": 176, "cost_micros": 77673, "latency_ms": 14359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (PageID.16 Page 16 of 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5579, "output_tokens": 52, "cost_micros": 17517, "latency_ms": 3594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "According to the verification section at the end of the document, the officer who verified and signed the complaint was:\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 6098, "output_tokens": 34, "cost_micros": 18804, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16):\n\nThe verification states:\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury...\"\n\nIt is signed by:\n\"Special Agent Marc Silski\nFederal Bureau of Investigation\nDated: September 6, 2018\"\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 5228, "output_tokens": 109, "cost_micros": 17319, "latency_ms": 3780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5778, "output_tokens": 43, "cost_micros": 17979, "latency_ms": 2312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find which officer verified and signed the complaint.Based on the document, I found the verification section on page 16. The officer who verified and signed the complaint is:\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23546, "error": null, "extra": {"thread_id": 2060, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890676fb4dc0465f94bad1a9648bd51a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 82, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I need to find all the checks issued to Mont Blanc company mentioned in the document.\n\nLooking through the document:\n\nOn page 11 (PageID.11), paragraph e states:\n\"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nOn page 11 (PageID.11), paragraph f states:\n\"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nThese are the two checks issued to Mont Blanc company.\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 25031, "output_tokens": 170, "cost_micros": 77643, "latency_ms": 10219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5599, "output_tokens": 129, "cost_micros": 18732, "latency_ms": 3389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32, subsections e and f:\n\n1. Check number 150301 issued in September 2012: $35,000\n2. Check number 151545 issued in February 2013: $40,684\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 6118, "output_tokens": 79, "cost_micros": 19539, "latency_ms": 3640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for checks issued to Mont Blanc company:\n\nFrom paragraph 32.e: \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32.f: \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5248, "output_tokens": 128, "cost_micros": 17664, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5798, "output_tokens": 127, "cost_micros": 19299, "latency_ms": 3592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about checks issued to Mont Blanc company.Let me continue reading the document to find information about checks issued to Mont Blanc company.Let me continue reading to find information about checks issued to Mont Blanc.Perfect! I found the information about checks issued to Mont Blanc. Let me verify by reading a bit more to ensure I have all the check amounts.\n\nBased on the document, I found the following information about checks issued to Mont Blanc:\n\n1. Check number 150301 - $35,000 (issued in September 2012)\n2. Check number 151545 - $40,684 (issued in February 2013)\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32530, "error": null, "extra": {"thread_id": 2061, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5f1b7e2037a34d839cda54ca371fcea3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 134, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the CA Bar number in the document.\n\nLooking at page 15, I can see the signature page where it states:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 25014, "output_tokens": 103, "cost_micros": 76587, "latency_ms": 10625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 15 (PageID.15), I can find the CA Bar number listed for Assistant United States Attorney Adriana Dydell.\n\nAnswer: 239516", "citations": [], "input_tokens": 5582, "output_tokens": 47, "cost_micros": 17451, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, in the signature block for the Assistant United States Attorney, I can see:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 6101, "output_tokens": 108, "cost_micros": 19923, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, the signature block shows:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nDated: September, 2018\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 5231, "output_tokens": 107, "cost_micros": 17298, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I can find the CA Bar number at the bottom of page 15, where it states:\n\n\"Dated: September 13, 2018 (CA. Bar No. 239516)\"\n\nThis appears next to the signature block for Adriana Dydell, Assistant United States Attorney.\n\nAnswer: 239516", "citations": [], "input_tokens": 5781, "output_tokens": 78, "cost_micros": 18513, "latency_ms": 3358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find the CA Bar number.Answer: 239516", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35797, "error": null, "extra": {"thread_id": 2062, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67c2db9144b348b6ba61d0ad2f21abaf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 115, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll examine each page to identify where information has been blacked out (redacted).\n\nPage 1: No redactions visible\nPage 2: No redactions visible\nPage 3: No redactions visible\nPage 4: No redactions visible\nPage 5: No redactions visible\nPage 6: No redactions visible\nPage 7: No redactions visible\nPage 8: No redactions visible\nPage 9: No redactions visible\nPage 10: No redactions visible\nPage 11: No redactions visible\nPage 12: No redactions visible\nPage 13: Contains multiple black redaction boxes blocking out names and information (in paragraph l and m)\nPage 14: Contains black redaction boxes blocking out information (in paragraph n)\nPage 15: No redactions visible\nPage 16: No redactions visible\n\nAnswer: 13, 14", "citations": [], "input_tokens": 25034, "output_tokens": 196, "cost_micros": 78042, "latency_ms": 11015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages have information that has been blacked out or redacted.\n\nLooking through the document:\n\n- Page 1 (PageID.1): No visible redactions\n- Page 2 (PageID.2): No visible redactions\n- Page 3 (PageID.3): No visible redactions\n- Page 4 (PageID.4): No visible redactions\n- Page 5 (PageID.5): No visible redactions\n- Page 6 (PageID.6): No visible redactions\n- Page 7 (PageID.7): No visible redactions\n- Page 8 (PageID.8): No visible redactions\n- Page 9 (PageID.9): No visible redactions\n- Page 10 (PageID.10): No visible redactions\n- Page 11 (PageID.11): No visible redactions\n- Page 12 (PageID.12): No visible redactions\n- Page 13 (PageID.13): In paragraph 1, there is redacted text after \"the last names of Iacobelli and\" and in paragraph m, there are multiple redactions of names\n- Page 14 (PageID.14): In paragraph n, there are redacted names at the beginning and within the text\n- Page 15 (PageID.15): No visible redactions\n- Page 16 (PageID.16): No visible redactions\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5602, "output_tokens": 339, "cost_micros": 21891, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for redacted/blacked out information:\n\nOn page 14 (PageID.14), paragraph m, there are multiple instances of redacted information indicated by blank spaces where names should be:\n- \"was interviewed by agents.\"\n- \"was a former co-worker\"\n- \"During the interview,\"\n- \"informed agents that Iacobelli bragged to her about a Mont Blanc pen that he carried in his shirt pocket costing $25,000. Iacobelli also informed that\"\n\nOn page 14 (PageID.14), paragraph n, there is also redacted information:\n- \"On July 31, 2017, agents contacted who stated that\"\n- \"he was in possession of the Lincoln commemorative Mont Blanc pen. added that he ordered the pen, but that Iacobelli paid for both and\"\n\nPage 13 (PageID.13), paragraph l mentions \"having the last names of Iacobelli and\" followed by what appears to be a redaction.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 6121, "output_tokens": 238, "cost_micros": 21933, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully review the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Page 2: No redactions visible\n- Page 3: No redactions visible\n- Page 4: No redactions visible\n- Page 5: No redactions visible\n- Page 6: No redactions visible\n- Page 7: No redactions visible\n- Page 8: No redactions visible\n- Page 9: No redactions visible\n- Page 10: No redactions visible\n- Page 11: No redactions visible\n- Page 12: No redactions visible\n- Page 13: Contains redacted/blacked out information (names are replaced with blank spaces/lines in sections l and m)\n- Page 14: Contains redacted/blacked out information (names are replaced with blank spaces/lines in section n)\n- Page 15: No redactions visible\n- Page 16: No redactions visible\n\nThe pages with blacked out information are pages 13 and 14, where personal names have been redacted.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5251, "output_tokens": 248, "cost_micros": 19473, "latency_ms": 5733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Pages 1-12: No redactions visible\n- Page 13 (PageID.13): Contains multiple redactions marked with \"\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\" or similar black bars in paragraph m and n. Specifically:\n - \"former co-worker \u2588\u2588\u2588\u2588\u2588\u2588\" \n - Multiple instances of \"\u2588\u2588\u2588\u2588\u2588\u2588\" referring to a person's name\n - \"podiatrist named \u2588\u2588\u2588\u2588\u2588\u2588\"\n - \"[\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588]\" in paragraph n\n - \"[\u2588\u2588\u2588\u2588]\" multiple times in paragraph n\n\n- Page 14 (PageID.14): No redactions visible\n- Pages 15-16: No redactions visible\n\nThe redactions appear only on page 13 of the document (which corresponds to PageID.13, Page 13 of 16).\n\nAnswer: 13", "citations": [], "input_tokens": 5801, "output_tokens": 206, "cost_micros": 20493, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "13", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify pages with blacked out information.Based on my review of the document, I can identify pages with redacted (blacked out) information:\n\n- **Page 13**: Contains multiple black redaction bars in paragraphs l and m, obscuring names and identifying information. The visual description explicitly notes \"Multiple horizontal black bars throughout the document\" and \"Redacted sections appear in paragraphs l and m.\"\n\n- **Page 14**: Contains three black redaction blocks in paragraph n, obscuring names/identifying information. The visual description notes \"Contains redacted sections shown as solid black rectangular blocks\" and \"Multiple solid black rectangular blocks throughout paragraph n.\"\n\nAnswer: 13, 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37265, "error": null, "extra": {"thread_id": 2063, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5190eed28c9d49e68875e654ed7fc489", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 163, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of this document.\n\nLooking at page 15 (PageID.15):\nI can see a handwritten date that appears to be \"September 12, 2018\" where \"12\" is written manually.\n\nLooking at page 16 (PageID.16):\nI can see a handwritten date that appears to be \"September 6, 2018\" where \"6\" is written manually.\n\nThe manually written numbers are:\n- Page 15: 12\n- Page 16: 6\n\nSum: 12 + 6 = 18\n\nAnswer: 18", "citations": [], "input_tokens": 25026, "output_tokens": 148, "cost_micros": 77298, "latency_ms": 8937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date shows \"September 3 2018\" - there appears to be a handwritten \"3\" in the date field\n\nLooking at page 16 (PageID.16):\n- The date shows \"September 6 2018\" - there appears to be a handwritten \"6\" in the date field\n\nThe sum of the manually written numbers is: 3 + 6 = 9\n\nAnswer: 9", "citations": [], "input_tokens": 5594, "output_tokens": 130, "cost_micros": 18732, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nThe date shows \"September 13 2018\" where \"13\" appears to be handwritten.\n\nLooking at page 16 (PageID.16):\nThe date shows \"September 6, 2018\" where \"6\" appears to be handwritten.\n\nSum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 6113, "output_tokens": 112, "cost_micros": 20019, "latency_ms": 3219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine pages 15 and 16 of the document to find manually written numbers.\n\nLooking at page 15:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.15 Page 15 of 16\"\n- The page contains typed text including \"Respectfully submitted,\" signature block for Matthew Schneider and Adriana Dydell\n- At the bottom it shows \"Dated: September, 2018\"\n- The number \"15\" appears at the bottom of the page\n\nLooking at page 16:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.16 Page 16 of 16\"\n- Contains a verification section signed by Marc Silski\n- At the bottom it shows \"Dated: September 6, 2018\"\n- The number \"16\" appears at the bottom of the page\n\nThe manually written/handwritten numbers visible on these pages are:\n- Page 15: The page number \"15\" at the bottom\n- Page 16: The page number \"16\" at the bottom\n\nSum: 15 + 16 = 31\n\nAnswer: 31", "citations": [], "input_tokens": 5243, "output_tokens": 307, "cost_micros": 20334, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "31", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date \"September 13, 2018\" appears to have \"13\" that could be handwritten, but examining the format, it appears to be typed text, not manually written.\n\nLooking at page 16 (PageID.16):\n- The date \"September 6, 2018\" appears, where \"6\" could potentially be handwritten.\n- There is a signature line with \"Special Agent Marc Silski\" and a signature.\n\nHowever, upon closer examination of the document format, these appear to be typed dates in the standard court document format. The document shows underlined spaces where information was filled in, but the actual numbers \"13\" on page 15 and \"6\" on page 16 appear in the standard typed format of the document.\n\nLooking more carefully, the date on page 15 shows \"September 13, 2018\" where \"13\" appears to be typed/filled in on an underlined space, and on page 16 \"September 6, 2018\" where \"6\" appears typed.\n\nIf these are considered the manually written/filled-in numbers:\n- Page 15: 13\n- Page 16: 6\n- Sum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 5793, "output_tokens": 316, "cost_micros": 22119, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I'll help you find the manually written numbers on pages 15 and 16 of the document. Let me first read the document that was mentioned in the context.Let me continue reading to find pages 15 and 16. I need to search through the document to locate these specific pages.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27906, "error": null, "extra": {"thread_id": 2064, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3dc2a747e29a4ced83b1f3d763f972af", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 79, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "19"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to analyze the document to identify all the colors present in it.\n\nLooking at the document pages provided:\n- The text appears in black\n- The background is white\n- There are blue hyperlinks/case numbers at the top of each page\n- There are black redaction marks/blocks in some areas (pages 13-14)\n\nThe colors present in this document are:\n- Black (text and redactions)\n- Blue (case numbers and headers)\n- White (background)\n\nAnswer: black, blue, white", "citations": [], "input_tokens": 25038, "output_tokens": 116, "cost_micros": 76854, "latency_ms": 10546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "black, blue, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this legal document.\n\nScanning through the document for color references:\n\n1. Page 11, paragraph d: \"red 2013 Ferrari 458 Spider convertible\"\n2. Page 12, paragraph j: mentions \"gold\" (portrait in gold) and \"sapphire\" (which is blue)\n\nLet me verify these are the only color mentions:\n- \"red\" - explicitly mentioned for the Ferrari\n- \"gold\" - mentioned for Lincoln's portrait on the pen clip\n- The sapphire mentioned would typically be blue, but the document says \"sapphire\" not \"blue\"\n\nLooking more carefully at the text:\n- \"red 2013 Ferrari\" (paragraph 32d)\n- \"gold\" (paragraph 32j - \"Lincoln's portrait in gold\")\n- \"sapphire\" (paragraph 32j - \"has a sapphire\")\n\nThe colors explicitly stated are:\n- gold\n- red\n\nAlphabetically ordered:\n\nAnswer: gold, red", "citations": [], "input_tokens": 5606, "output_tokens": 228, "cost_micros": 20238, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "gold, red", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine the document to identify all colors mentioned or visible in it.\n\nLooking through the document:\n\n1. The document itself appears to be a black and white legal document (black text on white background)\n2. In paragraph 32(d), a \"red 2013 Ferrari 458 Spider convertible\" is mentioned\n3. In paragraph 32(j), \"gold\" is mentioned (describing Lincoln's portrait in gold on the pen clip)\n4. In paragraph 32(j), \"sapphire\" is mentioned (the pen's clip has a sapphire)\n\nColors explicitly mentioned in the text:\n- Gold\n- Red\n- Sapphire (which is blue)\n\nColors of the document format itself:\n- Black (text)\n- White (background)\n\nListing all colors alphabetically:\n- Black\n- Blue (sapphire)\n- Gold\n- Red\n- White\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 6125, "output_tokens": 214, "cost_micros": 21585, "latency_ms": 5405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this document.\n\nLet me search through the document for any color references:\n\n1. In paragraph 32.a, there is mention of \"a red 2013 Ferrari 458 Spider convertible\"\n2. In paragraph 32.j, there is mention of \"gold\" (describing Lincoln's portrait on the pen clip)\n3. The document is a legal court document that appears to be in black text on white paper (standard document format)\n\nThe colors explicitly mentioned in the text are:\n- Gold (mentioned in the description of the pen)\n- Red (mentioned in the description of the Ferrari)\n\nSince this is a standard black and white legal document with black text, and the question asks for colors in the document, I should include the standard document colors as well as the colors mentioned in the content.\n\nColors found:\n- Black (text)\n- Gold (pen description)\n- Red (Ferrari description)\n- White (paper/background)\n\nIn alphabetical order: black, gold, red, white\n\nAnswer: black, gold, red, white", "citations": [], "input_tokens": 5255, "output_tokens": 238, "cost_micros": 19335, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5714285714285715, "method": "list_set", "normalised_pred": "black, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in the document.\n\nGoing through the document carefully:\n\n1. Page 10, paragraph d: \"red 2013 Ferrari 458 Spider convertible\" - mentions **red**\n2. Page 12, paragraph j: \"sapphire\" (referring to a sapphire on the pen clip) - **blue** (sapphire is blue)\n3. Page 12, paragraph j: \"gold\" (portrait in gold on the pen's clip) - mentions **gold**\n\nLooking at the document format itself, this appears to be a standard black text on white background legal document, so:\n- **black** (text)\n- **white** (background)\n\nAlphabetically ordered:\n- black\n- blue\n- gold\n- red\n- white\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 5805, "output_tokens": 189, "cost_micros": 20250, "latency_ms": 6407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to identify all the colors present.Based on my careful reading of the document, I can identify the following colors mentioned or described:\n\nFrom the visual descriptions and content:\n- **Black** - mentioned multiple times in visual descriptions (\"black text\", \"black bars\", \"black rectangular blocks\" for redactions)\n- **Blue** - mentioned in the visual description (\"Blue text displaying case number\")\n- **Gold** - mentioned in the pen description (\"Lincoln's portrait in gold\")\n- **Red** - mentioned for the Ferrari (\"red 2013 Ferrari 458 Spider\")\n- **White** - mentioned in visual descriptions (\"white background\")\n\nThere's also a **sapphire** mentioned on the pen clip, which is typically blue, but since \"blue\" is already listed separately and sapphire is a gemstone rather than a color descriptor in this context, I'll include it as a separate item if we're being comprehensive about color-related terms.\n\nHowever, looking at the actual color words used as descriptors in the document:\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37108, "error": null, "extra": {"thread_id": 2065, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8c1d9833eaf14e8eae921556f7ecfecf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 186, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL, Assistant United States Attorney\"\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski, Federal Bureau of Investigation\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 25021, "output_tokens": 122, "cost_micros": 76893, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL\" (Assistant United States Attorney).\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski\" (Federal Bureau of Investigation).\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5589, "output_tokens": 124, "cost_micros": 18627, "latency_ms": 3781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15:\n- There is one signature above the typed name \"ADRIANA DYDELL\"\n\nLooking at page 16:\n- There is one signature above the typed name \"Special Agent Marc Silski\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 6108, "output_tokens": 100, "cost_micros": 19824, "latency_ms": 3750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures that appear on pages 15 and 16 of the document.\n\nLooking at page 15:\n- This page shows the document dated September, 2018\n- It has the names \"MATTHEW SCHNEIDER\" and \"ADRIANA DYDELL\" typed, but these appear to be typed names as part of the document format, not actual signatures\n- I do not see any actual handwritten or image signatures on page 15\n\nLooking at page 16:\n- This is the VERIFICATION page\n- It shows \"Special Agent Marc Silski\" and \"Federal Bureau of Investigation\" with a date of \"September 6, 2018\"\n- This also appears to be typed text rather than an actual signature image\n\nThe question asks for signatures \"appeared\" on these pages. Looking carefully at both pages, I can see typed names but no visible handwritten signatures or signature images in the document.\n\nHowever, these are signature blocks where signatures would typically appear. The document shows:\n- Page 15: 1 signature block (for Adriana Dydell)\n- Page 16: 1 signature block (for Marc Silski)\n\nTotal signature blocks: 2\n\nSince the question asks for the count in \"float format\" but then requests \"a single integer only,\" and given that there are 2 signature blocks present on these pages combined:\n\nAnswer: 2", "citations": [], "input_tokens": 5238, "output_tokens": 308, "cost_micros": 20334, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\n**Page 15 (PageID.15):**\nLooking at page 15, I can see:\n1. A signature above \"ADRIANA DYDELL\" - there is an underlined signature line with \"Adriana Dydell\" written above it\n\nSo there is 1 signature on page 15.\n\n**Page 16 (PageID.16):**\nLooking at page 16, I can see:\n1. A signature notation \"[Signature of Marc Silski]\" above \"Special Agent Marc Silski\"\n\nSo there is 1 signature on page 16.\n\n**Total signatures on pages 15 and 16:** 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5788, "output_tokens": 185, "cost_micros": 20139, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find the signatures on pages 15 and 16.Let me search for content related to pages 15 and 16 by reading more of the document:Let me read the entire document to find pages 15 and 16:Great! I found the relevant sections. Let me look more carefully at the content around pages 15 and 16. I can see:\n\n- Line 616: ``\n- Line 637: ``\n\nNow let me examine the signatures on these pages. From what I can see:\n\n**Page 15** (lines 590-618): Shows signatures for:\n1. Matthew Schneider (United States Attorney) \n2. Adriana Dydell (Assistant United States Attorney)\n\n**Page 16** (lines 619-638): Shows signature for:\n1. Marc Silski (Special Agent, Federal Bureau of Investigation)\n\nLet me read the section more carefully to confirm:Perfect! Now I can clearly see the signatures on both pages:\n\n**Page 15** (lines 596-616):\n- Shows \"Respectfully submitted,\" followed by:\n 1. MATTHEW SCHNEIDER (United States Attorney)\n 2. \"Adriana@iol\" (appears to be a signature)\n 3. ADRIANA DYDELL (Assistant United States Attorney)\n- The \"Adriana@iol\" on line 603 appears to be a signature representation\n- Dated: September 13, 2018\n\n**Page 16** (lines 619-637):\n- Shows VERIFICATION section with:\n 1. \"Josh,\" on line 631 (appears to be a signature)\n 2. Special Agent Marc Silski (Federal Bureau of Investigation)\n- Dated: September 6, 2018\n\nCounting the signatures:\n- Page 15: 1 signature (the \"Adriana@iol\" signature for Adriana Dydell)\n- Page 16: 1 signature (the \"Josh,\" signature for Marc Silski)\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42092, "error": null, "extra": {"thread_id": 2066, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ddcf7e386683499793cc579d23850dce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 267, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "According to the document, the telephone number for The Limes Residential Home is:\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 37738, "output_tokens": 30, "cost_micros": 113664, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10257, "output_tokens": 37, "cost_micros": 31326, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nThe address and contact details are:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 12045, "output_tokens": 79, "cost_micros": 37320, "latency_ms": 3766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nThe address block shows:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10599, "output_tokens": 73, "cost_micros": 32892, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10834, "output_tokens": 33, "cost_micros": 32997, "latency_ms": 3625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the telephone number for The Limes Residential Home.Let me search for \"The Limes Residential Home\" in the document:Let me search with more context to see the actual content:I can see from the search results that the telephone number appears on line 168. Let me read that section:Perfect! I found the telephone number for The Limes Residential Home on line 174.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34734, "error": null, "extra": {"thread_id": 2067, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_de5d9004414b47159eff08bc68030dbf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 151, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 3 which contains the section \"The five questions we ask about services and what we found\", I can identify the primary questions asked in this inspection report.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 37742, "output_tokens": 73, "cost_micros": 114321, "latency_ms": 7515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can identify the five primary questions that were asked about the services in this inspection report.\n\nThese questions are clearly listed in the \"Summary of findings\" section under \"The five questions we ask about services and what we found\":\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10261, "output_tokens": 84, "cost_micros": 32043, "latency_ms": 3844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically the \"Summary of findings\" section which contains \"The five questions we ask about services and what we found\", I can identify the primary questions asked about the services.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 12049, "output_tokens": 72, "cost_micros": 37227, "latency_ms": 4234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can see the \"five questions we ask about services\" section which lists the primary questions asked during the inspection.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10603, "output_tokens": 61, "cost_micros": 32724, "latency_ms": 3125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document, I can identify the five primary questions that were consistently asked about the services throughout the inspection report.\n\nThese questions appear in the \"Summary of findings\" section and structure the entire report, with each having its own detailed findings section:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10838, "output_tokens": 126, "cost_micros": 34404, "latency_ms": 4328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find the primary questions asked about the services.Looking at the document, I can see this is a Care Quality Commission inspection report. The report explicitly states \"The five questions we ask about services\" in a table starting around line 314. Let me identify these questions from the document structure:\n\nFrom the document, the five primary questions asked about the services are clearly listed:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27546, "error": null, "extra": {"thread_id": 2068, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bdf643e270448d6aadaffef3d9744b8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 108, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLet me examine each page:\n\nPage 1: I can see the CQC logo (the circular Q logo with \"Care Quality Commission\" text) at the top of the page.\n\nPage 2: No CQC logo visible on this page.\n\nPage 3: No CQC logo visible on this page.\n\nPage 4: No CQC logo visible on this page.\n\nPage 5: I can see the CQC logo at the top of this page.\n\nPages 6-17: Examining the remaining pages, I do not see the CQC logo appearing on any of these pages.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 37764, "output_tokens": 169, "cost_micros": 115827, "latency_ms": 7890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of certain pages, which typically indicates where a logo would be placed in the actual PDF document.\n\nLet me go through each page:\n\nPage 1: \"Care Quality Commission\" appears at the top - \"The Limes Residential Care Home Limited The Limes Residential Home\"\n\nPage 2: \"Summary of findings\" section continues\n\nPage 3: \"Summary of findings\" continues\n\nPage 4: \"Summary of findings\" continues\n\nPage 5: \"Care Quality Commission\" appears - \"The Limes Residential Home Detailed findings\"\n\nPage 6: Continues with \"Is the service safe?\"\n\nPage 7: Continues with \"Is the service safe?\"\n\nPage 8: \"Inadequate\" and \"Is the service effective?\"\n\nPage 9: Continues with \"Is the service effective?\"\n\nPage 10: \"Requires improvement\" and \"Is the service caring?\"\n\nPage 11: Continues with \"Is the service caring?\"\n\nPage 12: \"Requires improvement\" and \"Is the service responsive?\"\n\nPage 13: \"Requires improvement\" and \"Is the service well-led?\"\n\nPage 14: Continues with \"Is the service well-led?\"\n\nPage 15: \"Action we have told the provider to take\"\n\nPage 16: Continues with \"Action we have told the provider to take\"\n\nPage 17: \"Enforcement actions\"\n\nBased on the document structure, \"Care Quality Commission\" appears as a header on pages 1 and 5, which is where the CQC logo would typically be located.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10283, "output_tokens": 395, "cost_micros": 36774, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the Care Quality Commission (CQC) logo appears in this document.\n\nLooking through the document:\n\n1. Page 1 (cover page) - Yes, CQC logo appears at the top\n2. Page 2 - No visible logo\n3. Page 3 - No visible logo\n4. Page 4 - No visible logo\n5. Page 5 - Yes, CQC logo appears at the top (start of \"Detailed findings\" section)\n6. Page 6 - No visible logo\n7. Page 7 - No visible logo\n8. Page 8 - No visible logo\n9. Page 9 - No visible logo\n10. Page 10 - No visible logo\n11. Page 11 - No visible logo\n12. Page 12 - No visible logo\n13. Page 13 - No visible logo\n14. Page 14 - No visible logo\n15. Page 15 - No visible logo\n16. Page 16 - No visible logo\n17. Page 17 - No visible logo\n\nThe CQC logo appears on:\n- Page 1 (the title page/cover)\n- Page 5 (at the start of the detailed findings section)\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 12071, "output_tokens": 294, "cost_micros": 40623, "latency_ms": 5390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of several pages. The logo/header \"Care Quality Commission\" appears on:\n\n- Page 1 (the title page with \"The Limes Residential Care Home Limited\")\n- Page 5 (at the top before \"Detailed findings\")\n- Page 15 (at the top before \"Action we have told the provider to take\")\n\nLet me verify by checking each page systematically:\n\nPage 1: \"Care Quality Commission\" header present\nPage 2-4: Summary of findings pages - no clear CQC logo/header visible in the page headers\nPage 5: \"Care Quality Commission\" header present before \"Detailed findings\"\nPages 6-14: Detailed findings sections - these appear to be continuation pages\nPage 15: \"Care Quality Commission\" header present before enforcement actions section\nPages 16-17: Continuation of enforcement actions\n\nBased on the document structure, the CQC logo/header appears on:\n\nAnswer: Page 1, Page 5, Page 15", "citations": [], "input_tokens": 10625, "output_tokens": 262, "cost_micros": 35805, "latency_ms": 7218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "page 1, page 15, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document systematically:\n\nPage 1: \"Care Quality Commission\" appears at the top as a header\nPage 2: \"Summary of findings\" - no logo visible in header\nPage 3: \"Summary of findings\" continues - no logo visible\nPage 4: \"Summary of findings\" continues - no logo visible\nPage 5: \"CareQuality Commission\" appears at the top (note: slightly different formatting)\nPage 6-17: These pages show section headings but I need to check for the CQC logo/branding\n\nLooking more carefully at the document structure, the CQC logo/branding appears to be present on:\n- Page 1 (top of document with \"Care Quality Commission\")\n- Page 5 (shows \"CareQuality Commission\" at top of \"Detailed findings\" section)\n\nThese appear to be the only pages with the CQC branding/logo clearly marked at the top.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10860, "output_tokens": 243, "cost_micros": 36225, "latency_ms": 6234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify all pages where the CQC logo appears.Let me search for \"CQC\" or \"logo\" throughout the document to find all pages where the logo appears:Now let me search for mentions of \"logo\" or \"Care Quality Commission\" to find where the logo appears:Let me search more specifically for image and figure tags to identify all instances of the logo:Let me read through the entire document more systematically to find all logo occurrences. Let me check what page the first logo is on and look for patterns:The first logo appears before \"PageFooter=1\", so it's on Page 1. Now let me search the entire document systematically for all instances of the CQC logo. Let me search for figures or images:Good! Now I found 7 figure tags. Let me read the context around each one to identify which ones are the CQC logo:Now I've identified that several figures contain only page headers. Let me verify which ones actually contain the CQC logo by checking the content. Based on my readings:\n\n1. Line 121 (before PageFooter 1) - Contains \"Care Quality Commission\" logo with visual description - **Page 1**\n2. Line 409 (after PageBreak on line 406, before PageFooter 4 on line 405) - Contains \"Care Quality Commission\" - **Page 5** (since PageFooter 4 appears at line 405-406, this is after the page break, so it's on page 5)\n\nLet me verify by checking the page breaks more carefully:Perfect! So after PageFooter 4 and PageBreak, we get Page 5, which has the CQC logo. Now let me check if there are any other CQC logos. The other figures at lines 868, 981, 1036, 1134, and 1247 only contain PageHeaders, not the logo.\n\nLet me verify this is correct by checking one more time if there are any other embedded images:Let me search for any other embedded images:Good! Only one embedded image (I1.jpg) exists in the document. Now let me confirm the two locations of \"Care Quality Commission\" text in figures:So the CQC logo on Page 5 appears between PageFooter 4 (line 405) and PageFooter 5 (line 456). That confirms it's on Page 5.\n\nNow let me verify there's only one actual logo image (I1.jpg) on Page 1, and the Page 5 figure is just text without an image:I notice that on Page 5 (line 409-414), there's a figure with \"Care Quality Commission\" text but NO embedded image notation. Let me check if there's an image reference after this figure:No embedded image noted for Page 5's figure. So the Page 5 figure has \"Care Quality Commission\" text but the question asks specifically about the \"logo\" which includes the visual/graphical element. Let me re-read the Page 1 section to see if it explicitly identifies this as a logo:\n\nLooking back at lines 130-147, Page 1 clearly shows:\n- \"**Embedded image:** `I1.jpg`\"\n- \"**Visual description:**\"\n- \"Organizational logo combining a graphical symbol with text\"\n\nThis definitively identifies Page 1 as having the CQC logo (the actual image/visual logo).\n\nOn Page 5 (lines 409-414), there's only the text \"Care Quality Commission\" in a figure, but no embedded image or logo graphic.\n\nHowever, let me check if both figures might represent the same logo - the system might have extracted it once with full details (Page 1) and then just shown the text on Page 5. In typical inspection reports, logos often appear on multiple pages. Let me search more broadly to understand the document structure better. Let me see if there are image references I might have missed:Only one \"Visual description\" exists - on Page 1. The interpretation here is that:\n- Page 1 has the actual CQC logo image (I1.jpg with full visual description)\n- Page 5 has a figure with just \"Care Quality Commission\" text\n\nGiven that the question asks for \"the logo of CQC\" (which implies the graphical/visual logo, not just text), and given that only Page 1 has an embedded image with the visual logo description, the answer should be Page 1 only.\n\nHowever, both figures contain \"Care Quality Commission\" and in PDF documents, logos can appear as repeated elements. The figure on Page 5 likely also represents the logo (just extracted differently by the parser). Let me verify by checking if standard CQC inspection reports have logos on multiple pages.\n\nBased on the document structure, I have:\n1. **Page 1** - Clear CQC logo with embedded image I1.jpg and full visual description\n2. **Page 5** - Figure with \"Care Quality Commission\" text (likely the same logo, but extraction didn't capture the image separately)\n\nGiven the ambiguity, I should report both pages where the CQC element appears in a figure tag, as both likely represent the logo placement in the original PDF.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 135453, "error": null, "extra": {"thread_id": 2069, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a8af9e5bd4c48cebf2e6cbec000700b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1002, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this CQC inspection report.\n\nLet me go through the document systematically:\n\n**From \"Action we have told the provider to take\" section (pages 15-16):**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From \"Enforcement actions\" section (page 17):**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nCounting the unique regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 HSCA (RA) (Staffing)\n- Regulation 18 CQC (Registration) (Notification)\n\nThat's 8 regulations total.\n\nAnswer: 8", "citations": [], "input_tokens": 37740, "output_tokens": 419, "cost_micros": 119505, "latency_ms": 10562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches of HSCA (Regulated Activities) Regulations 2014:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - unlawful restraint and failure to respond to allegations/safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (mentioned twice - failure to assess risks, and failure to manage medicines safely)\n\n3. **Regulation 18** - Staffing (insufficient staffing levels)\n\n4. **Regulation 18** - Staffing (again, for induction, supervision and training issues)\n\n5. **Regulation 11** - Need for consent (MCA 2005 implementation)\n\n6. **Regulation 10** - Dignity and respect\n\n7. **Regulation 9** - Person-centred care\n\n8. **Regulation 17** - Good governance (mentioned twice - failure to assess/monitor quality, and failure to maintain accurate records)\n\n**Breach of HSCA (Registration) Regulations 2009:**\n\n9. **Regulation 18** - Notification of other incidents (CQC Registration Regulations 2009)\n\nNow let me count the distinct regulation numbers that were breached:\n- Regulation 13 (RA Regs 2014)\n- Regulation 12 (RA Regs 2014)\n- Regulation 18 (RA Regs 2014)\n- Regulation 11 (RA Regs 2014)\n- Regulation 10 (RA Regs 2014)\n- Regulation 9 (RA Regs 2014)\n- Regulation 17 (RA Regs 2014)\n- Regulation 18 (Registration Regs 2009)\n\nThat's 7 different regulations from the RA Regulations 2014 and 1 from the Registration Regulations 2009, making 8 total distinct regulations breached.\n\nAnswer: 8", "citations": [], "input_tokens": 10259, "output_tokens": 499, "cost_micros": 38262, "latency_ms": 8109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breached regulations mentioned in this inspection report.\n\nLet me go through the document systematically:\n\n**From \"Is the service safe?\" section:**\n1. Regulation 13 - Safeguarding (failure to respond to allegations and investigate safeguarding incidents)\n2. Regulation 12 - Safe care and treatment (failure to assess and mitigate risks)\n3. Regulation 18 - Staffing (insufficient staffing levels)\n4. Regulation 12 - Safe care and treatment (medicines not managed safely) - already counted above\n\n**From \"Is the service effective?\" section:**\n5. Regulation 18 - Staffing (induction, supervision and training) - already counted above\n6. Regulation 13 - Safeguarding (unlawful restraint) - already counted above\n7. Regulation 11 - Need for consent (MCA 2005 not implemented)\n\n**From \"Is the service caring?\" section:**\n8. Regulation 10 - Dignity and respect\n\n**From \"Is the service responsive?\" section:**\n9. Regulation 9 - Person-centred care (care records not reflecting current needs)\n\n**From \"Is the service well-led?\" section:**\n10. Regulation 17 - Good governance (failure to assess, monitor and improve quality)\n11. Regulation 17 - Good governance (failure to maintain accurate records) - already counted above\n12. Regulation 18 CQC (Registration) Regulations 2009 - Notification of incidents\n\nLet me count the unique HSCA 2008 regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 (Staffing)\n\nThat's 7 regulations under HSCA 2008 (Regulated Activities) Regulations 2014.\n\nPlus 1 regulation under CQC (Registration) Regulations 2009 (Regulation 18).\n\nThe question asks specifically about \"regulations of the", "citations": [], "input_tokens": 12047, "output_tokens": 512, "cost_micros": 43821, "latency_ms": 8780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this report.\n\nLet me go through the document systematically:\n\n**From the \"Action we have told the provider to take\" section:**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From the \"Enforcement actions\" section:**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nLet me verify by checking the main body of the report for all breach mentions:\n- Regulation 13 (safeguarding) - breach mentioned\n- Regulation 12 (safe care and treatment, including medicines and risk assessment) - breach mentioned\n- Regulation 18 (staffing) - breach mentioned\n- Regulation 11 (Mental Capacity Act) - breach mentioned\n- Regulation 10 (dignity and respect) - breach mentioned\n- Regulation 9 (person-centred care) - breach mentioned\n- Regulation 17 (good governance - two separate breaches mentioned for different aspects) - breach mentioned\n- Regulation 18 CQC (Registration) Regulations 2009 (notifications) - breach mentioned\n\nTotal unique regulations breached: 8\n\nAnswer: 8", "citations": [], "input_tokens": 10601, "output_tokens": 448, "cost_micros": 38523, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA 2008) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches found in the main body of the report:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (failure to respond to allegations and report safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (failure to assess, record and mitigate risks to people's health and safety)\n\n3. **Regulation 18** - Staffing (failure to ensure sufficient staffing levels)\n\n4. **Regulation 12** - Safe care and treatment (failure to manage medicines safely) - *same regulation as #2*\n\n5. **Regulation 18** - Staffing (failure to ensure staff received appropriate induction, supervision and training) - *same regulation as #3*\n\n6. **Regulation 13** - Safeguarding (failure to ensure people were not unlawfully restrained) - *same regulation as #1*\n\n7. **Regulation 11** - Need for consent (failure to ensure MCA 2005 code of practice was implemented)\n\n8. **Regulation 10** - Dignity and respect (failure to treat people with respect and dignity at all times)\n\n9. **Regulation 9** - Person-centred care (failure to ensure care records reflected current needs and preferences)\n\n10. **Regulation 17** - Good governance (failure to effectively assess, monitor and improve quality and safety of services)\n\n11. **Regulation 17** - Good governance (failure to maintain accurate and complete records) - *same regulation as #10*\n\n12. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - This is a different Act (Registration Regulations, not Regulated Activities Regulations)\n\n**Counting unique HSCA 2008 (Regulated Activities) Regulations 2014 breaches:**\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n-", "citations": [], "input_tokens": 10836, "output_tokens": 512, "cost_micros": 40188, "latency_ms": 9764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}} -{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the provided document to find information about HSCA 2008 regulations breaches.Let me continue reading to find any summary or conclusion section that might provide the total count.Based on my careful reading of the document, I need to count all the breaches of the Health and Social Care Act 2008 (Regulated Activities) Regulations 2014 (HSCA 2008).\n\nThe breaches mentioned in the report are:\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - for failing to respond to allegations and for unlawful restraint)\n2. **Regulation 12** - Safe care and treatment (mentioned twice - for failing to assess/mitigate risks and for failing to manage medicines safely)\n3. **Regulation 18** - Staffing (mentioned twice - for insufficient staffing levels and for inadequate training/supervision)\n4. **Regulation 11** - Need for consent (MCA 2005 implementation)\n5. **Regulation 10** - Dignity and respect\n6. **Regulation 9** - Person-centred care\n7. **Regulation 17** - Good governance (mentioned twice - for failing to assess/monitor quality and for failing to maintain accurate records)\n\nAdditionally, there is one breach of:\n8. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - Notification of other incidents\n\nCounting the distinct regulation numbers breached under HSCA (RA) Regulations 2014:\n- Regulation 13\n- Regulation 12\n- Regulation 18\n- Regulation 11\n- Regulation 10\n- Regulation 9\n- Regulation 17\n\nThat's 7 different regulations of the HSCA (RA) Regulations 2014 that were breached.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38483, "error": null, "extra": {"thread_id": 2070, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ce6489cb9fc4be0aad3905ed014d904", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 217, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "7"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from 3M's cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018 (page 60):\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount for FY2018 is shown as purchases of property, plant and equipment.\n\nAnswer: 1577", "citations": [], "input_tokens": 390262, "output_tokens": 116, "cost_micros": 2344182, "latency_ms": 44468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the years ended December 31, I can find the capital expenditures under \"Cash Flows from Investing Activities.\"\n\nFrom the document:\n\n**Cash Flows from Investing Activities:**\nYears ended December 31\n(Millions) 2018 2017 2016\n\nPurchases of property, plant and equipment (PP&E) $(1,577) $(1,373) $(1,420)\n\nThe capital expenditure for FY2018 is shown as purchases of property, plant and equipment (PP&E) which equals $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 140538, "output_tokens": 171, "cost_micros": 424179, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount is shown as a negative number in the cash flow statement because it represents a cash outflow.\n\nThe capital expenditure amount for FY2018 is $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 207953, "output_tokens": 130, "cost_micros": 1250643, "latency_ms": 8718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for FY2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 163241, "output_tokens": 77, "cost_micros": 490878, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for fiscal year 2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 177504, "output_tokens": 78, "cost_micros": 533682, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the capital expenditure amount from the cash flow statement.Let me search for the cash flow statement in this document.Great! I found the Consolidated Statement of Cash Flows. Let me read that specific chunk to find the capital expenditure line.Let me search for variations of capital expenditure terminology:Perfect! I found it. The \"Purchases of property, plant and equipment (PP&E)\" line shows the capital expenditure amounts. For 2018, it shows $(1,577) million. Let me read the actual chunk to confirm this is from the cash flow statement.Let me check a bit earlier to confirm this is the Consolidated Statement of Cash Flows:Let me check the title of this statement:Perfect! I've confirmed this is the \"3M Company and Subsidiaries Consolidated Statement of Cash Flows\" for years ended December 31, with columns for 2018, 2017, and 2016.\n\nIn the \"Cash Flows from Investing Activities\" section, the line item \"Purchases of property, plant and equipment (PP&E)\" shows:\n- 2018: (1,577)\n- 2017: (1,373)\n- 2016: (1,420)\n\nThe amounts are in millions of USD, and the parentheses indicate these are cash outflows. The FY2018 capital expenditure amount is 1,577 million USD.\n\nAnswer: 1577.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53671, "error": null, "extra": {"thread_id": 2071, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b68bd46e8dab41c29dc9086112158cbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 310, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for 3M's business segments in FY2018...\n\nFrom the document, specifically on pages discussing business segments (e.g., page 4, page 33-38), I can identify the following business segments:\n\n1. Industrial Business\n2. Safety and Graphics Business\n3. Health Care Business\n4. Electronics and Energy Business\n5. Consumer Business\n\nThese five segments are consistently mentioned throughout the document as 3M's reportable segments for fiscal year 2018.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 390260, "output_tokens": 126, "cost_micros": 2344395, "latency_ms": 49344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to identify 3M's business segments for FY2018.\n\nFrom the document, I can find the business segment information in multiple places. The clearest statement is in the Overview section and repeated throughout:\n\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nThis is also confirmed in the Business Segment Products table which lists the major products for each segment:\n- Industrial\n- Safety and Graphics\n- Health Care\n- Electronics and Energy\n- Consumer\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 140536, "output_tokens": 140, "cost_micros": 423708, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information clearly stated in multiple places:\n\nIn the \"BUSINESS SEGMENTS\" section (page 4), it states:\n\"3M manages its operations in five business segments. The reportable segments are Industrial, Safety and Graphics, Health Care, Electronics and Energy, and Consumer.\"\n\nThis is also confirmed in the \"OVERVIEW\" section (page 15):\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnd in the \"PERFORMANCE BY BUSINESS SEGMENT\" section (page 32):\n\"3M manages its operations in five business segments. The reportable segments are Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 207951, "output_tokens": 206, "cost_micros": 1252341, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018:\n\nFrom the document, I can find the business segments clearly stated in multiple locations, including:\n\n1. In the Business Segments section (page 4):\n\"3M manages its operations in five business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\n2. In the Table of Contents and throughout the financial statements, these five segments are consistently referenced.\n\n3. In Note 18 (Business Segments), the same five segments are listed with their products and financial information.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 163239, "output_tokens": 147, "cost_micros": 491922, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information in multiple sections, including:\n\n1. In the Executive Summary/Overview section\n2. In Note 18 - Business Segments\n3. In the Business Segments section of Item 1\n\nThe document clearly states that 3M manages its operations in **five business segments**:\n\n1. **Industrial** - serves automotive OEM, automotive aftermarket, electronics, appliance, paper and printing, packaging, food and beverage, and construction markets\n2. **Safety and Graphics** - serves markets for personal protection products, commercial solutions, transportation safety, and roofing granules\n3. **Health Care** - serves medical clinics and hospitals, pharmaceuticals, dental and orthodontic practitioners, health information systems, and food manufacturing and testing\n4. **Electronics and Energy** - serves electronics and energy markets, including display materials, electrical products, and power generation\n5. **Consumer** - serves consumer retail, office retail, home improvement, drug and pharmacy retail markets\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 177502, "output_tokens": 255, "cost_micros": 536331, "latency_ms": 14344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about their business segments.Perfect! I found the information. Let me read the specific section that clearly states the five business segments.Answer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32219, "error": null, "extra": {"thread_id": 2072, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5721bf743c374f36a4a98f84b6cf13eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 119, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nLooking through the document, I can find information about derivative instruments in Note 14 (Derivatives) and the tables showing \"Location and Fair Value Amount of Derivative Instruments.\"\n\nFrom page 104-105, I can see tables showing the fair value of derivative instruments as of December 31, 2018:\n\n**Assets (December 31, 2018):**\n- Foreign currency forward/option contracts: $127 million\n- Interest rate swap contracts: $19 million\n- Total derivative assets: $146 million\n\n**Liabilities (December 31, 2018):**\n- Foreign currency forward/option contracts: $22 million\n- Interest rate swap contracts: $31 million\n- Total derivative liabilities: $53 million\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 390258, "output_tokens": 225, "cost_micros": 2346610, "latency_ms": 34921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments as of December 31, 2018.\n\nFrom the section \"Location and Fair Value Amount of Derivative Instruments\" on page 104, I can see a table showing derivative instruments as of December 31, 2018:\n\n**Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative instruments (Assets): $146 million\n\n**Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative instruments (Liabilities): $53 million\n\nNet fair value = Total Assets - Total Liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 140534, "output_tokens": 198, "cost_micros": 424572, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom Note 14 \"Derivatives\" and Note 15 \"Fair Value Measurements\", I can find the fair value of derivative instruments.\n\nIn the section \"Location and Fair Value Amount of Derivative Instruments\" under Note 14, there is a table showing:\n\n**December 31, 2018:**\n\nAssets:\n- Foreign currency forward/option contracts (current): $74 million\n- Foreign currency forward/option contracts (non-current): $39 million\n- Interest rate swap contracts (current): $0 million\n- Interest rate swap contracts (non-current): $19 million\n- Foreign currency forward/option contracts (not designated): $14 million\n**Total derivative assets: $146 million**\n\nLiabilities:\n- Foreign currency forward/option contracts (current): $12 million\n- Foreign currency forward/option contracts (non-current): $4 million\n- Interest rate swap contracts (current): $14 million\n- Interest rate swap contracts (non-current): $17 million\n- Foreign currency forward/option contracts (not designated): $6 million\n**Total derivative liabilities: $53 million**\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 207949, "output_tokens": 317, "cost_micros": 1254826, "latency_ms": 11608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom the \"Location and Fair Value Amount of Derivative Instruments\" table for December 31, 2018:\n\n**Derivative Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative assets: $146 million\n\n**Derivative Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative liabilities: $53 million\n\n**Net Fair Value = Total Assets - Total Liabilities**\nNet Fair Value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 163237, "output_tokens": 185, "cost_micros": 492486, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the fair value of derivative instruments from the document:\n\nFrom the table \"Location and Fair Value Amount of Derivative Instruments\" on page 104:\n\n**December 31, 2018:**\n- Total derivative assets (Fair Value Amount): $146 million\n- Total derivative liabilities (Fair Value Amount): $53 million\n\nNet fair value = Assets - Liabilities = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 177500, "output_tokens": 105, "cost_micros": 534075, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about the net fair value of derivative instruments.Let me search for information about derivative instruments in the document.Let me search more specifically for derivative instruments and their fair values in a table format.Now let me read the section on derivatives to find the fair value information, particularly around line 15104 where it mentions \"Location and Fair Value Amount of Derivative Instruments\":Perfect! I found the table showing the fair value of derivative instruments for 3M in FY2018. Looking at the table on lines 15230-15235:\n\nFor December 31, 2018:\n- Total derivative instruments (Assets): $146 million\n- Total derivative instruments (Liabilities): $53 million\n\nThe net fair value would be: Assets - Liabilities = $146 - $53 = $93 million\n\nAnswer: 93.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50969, "error": null, "extra": {"thread_id": 2073, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890847b672154ab2beed6f213e7b03d2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 220, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for advertising and merchandising costs:\n\nFrom the document (page 65):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nTo calculate the growth rate for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635... \u00d7 100\nGrowth rate = -3.649635...\nRounded to two decimal places = -3.65\n\nAnswer: -3.65", "citations": [], "input_tokens": 390267, "output_tokens": 193, "cost_micros": 2345944, "latency_ms": 37280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 2018 and 2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- 2018: $396 million\n- 2017: $411 million\n\nGrowth rate = (2018 value - 2017 value) / 2017 value \u00d7 100\nGrowth rate = ($396 - $411) / $411 \u00d7 100\nGrowth rate = -$15 / $411 \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 140543, "output_tokens": 230, "cost_micros": 425079, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- FY2018: $396 million\n- FY2017: $411 million\n\nGrowth rate = (FY2018 - FY2017) / FY2017 \u00d7 100\nGrowth rate = (396 - 411) / 411 \u00d7 100\nGrowth rate = -15 / 411 \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 207958, "output_tokens": 224, "cost_micros": 1252788, "latency_ms": 9453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, under \"Advertising and merchandising\":\n- 2018: $396 million\n- 2017: $411 million\n- 2016: $385 million\n\nThe growth rate formula is: ((2018 - 2017) / 2017) \u00d7 100\n\nGrowth rate = ((396 - 411) / 411) \u00d7 100\nGrowth rate = (-15 / 411) \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 163246, "output_tokens": 178, "cost_micros": 492408, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the advertising and merchandising costs:\n\nFrom the document (page 64):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nGrowth rate calculation for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 177509, "output_tokens": 176, "cost_micros": 535167, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} -{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the advertising and merchandising costs for FY2018 and the prior year to calculate the growth rate.Let me search for \"advertising\" in the document to find the relevant information quickly.Great! I found a reference to advertising and merchandising costs. Let me read the chunk that contains this information.Perfect! I can see that advertising and merchandising costs were $396 million in 2018 and $411 million in 2017. Let me read more context to ensure I have the complete information.Perfect! I now have the complete information:\n- 2018: $396 million\n- 2017: $411 million\n\nNow I'll calculate the growth rate:\nGrowth rate = [(2018 value - 2017 value) / 2017 value] \u00d7 100\nGrowth rate = [(396 - 411) / 411] \u00d7 100\nGrowth rate = [-15 / 411] \u00d7 100\nGrowth rate = -0.036496... \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36500, "error": null, "extra": {"thread_id": 2074, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3f1bac0372cd48f5b95293aa87160368", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 175, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}} diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl deleted file mode 100644 index bc18da838c..0000000000 --- a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl +++ /dev/null @@ -1,37 +0,0 @@ -{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:15Z", "latency_ms": 20985, "error": null, "raw_text_chars": 696}]}, "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many adults rated Trump's government ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nLooking through the document, I find relevant information on page 4 (document page 6):\n\nThe document states: \"About four-in-ten Americans rate the ethical standards of Trump administration officials as excellent (9%) or good (30%), while 58% say they are not good (21%) or poor (36%).\"\n\nThe survey was conducted among 1,503 adults (as stated in the methodology section on page 14).\n\nTo find the number of adults who rated the ethical standards as \"poor\":\n36% of 1,503 = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 33894, "output_tokens": 191, "cost_micros": 104547, "latency_ms": 20968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}} -{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:16Z", "latency_ms": 20453, "error": null, "raw_text_chars": 544}]}, "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at page 5 of the document, which shows \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the data for \"very confident\" and \"not at all confident\" percentages across different domains.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 30%\n\nFor \"Not at all confident\" (highest percentage):\n- Make wise decisions about immigration policy: 55%\n\nAnswer: Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 33942, "output_tokens": 117, "cost_micros": 103581, "latency_ms": 20438, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}} -{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 41750, "error": null, "raw_text_chars": 1479}]}, "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I need to count the pictures in the document that contain only one person.\n\nLet me go through the document page by page:\n\nPage 1:\n- Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\nPage 2:\n- Photo of Capt. John W. Sanders - 1 person\n- Photo of Capt. Steven M. Wechsler - 1 person\n\nPage 3:\n- Photo of Afghan trainees in laboratory - multiple people\n\nPage 4:\n- Group photo of Pacific Partnership 2012 Internal Medicine Department staff - multiple people\n- Photo of Cmdr. Charmagne Beckett on flight deck - 1 person\n\nPage 5:\n- Photo of Petty Officer 1st Class Dennis Gonzales - 1 person\n\nPage 6:\n- Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n\nPage 7:\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Chris Martinez, Capt. Buhari Oyofo, Col. Vernon Graham, and Lt. Joseph Diclaro - 5 people\n\nPage 8:\n- Group photo of Kazakh scientists and NMRC staff - multiple people\n\nPage 9:\n- Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n- Photo of U.S. Marines and Sailors in aircraft - multiple people\n\nPage 10:\n- Group photo of Joint Combat Casualty Research Team - multiple people\n\nPage 12:\n- Photo of NMRC 2012 Dining Out Event - multiple people\n- Photo with Rear Adm. Bruce Doll, Capt. John Sanders and others - multiple people\n\nPage 13:\n- Photo with Ellen Flannery, Emmanuel Adu-Gyamfi, Eil", "citations": [], "input_tokens": 40387, "output_tokens": 512, "cost_micros": 128841, "latency_ms": 41735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}} -{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "original_error": "JSONDecodeError: Expecting value: line 169 column 1 (char 924)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 36889, "error": null, "raw_text_chars": 1214}]}, "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure, specifically the number of multi-head self-attention layers.\n\nLet me search through the document for information about DETR's architecture.\n\nFrom page 2, I can see that DETR has a transformer encoder-decoder architecture.\n\nOn page 10 (Section 4.1), I find: \"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nLooking at the detailed architecture description on page 22 (Section A.3) and Figure 10, I can see that:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing multi-head self-attention AND multi-head attention (cross-attention)\n\nFrom page 9-10, the baseline model uses \"6 encoder, 6 decoder layers\"\n\nEach encoder layer has one multi-head self-attention layer.\nEach decoder layer has one multi-head self-attention layer and one multi-head cross-attention layer.\n\nSo the total number of multi-head self-attention layers (not counting cross-attention) is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 59866, "output_tokens": 318, "cost_micros": 184368, "latency_ms": 36875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}} -{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "arm": "llamacloud_basic_lc", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:13Z", "latency_ms": 11328, "error": null, "raw_text_chars": 1376}]}, "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots.\n\nLooking through the figures in the document:\n\n1. **Figure 1**: Shows example images with text descriptions - no bar plots\n2. **Figure 2**: Shows data format comparison with images and text - no bar plots\n3. **Figure 3**: Shows the Syphus overview diagram - no bar plots\n4. **Figure 4**: This appears to show statistics and analysis. Let me examine it carefully:\n - Part (a): Shows verb-noun structure for instructions - this appears to be a bar chart\n - Part (b): Shows verb-noun structure for responses - this appears to be a bar chart\n - Part (c): Shows \"Diversity in our dataset\" with what appears to be distribution plots\n\nLooking at Figure 4 more carefully:\n- (a) contains what looks like a horizontal bar chart showing verbs and nouns from instructions\n- (b) contains what looks like a horizontal bar chart showing verbs and nouns from responses\n- (c) contains multiple distribution plots showing lengths and counts\n\n5. **Figure 5**: Shows response examples - no bar plots\n6. **Figure 6**: The document mentions Fig. 6(b) and Fig. 6(c) but these are not fully visible in the provided pages\n\nFrom what I can see clearly in Figure 4:\n- Subfigure (a): 1 bar plot (showing instruction verb-noun structure)\n- Subfigure (b): 1 bar plot (showing response verb-noun structure)\n\nTotal visible bar plots: 2\n\nAnswer: 2", "citations": [], "input_tokens": 20240, "output_tokens": 354, "cost_micros": 66030, "latency_ms": 11328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}} -{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:18Z", "latency_ms": 52514, "error": null, "raw_text_chars": 860}]}, "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll examine pages 5-10 to count the tables.\n\nPage 5: No tables visible in the text content shown.\n\nPage 6: Table 1 is present - \"Llama 2 family of models\" showing training data, parameters, context length, GQA, tokens, and LR for Llama 1 and Llama 2 models.\n\nPage 7: \n- Table 2: \"CO2 emissions during pretraining\" showing time, power consumption, and carbon emitted for different Llama 2 models.\n- Table 3: \"Overall performance on grouped academic benchmarks\" comparing MPT, Falcon, Llama 1, and Llama 2 models.\n\nPage 8:\n- Table 4: \"Comparison to closed-source models on academic benchmarks\" showing results for GPT-3.5, GPT-4, PaLM, PaLM-2-L, and Llama 2.\n- Table 5: \"SFT annotation\" showing examples of helpfulness and safety annotations.\n\nPage 9: No complete tables visible in the shown content.\n\nPage 10: No tables visible in the shown content.\n\nAnswer: 5", "citations": [], "input_tokens": 199213, "output_tokens": 280, "cost_micros": 601839, "latency_ms": 52468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:25Z", "latency_ms": 40452, "error": null, "raw_text_chars": 575}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 4, which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the bar chart showing \"Violation % (lower is safer)\":\n- Llama 2-70b-chat has the lowest violation percentage (approximately 0-5%)\n- Llama 2-13b-chat has the second lowest (approximately 0-5%)\n- Llama 2-34b-chat has the third lowest (approximately 5-10%)\n\nAnswer: Llama 2-70b-chat, Llama 2-13b-chat, Llama 2-34b-chat", "citations": [], "input_tokens": 199224, "output_tokens": 182, "cost_micros": 600402, "latency_ms": 40406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:05Z", "latency_ms": 10343, "error": null, "raw_text_chars": 376}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I can see the violation percentages (lower is safer):\n\nThe top-3 safest models (lowest violation %) are:\n1. Llama-2 7b-chat: 3%\n2. Llama-2 13b-chat: 3%\n3. Llama-2 70b-chat: 4%\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 70b-chat", "citations": [], "input_tokens": 105645, "output_tokens": 151, "cost_micros": 319200, "latency_ms": 10343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 70b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}} -{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:10Z", "latency_ms": 29391, "error": null, "raw_text_chars": 525}]}, "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the QUAC (f1) 0-shot scores are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 105649, "output_tokens": 274, "cost_micros": 321057, "latency_ms": 29391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}} -{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:15Z", "latency_ms": 43610, "error": null, "raw_text_chars": 650}]}, "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 9 and count the emojis in the left and right subfigures.\n\nLooking through the document, I find Figure 9 on page 16 (document page 16). The figure shows \"Issues with multi-turn memory (left) can be improved with GAtt (right).\"\n\nLooking at the left subfigure:\n- It shows a conversation with text-only responses (no emojis visible)\n\nLooking at the right subfigure:\n- First response: 4 emojis (\ud83d\udc4b\ud83d\udc41\ufe0f\ud83d\udc44\ud83d\udc41\ufe0f)\n- Second response: 4 emojis (\u2708\ufe0f\u2708\ufe0f\ud83d\udeeb\ud83d\udcba)\n- Third response: 4 emojis (\ud83e\uddb4\ud83e\udd95\ud83e\uddb4\ud83d\udd25)\n- Fourth response: 2 emojis (\ud83c\uddee\ud83c\uddf9\ud83c\udf5d\ud83d\udc90)\n\nTotal in right subfigure: 4 + 4 + 4 + 2 = 14 emojis\n\nTotal in left subfigure: 0 emojis\n\nDifference: 14 - 0 = 14\n\nAnswer: 14", "citations": [], "input_tokens": 199223, "output_tokens": 267, "cost_micros": 601674, "latency_ms": 43578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}} -{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "original_error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:40Z", "latency_ms": 43593, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:53:24Z", "latency_ms": 44391, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:54:10Z", "latency_ms": 35797, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:54:48Z", "latency_ms": 42266, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T01:55:41Z", "latency_ms": 31983, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 31843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "blue"}} -{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:59Z", "latency_ms": 33250, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:53:33Z", "latency_ms": 40889, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:54:16Z", "latency_ms": 51515, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:55:10Z", "latency_ms": 54500, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T01:56:14Z", "latency_ms": 32641, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "youtube music"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:56:13Z", "latency_ms": 42110, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:56:56Z", "latency_ms": 52202, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:57:50Z", "latency_ms": 40469, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:58:34Z", "latency_ms": 49766, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T01:59:30Z", "latency_ms": 42110, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "arm": "azure_premium_lc", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:56:47Z", "latency_ms": 24578, "error": null, "raw_text_chars": 640}]}, "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in the screenshot.\n\nLooking through the document, I can find Figure 111 which is part of Section 9.8 on GUI Navigation, specifically about Online Shopping for an Ergonomic Keyboard.\n\nIn Figure 111, the screenshot shows a shopping cart with a keyboard item. The text indicates:\n\n\"Subtotal $4999\"\n\nAnd in the cart details, it shows:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...\n$4999\"\n\nThe price shown is $49.99 (represented as $4999 in the format used in the screenshot, which appears to be missing the decimal point).\n\nAnswer: 49.99", "citations": [], "input_tokens": 133648, "output_tokens": 176, "cost_micros": 403584, "latency_ms": 24578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:57:11Z", "latency_ms": 69062, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:58:21Z", "latency_ms": 35639, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:58:59Z", "latency_ms": 51312, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:59:54Z", "latency_ms": 49218, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:00:48Z", "latency_ms": 56375, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "arm": "azure_basic_lc", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:00:12Z", "latency_ms": 7967, "error": null, "raw_text_chars": 594}]}, "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and identify the battery percentage shown in that screenshot.\n\nLooking through the document for Figure 107, I can see it's in Section 9.8 on GUI Navigation, specifically related to online shopping.\n\nIn the image caption list, Figure 107 is described as \"Section 9.8: online shopping.\"\n\nLooking at the actual Figure 107 in the document, I can see it's a smartphone screenshot showing an Amazon shopping interface. At the top of the screenshot, there is a time display \"5:32\" and next to it shows \"75\" which represents the battery percentage.\n\nAnswer: 75", "citations": [], "input_tokens": 117429, "output_tokens": 143, "cost_micros": 354432, "latency_ms": 7967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.0", "normalised_gold": "76.0"}} -{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:00:20Z", "latency_ms": 33344, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:00:54Z", "latency_ms": 43733, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:01:39Z", "latency_ms": 33390, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:02:16Z", "latency_ms": 33641, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:02:59Z", "latency_ms": 38000, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "ukraine"}} -{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:01:45Z", "latency_ms": 35218, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:02:21Z", "latency_ms": 44921, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:03:08Z", "latency_ms": 52842, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:04:05Z", "latency_ms": 27250, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:04:41Z", "latency_ms": 48031, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "jooodeee"}} -{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:37Z", "latency_ms": 20843, "error": null, "raw_text_chars": 345}]}, "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 2, the task formulation section discusses the \"Setting of Conscious Incompetence.\"\n\nThe relevant text states: \"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 60205, "output_tokens": 80, "cost_micros": 181815, "latency_ms": 20828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}} -{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:57Z", "latency_ms": 14639, "error": null, "raw_text_chars": 1219}]}, "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to identify which modules use Large Language Models (LLMs) as their backbone.\n\nFrom Section 3, the QACHECK system has 5 modules:\n1. **Claim Verifier** - Section 3.1 states it's \"based on InstructGPT\" (an LLM)\n2. **Question Generator** - Section 3.2 states \"Similar to the claim verifier, we also leverage InstructGPT\" (an LLM)\n3. **Question Answering Model** - Section 3.3 describes three implementations, with the default being \"GPT Reciter-Reader\" which uses InstructGPT (an LLM). However, this module also has non-LLM options (Retriever-Reader, FLAN-T5).\n4. **QA Validator** - Section 3.4 states \"We again implement the validator by prompting InstructGPT\" (an LLM)\n5. **Reasoner** - Section 3.5 provides \"two different implementations: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\"\n\nCounting the modules that use LLM backbones:\n- Claim Verifier: Yes (InstructGPT)\n- Question Generator: Yes (InstructGPT)\n- Question Answering Model: Yes (uses GPT Reciter-Reader by default)\n- QA Validator: Yes (InstructGPT)\n- Reasoner: Yes (InstructGPT is one of the two implementations)\n\nAll 5 modules have LLM implementations, with some offering alternatives.\n\nAnswer: 5", "citations": [], "input_tokens": 27149, "output_tokens": 403, "cost_micros": 87492, "latency_ms": 14639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}} -{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:12Z", "latency_ms": 6562, "error": null, "raw_text_chars": 406}]}, "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the sociology subject in MMMU.\n\nIn Figure 3, which shows the distribution of subjects across disciplines, I can see:\n\nUnder \"Humanities & Social Sci. (9%)\":\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- **Sociology (287, 2.48%)**\n\nThe number 287 represents the number of samples in the sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 103232, "output_tokens": 123, "cost_micros": 311541, "latency_ms": 6562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}} -{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:19Z", "latency_ms": 62062, "error": null, "raw_text_chars": 760}]}, "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Economics-related error cases that fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\nFrom the Case Study section (pages 20-21), I can see Table 10 which shows the index of case study figures by subjects with associated error categories.\n\nLooking at the Economics row in Table 10:\n- Economics has: Correct Case (22), Perceptual Error (23, 24)\n\nThis indicates that there are 2 Economics-related error cases in the Perceptual Error category (Figures 23 and 24).\n\nLet me verify by checking the actual figures:\n- Figure 23 (page 37): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n- Figure 24 (page 38): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n\nAnswer: 2", "citations": [], "input_tokens": 259127, "output_tokens": 212, "cost_micros": 1559532, "latency_ms": 62014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}} -{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_basic_lc", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:21Z", "latency_ms": 5093, "error": null, "raw_text_chars": 174}]}, "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists the subfields of each subject:\n\nFor Energy & Power, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 83153, "output_tokens": 47, "cost_micros": 250164, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}} -{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:26Z", "latency_ms": 64485, "error": null, "raw_text_chars": 1513}]}, "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 1 in the document and count the different icons shown as different image types.\n\nLooking at page 1, Figure 1 is titled \"Overview of the MMMU dataset\" and shows four main sections:\n\n1. **Comprehensive Disciplines** - Shows icons/images representing different disciplines (Engineering, Science, Art & Design, Business, Humanities & Social Sci., Medicine)\n\n2. **Heterogeneous Image Types** - Shows various image type icons including:\n - Diagrams\n - Tables\n - Plots and Charts\n - Photographs\n - Chemical Structures\n - Paintings\n - Medical Images\n - Sheet Music\n - Geometric\n - Pathology images\n - Microscopic Images\n - Comics\n\n3. **Interleaved Text and Images** - Shows a medical question example with 3 MRI images\n\n4. **Expert-level Skills Test** - Shows a diagram with three components: Perception, Knowledge, and Reasoning\n\nThe question asks specifically about \"different icon are shown as different image types\" in Figure 1. Looking at the \"Heterogeneous Image Types\" section, I can count the distinct image type icons shown.\n\nCounting the image types listed in the \"Heterogeneous Image Types\" section:\nDiagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, and there's an ellipsis (...) suggesting more types.\n\nBased on what's explicitly visible and labeled in Figure 1, I can count approximately 12 distinct image type representations shown.\n\nAnswer: 12", "citations": [], "input_tokens": 259123, "output_tokens": 378, "cost_micros": 1563243, "latency_ms": 64406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}} -{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:29Z", "latency_ms": 75843, "error": null, "raw_text_chars": 831}]}, "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts that appear in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Figure 4: A comparison chart showing benchmarks plotted on a 2D graph with \"Breadth (Knowledge)\" on x-axis and \"Depth (Reasoning)\" on y-axis\n- A table comparing different datasets\nTotal: 1 chart\n\n**Page 6:**\n- Table 2: Results table (not a chart)\nTotal: 0 charts\n\n**Page 7:**\n- Figure 5: Bar chart showing \"Performance of models on different types of images\"\n- Table 3: Results table (not a chart)\nTotal: 1 chart\n\n**Page 8:**\n- Figure 6: Pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\nTotal: 1 chart\n\n**Page 9:**\n- No charts, only text\nTotal: 0 charts\n\n**Page 10:**\n- No charts, only text (references section)\nTotal: 0 charts\n\nTotal charts in pages 5-10: 1 + 0 + 1 + 1 + 0 + 0 = 3\n\nAnswer: 3", "citations": [], "input_tokens": 259120, "output_tokens": 289, "cost_micros": 1561222, "latency_ms": 75796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:30Z", "latency_ms": 77217, "error": null, "raw_text_chars": 404}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 on page 8 of the document, which shows the \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe error categories and their percentages are:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 259144, "output_tokens": 133, "cost_micros": 1557856, "latency_ms": 77171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "azure_premium_lc", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:45Z", "latency_ms": 7750, "error": null, "raw_text_chars": 387}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe pie chart shows the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 109199, "output_tokens": 125, "cost_micros": 329472, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:53Z", "latency_ms": 59125, "error": null, "raw_text_chars": 830}]}, "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 for error types mentioned in the case studies.\n\nFrom page 95 (Figure 80 - Architecture and Engineering: Surveying and Mapping):\n- This is a correct case, no error type mentioned\n\nFrom page 96 (Figure 82 - Architecture and Engineering: Civil Engineering):\n- Error Category: Reasoning Error\n\nFrom page 97 (Figure 83 - Computer Science: Data Structure and Algorithm):\n- This is a correct case, no error type mentioned\n\nFrom page 98 (Figure 84 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error, Lack of Knowledge\n\nFrom page 99 (Figure 85 - Computer Science: Operating System):\n- Error Category: Perceptual Error\n\nFrom page 100 (Figure 86 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 259136, "output_tokens": 226, "cost_micros": 1559901, "latency_ms": 59077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}} -{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:48Z", "latency_ms": 11110, "error": null, "raw_text_chars": 1296}]}, "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (correct predictions by GPT-4V) in Pages 65-70.\n\nLet me go through each figure in this range:\n\n**Page 65 - Figure 51 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (E) Familial polyposis coli\n- Ground Truth: (E) Familial polyposis coli\n- **SUCCESS**\n\n**Page 66 - Figure 52 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (D) Ruptured berry aneurysm\n- Ground Truth: (D) Ruptured berry aneurysm\n- **SUCCESS**\n\n**Page 67 - Figure 53 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 68 - Figure 54 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 69 - Figure 55 (Clinical Medicine: Heart Disease)**\n- GPT-4V answered (B) Mitral regurgitation\n- Ground Truth: (C) Aortic value regurgitation\n- Error Category: Lack of Knowledge\n- **FAILURE**\n\n**Page 70 - Figure 56 (Diagnostics and Laboratory Medicine: Radiology)**\n- GPT-4V correctly identified (C) Dandy-Walker syndrome\n- Ground Truth: (C) Dandy-Walker syndrome\n- **SUCCESS**\n\nTotal successful cases: 3\n\nAnswer: 3", "citations": [], "input_tokens": 103232, "output_tokens": 424, "cost_micros": 316056, "latency_ms": 11110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}} -{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:52Z", "latency_ms": 23593, "error": null, "raw_text_chars": 1091}]}, "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the figure with a tree shape and identify which branch has the least leaves.\n\nLooking through the document, I can see Figure 1 on page 2, which shows a \"Technology tree of RAG research\" with a tree-like structure.\n\nExamining this tree diagram, I can see it has a trunk labeled \"Retrieval-Augmented Generation\" and branches extending upward. The main branches appear to be:\n\n1. A left branch (colored in teal/green) showing the evolution from pre-training through fine-tuning stages\n2. A middle branch (colored in orange) labeled \"Instructlens\" \n3. A right branch (colored in blue) showing the inference stage with multiple methods\n\nLooking at the number of \"leaves\" (individual methods/papers) on each branch:\n- The left branch (Pre-training/Fine-tuning) has several leaves\n- The middle branch (Instructlens) appears to have the fewest leaves - it's relatively short with minimal branching\n- The right branch (Inference) has many leaves with extensive branching\n\nThe branch labeled \"Instructlens\" in the middle appears to have the least number of leaves.\n\nAnswer: Instructlens", "citations": [], "input_tokens": 64633, "output_tokens": 269, "cost_micros": 197934, "latency_ms": 23577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "instructlens", "normalised_gold": "pre training"}} -{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "arm": "azure_premium_lc", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:59Z", "latency_ms": 7156, "error": null, "raw_text_chars": 752}]}, "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Appendix F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 Tree Traversal Algorithm:**\n- Lines: function declaration, initialization of Scurrent, for layer loop, initialization of topk, for node loop, score calculation, append to top_k, end for, Slayer assignment, Scurrent update, end for, return statement, end function\n- Total: 13 lines\n\n**Algorithm 2 Collapsed Tree Algorithm:**\n- Lines: function declaration, flatten tree, initialization of top_nodes, for node loop, append with dot product, end for, sort top_nodes, result initialization, total_tokens initialization, for node loop, if condition, result append, end if, total_tokens update, end for, return statement, end function\n- Total: 16 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 27211, "output_tokens": 190, "cost_micros": 84483, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}} -{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:08:06Z", "latency_ms": 16641, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:08:23Z", "latency_ms": 11860, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:08:36Z", "latency_ms": 12422, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:08:54Z", "latency_ms": 19625, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:09:25Z", "latency_ms": 14078, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "figure 5, figure 6"}} -{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:08:16Z", "latency_ms": 13922, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:08:30Z", "latency_ms": 25952, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:08:57Z", "latency_ms": 14718, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:09:14Z", "latency_ms": 33452, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:09:54Z", "latency_ms": 22093, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "text tokens"}} -{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:09:39Z", "latency_ms": 11906, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:09:52Z", "latency_ms": 15921, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:10:10Z", "latency_ms": 21921, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:10:34Z", "latency_ms": 11906, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:10:53Z", "latency_ms": 13686, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "18"}} -{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:10:16Z", "latency_ms": 14281, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:10:32Z", "latency_ms": 12125, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:10:45Z", "latency_ms": 11952, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:11:01Z", "latency_ms": 20641, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:11:30Z", "latency_ms": 13985, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}} -{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:11:06Z", "latency_ms": 17157, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"{\\\"message\\\":\\\"Input is too long.\\\"}\",\"provider_name\":\"Amazon Bedrock\",\"is_byok\":false}},\"user_id\":\"user_3CNdnY1vL3Ln9TYRiGAii5kmBvu\"}", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:11:25Z", "latency_ms": 12125, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:11:40Z", "latency_ms": 13734, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:11:59Z", "latency_ms": 15828, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:12:25Z", "latency_ms": 19985, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "no"}} -{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "original_error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:11:44Z", "latency_ms": 15875, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:12:00Z", "latency_ms": 16625, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:12:19Z", "latency_ms": 12937, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:12:35Z", "latency_ms": 14625, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:12:57Z", "latency_ms": 12156, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n \\\"error\\\": {\\n \\\"code\\\": 400,\\n \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "150k"}} diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json deleted file mode 100644 index 7c99def5f2..0000000000 --- a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json +++ /dev/null @@ -1,100 +0,0 @@ -{ - "config": { - "base_delay": 1.0, - "concurrency": 2, - "llm_model": "anthropic/claude-sonnet-4.5", - "max_attempts": 5, - "max_delay": 30.0, - "max_output_tokens": 512, - "pdf_engine": "native" - }, - "elapsed_s": 1373.6, - "n_failed_rows_input": 37, - "n_retried": 37, - "per_arm": { - "azure_basic_lc": { - "attempts_distribution": [ - 1 - ], - "recovered": 1, - "recovery_rate": 1.0, - "still_failed": 0, - "tried": 1 - }, - "azure_premium_lc": { - "attempts_distribution": [ - 1, - 1, - 1 - ], - "recovered": 3, - "recovery_rate": 1.0, - "still_failed": 0, - "tried": 3 - }, - "llamacloud_basic_lc": { - "attempts_distribution": [ - 1, - 1 - ], - "recovered": 2, - "recovery_rate": 1.0, - "still_failed": 0, - "tried": 2 - }, - "llamacloud_premium_lc": { - "attempts_distribution": [ - 1, - 1, - 1, - 1 - ], - "recovered": 4, - "recovery_rate": 1.0, - "still_failed": 0, - "tried": 4 - }, - "native_pdf": { - "attempts_distribution": [ - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5 - ], - "recovered": 15, - "recovery_rate": 0.5555555555555556, - "still_failed": 12, - "tried": 27 - } - }, - "raw_retries_path": "data\\multimodal_doc\\runs\\2026-05-14T00-53-19Z\\parser_compare\\raw_retries.jsonl", - "run_id": "2026-05-14T00-53-19Z", - "totals": { - "recovered": 25, - "still_failed": 12, - "tried": 37 - } -} diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json deleted file mode 100644 index b6c59e2bc7..0000000000 --- a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json +++ /dev/null @@ -1,1022 +0,0 @@ -{ - "benchmark": "parser_compare", - "extra": { - "active_arms": [ - "native_pdf", - "azure_basic_lc", - "azure_premium_lc", - "llamacloud_basic_lc", - "llamacloud_premium_lc", - "surfsense_agentic" - ], - "chat_model_id": -5138454, - "concurrency": 2, - "llm_model": "anthropic/claude-sonnet-4.5", - "n_pdfs": 30, - "n_questions": 171, - "no_mentions": false, - "pdf_engine": "native", - "preprocess_tariff": { - "basic_per_1k_pages": 1.0, - "premium_per_1k_pages": 10.0 - }, - "provider_model": "anthropic/claude-sonnet-4.5", - "scenario": "head-to-head", - "vision_provider_model": "anthropic/claude-sonnet-4.5" - }, - "metrics": { - "n_questions": 171, - "n_unique_pdfs": 30, - "per_arm": { - "azure_basic_lc": { - "accuracy": 0.543859649122807, - "ci_high": 0.6167298584106974, - "ci_low": 0.46906215053943956, - "f1_mean": 0.5655925434323409, - "input_tokens_mean": 31882.88888888889, - "latency_ms_median": 7094, - "latency_ms_p95": 11984, - "llm_cost_per_q": 0.09939822807017544, - "n": 171, - "n_correct": 93, - "n_total": 171, - "output_tokens_mean": 249.97076023391813, - "preprocess_cost_per_q": 0.006771929824561403, - "preprocess_cost_total": 1.158, - "preprocess_label": "basic tier ($/basic/page = $0.0010)", - "preprocess_per_page_usd": 0.001, - "total_cost_per_q": 0.10617015789473684 - }, - "azure_premium_lc": { - "accuracy": 0.5672514619883041, - "ci_high": 0.6392285382926538, - "ci_low": 0.4923192087642231, - "f1_mean": 0.5956074243139755, - "input_tokens_mean": 39786.783625731, - "latency_ms_median": 6858, - "latency_ms_p95": 11608, - "llm_cost_per_q": 0.13734295321637427, - "n": 171, - "n_correct": 97, - "n_total": 171, - "output_tokens_mean": 223.39766081871346, - "preprocess_cost_per_q": 0.06771929824561404, - "preprocess_cost_total": 11.58, - "preprocess_label": "premium tier ($/premium/page = $0.0100)", - "preprocess_per_page_usd": 0.01, - "total_cost_per_q": 0.2050622514619883 - }, - "llamacloud_basic_lc": { - "accuracy": 0.5029239766081871, - "ci_high": 0.5769717486785515, - "ci_low": 0.42874771858479094, - "f1_mean": 0.5323894603029942, - "input_tokens_mean": 31493.333333333332, - "latency_ms_median": 7125, - "latency_ms_p95": 11922, - "llm_cost_per_q": 0.09812833333333335, - "n": 171, - "n_correct": 86, - "n_total": 171, - "output_tokens_mean": 243.22222222222223, - "preprocess_cost_per_q": 0.006771929824561403, - "preprocess_cost_total": 1.158, - "preprocess_label": "basic tier ($/basic/page = $0.0010)", - "preprocess_per_page_usd": 0.001, - "total_cost_per_q": 0.10490026315789475 - }, - "llamacloud_premium_lc": { - "accuracy": 0.5847953216374269, - "ci_high": 0.6559957259750998, - "ci_low": 0.5098688246618316, - "f1_mean": 0.6109434348065313, - "input_tokens_mean": 39130.666666666664, - "latency_ms_median": 6844, - "latency_ms_p95": 12656, - "llm_cost_per_q": 0.12080787719298246, - "n": 171, - "n_correct": 100, - "n_total": 171, - "output_tokens_mean": 227.7251461988304, - "preprocess_cost_per_q": 0.06771929824561404, - "preprocess_cost_total": 11.58, - "preprocess_label": "premium tier ($/premium/page = $0.0100)", - "preprocess_per_page_usd": 0.01, - "total_cost_per_q": 0.18852717543859648 - }, - "native_pdf": { - "accuracy": 0.47953216374269003, - "ci_high": 0.5540343616319661, - "ci_low": 0.40592936752463654, - "f1_mean": 0.5040888253091556, - "input_tokens_mean": 65772.61988304094, - "latency_ms_median": 29484, - "latency_ms_p95": 60530, - "llm_cost_per_q": 0.25520109356725146, - "n": 171, - "n_correct": 82, - "n_total": 171, - "output_tokens_mean": 209.09941520467837, - "preprocess_cost_per_q": 0.0, - "preprocess_cost_total": 0.0, - "preprocess_label": "n/a (PDF attached natively)", - "preprocess_per_page_usd": 0.0, - "total_cost_per_q": 0.25520109356725146 - }, - "surfsense_agentic": { - "accuracy": 0.5321637426900585, - "ci_high": 0.6054202002109953, - "ci_low": 0.4574939396857719, - "f1_mean": 0.5430214722229827, - "input_tokens_mean": 0.0, - "latency_ms_median": 52827, - "latency_ms_p95": 164109, - "llm_cost_per_q": 0.0, - "n": 171, - "n_correct": 91, - "n_total": 171, - "output_tokens_mean": 0.0, - "preprocess_cost_per_q": 0.06771929824561404, - "preprocess_cost_total": 11.58, - "preprocess_label": "premium tier (ingested by SurfSense at processing_mode=premium + vision_llm=on)", - "preprocess_per_page_usd": 0.01, - "total_cost_per_q": 0.06771929824561404 - } - }, - "per_pdf": { - "05-03-18-political-release.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 17 - }, - "0b85477387a9d0cc33fca0f4becaa0e5.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 0.888888888888889 - }, - "azure_premium_lc": { - "correct": true, - "f1": 0.888888888888889 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.7272727272727273 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 0.888888888888889 - }, - "native_pdf": { - "correct": true, - "f1": 0.888888888888889 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.28571428571428575 - } - }, - "pages": 16 - }, - "0e94b4197b10096b1f4c699701570fbf.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 15 - }, - "11-21-16-Updated-Post-Election-Release.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.6666666666666666 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.6666666666666666 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 45 - }, - "12-15-15-ISIS-and-terrorism-release-final.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 28 - }, - "2005.12872v3.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 26 - }, - "2021-Apple-Catalog.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 55 - }, - "2023.acl-long.386.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.5 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.5 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.5 - } - }, - "pages": 24 - }, - "2023.findings-emnlp.248.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 19 - }, - "2024.ug.eprospectus.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.8 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 27 - }, - "2210.02442v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 24 - }, - "2303.05039v2.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 9 - }, - "2303.08559v2.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 30 - }, - "2305.13186v3.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.5 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.5 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 27 - }, - "2305.14160v4.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 16 - }, - "2306.05425v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.3333333333333333 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.4 - }, - "native_pdf": { - "correct": false, - "f1": 0.4 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 22 - }, - "2307.09288v2.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 77 - }, - "2309.17421v2.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 166 - }, - "2310.05634v2.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.28571428571428575 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.5 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.6 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 24 - }, - "2310.07609v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 10 - }, - "2310.09158v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 23 - }, - "2311.16502v3.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 117 - }, - "2312.04350v3.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 28 - }, - "2312.09390v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.11764705882352941 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.25 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.2222222222222222 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.16666666666666666 - }, - "native_pdf": { - "correct": false, - "f1": 0.11764705882352941 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.16666666666666666 - } - }, - "pages": 49 - }, - "2312.10997v5.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 21 - }, - "2401.18059v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": false, - "f1": 0.28571428571428575 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.3333333333333333 - }, - "llamacloud_basic_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.4 - }, - "surfsense_agentic": { - "correct": false, - "f1": 0.0 - } - }, - "pages": 23 - }, - "2405.09818v1.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": false, - "f1": 0.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": false, - "f1": 0.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 27 - }, - "3276a5b991c49cf5f9a4af0f7d6fce67.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 0.6666666666666666 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 0.6666666666666666 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 0.6666666666666666 - } - }, - "pages": 16 - }, - "379f44022bb27aa53efd5d322c7b57bf.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 17 - }, - "3M_2018_10K.pdf": { - "arms": { - "azure_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "azure_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_basic_lc": { - "correct": true, - "f1": 1.0 - }, - "llamacloud_premium_lc": { - "correct": true, - "f1": 1.0 - }, - "native_pdf": { - "correct": true, - "f1": 1.0 - }, - "surfsense_agentic": { - "correct": true, - "f1": 1.0 - } - }, - "pages": 160 - } - }, - "total_pages_in_scope": 1158 - }, - "raw_path": "raw.jsonl", - "suite": "multimodal_doc" -} diff --git a/surfsense_evals/pyproject.toml b/surfsense_evals/pyproject.toml deleted file mode 100644 index 3a03e1ed84..0000000000 --- a/surfsense_evals/pyproject.toml +++ /dev/null @@ -1,63 +0,0 @@ -[project] -name = "surfsense-evals" -version = "0.1.0" -description = "Domain-agnostic evaluation harness for SurfSense (medical RAG suite ships first; legal/finance/code suites slot in under suites/)." -readme = "README.md" -requires-python = ">=3.12" -license = { text = "Apache-2.0" } -authors = [{ name = "SurfSense" }] - -dependencies = [ - "httpx>=0.27.0", - "httpx-sse>=0.4.0", - "datasets>=2.21.0", - "huggingface_hub>=0.24.0", - "reportlab>=4.0.0", - "Pillow>=10.0.0", - "pyarrow>=15.0.0", - "pydantic>=2.6.0", - "tqdm>=4.66.0", - "numpy>=1.26.0", - "scikit-learn>=1.4.0", - "scipy>=1.12.0", - "python-dotenv>=1.0.0", - "rich>=13.7.0", - "trafilatura>=1.12.0", - "pypdf>=5.1.0", - "azure-ai-documentintelligence>=1.0.2", - "llama-cloud-services>=0.6.25", -] - -[project.optional-dependencies] -dev = [ - "pytest>=8.0.0", - "pytest-asyncio>=0.23.0", - "respx>=0.21.0", - "ruff>=0.5.0", -] - -[project.scripts] -surfsense-evals = "surfsense_evals.core.cli:main" - -[build-system] -requires = ["setuptools>=61.0", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.setuptools.packages.find] -where = ["src"] -include = ["surfsense_evals*"] - -[tool.pytest.ini_options] -asyncio_mode = "auto" -testpaths = ["tests"] -markers = [ - "integration: opt-in tests that hit a live SurfSense instance (run with `-m integration`)", -] - -[tool.ruff] -line-length = 100 -target-version = "py312" - -[tool.ruff.lint] -select = ["E", "F", "I", "B", "UP", "SIM", "ASYNC"] -ignore = ["E501"] diff --git a/surfsense_evals/reports/.gitignore b/surfsense_evals/reports/.gitignore deleted file mode 100644 index e33c279369..0000000000 --- a/surfsense_evals/reports/.gitignore +++ /dev/null @@ -1,13 +0,0 @@ -# Default: don't track auto-generated `summary.md` / `summary.json` from -# every benchmark run — those are derivative of `data/.../runs//`. -* -!.gitignore - -# Hand-curated sample report kept as a reference for the medical suite. -!medical/ -!medical/sample_summary.md - -# Hand-curated blog-ready writeups (one per experiment) — these *are* -# the public citation surface and must travel with the repo. -!blog/ -!blog/*.md diff --git a/surfsense_evals/scripts/analyze_failure_timing.py b/surfsense_evals/scripts/analyze_failure_timing.py deleted file mode 100644 index 14b76852f8..0000000000 --- a/surfsense_evals/scripts/analyze_failure_timing.py +++ /dev/null @@ -1,139 +0,0 @@ -"""Were the SSL failures clustered in time (network blip) or evenly -distributed (sustained limit)? Group failures by 1-min buckets using -the run start time and the per-row latency_ms / answer order. - -Also: for the one *real* intrinsic failure — the 30MB Anthropic limit -on 2405.09818v1.pdf::Q007 — print the full error message + raw payload -sizes so the blog has a clean root cause. -""" - -from __future__ import annotations - -import json -from collections import Counter, defaultdict -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] -RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare" -RAW = RUN / "raw.jsonl" -PDFS = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs" - - -def main() -> None: - rows = [ - json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip() - ] - - # 1) SSL clustering: failures by question index per arm - by_arm_idx: dict[str, list[tuple[int, str]]] = defaultdict(list) - qid_order: dict[str, int] = {} - arm_seen_count: dict[str, int] = defaultdict(int) - for row in rows: - arm = row["arm"] - idx = arm_seen_count[arm] - arm_seen_count[arm] += 1 - qid_order[f"{arm}::{row['qid']}"] = idx - err = row.get("error") or "" - cluster = ( - "ssl" - if "SSLError" in err - else ( - "empty" - if not (row.get("raw_text") or "").strip() and not err - else ( - "5xx" - if "502" in err or "503" in err - else ( - "size_limit" - if "exceeds" in err.lower() and "limit" in err.lower() - else ("other_err" if err else "ok") - ) - ) - ) - ) - if cluster != "ok": - by_arm_idx[arm].append((idx, cluster)) - - print("=" * 80) - print("SSL/network-error indices per arm (each arm processes 171 questions in") - print("order; index = sequential position within that arm). Tight clustering") - print("in time = transient blip, even spread = sustained limit.") - print("=" * 80) - for arm in sorted(by_arm_idx): - items = by_arm_idx[arm] - if not items: - continue - idxs = sorted(set(i for i, _ in items)) - print(f"\n{arm}: {len(items)} failures at indices {idxs}") - # show clusters - cluster_runs = [] - cur = [idxs[0]] - for i in idxs[1:]: - if i - cur[-1] <= 5: # within 5 questions = same time window - cur.append(i) - else: - cluster_runs.append(cur) - cur = [i] - cluster_runs.append(cur) - print(f" clusters (gap<=5): {len(cluster_runs)}: {cluster_runs}") - - # 2) The 30MB intrinsic failure — full details - print() - print("=" * 80) - print("Intrinsic failure: 30MB Anthropic input limit on 2405.09818v1.pdf::Q007") - print("=" * 80) - for row in rows: - if row["qid"] == "2405.09818v1.pdf::Q007" and row["arm"] == "native_pdf": - err = row.get("error") or "" - print(f" qid: {row['qid']}") - print(f" doc: {row['doc_id']}, pages: {row.get('pages')}") - pdf_path = PDFS / row["doc_id"] - if pdf_path.exists(): - size_mb = pdf_path.stat().st_size / (1024 * 1024) - print(f" PDF size on disk: {size_mb:.1f} MB") - # base64 inflates ~33% - est_b64 = size_mb * 1.33 - print(f" estimated base64 wire size: {est_b64:.1f} MB") - print(f" full error: {err[:600]}") - break - - # 3) Per-PDF: which PDFs are pathological? - print() - print("=" * 80) - print("Per-PDF failure breakdown across all 6 arms (only PDFs with failures)") - print("=" * 80) - by_pdf: dict[str, list[dict]] = defaultdict(list) - for row in rows: - err = row.get("error") or "" - empty = not (row.get("raw_text") or "").strip() - if err or empty: - by_pdf[row["doc_id"]].append( - { - "arm": row["arm"], - "qid": row["qid"], - "err_kind": ( - "ssl" - if "SSLError" in err - else "size_limit" - if "exceeds" in err.lower() and "limit" in err.lower() - else "5xx" - if "502" in err or "503" in err - else "json_decode" - if "JSONDecodeError" in err - else "empty" - if empty and not err - else "other" - ), - "pages": row.get("pages"), - } - ) - for doc, items in sorted(by_pdf.items(), key=lambda x: (-len(x[1]), x[0])): - kinds = Counter(i["err_kind"] for i in items) - arms = sorted({i["arm"] for i in items}) - pages = items[0]["pages"] - print(f" {doc} pages={pages} failures={len(items)} arms={arms}") - print(f" kinds: {dict(kinds)}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/analyze_failures.py b/surfsense_evals/scripts/analyze_failures.py deleted file mode 100644 index f60038c006..0000000000 --- a/surfsense_evals/scripts/analyze_failures.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Drill into the parser_compare n=171 raw.jsonl to surface every -failure, group by arm + PDF, and dump the underlying error strings so -we can write up a clean failure-mode taxonomy for the blog post. - -Outputs (printed to stdout + written to `failures_n171.json`): -* per-arm failure count and rate -* per-PDF failure count across all arms (which docs are pathological?) -* error-string clusters per arm (so we can give human-readable causes) -* sample failure rows (one per cluster) for the appendix -""" - -from __future__ import annotations - -import json -from collections import Counter, defaultdict -from pathlib import Path -from typing import Any - -REPO = Path(__file__).resolve().parents[1] -RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare" -RAW = RUN / "raw.jsonl" -OUT = REPO / "scripts" / "failures_n171.json" - - -def _classify(error: str | None, raw_text: str) -> str: - """Coarse-grained bucket for an error message.""" - - blob = (error or "").lower() - if not blob and not raw_text.strip(): - return "empty_response" - if "rate limit" in blob or "429" in blob: - return "rate_limit" - if "context_length" in blob or "context window" in blob or "too many tokens" in blob: - return "context_overflow" - if "could not process image" in blob or "invalid image" in blob: - return "image_decode_failure" - if "could not process pdf" in blob or "invalid_request_error" in blob and "pdf" in blob: - return "pdf_decode_failure" - if "timeout" in blob or "timed out" in blob: - return "timeout" - if "5xx" in blob or "internal server error" in blob or "503" in blob or "502" in blob: - return "provider_5xx" - if "filenotfound" in blob: - return "missing_extraction" - if "badrequest" in blob: - return "provider_400" - if blob: - return "other_error" - return "unknown" - - -def main() -> None: - rows = [ - json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip() - ] - - by_arm_failures: dict[str, list[dict]] = defaultdict(list) - by_pdf_failures: dict[str, list[dict]] = defaultdict(list) - error_clusters: dict[str, dict[str, list[dict]]] = defaultdict(lambda: defaultdict(list)) - - n_per_arm: dict[str, int] = defaultdict(int) - for row in rows: - arm = row["arm"] - n_per_arm[arm] += 1 - err = row.get("error") - raw_text = row.get("raw_text") or "" - if err or not raw_text.strip(): - cluster = _classify(err, raw_text) - entry = { - "qid": row["qid"], - "doc_id": row["doc_id"], - "answer_format": row["answer_format"], - "gold": row["gold"], - "error": err, - "cluster": cluster, - "raw_text_len": len(raw_text), - "pages": row.get("pages"), - } - by_arm_failures[arm].append(entry) - by_pdf_failures[row["doc_id"]].append({**entry, "arm": arm}) - error_clusters[arm][cluster].append(entry) - - print("=" * 90) - print("Per-arm failure count & rate") - print("=" * 90) - print(f"{'arm':<25} {'n':>4} {'fail':>5} {'rate%':>6}") - for arm in sorted(n_per_arm): - f = len(by_arm_failures[arm]) - n = n_per_arm[arm] - print(f"{arm:<25} {n:>4} {f:>5} {f / n * 100:>5.1f}%") - - print() - print("=" * 90) - print("Failure clusters per arm") - print("=" * 90) - for arm in sorted(error_clusters): - print(f"\n{arm}:") - for cluster, items in sorted(error_clusters[arm].items()): - print(f" {cluster:<22} {len(items):>3}") - sample = items[0] - err_short = (sample["error"] or "")[:200].replace("\n", " ") - print(f" example: {sample['qid']} doc={sample['doc_id']} pages={sample['pages']}") - print(f" error: {err_short}") - - print() - print("=" * 90) - print("Per-PDF failure totals (PDFs with >=2 failures)") - print("=" * 90) - pdf_counts = Counter({pdf: len(rows) for pdf, rows in by_pdf_failures.items()}) - for pdf, count in pdf_counts.most_common(): - if count < 2: - break - arms_failed = sorted({r["arm"] for r in by_pdf_failures[pdf]}) - pages = by_pdf_failures[pdf][0].get("pages") - print(f" {pdf} pages={pages} failures={count} arms={arms_failed}") - - print() - print("=" * 90) - print("All native_pdf failures (one row per failure)") - print("=" * 90) - for entry in by_arm_failures.get("native_pdf", []): - err = (entry["error"] or "(no error string)")[:240].replace("\n", " ") - print( - f" {entry['qid']} doc={entry['doc_id']} pages={entry['pages']} cluster={entry['cluster']}" - ) - print(f" err: {err}") - - summary: dict[str, Any] = { - "per_arm": { - arm: { - "n": n_per_arm[arm], - "failures": len(by_arm_failures[arm]), - "rate": len(by_arm_failures[arm]) / n_per_arm[arm], - "clusters": {cluster: len(items) for cluster, items in error_clusters[arm].items()}, - "rows": by_arm_failures[arm], - } - for arm in sorted(n_per_arm) - }, - "per_pdf": { - pdf: [{**r, "arm": r["arm"]} for r in failures] - for pdf, failures in by_pdf_failures.items() - }, - } - OUT.write_text(json.dumps(summary, indent=2), encoding="utf-8") - print(f"\nWrote: {OUT}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/check_extraction_sizes.py b/surfsense_evals/scripts/check_extraction_sizes.py deleted file mode 100644 index 1755e9b6cc..0000000000 --- a/surfsense_evals/scripts/check_extraction_sizes.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Sanity check extraction sizes against Sonnet 4.5's context window. - -Sonnet 4.5 supports ~200k tokens. As a *very* rough heuristic, English -markdown is ~4 chars/token, so anything over ~750k chars likely won't -fit alongside the system + question + 512 max_output_tokens. Print -warnings for any extraction that's at risk. -""" - -from __future__ import annotations - -import json -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] -MAP = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl" - -CHARS_PER_TOKEN = 4 -CTX_TOKENS = 200_000 -PROMPT_OVERHEAD_TOKENS = 1_000 # system + question + format hint -MAX_OUTPUT_TOKENS = 512 -SAFE_CHARS = (CTX_TOKENS - PROMPT_OVERHEAD_TOKENS - MAX_OUTPUT_TOKENS) * CHARS_PER_TOKEN - - -def main() -> None: - rows = [ - json.loads(line) for line in MAP.read_text(encoding="utf-8").splitlines() if line.strip() - ] - - total = len(rows) - arm_max: dict[str, tuple[int, str]] = {} - overflows: list[tuple[str, str, int]] = [] - for row in rows: - for arm, ext in (row.get("extractions") or {}).items(): - chars = int(ext.get("chars") or 0) - if arm not in arm_max or arm_max[arm][0] < chars: - arm_max[arm] = (chars, row["doc_id"]) - if chars > SAFE_CHARS: - overflows.append((row["doc_id"], arm, chars)) - - print(f"PDFs in manifest: {total}") - print(f"safe char budget: {SAFE_CHARS:,} (~{(SAFE_CHARS // CHARS_PER_TOKEN):,} tokens)") - print() - print("largest extraction per arm:") - for arm, (chars, doc_id) in sorted(arm_max.items()): - print(f" {arm:25s} {chars:>10,} chars ({doc_id})") - - print() - if overflows: - print(f"OVERFLOW RISK ({len(overflows)} extractions > safe budget):") - for doc_id, arm, chars in overflows: - est_tokens = chars // CHARS_PER_TOKEN - print(f" {doc_id} :: {arm} :: {chars:,} chars (~{est_tokens:,} tokens)") - else: - print("no overflow risk — all extractions fit Sonnet 4.5's 200k context.") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/check_uploaded_status.py b/surfsense_evals/scripts/check_uploaded_status.py deleted file mode 100644 index 1903502eda..0000000000 --- a/surfsense_evals/scripts/check_uploaded_status.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Query SurfSense for the status of every MMLongBench PDF in scope. - -Uses the existing SurfSense documents client to query -``/documents/status?document_ids=...`` for both the known-existing 5 -PDFs (doc ids 5219-5223) and the recently-uploaded mmlongbench batch -(7577-7600 range). -""" - -from __future__ import annotations - -import asyncio -import os -from pathlib import Path - -import httpx -from dotenv import load_dotenv - -REPO = Path(__file__).resolve().parents[1] -PDF_DIR = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs" - - -async def main() -> None: - load_dotenv(REPO / ".env") - base = os.environ.get("SURFSENSE_API_BASE", "http://localhost:8000").rstrip("/") - token = os.environ.get("SURFSENSE_JWT") - if not token: - raise SystemExit("SURFSENSE_JWT missing from .env") - - pdf_names = sorted(p.name for p in PDF_DIR.glob("*.pdf")) - print(f"local cached PDFs: {len(pdf_names)}") - - candidate_ids = list(range(5219, 5224)) + list(range(7577, 7625)) - - headers = { - "Authorization": f"Bearer {token}", - "Accept": "application/json", - } - async with httpx.AsyncClient(timeout=30.0) as http: - r = await http.get( - f"{base}/api/v1/documents/status", - params={ - "search_space_id": 55, - "document_ids": ",".join(str(d) for d in candidate_ids), - }, - headers=headers, - ) - r.raise_for_status() - items = r.json().get("items", []) - - by_title: dict[str, dict] = {} - for it in items: - by_title[it.get("title", "")] = { - "id": it.get("id"), - "state": (it.get("status") or {}).get("state"), - "reason": (it.get("status") or {}).get("reason"), - } - - by_state: dict[str, int] = {} - print() - for name in pdf_names: - info = by_title.get(name) - if info is None: - print(f" [missing ] {name}") - by_state["missing"] = by_state.get("missing", 0) + 1 - else: - tag = info["state"] or "?" - print(f" [{tag:13s}] doc_id={info['id']:>5} {name}") - by_state[tag] = by_state.get(tag, 0) + 1 - print() - print("summary:") - for k, v in sorted(by_state.items()): - print(f" {k}: {v}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/surfsense_evals/scripts/compute_adjusted_accuracy.py b/surfsense_evals/scripts/compute_adjusted_accuracy.py deleted file mode 100644 index 0cd4b30732..0000000000 --- a/surfsense_evals/scripts/compute_adjusted_accuracy.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Compute "intrinsic" accuracy by removing transient network errors. - -A failure is *transient* if it's: - * SSLError: SSL bad-record-mac (TLS hiccup) - * Cloudflare 502 / 503 (provider-side load shedding) - * empty_response with no error string and no other signal (likely - connection reset mid-stream) - * JSONDecodeError (parse error mid-stream) - -A failure is *intrinsic* if it's a hard limit: - * "exceeds .* limit" (size limits) - * context_length errors - * provider 400 with image / pdf decode failure - * malformed-input failures - -We re-compute accuracy with two denominators: - * raw acc = correct / 171 (what the headline reports) - * adjusted acc = correct / (171 - transient_failures) (intrinsic) - -Outputs a table that we can drop straight into the blog. -""" - -from __future__ import annotations - -import json -from collections import defaultdict -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] -RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare" -RAW = RUN / "raw.jsonl" - - -TRANSIENT_HINTS = ( - "sslv3_alert_bad_record_mac", - "ssl_alert_bad_record_mac", - "ssl: ssl", - "cloudflare", - "error 502", - "error 503", - "bad gateway", - "service unavailable", - "gateway timeout", - "jsondecodeerror", -) -INTRINSIC_HINTS = ( - "exceeds", - "context_length", - "context window", - "could not process pdf", - "could not process image", -) - - -def classify(error: str | None, raw_text: str) -> str: - err = (error or "").lower() - if not err and not raw_text.strip(): - return "transient_empty" - if any(h in err for h in TRANSIENT_HINTS): - return "transient_ssl_or_5xx" - if any(h in err for h in INTRINSIC_HINTS): - return "intrinsic_limit" - if err: - return "other_error" - return "ok" - - -def main() -> None: - rows = [ - json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip() - ] - by_arm: dict[str, dict] = defaultdict( - lambda: { - "n": 0, - "correct": 0, - "transient_ssl_or_5xx": 0, - "transient_empty": 0, - "intrinsic_limit": 0, - "other_error": 0, - } - ) - for row in rows: - arm = row["arm"] - m = by_arm[arm] - m["n"] += 1 - graded = row.get("graded") or {} - if graded.get("correct"): - m["correct"] += 1 - kind = classify(row.get("error"), row.get("raw_text") or "") - if kind != "ok": - m[kind] += 1 - - print( - f"{'arm':<25} {'raw acc%':>8} {'transient':>10} {'intrinsic':>10} {'other':>6} {'adj acc% (no transient)':>22}" - ) - print("-" * 88) - for arm in sorted(by_arm): - m = by_arm[arm] - raw = m["correct"] / m["n"] * 100 - transient = m["transient_ssl_or_5xx"] + m["transient_empty"] - intrinsic = m["intrinsic_limit"] - other = m["other_error"] - usable = m["n"] - transient - adj = m["correct"] / usable * 100 if usable else 0 - print(f"{arm:<25} {raw:>7.1f}% {transient:>10} {intrinsic:>10} {other:>6} {adj:>21.1f}%") - - print() - print("transient = SSLError / 502 / 503 / empty stream / mid-stream JSON decode (would") - print(" succeed on retry; eval harness has no built-in retry today).") - print("intrinsic = hard limit (e.g. >30MB Anthropic request, model context overflow).") - print("adj acc% = correct / (n - transient) — what the arm scores when network noise") - print(" is removed; closest thing we have to a like-for-like quality number.") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/compute_blog_extras.py b/surfsense_evals/scripts/compute_blog_extras.py deleted file mode 100644 index 29922f54cd..0000000000 --- a/surfsense_evals/scripts/compute_blog_extras.py +++ /dev/null @@ -1,401 +0,0 @@ -"""Compute the deeper statistics the blog needs: McNemar pairwise tests, -per-PDF heterogeneity, latency/token distribution percentiles. - -Reads the merged post-retry artifact: - - data/multimodal_doc/runs//parser_compare/raw_post_retry.jsonl - -Outputs to stdout: - - 1) Per-arm latency distribution (n, mean, std, p10, p25, p50, p75, p90, p95, p99, max). - 2) Per-arm input/output token distribution (mean, p50, p95, max). - 3) McNemar pairwise table: for every (arm_i, arm_j) ordered pair on the - same 171 questions, count b_ij = #(arm_i correct & arm_j wrong) and - b_ji = #(arm_i wrong & arm_j correct), and report the exact-binomial - two-sided p-value. We include both raw (using the original raw.jsonl) - and post-retry results. - 4) Per-PDF accuracy variance per arm (n_pdfs=30): mean, std, min, max. - -Pure stdlib — no scipy/numpy. -""" - -from __future__ import annotations - -import argparse -import json -import math -import statistics -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] - - -# --------------------------------------------------------------------------- -# I/O -# --------------------------------------------------------------------------- - - -def _read_jsonl(path: Path) -> list[dict]: - out: list[dict] = [] - with path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - out.append(json.loads(line)) - return out - - -# --------------------------------------------------------------------------- -# Distribution helpers -# --------------------------------------------------------------------------- - - -def _percentile(values: list[float], p: float) -> float: - """Linear-interpolation percentile (p in [0, 100]).""" - - if not values: - return 0.0 - s = sorted(values) - if len(s) == 1: - return float(s[0]) - k = (len(s) - 1) * (p / 100.0) - lo, hi = math.floor(k), math.ceil(k) - if lo == hi: - return float(s[int(k)]) - return float(s[lo] + (s[hi] - s[lo]) * (k - lo)) - - -# --------------------------------------------------------------------------- -# McNemar exact-binomial p-value -# --------------------------------------------------------------------------- - - -def _binom_coef(n: int, k: int) -> int: - if k < 0 or k > n: - return 0 - return math.comb(n, k) - - -def _mcnemar_exact_pvalue(b: int, c: int) -> float: - """Two-sided exact-binomial McNemar p-value. - - Tests H0: P(arm_i wrong, arm_j right) == P(arm_i right, arm_j wrong) - on discordant pairs only. Under H0 the count b ~ Bin(b+c, 0.5). - The two-sided p-value is - - P(X <= min(b, c)) + P(X >= max(b, c)) - - computed exactly (cheap because b+c <= 27 in our run). - """ - - n = b + c - if n == 0: - return 1.0 - k = min(b, c) - # Two-sided exact: 2 * P(X <= k) clipped at 1.0 - cdf = sum(_binom_coef(n, i) for i in range(k + 1)) - p = 2.0 * cdf / (2**n) - return min(1.0, p) - - -def _mcnemar_table(rows: list[dict]) -> dict: - """Group rows -> {qid: {arm: bool_correct}} and compute pairwise.""" - - by_qid: dict[str, dict[str, bool]] = {} - arms_seen: set[str] = set() - for r in rows: - qid = r["qid"] - arm = r["arm"] - graded = r.get("graded") or {} - correct = bool(graded.get("correct")) - by_qid.setdefault(qid, {})[arm] = correct - arms_seen.add(arm) - - arms = sorted(arms_seen) - qids = sorted(by_qid) - out: dict[str, dict] = {"arms": arms, "n_qids": len(qids), "pairs": []} - for i, ai in enumerate(arms): - for aj in arms[i + 1 :]: - b = c = both = neither = 0 - for q in qids: - row = by_qid[q] - if ai not in row or aj not in row: - continue - ci, cj = row[ai], row[aj] - if ci and not cj: - b += 1 - elif cj and not ci: - c += 1 - elif ci and cj: - both += 1 - else: - neither += 1 - p = _mcnemar_exact_pvalue(b, c) - out["pairs"].append( - { - "arm_i": ai, - "arm_j": aj, - "b_i_only": b, - "c_j_only": c, - "both_correct": both, - "both_wrong": neither, - "p_value": p, - } - ) - return out - - -# --------------------------------------------------------------------------- -# Per-PDF heterogeneity -# --------------------------------------------------------------------------- - - -def _per_pdf_stats(rows: list[dict]) -> dict[str, dict]: - """For each arm, per-PDF accuracy = correct/total questions on that PDF.""" - - bucket: dict[str, dict[str, list[bool]]] = {} - for r in rows: - arm = r["arm"] - pdf = r["doc_id"] - graded = r.get("graded") or {} - bucket.setdefault(arm, {}).setdefault(pdf, []).append(bool(graded.get("correct"))) - - out: dict[str, dict] = {} - for arm, pdfs in bucket.items(): - accs = [sum(b) / len(b) for b in pdfs.values() if b] - if not accs: - continue - out[arm] = { - "n_pdfs": len(accs), - "mean": statistics.mean(accs), - "std": statistics.stdev(accs) if len(accs) > 1 else 0.0, - "min": min(accs), - "max": max(accs), - "p25": _percentile(accs, 25), - "p50": _percentile(accs, 50), - "p75": _percentile(accs, 75), - "n_pdfs_zero": sum(1 for a in accs if a == 0.0), - "n_pdfs_perfect": sum(1 for a in accs if a == 1.0), - } - return out - - -# --------------------------------------------------------------------------- -# Latency / token distributions -# --------------------------------------------------------------------------- - - -def _per_arm_latency(rows: list[dict]) -> dict[str, dict]: - by_arm: dict[str, list[float]] = {} - for r in rows: - lat = r.get("latency_ms") - if lat is None or lat == 0: - continue - by_arm.setdefault(r["arm"], []).append(float(lat)) - out: dict[str, dict] = {} - for arm, lats in by_arm.items(): - out[arm] = { - "n": len(lats), - "mean_s": statistics.mean(lats) / 1000, - "std_s": (statistics.stdev(lats) / 1000) if len(lats) > 1 else 0.0, - "p10_s": _percentile(lats, 10) / 1000, - "p25_s": _percentile(lats, 25) / 1000, - "p50_s": _percentile(lats, 50) / 1000, - "p75_s": _percentile(lats, 75) / 1000, - "p90_s": _percentile(lats, 90) / 1000, - "p95_s": _percentile(lats, 95) / 1000, - "p99_s": _percentile(lats, 99) / 1000, - "max_s": max(lats) / 1000, - # Coefficient of variation: std / mean (unitless tail-fatness). - "cv": ( - statistics.stdev(lats) / statistics.mean(lats) - if len(lats) > 1 and statistics.mean(lats) > 0 - else 0.0 - ), - } - return out - - -def _per_arm_tokens(rows: list[dict]) -> dict[str, dict]: - by_arm_in: dict[str, list[float]] = {} - by_arm_out: dict[str, list[float]] = {} - for r in rows: - t_in = r.get("input_tokens") or 0 - t_out = r.get("output_tokens") or 0 - if t_in: - by_arm_in.setdefault(r["arm"], []).append(float(t_in)) - if t_out: - by_arm_out.setdefault(r["arm"], []).append(float(t_out)) - out: dict[str, dict] = {} - for arm in sorted(set(by_arm_in) | set(by_arm_out)): - in_vals = by_arm_in.get(arm, []) - out_vals = by_arm_out.get(arm, []) - if not in_vals and not out_vals: - continue - entry: dict = {} - if in_vals: - entry["input"] = { - "n": len(in_vals), - "mean": statistics.mean(in_vals), - "p50": _percentile(in_vals, 50), - "p95": _percentile(in_vals, 95), - "max": max(in_vals), - } - if out_vals: - entry["output"] = { - "n": len(out_vals), - "mean": statistics.mean(out_vals), - "p50": _percentile(out_vals, 50), - "p95": _percentile(out_vals, 95), - "max": max(out_vals), - } - out[arm] = entry - return out - - -# --------------------------------------------------------------------------- -# Pretty-printing -# --------------------------------------------------------------------------- - - -def _print_latency(title: str, lat: dict[str, dict]) -> None: - print() - print(title) - print("-" * len(title)) - header = ( - f"{'arm':<25} {'n':>4} {'mean':>7} {'std':>7} " - f"{'p50':>7} {'p90':>7} {'p95':>7} {'p99':>7} {'max':>7} {'CV':>5}" - ) - print(header) - print("-" * len(header)) - for arm in sorted(lat, key=lambda a: lat[a]["mean_s"]): - s = lat[arm] - print( - f"{arm:<25} {s['n']:>4} " - f"{s['mean_s']:>6.1f}s {s['std_s']:>6.1f}s " - f"{s['p50_s']:>6.1f}s {s['p90_s']:>6.1f}s {s['p95_s']:>6.1f}s " - f"{s['p99_s']:>6.1f}s {s['max_s']:>6.1f}s {s['cv']:>5.2f}" - ) - - -def _print_tokens(title: str, toks: dict[str, dict]) -> None: - print() - print(title) - print("-" * len(title)) - header = ( - f"{'arm':<25} {'in mean':>9} {'in p50':>9} {'in p95':>9} {'in max':>9}" - f" {'out mean':>9} {'out p95':>9}" - ) - print(header) - print("-" * len(header)) - for arm in sorted(toks): - e = toks[arm] - ein = e.get("input") - eout = e.get("output") - if not ein: - continue - print( - f"{arm:<25} " - f"{ein['mean']:>9,.0f} {ein['p50']:>9,.0f} {ein['p95']:>9,.0f} {ein['max']:>9,.0f} " - f"{(eout or {}).get('mean', 0):>9,.0f} {(eout or {}).get('p95', 0):>9,.0f}" - ) - - -def _print_pdf_var(title: str, var: dict[str, dict]) -> None: - print() - print(title) - print("-" * len(title)) - header = ( - f"{'arm':<25} {'n_pdfs':>7} {'mean':>7} {'std':>7} {'min':>7} " - f"{'p25':>7} {'p50':>7} {'p75':>7} {'max':>7} {'#0%':>5} {'#100%':>6}" - ) - print(header) - print("-" * len(header)) - for arm in sorted(var, key=lambda a: -var[a]["mean"]): - s = var[arm] - print( - f"{arm:<25} {s['n_pdfs']:>7} " - f"{s['mean'] * 100:>6.1f}% {s['std'] * 100:>6.1f}% {s['min'] * 100:>6.1f}% " - f"{s['p25'] * 100:>6.1f}% {s['p50'] * 100:>6.1f}% {s['p75'] * 100:>6.1f}% " - f"{s['max'] * 100:>6.1f}% {s['n_pdfs_zero']:>5} {s['n_pdfs_perfect']:>6}" - ) - - -def _print_mcnemar(title: str, table: dict) -> None: - print() - print(title) - print("-" * len(title)) - print(f"n_qids on which all arms have a graded row: {table['n_qids']}") - header = ( - f"{'arm_i':<25} {'arm_j':<25} {'b':>4} {'c':>4} " - f"{'both ok':>8} {'both wr':>8} {'p (2-sided)':>13} {'sig':>4}" - ) - print(header) - print("-" * len(header)) - for pair in sorted(table["pairs"], key=lambda p: p["p_value"]): - sig = "" - if pair["p_value"] < 0.001: - sig = "***" - elif pair["p_value"] < 0.01: - sig = "**" - elif pair["p_value"] < 0.05: - sig = "*" - print( - f"{pair['arm_i']:<25} {pair['arm_j']:<25} " - f"{pair['b_i_only']:>4} {pair['c_j_only']:>4} " - f"{pair['both_correct']:>8} {pair['both_wrong']:>8} " - f"{pair['p_value']:>13.4f} {sig:>4}" - ) - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--run-id", default="2026-05-14T00-53-19Z") - args = parser.parse_args() - - run_dir = REPO / "data" / "multimodal_doc" / "runs" / args.run_id / "parser_compare" - raw_path = run_dir / "raw.jsonl" - post_path = run_dir / "raw_post_retry.jsonl" - if not raw_path.exists() or not post_path.exists(): - raise SystemExit( - "Missing raw.jsonl or raw_post_retry.jsonl. " - "Run scripts/compute_post_retry_accuracy.py first." - ) - - raw_rows = _read_jsonl(raw_path) - post_rows = _read_jsonl(post_path) - - print(f"Run: {args.run_id}") - print(f"raw rows: {len(raw_rows)}, post-retry rows: {len(post_rows)}") - - # Latency uses post-retry rows (post-retry rows include the retry's own - # latency for recovered rows). For raw, recovered rows have latency=0 - # because the harness recorded a failure. - _print_latency("Per-arm latency (post-retry)", _per_arm_latency(post_rows)) - - _print_tokens("Per-arm token distribution (post-retry)", _per_arm_tokens(post_rows)) - - _print_pdf_var( - "Per-PDF accuracy heterogeneity (post-retry)", - _per_pdf_stats(post_rows), - ) - - _print_mcnemar( - "McNemar pairwise (RAW, no retries)", - _mcnemar_table(raw_rows), - ) - _print_mcnemar( - "McNemar pairwise (POST-RETRY)", - _mcnemar_table(post_rows), - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/surfsense_evals/scripts/compute_post_retry_accuracy.py b/surfsense_evals/scripts/compute_post_retry_accuracy.py deleted file mode 100644 index 29007ed412..0000000000 --- a/surfsense_evals/scripts/compute_post_retry_accuracy.py +++ /dev/null @@ -1,178 +0,0 @@ -"""Recompute per-arm accuracy/F1 after merging retry survivors into raw.jsonl. - -Reads: - - data/multimodal_doc/runs//parser_compare/raw.jsonl - - data/multimodal_doc/runs//parser_compare/raw_retries.jsonl - -For each (arm, qid) present in the retry artifact: - - if the retry RECOVERED, the retry row replaces the original row (same - grader is reused — see ``mmlongbench/grader.py``); - - if the retry did NOT recover, the original row stays (still a failure, - so ``correct=False`` and ``f1=0``). - -Prints two tables side by side: - * Raw run (no retries) — matches §1 of the blog. - * Post-retry run — final, "what would the headline have been if - the harness had had retries from day one". - -It also writes ``data/multimodal_doc/runs//parser_compare/raw_post_retry.jsonl`` -so any downstream notebook / report can join straight on it. -""" - -from __future__ import annotations - -import argparse -import json -import sys -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] - - -def _read_jsonl(path: Path) -> list[dict]: - out: list[dict] = [] - with path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - out.append(json.loads(line)) - return out - - -def _row_key(row: dict) -> tuple[str, str]: - return (str(row["arm"]), str(row["qid"])) - - -def _is_failure(row: dict) -> bool: - if row.get("error"): - return True - return bool(not (row.get("raw_text") or "").strip()) - - -def _summarise(rows_by_arm: dict[str, list[dict]]) -> dict[str, dict]: - out: dict[str, dict] = {} - for arm, rows in rows_by_arm.items(): - n = len(rows) - n_correct = sum(1 for r in rows if r.get("graded", {}).get("correct")) - f1_sum = sum(float(r.get("graded", {}).get("f1") or 0.0) for r in rows) - n_fail = sum(1 for r in rows if _is_failure(r)) - out[arm] = { - "n": n, - "n_correct": n_correct, - "n_failures": n_fail, - "accuracy": (n_correct / n) if n else 0.0, - "f1_mean": (f1_sum / n) if n else 0.0, - "failure_rate": (n_fail / n) if n else 0.0, - } - return out - - -def _print_table(title: str, summary: dict[str, dict]) -> None: - print() - print(title) - print("-" * len(title)) - header = f"{'arm':<25} {'n':>4} {'n_corr':>7} {'acc':>7} {'F1':>7} {'fails':>6} {'fail%':>7}" - print(header) - print("-" * len(header)) - # stable order: highest accuracy first - arms_sorted = sorted(summary.items(), key=lambda kv: -kv[1]["accuracy"]) - for arm, s in arms_sorted: - print( - f"{arm:<25} {s['n']:>4} {s['n_correct']:>7} " - f"{s['accuracy'] * 100:>6.1f}% {s['f1_mean'] * 100:>6.1f}% " - f"{s['n_failures']:>6} {s['failure_rate'] * 100:>6.1f}%" - ) - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--run-id", default="2026-05-14T00-53-19Z") - args = parser.parse_args() - - run_dir = REPO / "data" / "multimodal_doc" / "runs" / args.run_id / "parser_compare" - raw_path = run_dir / "raw.jsonl" - retry_path = run_dir / "raw_retries.jsonl" - out_path = run_dir / "raw_post_retry.jsonl" - - if not raw_path.exists(): - print(f"raw.jsonl not found at {raw_path}", file=sys.stderr) - return 1 - if not retry_path.exists(): - print(f"raw_retries.jsonl not found at {retry_path}", file=sys.stderr) - return 1 - - raw_rows = _read_jsonl(raw_path) - retry_rows = _read_jsonl(retry_path) - - retry_by_key: dict[tuple[str, str], dict] = {_row_key(r): r for r in retry_rows} - - merged_rows: list[dict] = [] - n_replaced_recovered = 0 - n_replaced_still_failed = 0 - n_unchanged = 0 - for row in raw_rows: - key = _row_key(row) - retry = retry_by_key.get(key) - if retry is None: - merged_rows.append(row) - n_unchanged += 1 - continue - # The retry artifact carries a fresh ArmResult + grade in the same - # shape, plus a "retry" sub-object. We use the retry row whenever - # it represents a recovery; otherwise we keep the original (the - # retry confirms it is intrinsic, but the original row is the one - # the headline numbers were computed from, and the failure verdict - # is identical either way). - recovered = bool(retry.get("retry", {}).get("recovered")) - if recovered: - merged_rows.append(retry) - n_replaced_recovered += 1 - else: - merged_rows.append(row) - n_replaced_still_failed += 1 - - # Persist merged jsonl for downstream consumers - with out_path.open("w", encoding="utf-8") as fh: - for r in merged_rows: - fh.write(json.dumps(r) + "\n") - - # Bucket per arm - raw_by_arm: dict[str, list[dict]] = {} - for r in raw_rows: - raw_by_arm.setdefault(r["arm"], []).append(r) - post_by_arm: dict[str, list[dict]] = {} - for r in merged_rows: - post_by_arm.setdefault(r["arm"], []).append(r) - - raw_summary = _summarise(raw_by_arm) - post_summary = _summarise(post_by_arm) - - print() - print(f"Run: {args.run_id}") - print(f"Replaced (retry recovered): {n_replaced_recovered}") - print(f"Kept original (retry still failed): {n_replaced_still_failed}") - print(f"Untouched rows: {n_unchanged}") - print(f"Wrote merged artifact: {out_path.relative_to(REPO)}") - - _print_table("Raw run (no retries)", raw_summary) - _print_table("Post-retry run (final)", post_summary) - - print() - print("Delta (post-retry minus raw):") - print(f"{'arm':<25} {'d_acc':>7} {'d_fails':>8}") - print("-" * 42) - for arm in sorted(set(raw_summary) | set(post_summary)): - r = raw_summary.get(arm) - p = post_summary.get(arm) - if not r or not p: - continue - d_acc = (p["accuracy"] - r["accuracy"]) * 100 - d_fail = p["n_failures"] - r["n_failures"] - print(f"{arm:<25} {d_acc:>+6.1f}p {d_fail:>+7d}") - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/surfsense_evals/scripts/inspect_first30.py b/surfsense_evals/scripts/inspect_first30.py deleted file mode 100644 index b3caedca65..0000000000 --- a/surfsense_evals/scripts/inspect_first30.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Inspect what the first 30 MMLongBench-Doc PDFs would look like for scoping. - -Run from surfsense_evals/ root via: - python scripts/inspect_first30.py - -Prints which docs are already ingested (existing 5), which are new (25 to -upload), how many questions cover those 30 PDFs, and the answerable / -unanswerable + format mix. -""" - -from __future__ import annotations - -import json -from collections import Counter -from pathlib import Path - - -def main() -> None: - qpath = Path("data/multimodal_doc/mmlongbench/questions.jsonl") - lines = qpath.read_text(encoding="utf-8").splitlines() - rows = [json.loads(line) for line in lines if line.strip()] - - docs_by_id = sorted({r["doc_id"] for r in rows}) - first30 = docs_by_id[:30] - existing5 = { - "05-03-18-political-release.pdf", - "0b85477387a9d0cc33fca0f4becaa0e5.pdf", - "0e94b4197b10096b1f4c699701570fbf.pdf", - "11-21-16-Updated-Post-Election-Release.pdf", - "12-15-15-ISIS-and-terrorism-release-final.pdf", - } - new25 = [d for d in first30 if d not in existing5] - print( - f"first 30 docs (alphabetical) — {len(new25)} new, " - f"{len(first30) - len(new25)} already in SurfSense" - ) - - qs_in_30 = [r for r in rows if r["doc_id"] in set(first30)] - fmts = Counter((r.get("answer_format") or "").lower() for r in qs_in_30) - answerable = sum(v for k, v in fmts.items() if k != "none") - unanswerable = fmts.get("none", 0) - - print( - f"questions covering first 30 docs: total={len(qs_in_30)} " - f"answerable={answerable} unanswerable={unanswerable}" - ) - print(f"avg Qs/PDF: {len(qs_in_30) / 30:.1f} answerable/PDF: {answerable / 30:.1f}") - print(f"format mix in scope: {dict(fmts)}") - print() - print("25 new PDFs to ingest:") - for d in new25: - print(f" - {d}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py b/surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py deleted file mode 100644 index 8cfb13eb78..0000000000 --- a/surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py +++ /dev/null @@ -1,99 +0,0 @@ -"""Stub the mmlongbench manifest so parser_compare can extract in parallel. - -The mmlongbench Surfsense ingest writes its manifest only at the very -end of the upload pipeline (~hours of celery work). parser_compare's -ingest, on the other hand, just needs a list of (doc_id, pdf_path) -tuples to know which PDFs to extract — it doesn't care about the -SurfSense ``document_id`` (the runner does, later, after a refresh). - -This script extends the existing manifest with the *additional* PDFs -that mmlongbench has already cached on disk (i.e. all 30 PDFs in -``data/multimodal_doc/mmlongbench/pdfs/`` even though only 5 have -SurfSense ``document_id``s yet) so parser_compare can run all four -extractions for them in parallel with the SurfSense ingest. - -After mmlongbench finishes, re-run:: - - python -m surfsense_evals ingest multimodal_doc parser_compare \ - --max-docs 30 - -…to refresh ``parser_compare_doc_map.jsonl`` with the now-populated -``document_id`` values for the 25 new PDFs. The extractions -themselves are cached on disk so the second pass is essentially free. -""" - -from __future__ import annotations - -import json -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] -MAP_PATH = REPO / "data" / "multimodal_doc" / "maps" / "mmlongbench_doc_map.jsonl" -PDF_DIR = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs" -QUESTIONS = REPO / "data" / "multimodal_doc" / "mmlongbench" / "questions.jsonl" - - -def _question_count_per_doc() -> dict[str, int]: - counts: dict[str, int] = {} - with QUESTIONS.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - counts[row["doc_id"]] = counts.get(row["doc_id"], 0) + 1 - return counts - - -def main() -> None: - if not MAP_PATH.exists(): - raise SystemExit( - f"manifest not found at {MAP_PATH} — " - "run `surfsense_evals ingest multimodal_doc mmlongbench` first." - ) - - existing_lines = MAP_PATH.read_text(encoding="utf-8").splitlines() - existing_rows: list[dict] = [] - settings_line = None - for line in existing_lines: - line = line.strip() - if not line: - continue - row = json.loads(line) - if "__settings__" in row: - settings_line = line - else: - existing_rows.append(row) - - by_doc_id = {r["doc_id"]: r for r in existing_rows} - counts = _question_count_per_doc() - - cached_pdfs = sorted(p for p in PDF_DIR.glob("*.pdf")) - print(f"existing manifest entries: {len(existing_rows)}") - print(f"cached PDFs on disk: {len(cached_pdfs)}") - - added = 0 - for pdf in cached_pdfs: - if pdf.name in by_doc_id: - continue - by_doc_id[pdf.name] = { - "doc_id": pdf.name, - "document_id": None, - "pdf_path": str(pdf), - "n_questions": counts.get(pdf.name, 0), - } - added += 1 - - out_lines: list[str] = [] - if settings_line: - out_lines.append(settings_line) - for doc_id in sorted(by_doc_id): - out_lines.append(json.dumps(by_doc_id[doc_id])) - MAP_PATH.write_text("\n".join(out_lines) + "\n", encoding="utf-8") - - print(f"added {added} stub rows; manifest now has {len(by_doc_id)} PDFs") - print(f"wrote: {MAP_PATH}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/peek_crag_run.py b/surfsense_evals/scripts/peek_crag_run.py deleted file mode 100644 index 0720e24cf7..0000000000 --- a/surfsense_evals/scripts/peek_crag_run.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Tiny helper to inspect the latest CRAG run's per-question outputs.""" - -from __future__ import annotations - -import glob -import json -from collections import defaultdict - - -def main() -> None: - raw_path = sorted(glob.glob("data/research/runs/*/crag/raw.jsonl"))[-1] - print(f"Reading: {raw_path}") - with open(raw_path, encoding="utf-8") as fh: - rows = [json.loads(line) for line in fh if line.strip()] - by_q: dict[str, dict[str, dict]] = defaultdict(dict) - for r in rows: - by_q[r["qid"]][r["arm"]] = r - - for qid, arms in list(by_q.items()): - b = arms.get("bare_llm", {}) - lc = arms.get("long_context", {}) - s = arms.get("surfsense", {}) - print(f"\n=== {qid} ({b.get('domain')}/{b.get('question_type')}) ===") - print(f" question: {b.get('extra', {}).get('question', '?')!r}") - print(f" gold: {b.get('gold')!r}") - for arm_name, a in (("bare_llm", b), ("long_context", lc), ("surfsense", s)): - grade = a.get("graded", {}) - text = (a.get("raw_text") or "").strip() - tail = text[-200:] if text else "" - print(f" [{arm_name}] grade={grade.get('grade')} method={grade.get('method')}") - print(f" -> {tail!r}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/peek_disagreements.py b/surfsense_evals/scripts/peek_disagreements.py deleted file mode 100644 index b6497570ee..0000000000 --- a/surfsense_evals/scripts/peek_disagreements.py +++ /dev/null @@ -1,65 +0,0 @@ -"""Show questions where SurfSense was wrong but long-context was right (and vice versa).""" - -from __future__ import annotations - -import glob -import json -from collections import defaultdict - - -def main() -> None: - raw_path = sorted(glob.glob("data/research/runs/*/crag/raw.jsonl"))[-1] - print(f"Reading: {raw_path}") - with open(raw_path, encoding="utf-8") as fh: - rows = [json.loads(line) for line in fh if line.strip()] - by_q: dict[str, dict[str, dict]] = defaultdict(dict) - for r in rows: - by_q[r["qid"]][r["arm"]] = r - - surf_wrong_lc_right = [] - lc_wrong_surf_right = [] - surf_wrong_bare_right = [] - for qid, arms in by_q.items(): - b = arms.get("bare_llm", {}).get("graded", {}).get("grade") - lc = arms.get("long_context", {}).get("graded", {}).get("grade") - s = arms.get("surfsense", {}).get("graded", {}).get("grade") - if s == "incorrect" and lc == "correct": - surf_wrong_lc_right.append(qid) - if lc == "incorrect" and s == "correct": - lc_wrong_surf_right.append(qid) - if s == "incorrect" and b == "correct": - surf_wrong_bare_right.append(qid) - - print(f"\nSurfSense INCORRECT but Long-Context CORRECT: {len(surf_wrong_lc_right)}") - print(f"Long-Context INCORRECT but SurfSense CORRECT: {len(lc_wrong_surf_right)}") - print(f"SurfSense INCORRECT but Bare CORRECT: {len(surf_wrong_bare_right)}") - - print("\n=== Where SurfSense is wrong but long-context is right (top 5) ===") - for qid in surf_wrong_lc_right[:5]: - arms = by_q[qid] - b = arms.get("bare_llm", {}) - print(f"\n[{qid}] domain={b.get('domain')} qtype={b.get('question_type')}") - print(f" GOLD: {b.get('gold')!r}") - for arm_name in ("bare_llm", "long_context", "surfsense"): - a = arms.get(arm_name, {}) - t = (a.get("raw_text") or "").strip() - tail = t[-180:] if t else "" - grade = a.get("graded", {}) - print(f" [{arm_name}] {grade.get('grade')} ({grade.get('method')}): {tail!r}") - - print("\n=== Where Long-Context is wrong but SurfSense is right (top 5) ===") - for qid in lc_wrong_surf_right[:5]: - arms = by_q[qid] - b = arms.get("bare_llm", {}) - print(f"\n[{qid}] domain={b.get('domain')} qtype={b.get('question_type')}") - print(f" GOLD: {b.get('gold')!r}") - for arm_name in ("bare_llm", "long_context", "surfsense"): - a = arms.get(arm_name, {}) - t = (a.get("raw_text") or "").strip() - tail = t[-180:] if t else "" - grade = a.get("graded", {}) - print(f" [{arm_name}] {grade.get('grade')} ({grade.get('method')}): {tail!r}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/retry_failed_questions.py b/surfsense_evals/scripts/retry_failed_questions.py deleted file mode 100644 index d65f9f0fbb..0000000000 --- a/surfsense_evals/scripts/retry_failed_questions.py +++ /dev/null @@ -1,680 +0,0 @@ -"""Retry only the failed (arm, question) pairs from a previous parser_compare run. - -The original parser_compare run records one row per (arm, qid) in -``raw.jsonl``. Some of those rows came back with transient transport -errors (SSL alerts, gateway 502s, empty SSE streams) or empty -``raw_text``. This script re-issues *only* those calls with exponential -backoff so we can see how many recover. - -Design constraints / choices: - -* **No re-ingest.** All cached PDFs and parser-extracted markdown stay - on disk. We rebuild ``ArmRequest`` objects from the existing manifest - + the original ``mmlongbench/questions.jsonl``. -* **No SurfSense backend or celery required.** SurfSense had 0 - reported failures; this script will skip any ``surfsense_agentic`` - rows it encounters and warn rather than try to start the backend. -* **Original ``raw.jsonl`` is never mutated.** Retries land in a - sibling ``raw_retries.jsonl`` so the original artifact stays - citeable. -* **Idempotent.** Re-running this script re-tries the same set of - failed rows from ``raw.jsonl``. If you want to merge survivor rows - back in, do that as a separate aggregation step. - -Usage: - - python scripts/retry_failed_questions.py \ - --run-id 2026-05-14T00-53-19Z \ - --max-attempts 5 \ - --concurrency 2 - -Outputs (written next to the original raw.jsonl): - -* ``raw_retries.jsonl`` — one line per retried (arm, qid). Each line - carries the original error, every retry attempt's timing/error, - and the final result (incl. grade) so you can drop it straight - into a notebook. -* ``raw_retries_summary.json`` — per-arm tried/recovered/still-failed - counts and an aggregated retry-success rate. -""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import logging -import os -import random -import sys -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -REPO = Path(__file__).resolve().parents[1] -SRC = REPO / "src" -if str(SRC) not in sys.path: - sys.path.insert(0, str(SRC)) - -from dotenv import load_dotenv # noqa: E402 - -from surfsense_evals.core.arms import ( # noqa: E402 - ArmRequest, - ArmResult, - BareLlmArm, - NativePdfArm, -) -from surfsense_evals.core.parse.freeform_answer import ( # noqa: E402 - extract_freeform_answer, -) -from surfsense_evals.core.providers.openrouter_chat import ( # noqa: E402 - OpenRouterChatProvider, -) -from surfsense_evals.core.providers.openrouter_pdf import ( # noqa: E402 - OpenRouterPdfProvider, - PdfEngine, -) -from surfsense_evals.suites.multimodal_doc.mmlongbench.grader import grade # noqa: E402 -from surfsense_evals.suites.multimodal_doc.parser_compare.prompt import ( # noqa: E402 - build_long_context_prompt, - build_native_pdf_prompt, -) - -logger = logging.getLogger("retry_failed_questions") - -LC_ARMS = { - "azure_basic_lc", - "azure_premium_lc", - "llamacloud_basic_lc", - "llamacloud_premium_lc", -} - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _is_failure_row(row: dict[str, Any]) -> bool: - """A row counts as failed if it raised an error OR returned empty text. - - We retry both because the empty-stream case is the same operational - failure mode (the call returned nothing usable) — we just didn't - raise it as an exception. - """ - - if row.get("error"): - return True - return bool(not (row.get("raw_text") or "").strip()) - - -@dataclass -class FailedRow: - arm: str - qid: str - doc_id: str - answer_format: str - gold: str - pages: int - document_id: int | None - original_error: str | None - original_row: dict[str, Any] - - -def _load_failed_rows(raw_path: Path) -> list[FailedRow]: - out: list[FailedRow] = [] - with raw_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - if not _is_failure_row(row): - continue - out.append( - FailedRow( - arm=str(row["arm"]), - qid=str(row["qid"]), - doc_id=str(row["doc_id"]), - answer_format=str(row.get("answer_format") or ""), - gold=str(row.get("gold") or ""), - pages=int(row.get("pages") or 0), - document_id=row.get("document_id"), - original_error=row.get("error"), - original_row=row, - ) - ) - return out - - -def _load_doc_map(map_path: Path) -> dict[str, dict[str, Any]]: - out: dict[str, dict[str, Any]] = {} - with map_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - out[str(row["doc_id"])] = row - return out - - -def _load_question_text_index( - questions_jsonl: Path, -) -> dict[tuple[str, int], dict[str, Any]]: - """Map (doc_id, per_doc_index) -> raw question row. - - qids in raw.jsonl are formatted ``{doc_id}::Q{NNN}`` where NNN is - the per-doc index. Reproducing the runner's question selection - requires walking ``questions.jsonl`` in order and assigning - indices per doc_id (so we match the runner's ``per_doc_idx`` logic - in ``_select_questions``). - """ - - out: dict[tuple[str, int], dict[str, Any]] = {} - per_doc_idx: dict[str, int] = {} - with questions_jsonl.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - doc_id = str(row.get("doc_id") or "") - if not doc_id: - continue - idx = per_doc_idx.get(doc_id, 0) - per_doc_idx[doc_id] = idx + 1 - out[(doc_id, idx)] = row - return out - - -def _qid_index(qid: str) -> int: - """Parse the per-doc question index out of a qid like ``foo.pdf::Q007``.""" - - _, _, q_part = qid.rpartition("::") - if not q_part.startswith("Q"): - raise ValueError(f"unexpected qid shape: {qid!r}") - return int(q_part[1:]) - - -# --------------------------------------------------------------------------- -# Request building (mirrors runner.py exactly so prompts are byte-identical) -# --------------------------------------------------------------------------- - - -def _build_native_request( - qid: str, - question: str, - answer_format: str, - pdf_path: Path, - *, - max_output_tokens: int, -) -> ArmRequest: - return ArmRequest( - question_id=qid, - prompt=build_native_pdf_prompt(question, answer_format=answer_format), - pdf_paths=[pdf_path], - options={"max_tokens": max_output_tokens}, - ) - - -def _build_lc_request( - qid: str, - question: str, - answer_format: str, - doc_id: str, - md_path: Path, -) -> ArmRequest: - if not md_path.exists(): - raise FileNotFoundError(f"Missing parser extraction at {md_path}; cannot retry LC arm.") - markdown = md_path.read_text(encoding="utf-8") - return ArmRequest( - question_id=qid, - prompt=build_long_context_prompt( - question, - answer_format=answer_format, - document_markdown=markdown, - document_label=doc_id, - ), - ) - - -# --------------------------------------------------------------------------- -# Retry driver -# --------------------------------------------------------------------------- - - -@dataclass -class AttemptLog: - attempt: int - started_iso: str - latency_ms: int - error: str | None - raw_text_chars: int - - -@dataclass -class RetryOutcome: - arm: str - qid: str - attempts: list[AttemptLog] - final_result: ArmResult - recovered: bool - - -async def _retry_one( - arm_obj: Any, - request: ArmRequest, - *, - arm_name: str, - qid: str, - max_attempts: int, - base_delay: float, - max_delay: float, -) -> RetryOutcome: - attempts: list[AttemptLog] = [] - final: ArmResult | None = None - for attempt in range(1, max_attempts + 1): - started_iso = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) - t0 = time.monotonic() - result = await arm_obj.answer(request) - latency_ms = int((time.monotonic() - t0) * 1000) - raw_text = (result.raw_text or "").strip() - attempt_error = result.error - if not attempt_error and not raw_text: - attempt_error = "EmptyResponse: stream ended with no text" - attempts.append( - AttemptLog( - attempt=attempt, - started_iso=started_iso, - latency_ms=latency_ms, - error=attempt_error, - raw_text_chars=len(raw_text), - ) - ) - final = result - if not attempt_error and raw_text: - return RetryOutcome( - arm=arm_name, - qid=qid, - attempts=attempts, - final_result=result, - recovered=True, - ) - if attempt < max_attempts: - delay = min(max_delay, base_delay * (2 ** (attempt - 1))) - delay = delay * (0.5 + random.random()) - logger.info( - "[%s::%s] attempt %d/%d failed (%s); sleeping %.1fs", - arm_name, - qid, - attempt, - max_attempts, - attempt_error, - delay, - ) - await asyncio.sleep(delay) - assert final is not None - return RetryOutcome( - arm=arm_name, - qid=qid, - attempts=attempts, - final_result=final, - recovered=False, - ) - - -async def _gather_with_limit(coros: list, *, concurrency: int) -> list[Any]: - sem = asyncio.Semaphore(max(1, concurrency)) - - async def _wrap(coro): - async with sem: - return await coro - - return await asyncio.gather(*(_wrap(c) for c in coros)) - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - - -async def _run(args: argparse.Namespace) -> int: - load_dotenv(REPO / ".env") - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s %(levelname)s %(name)s %(message)s", - ) - - run_dir = REPO / "data" / "multimodal_doc" / "runs" / args.run_id / "parser_compare" - raw_path = run_dir / "raw.jsonl" - if not raw_path.exists(): - raise SystemExit(f"raw.jsonl not found at {raw_path}") - - map_path = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl" - questions_jsonl = REPO / "data" / "multimodal_doc" / "mmlongbench" / "questions.jsonl" - if not map_path.exists(): - raise SystemExit(f"parser_compare manifest not found at {map_path}") - if not questions_jsonl.exists(): - raise SystemExit(f"mmlongbench questions not found at {questions_jsonl}") - - failed = _load_failed_rows(raw_path) - if not failed: - logger.info("No failed rows in %s — nothing to retry.", raw_path) - return 0 - - # SurfSense rows: warn and skip; we don't want to start backend just to - # defensively retry a 0-failure arm. - surf_failed = [f for f in failed if f.arm == "surfsense_agentic"] - if surf_failed: - logger.warning( - "Skipping %d surfsense_agentic failures; this script doesn't drive the backend. " - "If you want those retried too, start backend + celery and rerun " - "with --include-surfsense.", - len(surf_failed), - ) - if not args.include_surfsense: - failed = [f for f in failed if f.arm != "surfsense_agentic"] - else: - logger.info("No surfsense_agentic failures; backend/celery not needed for this retry.") - - if not failed: - logger.info("Nothing left to retry after filtering.") - return 0 - - by_arm_count: dict[str, int] = {} - for f in failed: - by_arm_count[f.arm] = by_arm_count.get(f.arm, 0) + 1 - logger.info( - "Loaded %d failed rows across %d arms: %s", - len(failed), - len(by_arm_count), - ", ".join(f"{a}={n}" for a, n in sorted(by_arm_count.items())), - ) - - doc_map = _load_doc_map(map_path) - qtext_idx = _load_question_text_index(questions_jsonl) - - api_key = os.environ.get("OPENROUTER_API_KEY") - if not api_key: - raise SystemExit("OPENROUTER_API_KEY missing from environment / .env") - - native_provider = OpenRouterPdfProvider( - api_key=api_key, - base_url=os.environ.get("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"), - model=args.llm_model, - engine=PdfEngine(args.pdf_engine), - ) - native_arm = NativePdfArm( - provider=native_provider, - max_output_tokens=args.max_output_tokens, - ) - - lc_arms: dict[str, BareLlmArm] = {} - for arm_name in sorted({f.arm for f in failed} & LC_ARMS): - lc_provider = OpenRouterChatProvider( - api_key=api_key, - base_url=os.environ.get("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"), - model=args.llm_model, - ) - lc_arms[arm_name] = BareLlmArm( - provider=lc_provider, - max_output_tokens=args.max_output_tokens, - name=arm_name, - ) - - coros: list = [] - plan: list[tuple[FailedRow, ArmRequest, Any]] = [] - - for f in failed: - # Look up the question text from questions.jsonl - try: - q_idx = _qid_index(f.qid) - except Exception: - logger.error("Bad qid %r — skipping", f.qid) - continue - qrow = qtext_idx.get((f.doc_id, q_idx)) - if qrow is None: - logger.error( - "Could not find question text for %s (idx %d) — skipping", - f.doc_id, - q_idx, - ) - continue - question_text = str(qrow.get("question") or "").strip() - answer_format = str(qrow.get("answer_format") or f.answer_format or "").strip().lower() - - map_row = doc_map.get(f.doc_id) - if map_row is None: - logger.error("doc_id %s not in manifest — skipping", f.doc_id) - continue - - if f.arm == "native_pdf": - pdf_path = Path(map_row["pdf_path"]) - if not await asyncio.to_thread(pdf_path.exists): - logger.error("PDF missing on disk: %s — skipping", pdf_path) - continue - request = _build_native_request( - f.qid, - question_text, - answer_format, - pdf_path, - max_output_tokens=args.max_output_tokens, - ) - arm_obj = native_arm - elif f.arm in LC_ARMS: - ext_blob = (map_row.get("extractions") or {}).get(f.arm) or {} - md_path_str = ext_blob.get("markdown_path") - if not md_path_str or ext_blob.get("status") != "ok": - logger.error( - "Missing extraction for %s on %s — cannot retry; skipping", - f.arm, - f.doc_id, - ) - continue - request = _build_lc_request( - f.qid, - question_text, - answer_format, - f.doc_id, - Path(md_path_str), - ) - arm_obj = lc_arms[f.arm] - else: - logger.warning("Unhandled arm %s — skipping", f.arm) - continue - - plan.append((f, request, arm_obj)) - coros.append( - _retry_one( - arm_obj, - request, - arm_name=f.arm, - qid=f.qid, - max_attempts=args.max_attempts, - base_delay=args.base_delay, - max_delay=args.max_delay, - ) - ) - - if not coros: - logger.warning("Nothing to retry after request building.") - return 0 - - logger.info( - "Retrying %d failed rows with up to %d attempts each " - "(base_delay=%.1fs, max_delay=%.1fs, concurrency=%d).", - len(coros), - args.max_attempts, - args.base_delay, - args.max_delay, - args.concurrency, - ) - - started = time.monotonic() - outcomes: list[RetryOutcome] = await _gather_with_limit( - coros, - concurrency=args.concurrency, - ) - elapsed = time.monotonic() - started - logger.info("Retry pass finished in %.1fs.", elapsed) - - out_path = run_dir / "raw_retries.jsonl" - summary_path = run_dir / "raw_retries_summary.json" - - per_arm_recovered: dict[str, int] = {} - per_arm_total: dict[str, int] = {} - per_arm_attempts_dist: dict[str, list[int]] = {} - - with out_path.open("w", encoding="utf-8") as fh: - for (f, _req, _arm_obj), outcome in zip(plan, outcomes, strict=True): - per_arm_total[outcome.arm] = per_arm_total.get(outcome.arm, 0) + 1 - if outcome.recovered: - per_arm_recovered[outcome.arm] = per_arm_recovered.get(outcome.arm, 0) + 1 - per_arm_attempts_dist.setdefault(outcome.arm, []).append(len(outcome.attempts)) - - g = grade( - pred=extract_freeform_answer(outcome.final_result.raw_text or ""), - gold=f.gold, - answer_format=f.answer_format, - ) - row = { - "qid": f.qid, - "doc_id": f.doc_id, - "arm": f.arm, - "answer_format": f.answer_format, - "gold": f.gold, - "pages": f.pages, - "document_id": f.document_id, - "original_error": f.original_error, - "retry": { - "max_attempts": args.max_attempts, - "n_attempts": len(outcome.attempts), - "recovered": outcome.recovered, - "attempts": [ - { - "attempt": a.attempt, - "started_iso": a.started_iso, - "latency_ms": a.latency_ms, - "error": a.error, - "raw_text_chars": a.raw_text_chars, - } - for a in outcome.attempts - ], - }, - **outcome.final_result.to_jsonl(), - "graded": { - "correct": g.correct, - "f1": g.f1, - "method": g.method, - "normalised_pred": g.normalised_pred, - "normalised_gold": g.normalised_gold, - }, - } - fh.write(json.dumps(row) + "\n") - - summary = { - "run_id": args.run_id, - "raw_retries_path": str(out_path.relative_to(REPO)), - "n_failed_rows_input": len(failed), - "n_retried": len(coros), - "elapsed_s": round(elapsed, 1), - "config": { - "max_attempts": args.max_attempts, - "base_delay": args.base_delay, - "max_delay": args.max_delay, - "concurrency": args.concurrency, - "llm_model": args.llm_model, - "pdf_engine": args.pdf_engine, - "max_output_tokens": args.max_output_tokens, - }, - "per_arm": { - arm: { - "tried": per_arm_total.get(arm, 0), - "recovered": per_arm_recovered.get(arm, 0), - "still_failed": (per_arm_total.get(arm, 0) - per_arm_recovered.get(arm, 0)), - "recovery_rate": ( - per_arm_recovered.get(arm, 0) / per_arm_total[arm] - if per_arm_total.get(arm) - else 0.0 - ), - "attempts_distribution": sorted(per_arm_attempts_dist.get(arm, [])), - } - for arm in sorted(per_arm_total) - }, - "totals": { - "tried": sum(per_arm_total.values()), - "recovered": sum(per_arm_recovered.values()), - "still_failed": sum(per_arm_total.values()) - sum(per_arm_recovered.values()), - }, - } - summary_path.write_text( - json.dumps(summary, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - - print() - print("=" * 78) - print("Retry pass summary") - print("=" * 78) - header = f"{'arm':<25} {'tried':>6} {'recovered':>10} {'still fail':>11} {'rate':>7}" - print(header) - print("-" * len(header)) - for arm in sorted(per_arm_total): - tried = per_arm_total[arm] - rec = per_arm_recovered.get(arm, 0) - rate = (rec / tried * 100) if tried else 0.0 - print(f"{arm:<25} {tried:>6} {rec:>10} {tried - rec:>11} {rate:>6.1f}%") - total = sum(per_arm_total.values()) - rec_total = sum(per_arm_recovered.values()) - rate_total = (rec_total / total * 100) if total else 0.0 - print("-" * len(header)) - print(f"{'TOTAL':<25} {total:>6} {rec_total:>10} {total - rec_total:>11} {rate_total:>6.1f}%") - print() - print(f"Wrote {out_path.relative_to(REPO)}") - print(f"Wrote {summary_path.relative_to(REPO)}") - return 0 - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "--run-id", - default="2026-05-14T00-53-19Z", - help="Run timestamp under data/multimodal_doc/runs/. Default is the " - "n=171 production run we wrote up in the blog.", - ) - parser.add_argument("--max-attempts", type=int, default=5) - parser.add_argument( - "--base-delay", - type=float, - default=1.0, - help="Base seconds for exponential backoff (default 1s).", - ) - parser.add_argument( - "--max-delay", type=float, default=30.0, help="Cap on per-retry sleep (default 30s)." - ) - parser.add_argument( - "--concurrency", - type=int, - default=2, - help="Parallel retries in flight (default 2 — keep low " - "to avoid the same transport stress that caused " - "the original failures).", - ) - parser.add_argument("--llm-model", default="anthropic/claude-sonnet-4.5") - parser.add_argument("--pdf-engine", default="native", choices=[e.value for e in PdfEngine]) - parser.add_argument("--max-output-tokens", type=int, default=512) - parser.add_argument( - "--include-surfsense", - action="store_true", - help="Also retry surfsense_agentic failures (requires backend + celery up). " - "Default is to skip them since the n=171 run had 0 SurfSense failures.", - ) - args = parser.parse_args() - raise SystemExit(asyncio.run(_run(args))) - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/summarise_crag_run.py b/surfsense_evals/scripts/summarise_crag_run.py deleted file mode 100644 index d15c4996f9..0000000000 --- a/surfsense_evals/scripts/summarise_crag_run.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Render a quick textual summary of the latest CRAG run.""" - -from __future__ import annotations - -import glob -import json - - -def main() -> None: - runs = sorted(glob.glob("data/research/runs/*/crag/run_artifact.json")) - if not runs: - print("(no CRAG runs found)") - return - with open(runs[-1], encoding="utf-8") as fh: - m = json.load(fh) - metrics = m["metrics"] - - print(f"Reading: {runs[-1]}") - print(f"n_questions: {m['extra']['n_questions']}") - print() - print("=== ARMS ===") - for arm in ("bare_llm", "long_context", "surfsense"): - d = metrics[arm] - print( - f"{arm:14s}: " - f"acc={d['accuracy'] * 100:5.1f}% (Wilson 95% CI " - f"{d['ci_low'] * 100:.1f}-{d['ci_high'] * 100:.1f}) | " - f"correct={d['correct_rate'] * 100:5.1f}% " - f"missing={d['missing_rate'] * 100:5.1f}% " - f"incorrect={d['incorrect_rate'] * 100:5.1f}% | " - f"truth={d['truthfulness_score'] * 100:+5.1f}%" - ) - - print() - print("=== DELTAS ===") - for key, d in metrics["deltas"].items(): - print( - f"{key:30s}: acc={d['accuracy_pp']:+5.1f}pp " - f"truth={d['truthfulness_score_pp']:+5.1f}pp " - f"McNemar p={d['mcnemar_p_value']:.4f} ({d['mcnemar_method']}) " - f"bootstrap CI [{d['bootstrap_ci_low']:+.1f}, {d['bootstrap_ci_high']:+.1f}]" - ) - - print() - print("=== PER-QUESTION-TYPE TRUTHFULNESS ===") - for qt, row in sorted(metrics["per_question_type"].items()): - n = row["n"] - pieces = [f"{qt:20s} (n={n:3d}):"] - for arm in ("bare_llm", "long_context", "surfsense"): - if arm in row: - pieces.append(f"{arm}={row[arm]['truthfulness_score'] * 100:+7.1f}%") - print(" ".join(pieces)) - - print() - print("=== PER-DOMAIN TRUTHFULNESS ===") - for dom, row in sorted(metrics["per_domain"].items()): - n = row["n"] - pieces = [f"{dom:10s} (n={n:3d}):"] - for arm in ("bare_llm", "long_context", "surfsense"): - if arm in row: - pieces.append(f"{arm}={row[arm]['truthfulness_score'] * 100:+7.1f}%") - print(" ".join(pieces)) - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/summarise_parser_compare_run.py b/surfsense_evals/scripts/summarise_parser_compare_run.py deleted file mode 100644 index 7801a13187..0000000000 --- a/surfsense_evals/scripts/summarise_parser_compare_run.py +++ /dev/null @@ -1,133 +0,0 @@ -"""Slice the parser_compare raw.jsonl for the n=171 run. - -Reports per-arm: - * tokens & cost stats (input/output mean, $/Q distribution) - * failures (status != ok or empty raw_text) - * answer_format breakdown (accuracy by str/int/float/list) - -Plus surfsense agentic breakdown so we can compare apples to apples -even though the new_chat SSE doesn't surface per-call token counts. -""" - -from __future__ import annotations - -import json -import statistics -from collections import defaultdict -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] -RUN_DIR = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare" -RAW = RUN_DIR / "raw.jsonl" -ARTIFACT = RUN_DIR / "run_artifact.json" - - -def main() -> None: - rows = [ - json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip() - ] - print(f"raw rows: {len(rows)}") - - by_qid: dict[str, list[dict]] = defaultdict(list) - for row in rows: - by_qid[row["qid"]].append(row) - print(f"unique questions: {len(by_qid)}") - - arm_metrics: dict[str, dict] = defaultdict( - lambda: { - "n": 0, - "n_correct": 0, - "n_failed": 0, - "n_empty": 0, - "costs": [], - "in_tokens": [], - "out_tokens": [], - "latency_ms": [], - "by_format": defaultdict(lambda: {"n": 0, "correct": 0}), - } - ) - - for row in rows: - arm = row["arm"] - m = arm_metrics[arm] - m["n"] += 1 - graded = row.get("graded") or {} - if graded.get("correct"): - m["n_correct"] += 1 - - err = row.get("error") - raw_text = row.get("raw_text") or "" - if err: - m["n_failed"] += 1 - elif not raw_text.strip(): - m["n_empty"] += 1 - - cost = row.get("cost_usd") - if cost is not None: - m["costs"].append(float(cost)) - ut = row.get("usage") or {} - if ut.get("prompt_tokens"): - m["in_tokens"].append(ut["prompt_tokens"]) - if ut.get("completion_tokens"): - m["out_tokens"].append(ut["completion_tokens"]) - if row.get("latency_ms"): - m["latency_ms"].append(row["latency_ms"]) - - fmt = row.get("answer_format") or "unknown" - m["by_format"][fmt]["n"] += 1 - if graded.get("correct"): - m["by_format"][fmt]["correct"] += 1 - - print() - print("=" * 100) - print( - f"{'arm':<25} {'n':>4} {'acc%':>6} {'F1%':>6} {'fail':>5} {'$ mean':>10} {'$ median':>10} {'in tok mean':>12} {'out tok mean':>12} {'p50 ms':>8}" - ) - print("=" * 100) - art = json.loads(ARTIFACT.read_text(encoding="utf-8")) - per_arm_art = art["metrics"]["per_arm"] - for arm, m in sorted(arm_metrics.items()): - acc = m["n_correct"] / m["n"] * 100 - fail = m["n_failed"] - cost_mean = statistics.mean(m["costs"]) if m["costs"] else 0.0 - cost_med = statistics.median(m["costs"]) if m["costs"] else 0.0 - in_mean = statistics.mean(m["in_tokens"]) if m["in_tokens"] else 0 - out_mean = statistics.mean(m["out_tokens"]) if m["out_tokens"] else 0 - lat_p50 = statistics.median(m["latency_ms"]) if m["latency_ms"] else 0 - f1 = per_arm_art.get(arm, {}).get("f1_mean", 0.0) * 100 - print( - f"{arm:<25} {m['n']:>4} {acc:>5.1f}% {f1:>5.1f}% {fail:>5} " - f"${cost_mean:>9.4f} ${cost_med:>9.4f} {in_mean:>12.0f} {out_mean:>12.0f} {lat_p50:>8.0f}" - ) - - print() - print("by answer_format (accuracy):") - formats = sorted({f for m in arm_metrics.values() for f in m["by_format"]}) - header = f"{'arm':<25} " + " ".join(f"{f:>10}" for f in formats) - print(header) - print("-" * len(header)) - for arm, m in sorted(arm_metrics.items()): - cells = [] - for f in formats: - row = m["by_format"][f] - if row["n"] == 0: - cells.append(f"{'-':>10}") - else: - pct = row["correct"] / row["n"] * 100 - cells.append(f"{pct:>5.0f}% ({row['correct']:>2}/{row['n']:>2})") - print(f"{arm:<25} " + " ".join(cells)) - - print() - print("=" * 100) - print("Aggregated cost (from run_artifact.json):") - for arm, row in per_arm_art.items(): - print( - f" {arm:<25} acc={row['accuracy'] * 100:5.1f}% " - f" $/Q LLM={row['llm_cost_per_q']:.4f} " - f" preprocess total=${row['preprocess_cost_total']:.2f} " - f" $/Q total={row['total_cost_per_q']:.4f}" - ) - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/scripts/test_context_overflow_hypothesis.py b/surfsense_evals/scripts/test_context_overflow_hypothesis.py deleted file mode 100644 index 8ccccba45b..0000000000 --- a/surfsense_evals/scripts/test_context_overflow_hypothesis.py +++ /dev/null @@ -1,154 +0,0 @@ -"""Test the hypothesis: were the LC-arm errors actually context-window -overflow errors disguised as SSL / network failures? - -If true, we'd expect: - (a) literal "prompt is too long" / "context_length_exceeded" / "exceeds .* tokens" strings, - (b) failures correlated with extraction size / input_tokens (large doc -> failure), - (c) failing requests near or over Sonnet 4.5's 200k input-token limit. - -If false (transport-layer hypothesis), we'd expect: - (a) only SSL / 502 / empty stream / JSONDecode strings, - (b) failures NOT correlated with size (uniform across PDFs by time, not by tokens), - (c) failing requests well below the 200k limit. -""" - -from __future__ import annotations - -import json -import statistics -from collections import defaultdict -from pathlib import Path - -REPO = Path(__file__).resolve().parents[1] -RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare" -RAW = RUN / "raw.jsonl" -MANIFEST = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl" - -CONTEXT_HINTS = ( - "context_length", - "context window", - "prompt is too long", - "exceeds", - "maximum context", - "input tokens", - "too many tokens", - "over the maximum", - "200000", - "200_000", -) - - -def main() -> None: - rows = [ - json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip() - ] - - extraction_size: dict[tuple[str, str], int] = {} - for line in MANIFEST.read_text(encoding="utf-8").splitlines(): - if not line.strip(): - continue - m = json.loads(line) - for arm, ext in (m.get("extractions") or {}).items(): - extraction_size[(m["doc_id"], arm)] = int(ext.get("chars") or 0) - - print("=" * 80) - print("(a) Literal 'context window' / 'prompt too long' error strings?") - print("=" * 80) - found = 0 - for row in rows: - err = (row.get("error") or "").lower() - if not err: - continue - for hint in CONTEXT_HINTS: - if hint in err: - print(f" {row['arm']:<25} {row['qid']:<50}") - print(f" -> {err[:240]}") - found += 1 - break - if not found: - print(" none found.") - - print() - print("=" * 80) - print("(b) Extraction size for OK vs FAILED rows per arm") - print("=" * 80) - arm_buckets: dict[str, dict[str, list[int]]] = defaultdict(lambda: {"ok": [], "fail": []}) - parser_arms = ( - "azure_basic_lc", - "azure_premium_lc", - "llamacloud_basic_lc", - "llamacloud_premium_lc", - ) - for row in rows: - arm = row["arm"] - if arm not in parser_arms: - continue - size = extraction_size.get((row["doc_id"], arm), 0) - bucket = "fail" if (row.get("error") or not (row.get("raw_text") or "").strip()) else "ok" - arm_buckets[arm][bucket].append(size) - - print(f"{'arm':<25} {'bucket':<5} {'n':>4} {'mean chars':>12} {'median':>10} {'max':>10}") - for arm in parser_arms: - for bucket in ("ok", "fail"): - sizes = arm_buckets[arm][bucket] - if not sizes: - print(f" {arm:<23} {bucket:<5} {0:>4} -") - continue - print( - f" {arm:<23} {bucket:<5} {len(sizes):>4} " - f"{statistics.mean(sizes):>12,.0f} " - f"{statistics.median(sizes):>10,.0f} " - f"{max(sizes):>10,}" - ) - - print() - print("=" * 80) - print("(c) Largest extraction each arm processed *successfully* vs *failed*") - print("=" * 80) - print( - "(Sonnet 4.5 input limit ~200k tokens ~= 800k chars. If failures were " - "context-overflow, max-OK would be near that cap. If max-OK is well " - "above max-FAIL, the model handled bigger contexts than the failed " - "ones, so size cannot be the cause.)" - ) - print() - for arm in parser_arms: - ok_sizes = arm_buckets[arm]["ok"] - fail_sizes = arm_buckets[arm]["fail"] - if not ok_sizes: - continue - max_ok = max(ok_sizes) - max_fail = max(fail_sizes) if fail_sizes else 0 - print( - f" {arm:<25} max OK = {max_ok:>10,} chars (~{max_ok / 4:>7,.0f} tokens) " - f"max FAIL = {max_fail:>10,} chars (~{max_fail / 4:>7,.0f} tokens)" - ) - - print() - print("=" * 80) - print("(d) Did the *known* overflow candidate fail?") - print("=" * 80) - print( - " 3M_2018_10K x llamacloud_premium = 908,733 chars (~227k tokens) " - "-- this is above Sonnet 4.5's 200k window." - ) - print( - " If transport hypothesis is correct, this should still fail with a real overflow error." - ) - print( - " If transport hypothesis is correct AND the model truncates silently, " - "it might 'succeed' but be wrong." - ) - print() - for row in rows: - if row["doc_id"] != "3M_2018_10K.pdf": - continue - if row["arm"] != "llamacloud_premium_lc": - continue - err = row.get("error") or "(none)" - graded = row.get("graded") or {} - print(f" {row['qid']:<40} correct={graded.get('correct')!s:<5} err={err[:100]}") - - -if __name__ == "__main__": - main() diff --git a/surfsense_evals/src/surfsense_evals/__init__.py b/surfsense_evals/src/surfsense_evals/__init__.py deleted file mode 100644 index fc8a814825..0000000000 --- a/surfsense_evals/src/surfsense_evals/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -"""SurfSense Evals — domain-agnostic eval harness. - -Public entry-point is the ``surfsense_evals`` CLI (``python -m surfsense_evals``). -Programmatic embedding is a non-goal for now; everything goes through the CLI -+ filesystem outputs (state.json, raw run JSONL, summary.md/json reports). -""" - -from __future__ import annotations - -__version__ = "0.1.0" diff --git a/surfsense_evals/src/surfsense_evals/__main__.py b/surfsense_evals/src/surfsense_evals/__main__.py deleted file mode 100644 index 0efb932ddc..0000000000 --- a/surfsense_evals/src/surfsense_evals/__main__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Module entry point: ``python -m surfsense_evals ...``. - -Delegates to ``core.cli.main``. ``core.cli`` lazily imports -``surfsense_evals.suites`` so every benchmark gets a chance to register -before argparse builds its subcommand groups. -""" - -from __future__ import annotations - -from surfsense_evals.core.cli import main - -if __name__ == "__main__": # pragma: no cover - raise SystemExit(main()) diff --git a/surfsense_evals/src/surfsense_evals/core/__init__.py b/surfsense_evals/src/surfsense_evals/core/__init__.py deleted file mode 100644 index b5cc64a563..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -"""Domain-agnostic infrastructure shared by every suite. - -Nothing under ``core/`` knows or cares about a specific evaluation domain. -Suites live under ``surfsense_evals.suites..`` and -register themselves with ``core.registry`` on import. -""" - -from __future__ import annotations diff --git a/surfsense_evals/src/surfsense_evals/core/arms/__init__.py b/surfsense_evals/src/surfsense_evals/core/arms/__init__.py deleted file mode 100644 index 0e7ce46e4b..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/arms/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Arm protocol + concrete arms shared across suites. - -Concrete arms (``NativePdfArm``, ``SurfSenseArm``, ``BareLlmArm``) are -imported lazily via ``__getattr__`` so consumers that only need the -protocol — e.g. the registry's ``Arm`` re-export — don't transitively -pull in ``httpx`` providers or the SurfSense client unless they -actually use those arms. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -from .base import Arm, ArmRequest, ArmResult - -if TYPE_CHECKING: # pragma: no cover - from .bare_llm import BareLlmArm - from .native_pdf import NativePdfArm - from .surfsense import SurfSenseArm - -__all__ = [ - "Arm", - "ArmRequest", - "ArmResult", - "BareLlmArm", - "NativePdfArm", - "SurfSenseArm", -] - - -def __getattr__(name: str): # PEP 562 - if name == "NativePdfArm": - from .native_pdf import NativePdfArm - - return NativePdfArm - if name == "SurfSenseArm": - from .surfsense import SurfSenseArm - - return SurfSenseArm - if name == "BareLlmArm": - from .bare_llm import BareLlmArm - - return BareLlmArm - raise AttributeError(f"module 'surfsense_evals.core.arms' has no attribute {name!r}") diff --git a/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py b/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py deleted file mode 100644 index 1e32154159..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py +++ /dev/null @@ -1,100 +0,0 @@ -"""Bare-LLM arm: chat completion with prompt-only input, no retrieval. - -Pairs with ``SurfSenseArm`` for any benchmark that wants to measure -"how much does the model already know without RAG?". For factuality / -multi-hop benchmarks (FRAMES, MuSiQue, …) this produces the published -"naive prompting" baseline — e.g. FRAMES's 40.8% on Gemini-Pro-1.5. - -Symmetric with ``NativePdfArm`` in shape, but the request carries no -``pdf_paths``: the prompt itself is the only input the model gets. -""" - -from __future__ import annotations - -import logging - -from ..providers.openrouter_chat import OpenRouterChatProvider -from .base import Arm, ArmRequest, ArmResult - -logger = logging.getLogger(__name__) - - -class BareLlmArm(Arm): - """``Arm`` implementation backed by ``OpenRouterChatProvider``. - - ``name`` defaults to ``"bare_llm"`` but is overridable per-instance. - Suites that want two distinct OpenRouter chat arms (e.g. CRAG's - ``bare_llm`` vs ``long_context`` — both backed by chat-completions - but exercising different prompt strategies) instantiate twice with - different names so the metrics aggregator can keep them separate. - """ - - name: str = "bare_llm" - - def __init__( - self, - *, - provider: OpenRouterChatProvider, - max_output_tokens: int | None = 1024, - system_prompt: str | None = None, - name: str | None = None, - ) -> None: - self._provider = provider - self._max_output = max_output_tokens - self._system_prompt = system_prompt - if name: - self.name = name - - @classmethod - def from_env( - cls, - *, - api_key: str, - model: str, - base_url: str = "https://openrouter.ai/api/v1", - max_output_tokens: int | None = 1024, - system_prompt: str | None = None, - name: str | None = None, - ) -> BareLlmArm: - provider = OpenRouterChatProvider( - api_key=api_key, - base_url=base_url, - model=model, - ) - return cls( - provider=provider, - max_output_tokens=max_output_tokens, - system_prompt=system_prompt, - name=name, - ) - - async def answer(self, request: ArmRequest) -> ArmResult: - try: - response = await self._provider.complete( - prompt=request.prompt, - system_prompt=self._system_prompt, - max_tokens=self._max_output, - ) - except Exception as exc: # noqa: BLE001 - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text="", - error=f"{type(exc).__name__}: {exc}", - ) - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text=response.text, - input_tokens=response.input_tokens, - output_tokens=response.output_tokens, - cost_micros=response.cost_micros, - latency_ms=response.latency_ms, - extra={ - "model": self._provider.model, - "finish_reason": response.finish_reason, - }, - ) - - -__all__ = ["BareLlmArm"] diff --git a/surfsense_evals/src/surfsense_evals/core/arms/base.py b/surfsense_evals/src/surfsense_evals/core/arms/base.py deleted file mode 100644 index 3e327fef24..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/arms/base.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Arm protocol + the value types every arm exchanges with a runner. - -An ``Arm`` is "one way to answer one question". Two ship in this PR: - -* ``NativePdfArm`` — drop the PDF straight into an OpenRouter - chat-completions request with ``plugins=[{file-parser, engine: - native}]``. Used for the head-to-head "is the model good enough on - its own?" measurement. -* ``SurfSenseArm`` — POST ``/api/v1/new_chat`` with the question - scoped to the relevant ``mentioned_document_ids``; consume the SSE - stream and parse citations. - -Both implement the same protocol so a benchmark runner only sees -``Arm.answer(request) -> ArmResult``. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any, Protocol - - -@dataclass -class ArmRequest: - """One arm-call worth of input. - - * ``question_id`` is opaque — used for logging and joining results. - * ``prompt`` is the fully-formatted text the arm should send. The - runner is responsible for prompt construction so head-to-head - comparisons use byte-identical text. - * ``pdf_paths`` is the per-question source PDFs (used by - ``NativePdfArm``). Empty for retrieval-only / corpus-wide - benchmarks. - * ``mentioned_document_ids`` is the SurfSense document scoping list - (used by ``SurfSenseArm``). When ``None`` SurfSense retrieves - across the whole search space. - * ``options`` is a free-form bag of arm-specific overrides - (e.g. SurfSense's ``disabled_tools``). - """ - - question_id: str - prompt: str - pdf_paths: list[Path] = field(default_factory=list) - mentioned_document_ids: list[int] | None = None - options: dict[str, Any] = field(default_factory=dict) - - -@dataclass -class ArmResult: - """Outcome of one ``Arm.answer`` invocation.""" - - arm: str - question_id: str - raw_text: str - answer_letter: str | None = None - citations: list[dict[str, Any]] = field(default_factory=list) - input_tokens: int = 0 - output_tokens: int = 0 - cost_micros: int = 0 - latency_ms: int = 0 - error: str | None = None - extra: dict[str, Any] = field(default_factory=dict) - - @property - def ok(self) -> bool: - return self.error is None - - def to_jsonl(self) -> dict[str, Any]: - """Stable dict shape for ``data//runs//_raw.jsonl``.""" - - return { - "arm": self.arm, - "question_id": self.question_id, - "answer_letter": self.answer_letter, - "raw_text": self.raw_text, - "citations": self.citations, - "input_tokens": self.input_tokens, - "output_tokens": self.output_tokens, - "cost_micros": self.cost_micros, - "latency_ms": self.latency_ms, - "error": self.error, - "extra": self.extra, - } - - -class Arm(Protocol): - """One concrete way to answer questions for a given run.""" - - name: str - - async def answer(self, request: ArmRequest) -> ArmResult: # pragma: no cover - protocol - ... diff --git a/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py b/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py deleted file mode 100644 index 9294ed0325..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py +++ /dev/null @@ -1,104 +0,0 @@ -"""Native-PDF arm: drop the PDF straight into OpenRouter chat-completions. - -Generic across suites — a benchmark just supplies the prompt and the -single PDF path. Multi-PDF questions concatenate in the runner before -calling this arm so each ``answer`` invocation feeds the model exactly -one ``data:application/pdf;base64,...`` block (matches the human -"drag-and-drop one PDF into Claude" intent). -""" - -from __future__ import annotations - -import logging - -from ..parse.answer_letter import extract_answer_letter -from ..providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine -from .base import Arm, ArmRequest, ArmResult - -logger = logging.getLogger(__name__) - - -class NativePdfArm(Arm): - """``Arm`` implementation backed by ``OpenRouterPdfProvider``.""" - - name: str = "native_pdf" - - def __init__( - self, - *, - provider: OpenRouterPdfProvider, - max_output_tokens: int | None = 1024, - ) -> None: - self._provider = provider - self._max_output = max_output_tokens - - @classmethod - def from_env( - cls, - *, - api_key: str, - model: str, - engine: PdfEngine = PdfEngine.NATIVE, - base_url: str = "https://openrouter.ai/api/v1", - max_output_tokens: int | None = 1024, - ) -> NativePdfArm: - provider = OpenRouterPdfProvider( - api_key=api_key, - base_url=base_url, - model=model, - engine=engine, - ) - return cls(provider=provider, max_output_tokens=max_output_tokens) - - async def answer(self, request: ArmRequest) -> ArmResult: - if not request.pdf_paths: - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text="", - error="native_pdf arm requires at least one pdf_path", - ) - if len(request.pdf_paths) > 1: - # The plan calls out one-PDF-per-question so the head-to-head - # is fair; runners are responsible for upstream concatenation. - logger.debug( - "qid=%s native_pdf got %d pdfs; using first only", - request.question_id, - len(request.pdf_paths), - ) - pdf = request.pdf_paths[0] - try: - response = await self._provider.complete( - prompt=request.prompt, - pdf_path=pdf, - max_tokens=self._max_output, - ) - except Exception as exc: # noqa: BLE001 - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text="", - error=f"{type(exc).__name__}: {exc}", - ) - - letter = extract_answer_letter(response.text) - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text=response.text, - answer_letter=letter.letter, - input_tokens=response.input_tokens, - output_tokens=response.output_tokens, - cost_micros=response.cost_micros, - latency_ms=response.latency_ms, - extra={ - "model": self._provider.model, - "engine": self._provider.engine.value, - "answer_letter_strategy": letter.strategy, - "finish_reason": response.finish_reason, - "pdf_filename": pdf.name, - }, - ) - - -__all__ = ["NativePdfArm"] diff --git a/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py b/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py deleted file mode 100644 index f63b5cbe6e..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py +++ /dev/null @@ -1,102 +0,0 @@ -"""SurfSense arm: per-question fresh thread + ``/api/v1/new_chat`` stream. - -For every question: - -* Create a fresh ``NewChatThread`` on the suite's pinned SearchSpace. - This sidesteps the per-thread ``THREAD_BUSY`` 409 (a single thread - serialises turns, see ``surfsense_backend/app/routes/new_chat_routes.py:191-220``). -* POST ``/api/v1/new_chat`` with the prompt and the per-question - ``mentioned_document_ids`` (``surfsense_backend/app/schemas/new_chat.py:241-243``). -* Consume the SSE stream via ``NewChatClient.ask`` which accumulates - text deltas and returns ``StreamedAnswer``. -* Optionally delete the thread (default ON for ephemeral runs). - -Citations are parsed from the streamed assistant text via the -canonical regex port; chunk ids are returned in ``ArmResult.citations`` -for the runner to map back to corpus ids. -""" - -from __future__ import annotations - -import logging - -from ..clients import NewChatClient -from ..parse.answer_letter import extract_answer_letter -from .base import Arm, ArmRequest, ArmResult - -logger = logging.getLogger(__name__) - - -class SurfSenseArm(Arm): - """``Arm`` implementation backed by ``NewChatClient``.""" - - name: str = "surfsense" - - def __init__( - self, - *, - client: NewChatClient, - search_space_id: int, - ephemeral_threads: bool = True, - thread_title_prefix: str = "eval", - ) -> None: - self._client = client - self._search_space_id = search_space_id - self._ephemeral = ephemeral_threads - self._title_prefix = thread_title_prefix - - async def answer(self, request: ArmRequest) -> ArmResult: - thread_id: int | None = None - try: - thread_id = await self._client.create_thread( - search_space_id=self._search_space_id, - title=f"{self._title_prefix}:{request.question_id}", - ) - answer = await self._client.ask( - thread_id=thread_id, - search_space_id=self._search_space_id, - user_query=request.prompt, - mentioned_document_ids=request.mentioned_document_ids, - disabled_tools=request.options.get("disabled_tools"), - ) - except Exception as exc: # noqa: BLE001 - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text="", - error=f"{type(exc).__name__}: {exc}", - extra={"thread_id": thread_id}, - ) - finally: - if self._ephemeral and thread_id is not None: - try: - await self._client.delete_thread(thread_id) - except Exception as exc: # noqa: BLE001 - logger.debug("Failed to delete thread %s: %s", thread_id, exc) - - letter = extract_answer_letter(answer.text) - return ArmResult( - arm=self.name, - question_id=request.question_id, - raw_text=answer.text, - answer_letter=letter.letter, - citations=answer.citations, - latency_ms=answer.latency_ms, - # SurfSense doesn't surface input/output token counts in the - # SSE stream today; leaving the cost / token fields at 0 - # documents that gap. Estimating from the raw text would - # bias the comparison against the SurfSense arm. - extra={ - "thread_id": thread_id, - "search_space_id": self._search_space_id, - "answer_letter_strategy": letter.strategy, - "user_message_id": answer.user_message_id, - "assistant_message_id": answer.assistant_message_id, - "finished_normally": answer.finished_normally, - "n_raw_events": len(answer.raw_events), - "n_mentioned_documents": len(request.mentioned_document_ids or []), - }, - ) - - -__all__ = ["SurfSenseArm"] diff --git a/surfsense_evals/src/surfsense_evals/core/auth.py b/surfsense_evals/src/surfsense_evals/core/auth.py deleted file mode 100644 index cf348e3ff9..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/auth.py +++ /dev/null @@ -1,270 +0,0 @@ -"""Dual-mode credential resolver + httpx client factory with 401 auto-refresh. - -SurfSense supports ``AUTH_TYPE=LOCAL`` (email + password) and -``AUTH_TYPE=GOOGLE`` (Google OAuth → frontend stores JWT in ``localStorage``). -There is no headless equivalent of the Google flow, so the harness handles -both modes by treating the JWT as the universal credential: - -* **LOCAL**: harness POSTs JSON ``email`` + ``password`` to - ``/auth/desktop/login``, reads ``{access_token, refresh_token}``. -* **GOOGLE / pre-issued JWT**: operator pastes their existing JWT (and - optionally refresh token) into ``SURFSENSE_JWT`` / - ``SURFSENSE_REFRESH_TOKEN``; harness skips login. - -Either way ``client_with_auth`` returns one shared -``httpx.AsyncClient`` with ``Authorization: Bearer `` set and an -event hook that, on a 401 with a refresh token in scope, calls -``POST /auth/jwt/refresh`` and retries the original request once. JWT -lifetime defaults to one day backend-side, so this matters for long -MIRAGE runs. -""" - -from __future__ import annotations - -import logging -from dataclasses import dataclass -from typing import Any - -import httpx - -from .config import Config - -logger = logging.getLogger(__name__) - - -class CredentialError(RuntimeError): - """Raised when no credential mode is configured.""" - - -_NO_CREDENTIALS_MESSAGE = ( - "No SurfSense credentials configured. Set ONE of:\n" - " (LOCAL) SURFSENSE_USER_EMAIL + SURFSENSE_USER_PASSWORD\n" - " (GOOGLE) SURFSENSE_JWT (and optionally SURFSENSE_REFRESH_TOKEN)\n" - "For GOOGLE: use a PAT or operator-issued bearer token and set " - "SURFSENSE_JWT (plus SURFSENSE_REFRESH_TOKEN if available)." -) - - -@dataclass -class TokenBundle: - """Mutable token state — refresh hook updates ``access_token`` in place.""" - - access_token: str - refresh_token: str | None = None - # ``mode`` is informational only ("local" or "jwt"); used in error messages. - mode: str = "jwt" - - -# --------------------------------------------------------------------------- -# Token acquisition -# --------------------------------------------------------------------------- - - -async def acquire_token(config: Config, *, http: httpx.AsyncClient | None = None) -> TokenBundle: - """Resolve credentials → ``TokenBundle``. - - Precedence: - - 1. ``SURFSENSE_JWT`` set → use it directly. Refresh token captured if - supplied. - 2. ``SURFSENSE_USER_EMAIL`` + ``SURFSENSE_USER_PASSWORD`` set → - JSON POST to ``/auth/desktop/login``. - 3. Neither → raise ``CredentialError``. - - The optional ``http`` argument lets tests inject a mocked client; if - omitted a one-shot client is created for the login call only. - """ - - if config.has_jwt_mode(): - return TokenBundle( - access_token=config.surfsense_jwt or "", - refresh_token=config.surfsense_refresh_token, - mode="jwt", - ) - - if config.has_local_mode(): - - async def _login(client: httpx.AsyncClient) -> TokenBundle: - response = await client.post( - f"{config.surfsense_api_base}/auth/desktop/login", - json={ - "email": config.surfsense_user_email, - "password": config.surfsense_user_password, - }, - headers={"Accept": "application/json"}, - ) - if response.status_code != 200: - raise CredentialError( - f"LOCAL login failed (HTTP {response.status_code}): {_safe_text(response)}" - ) - payload = response.json() - access = payload.get("access_token") - if not access: - raise CredentialError(f"LOCAL login response missing access_token: {payload!r}") - return TokenBundle( - access_token=access, - refresh_token=payload.get("refresh_token") or None, - mode="local", - ) - - if http is not None: - return await _login(http) - async with httpx.AsyncClient(timeout=httpx.Timeout(30.0, connect=10.0)) as client: - return await _login(client) - - raise CredentialError(_NO_CREDENTIALS_MESSAGE) - - -def _safe_text(response: httpx.Response, *, limit: int = 200) -> str: - body = response.text or "" - if len(body) > limit: - return body[:limit] + "…" - return body - - -# --------------------------------------------------------------------------- -# httpx client + 401 auto-refresh -# --------------------------------------------------------------------------- - - -class _AuthState: - """Shared mutable holder closed over by the auth event hook. - - Kept private so callers can't accidentally mutate the access token - out-of-band; ``client_with_auth`` returns the client directly. - """ - - def __init__(self, config: Config, tokens: TokenBundle) -> None: - self.config = config - self.tokens = tokens - self._refresh_in_flight: bool = False - - -def _build_auth_request(state: _AuthState, request: httpx.Request) -> None: - """Stamp the current bearer onto ``request`` (request-event hook).""" - - request.headers["Authorization"] = f"Bearer {state.tokens.access_token}" - - -async def _refresh_access_token( - state: _AuthState, transport: httpx.AsyncBaseTransport | None = None -) -> bool: - """POST ``/auth/jwt/refresh`` with the current refresh token. - - Returns ``True`` on success and updates ``state.tokens`` in place. - Returns ``False`` if no refresh token is configured or the call fails. - Recursive 401s are avoided by using a *new* client without the auth - hook. - """ - - refresh = state.tokens.refresh_token - if not refresh: - return False - try: - async with httpx.AsyncClient( - timeout=httpx.Timeout(15.0, connect=5.0), - transport=transport, - ) as inner: - response = await inner.post( - f"{state.config.surfsense_api_base}/auth/jwt/refresh", - json={"refresh_token": refresh}, - headers={"Accept": "application/json"}, - ) - except httpx.HTTPError as exc: - logger.warning("Token refresh transport error: %s", exc) - return False - if response.status_code != 200: - logger.warning( - "Token refresh rejected (HTTP %s): %s", - response.status_code, - _safe_text(response), - ) - return False - payload = response.json() - new_access = payload.get("access_token") - if not new_access: - logger.warning("Refresh response missing access_token: %r", payload) - return False - state.tokens.access_token = new_access - new_refresh = payload.get("refresh_token") - if new_refresh: - state.tokens.refresh_token = new_refresh - return True - - -def client_with_auth( - config: Config, - tokens: TokenBundle, - *, - timeout: float = 60.0, - transport: httpx.AsyncBaseTransport | None = None, - base_url: str | None = None, -) -> httpx.AsyncClient: - """Build a single shared ``httpx.AsyncClient`` for the SurfSense API. - - * Stamps ``Authorization: Bearer `` on every outgoing request. - * On any 401 response, attempts a single refresh (if a refresh token - is configured) and retries the original request once. The retry - uses a fresh stamping of the bearer header, so a successful - refresh transparently unblocks long runs. - * The retry is best-effort — repeated 401s after a refresh attempt - are surfaced to the caller so they can re-auth manually. - - Pass ``base_url`` to scope a sub-client (e.g. tests). The default - keeps full URLs in calling code, which makes route-spec citations in - the codebase easier to grep. - """ - - state = _AuthState(config, tokens) - - async def _request_hook(request: httpx.Request) -> None: - _build_auth_request(state, request) - - # ``send`` is overridden in ``_AuthAwareClient`` to retry once on 401 - # after refreshing the bearer. httpx's response event-hook can't - # *replace* a response, so we need a subclass to do the replay. - client = _AuthAwareClient( - state=state, - transport=transport, - timeout=httpx.Timeout(timeout, connect=10.0), - base_url=base_url or "", - event_hooks={"request": [_request_hook]}, - ) - return client - - -class _AuthAwareClient(httpx.AsyncClient): - """``AsyncClient`` that retries once on 401 after refreshing the token.""" - - def __init__(self, *, state: _AuthState, **kwargs: Any) -> None: - super().__init__(**kwargs) - self._auth_state = state - - async def send( # type: ignore[override] - self, request: httpx.Request, **kwargs: Any - ) -> httpx.Response: - response = await super().send(request, **kwargs) - if response.status_code != 401: - return response - # Don't refresh while a refresh is itself in flight. - if self._auth_state._refresh_in_flight: - return response - self._auth_state._refresh_in_flight = True - try: - refreshed = await _refresh_access_token(self._auth_state) - finally: - self._auth_state._refresh_in_flight = False - if not refreshed: - return response - # Re-stamp and replay once. ``request`` is reusable. - await response.aclose() - request.headers["Authorization"] = f"Bearer {self._auth_state.tokens.access_token}" - return await super().send(request, **kwargs) - - -__all__ = [ - "CredentialError", - "TokenBundle", - "acquire_token", - "client_with_auth", -] diff --git a/surfsense_evals/src/surfsense_evals/core/cli.py b/surfsense_evals/src/surfsense_evals/core/cli.py deleted file mode 100644 index 21e706c491..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/cli.py +++ /dev/null @@ -1,786 +0,0 @@ -"""Argparse CLI for ``python -m surfsense_evals``. - -Subcommands: - -* ``setup --suite --provider-model [--chat-model-id ]`` -* ``teardown --suite `` -* ``models list [--provider openrouter] [--grep ]`` -* ``suites list`` -* ``benchmarks list [--suite ]`` -* ``ingest [benchmark flags]`` -* ``run [benchmark flags]`` -* ``report --suite [--benchmark ]`` - -The ``ingest`` / ``run`` subparsers are built dynamically from the -registry — adding a new benchmark only requires registering it; the -CLI surface comes for free. ``add_run_args`` lets each benchmark -publish its own flags. - -Design choices worth flagging: - -* ``setup`` rejects ``chat_model_id == 0`` (Auto / LiteLLM router) so - per-question accuracy is reproducible. -* ``setup`` validates that the picked LLM config has - ``provider == "OPENROUTER"`` and ``model_name == --provider-model`` - before declaring success — both arms of the head-to-head must hit - the same OpenRouter slug. -* Lifecycle state is keyed by suite, so ``setup --suite legal`` does - not touch ``medical``'s SearchSpace, and vice versa. -""" - -from __future__ import annotations - -import argparse -import asyncio -import contextlib -import json -import logging -import sys -from dataclasses import dataclass -from typing import Any - -import httpx -from rich.console import Console -from rich.table import Table - -# Windows' legacy console (cp1252) crashes when Rich tries to write characters -# outside the active codepage (e.g. '->', em-dashes, box-drawing). Force UTF-8 -# on stdout/stderr and disable Rich's legacy_windows render path so the file -# stream is used directly. Modern Windows (>=10, VS Code terminal, Windows -# Terminal, PowerShell, cmd) all interpret ANSI escapes natively. -if sys.platform == "win32": - for _stream in (sys.stdout, sys.stderr): - with contextlib.suppress(AttributeError, ValueError): - _stream.reconfigure(encoding="utf-8", errors="replace") - -from . import registry -from .auth import CredentialError, acquire_token, client_with_auth -from .clients import SearchSpaceClient -from .config import ( - DEFAULT_SCENARIO, - SCENARIOS, - Config, - SuiteState, - clear_suite_state, - get_suite_state, - load_config, - set_suite_state, - utc_iso_timestamp, -) -from .vision_llm import VisionConfigError, resolve_vision_llm - -logger = logging.getLogger("surfsense_evals") -console = Console(legacy_windows=False) - - -# --------------------------------------------------------------------------- -# Discovery -# --------------------------------------------------------------------------- - - -def _discover_suites() -> list[str]: - """Trigger ``register(...)`` for every benchmark. - - Imported lazily so ``models list`` (which doesn't need any - benchmark) still runs fast. - """ - - from surfsense_evals.suites import discover_suites - - return discover_suites() - - -# --------------------------------------------------------------------------- -# Global LLM config fetcher (used by setup + models list) -# --------------------------------------------------------------------------- - - -@dataclass -class LlmConfigEntry: - id: int - name: str - provider: str - model_name: str - raw: dict[str, Any] - - @classmethod - def from_payload(cls, payload: dict[str, Any]) -> LlmConfigEntry: - return cls( - id=int(payload["id"]), - name=str(payload.get("display_name") or payload.get("name") or ""), - provider=str(payload.get("provider", "")).upper(), - model_name=str(payload.get("model_id") or payload.get("model_name") or ""), - raw=payload, - ) - - -async def _list_global_llm_configs(http: httpx.AsyncClient, base: str) -> list[LlmConfigEntry]: - response = await http.get( - f"{base}/api/v1/model-connections/global", - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - payload = response.json() - if not isinstance(payload, list): - raise RuntimeError(f"Unexpected /model-connections/global payload: {payload!r}") - entries: list[LlmConfigEntry] = [] - for connection in payload: - provider = connection.get("provider", "") - for model in connection.get("models") or []: - if not model.get("enabled", True) or not model.get("supports_chat"): - continue - entries.append(LlmConfigEntry.from_payload({**model, "provider": provider})) - return entries - - -def _resolve_openrouter_id( - candidates: list[LlmConfigEntry], - provider_model: str, - *, - explicit_id: int | None, -) -> int: - """Resolve the SurfSense LLM id for ``provider_model``. - - Behaviour: - - * If ``explicit_id`` is given: return it directly. The caller is - then expected to GET-validate that the row's - ``provider == "OPENROUTER"`` and ``model_name`` matches the slug. - That branch supports positive BYOK model rows whose slugs may overlap - with global OpenRouter virtuals. - * Otherwise: filter to ``provider == "OPENROUTER"`` and - ``model_name == provider_model``. Expect exactly one match — - raise with a friendly message otherwise. - """ - - if explicit_id is not None: - return explicit_id - - matches = [ - c for c in candidates if c.provider == "OPENROUTER" and c.model_name == provider_model - ] - if not matches: - sample = ", ".join( - f"{c.model_name} (id={c.id})" for c in candidates if c.provider == "OPENROUTER" - )[:600] - raise RuntimeError( - f"No OpenRouter config found for slug '{provider_model}'. " - "Make sure `openrouter_integration.enabled: true` in " - "global_llm_config.yaml and that the Celery worker has " - "finished its first refresh (the catalogue is fetched at " - "Celery startup per `app/celery_app.py`). " - f"Available OpenRouter slugs (sample): {sample or ''}.\n" - "Browse with: python -m surfsense_evals models list --grep " - ) - if len(matches) > 1: - listing = "\n".join(f" id={c.id} name={c.name!r}" for c in matches) - raise RuntimeError( - f"Multiple OpenRouter configs for slug '{provider_model}':\n{listing}\n" - "Pass --chat-model-id to disambiguate." - ) - return matches[0].id - - -# --------------------------------------------------------------------------- -# Subcommand implementations -# --------------------------------------------------------------------------- - - -async def _cmd_setup(args: argparse.Namespace) -> int: - suite = args.suite - provider_model: str = args.provider_model - explicit_id: int | None = args.chat_model_id - scenario: str = args.scenario - vision_llm_slug: str | None = args.vision_llm - native_arm_model: str | None = args.native_arm_model - skip_vision_setup: bool = args.no_vision_llm_setup - - if explicit_id == 0: - console.print( - "[red]chat_model_id == 0 (Auto / LiteLLM router) is not allowed — " - "results would not be reproducible.[/red]" - ) - return 2 - - if scenario not in SCENARIOS: - console.print( - f"[red]Unknown scenario {scenario!r}. Pick one of: {', '.join(SCENARIOS)}[/red]" - ) - return 2 - - # Scenario-specific validation. Each branch documents WHY the rule - # exists so the operator's mental model matches what the runner does. - if scenario == "cost-arbitrage": - if not native_arm_model: - console.print( - "[red]--scenario cost-arbitrage requires --native-arm-model " - ".[/red] The native arm needs a vision " - "model to fairly answer image-bearing questions; SurfSense " - "answers from already-extracted text via --provider-model." - ) - return 2 - if native_arm_model == provider_model: - console.print( - "[yellow]--native-arm-model equals --provider-model in " - "cost-arbitrage; that's degenerate (same as head-to-head). " - "Pick a different slug or switch to --scenario head-to-head.[/yellow]" - ) - elif scenario in ("head-to-head", "symmetric-cheap"): - if native_arm_model: - console.print( - f"[yellow]--native-arm-model is ignored for --scenario {scenario} " - f"(both arms answer with --provider-model={provider_model!r}).[/yellow]" - ) - native_arm_model = None # don't persist a stale value - - config = load_config() - try: - token = await acquire_token(config) - except CredentialError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - - async with client_with_auth(config, token) as http: - candidates = await _list_global_llm_configs(http, config.surfsense_api_base) - - try: - chat_model_id = _resolve_openrouter_id( - candidates, provider_model, explicit_id=explicit_id - ) - except RuntimeError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - - ss_client = SearchSpaceClient(http, config.surfsense_api_base) - existing = get_suite_state(config, suite) - if existing is not None: - try: - row = await ss_client.get(existing.search_space_id) - console.print( - f"Reusing existing SearchSpace [cyan]{row.name}[/cyan] " - f"(id={row.id}) for suite [bold]{suite}[/bold]." - ) - search_space_id = row.id - except httpx.HTTPStatusError as exc: - if exc.response.status_code == 404: - console.print( - f"[yellow]state.json pointed at SearchSpace id={existing.search_space_id} " - f"but backend returned 404; creating a fresh one.[/yellow]" - ) - existing = None - else: - raise - if existing is None: - ss_name = f"eval-{suite}-{utc_iso_timestamp()}" - row = await ss_client.create( - ss_name, description=f"surfsense-evals lifecycle ({suite})" - ) - console.print( - f"Created SearchSpace [cyan]{row.name}[/cyan] (id={row.id}) " - f"for suite [bold]{suite}[/bold]." - ) - search_space_id = row.id - - # Resolve + attach the vision LLM config (unless explicitly skipped). - # Asymmetric scenarios make the vision LLM at ingest a hard - # requirement — without it, SurfSense's chunks have no image - # content and the entire framing collapses. - vision_required = scenario in ("symmetric-cheap", "cost-arbitrage") - vision_config_id: int | None = None - vision_provider_model: str | None = None - if not skip_vision_setup and (vision_required or vision_llm_slug is not None): - try: - vision_candidates = await ss_client.list_global_vision_models() - resolved = resolve_vision_llm(vision_candidates, explicit_slug=vision_llm_slug) - except VisionConfigError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - vision_config_id = resolved.config_id - vision_provider_model = resolved.provider_model - console.print( - f"Vision LLM at ingest: [cyan]{vision_provider_model}[/cyan] " - f"(id={vision_config_id}, selected_via={resolved.selected_via})." - ) - - role_kwargs: dict[str, Any] = {"chat_model_id": chat_model_id} - if vision_config_id is not None: - role_kwargs["vision_model_id"] = vision_config_id - - await ss_client.set_model_roles(search_space_id, **role_kwargs) - roles = await ss_client.get_model_roles(search_space_id) - if roles.chat_model_id != chat_model_id: - console.print( - f"[red]LLM pin validation FAILED.[/red] After PUT, " - f"chat_model_id={roles.chat_model_id!r}; expected {chat_model_id!r}." - ) - return 2 - if vision_config_id is not None and roles.vision_model_id != vision_config_id: - console.print( - f"[red]Vision LLM pin validation FAILED.[/red] After PUT, " - f"vision_model_id={roles.vision_model_id!r}; " - f"expected {vision_config_id!r}." - ) - return 2 - - suite_state = SuiteState( - search_space_id=search_space_id, - chat_model_id=chat_model_id, - provider_model=provider_model, - created_at=utc_iso_timestamp(), - ingestion_maps=existing.ingestion_maps if existing else {}, - scenario=scenario, - vision_model_id=vision_config_id, - vision_provider_model=vision_provider_model, - native_arm_model=native_arm_model, - ) - set_suite_state(config, suite, suite_state) - - summary_bits = [ - f"suite={suite!r}", - f"scenario={scenario!r}", - f"search_space_id={suite_state.search_space_id}", - f"chat_model_id={suite_state.chat_model_id}", - f"provider_model={suite_state.provider_model!r}", - ] - if suite_state.vision_provider_model: - summary_bits.append(f"vision_provider_model={suite_state.vision_provider_model!r}") - if suite_state.native_arm_model: - summary_bits.append(f"native_arm_model={suite_state.native_arm_model!r}") - console.print(f"[green]setup OK[/green] {' '.join(summary_bits)}") - return 0 - - -async def _cmd_teardown(args: argparse.Namespace) -> int: - suite = args.suite - config = load_config() - state = get_suite_state(config, suite) - if state is None: - console.print(f"[yellow]No state for suite {suite!r}; nothing to tear down.[/yellow]") - return 0 - try: - token = await acquire_token(config) - except CredentialError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - async with client_with_auth(config, token) as http: - ss_client = SearchSpaceClient(http, config.surfsense_api_base) - try: - await ss_client.delete(state.search_space_id) - except httpx.HTTPStatusError as exc: - console.print( - f"[yellow]DELETE failed (HTTP {exc.response.status_code}); " - "clearing state.json anyway.[/yellow]" - ) - clear_suite_state(config, suite) - console.print( - f"[green]teardown OK[/green] suite={suite!r} " - f"(SearchSpace soft-deleted, state.json slot cleared)." - ) - return 0 - - -async def _cmd_models_list(args: argparse.Namespace) -> int: - config = load_config() - try: - token = await acquire_token(config) - except CredentialError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - async with client_with_auth(config, token) as http: - entries = await _list_global_llm_configs(http, config.surfsense_api_base) - grep = (args.grep or "").lower() - provider_filter = (args.provider or "").upper() - rows: list[LlmConfigEntry] = [] - for e in entries: - if provider_filter and e.provider != provider_filter: - continue - if grep and grep not in e.model_name.lower() and grep not in e.name.lower(): - continue - rows.append(e) - table = Table( - title=f"Global LLM configs ({len(rows)} of {len(entries)})", - show_lines=False, - ) - table.add_column("id", justify="right", style="cyan") - table.add_column("provider", style="magenta") - table.add_column("model_name", style="green") - table.add_column("name") - for e in sorted(rows, key=lambda x: (x.provider, x.model_name)): - table.add_row(str(e.id), e.provider, e.model_name, e.name) - console.print(table) - return 0 - - -def _cmd_suites_list(_args: argparse.Namespace) -> int: - _discover_suites() - suites = registry.list_suites() - if not suites: - console.print( - "[yellow]No suites registered. Drop a benchmark under " - "src/surfsense_evals/suites///.[/yellow]" - ) - return 0 - table = Table(title=f"Registered suites ({len(suites)})") - table.add_column("suite", style="bold") - table.add_column("benchmarks", style="green") - for suite in suites: - names = [b.name for b in registry.list_benchmarks(suite)] - table.add_row(suite, ", ".join(names) or "") - console.print(table) - return 0 - - -def _cmd_benchmarks_list(args: argparse.Namespace) -> int: - _discover_suites() - benchmarks = registry.list_benchmarks(args.suite) - if not benchmarks: - console.print("[yellow]No benchmarks registered.[/yellow]") - return 0 - table = Table(title=f"Benchmarks ({len(benchmarks)})") - table.add_column("suite", style="bold") - table.add_column("name", style="cyan") - table.add_column("headline", justify="center") - table.add_column("description") - for b in benchmarks: - table.add_row( - b.suite, - b.name, - "yes" if b.headline else "no", - getattr(b, "description", ""), - ) - console.print(table) - return 0 - - -async def _cmd_ingest(args: argparse.Namespace) -> int: - benchmark = registry.get(args.suite, args.benchmark) - config = load_config() - state = get_suite_state(config, args.suite) - if state is None: - console.print( - f"[red]No setup for suite {args.suite!r}. Run " - f"`python -m surfsense_evals setup --suite {args.suite} " - f"--provider-model ` first.[/red]" - ) - return 2 - try: - token = await acquire_token(config) - except CredentialError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - - # Forward parsed CLI flags into ingest() so a benchmark can honour - # its own flags (e.g. MIRAGE's --skip-snippet-filter / --corpus). - extra_kwargs = { - k: v - for k, v in vars(args).items() - if k not in {"_func", "_async", "command", "subcommand", "suite", "benchmark", "log_level"} - } - async with client_with_auth(config, token) as http: - ctx = registry.RunContext( - suite=args.suite, - benchmark=args.benchmark, - config=config, - suite_state=state, - http=http, - ) - await benchmark.ingest(ctx, **extra_kwargs) - console.print(f"[green]ingest OK[/green] {args.suite}/{args.benchmark}") - return 0 - - -async def _cmd_run(args: argparse.Namespace) -> int: - benchmark = registry.get(args.suite, args.benchmark) - config = load_config() - state = get_suite_state(config, args.suite) - if state is None: - console.print( - f"[red]No setup for suite {args.suite!r}. Run " - f"`python -m surfsense_evals setup --suite {args.suite} " - f"--provider-model ` first.[/red]" - ) - return 2 - try: - token = await acquire_token(config) - except CredentialError as exc: - console.print(f"[red]{exc}[/red]") - return 2 - - extra_kwargs = { - k: v - for k, v in vars(args).items() - if k not in {"_func", "_async", "command", "subcommand", "suite", "benchmark", "log_level"} - } - async with client_with_auth(config, token) as http: - ctx = registry.RunContext( - suite=args.suite, - benchmark=args.benchmark, - config=config, - suite_state=state, - http=http, - ) - artifact = await benchmark.run(ctx, **extra_kwargs) - - console.print(f"[green]run OK[/green] {args.suite}/{args.benchmark} → {artifact.raw_path}") - return 0 - - -async def _cmd_report(args: argparse.Namespace) -> int: - from .report import write_report - - benchmark_filter = args.benchmark - config = load_config() - state = get_suite_state(config, args.suite) - if state is None: - console.print(f"[red]No setup for suite {args.suite!r}.[/red]") - return 2 - benchmarks = registry.list_benchmarks(args.suite) - if benchmark_filter: - benchmarks = [b for b in benchmarks if b.name == benchmark_filter] - if not benchmarks: - console.print( - f"[red]No registered benchmark named {benchmark_filter!r} in suite {args.suite!r}.[/red]" - ) - return 2 - - artifacts = _collect_artifacts(config, args.suite, [b.name for b in benchmarks]) - if not artifacts: - console.print( - "[yellow]No run artifacts found under " - f"{config.suite_runs_dir(args.suite)}. Run a benchmark first.[/yellow]" - ) - return 1 - - grouped: dict[str, list[registry.RunArtifact]] = {} - for art in artifacts: - grouped.setdefault(art.benchmark, []).append(art) - sections: list[registry.ReportSection] = [] - for benchmark in benchmarks: - if benchmark.name not in grouped: - continue - sections.append(benchmark.report_section(grouped[benchmark.name])) - - summary_path = write_report( - config=config, - suite=args.suite, - sections=sections, - run_timestamp=utc_iso_timestamp(), - ) - console.print(f"[green]report OK[/green] → {summary_path}") - return 0 - - -def _collect_artifacts( - config: Config, suite: str, benchmark_names: list[str] -) -> list[registry.RunArtifact]: - """Walk ``data//runs/*//`` for the latest artifacts. - - Reads any ``run_artifact.json`` written by a benchmark runner. The - runner is responsible for writing this manifest alongside its raw - JSONL so the report writer doesn't have to know benchmark-specific - metric shapes. - """ - - runs_dir = config.suite_runs_dir(suite) - if not runs_dir.exists(): - return [] - artifacts: list[registry.RunArtifact] = [] - by_bench: dict[str, registry.RunArtifact] = {} - for ts_dir in sorted(runs_dir.iterdir()): - if not ts_dir.is_dir(): - continue - for bench_name in benchmark_names: - bench_dir = ts_dir / bench_name - manifest = bench_dir / "run_artifact.json" - if not manifest.exists(): - continue - try: - with manifest.open("r", encoding="utf-8") as fh: - payload = json.load(fh) - except (OSError, json.JSONDecodeError): - continue - artifact = registry.RunArtifact( - suite=suite, - benchmark=bench_name, - run_timestamp=ts_dir.name, - raw_path=bench_dir / payload.get("raw_path", "raw.jsonl"), - metrics=payload.get("metrics", {}), - extra=payload.get("extra", {}), - ) - # Latest run wins per benchmark. - by_bench[bench_name] = artifact - artifacts = list(by_bench.values()) - return artifacts - - -# --------------------------------------------------------------------------- -# Argparse wiring -# --------------------------------------------------------------------------- - - -def _build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser( - prog="surfsense-evals", - description="SurfSense evaluation harness — domain-agnostic core + pluggable suites.", - ) - parser.add_argument( - "--log-level", default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"] - ) - sub = parser.add_subparsers(dest="command", required=True) - - p_setup = sub.add_parser("setup", help="Create per-suite SearchSpace + pin LLM.") - p_setup.add_argument("--suite", required=True) - p_setup.add_argument( - "--provider-model", - required=True, - help=( - "OpenRouter slug for the SurfSense answer LLM (and the native arm " - "too unless --native-arm-model is set), e.g. " - "'anthropic/claude-sonnet-4.5'." - ), - ) - p_setup.add_argument( - "--chat-model-id", - type=int, - default=None, - help="Optional explicit model id override.", - ) - p_setup.add_argument( - "--scenario", - choices=SCENARIOS, - default=DEFAULT_SCENARIO, - help=( - "head-to-head (default): both arms answer with --provider-model; " - "symmetric-cheap: both arms use the same cheap text-only slug, " - "SurfSense pre-extracted images at ingest with a vision LLM; " - "cost-arbitrage: native arm uses --native-arm-model (vision), " - "SurfSense uses --provider-model (cheap, text-only) over chunks " - "the vision LLM already extracted at ingest." - ), - ) - p_setup.add_argument( - "--vision-llm", - default=None, - metavar="SLUG", - help=( - "OpenRouter slug for the vision LLM SurfSense uses at ingest " - "when --use-vision-llm is on. If omitted in symmetric-cheap / " - "cost-arbitrage, the strongest registered vision config is " - "auto-picked (priority: claude-sonnet-4.5 > claude-opus-4.7 > " - "gpt-5 > gemini-2.5-pro)." - ), - ) - p_setup.add_argument( - "--native-arm-model", - default=None, - metavar="SLUG", - help=( - "Required for --scenario cost-arbitrage. OpenRouter slug used " - "by the native_pdf arm only; SurfSense answers with " - "--provider-model. Ignored for head-to-head / symmetric-cheap." - ), - ) - p_setup.add_argument( - "--no-vision-llm-setup", - action="store_true", - help=( - "Skip attaching a vision LLM config to the SearchSpace even if " - "the scenario would normally require one. Use when you want to " - "keep whatever is already attached (e.g. a per-user config)." - ), - ) - p_setup.set_defaults(_func=_cmd_setup, _async=True) - - p_teardown = sub.add_parser( - "teardown", help="Soft-delete the suite SearchSpace + clear state slot." - ) - p_teardown.add_argument("--suite", required=True) - p_teardown.set_defaults(_func=_cmd_teardown, _async=True) - - p_models = sub.add_parser("models", help="LLM-config discovery helpers.") - models_sub = p_models.add_subparsers(dest="subcommand", required=True) - p_models_list = models_sub.add_parser("list", help="List global LLM configs.") - p_models_list.add_argument( - "--provider", default=None, help="Filter by provider, e.g. openrouter" - ) - p_models_list.add_argument( - "--grep", default=None, help="Substring filter on name / model_name." - ) - p_models_list.set_defaults(_func=_cmd_models_list, _async=True) - - p_suites = sub.add_parser("suites", help="List registered suites.") - suites_sub = p_suites.add_subparsers(dest="subcommand", required=True) - p_suites_list = suites_sub.add_parser("list", help="List suites.") - p_suites_list.set_defaults(_func=_cmd_suites_list, _async=False) - - p_benchmarks = sub.add_parser("benchmarks", help="List registered benchmarks.") - bench_sub = p_benchmarks.add_subparsers(dest="subcommand", required=True) - p_bench_list = bench_sub.add_parser("list", help="List benchmarks.") - p_bench_list.add_argument("--suite", default=None) - p_bench_list.set_defaults(_func=_cmd_benchmarks_list, _async=False) - - # Dynamic ingest / run subcommands need the registry populated, so - # discover up-front (cheap on import — modules just register). - _discover_suites() - - p_ingest = sub.add_parser("ingest", help="Ingest a benchmark's corpus.") - ingest_sub = p_ingest.add_subparsers(dest="suite", required=True) - for suite in registry.list_suites(): - suite_parser = ingest_sub.add_parser(suite, help=f"Ingest a {suite} benchmark.") - suite_bench = suite_parser.add_subparsers(dest="benchmark", required=True) - for benchmark in registry.list_benchmarks(suite): - bp = suite_bench.add_parser( - benchmark.name, help=getattr(benchmark, "description", benchmark.name) - ) - if hasattr(benchmark, "add_run_args"): - benchmark.add_run_args(bp) - bp.set_defaults(_func=_cmd_ingest, _async=True) - - p_run = sub.add_parser("run", help="Run a benchmark.") - run_sub = p_run.add_subparsers(dest="suite", required=True) - for suite in registry.list_suites(): - suite_parser = run_sub.add_parser(suite, help=f"Run a {suite} benchmark.") - suite_bench = suite_parser.add_subparsers(dest="benchmark", required=True) - for benchmark in registry.list_benchmarks(suite): - bp = suite_bench.add_parser( - benchmark.name, help=getattr(benchmark, "description", benchmark.name) - ) - if hasattr(benchmark, "add_run_args"): - benchmark.add_run_args(bp) - bp.set_defaults(_func=_cmd_run, _async=True) - - p_report = sub.add_parser("report", help="Aggregate latest run artifacts into a summary.") - p_report.add_argument("--suite", required=True) - p_report.add_argument("--benchmark", default=None, help="Optional: report only this benchmark.") - p_report.set_defaults(_func=_cmd_report, _async=True) - - return parser - - -def main(argv: list[str] | None = None) -> int: - parser = _build_parser() - args = parser.parse_args(argv) - logging.basicConfig( - level=getattr(logging, args.log_level), - format="%(asctime)s %(levelname)s %(name)s %(message)s", - ) - func = getattr(args, "_func", None) - if func is None: - parser.print_help() - return 2 - is_async = getattr(args, "_async", False) - try: - if is_async: - return asyncio.run(func(args)) - return func(args) - except KeyboardInterrupt: - console.print("[yellow]Interrupted.[/yellow]") - return 130 - except Exception as exc: # noqa: BLE001 - logger.exception("CLI command failed") - console.print(f"[red]Command failed: {exc}[/red]") - return 1 - - -if __name__ == "__main__": # pragma: no cover - sys.exit(main()) diff --git a/surfsense_evals/src/surfsense_evals/core/clients/__init__.py b/surfsense_evals/src/surfsense_evals/core/clients/__init__.py deleted file mode 100644 index 37246c2212..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/clients/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""HTTP clients for the SurfSense API. All share one ``httpx.AsyncClient``.""" - -from __future__ import annotations - -from .documents import DocumentsClient -from .new_chat import NewChatClient, StreamedAnswer -from .search_space import SearchSpaceClient - -__all__ = [ - "DocumentsClient", - "NewChatClient", - "SearchSpaceClient", - "StreamedAnswer", -] diff --git a/surfsense_evals/src/surfsense_evals/core/clients/documents.py b/surfsense_evals/src/surfsense_evals/core/clients/documents.py deleted file mode 100644 index 2fd9b27666..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/clients/documents.py +++ /dev/null @@ -1,271 +0,0 @@ -"""Client for ``/api/v1/documents/{fileupload,status,{id}/chunks}``. - -Verified against: - -* ``surfsense_backend/app/routes/documents_routes.py:122-292`` (POST fileupload) -* ``surfsense_backend/app/routes/documents_routes.py:806-871`` (GET status batch) -* ``surfsense_backend/app/routes/documents_routes.py:1062-1128`` (GET {id}/chunks paginated) - -Document processing is asynchronous: -* ``POST /documents/fileupload`` returns immediately with - ``document_ids`` in ``pending``; -* a Celery worker moves each through ``processing → ready/failed``; -* the harness polls ``GET /documents/status?document_ids=...`` until - every doc is ``ready`` (otherwise the retriever sees an empty corpus - and accuracy numbers are meaningless). -""" - -from __future__ import annotations - -import asyncio -import contextlib -import logging -import mimetypes -from collections.abc import Iterable, Sequence -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -import httpx - -logger = logging.getLogger(__name__) - - -@dataclass -class FileUploadResult: - """Mirrors the JSON returned by ``POST /documents/fileupload``.""" - - document_ids: list[int] - duplicate_document_ids: list[int] - total_files: int - pending_files: int - skipped_duplicates: int - message: str = "" - - @classmethod - def from_payload(cls, payload: dict[str, Any]) -> FileUploadResult: - return cls( - document_ids=[int(x) for x in payload.get("document_ids", [])], - duplicate_document_ids=[int(x) for x in payload.get("duplicate_document_ids", [])], - total_files=int(payload.get("total_files", 0)), - pending_files=int(payload.get("pending_files", 0)), - skipped_duplicates=int(payload.get("skipped_duplicates", 0)), - message=str(payload.get("message", "")), - ) - - -@dataclass -class DocumentStatus: - document_id: int - title: str - document_type: str - state: str - reason: str | None = None - - @property - def is_ready(self) -> bool: - return self.state == "ready" - - @property - def is_failed(self) -> bool: - return self.state == "failed" - - -@dataclass -class ChunkRow: - id: int - document_id: int - content: str = "" - raw: dict[str, Any] = field(default_factory=dict) - - -class DocumentProcessingFailed(RuntimeError): - """Raised when a polled document lands in ``failed``.""" - - def __init__(self, statuses: Sequence[DocumentStatus]) -> None: - details = ", ".join( - f"id={s.document_id} ({s.title!r}): {s.reason or 'unknown'}" for s in statuses - ) - super().__init__(f"Document(s) failed to process: {details}") - self.statuses = list(statuses) - - -class DocumentProcessingTimeout(RuntimeError): - """Raised when polling exceeds the per-doc timeout budget.""" - - -class DocumentsClient: - """Document upload + status polling + chunk listing.""" - - def __init__(self, http: httpx.AsyncClient, base_url: str) -> None: - self._http = http - self._base = base_url.rstrip("/") - - # ------------------------------------------------------------------ - # upload - # ------------------------------------------------------------------ - - async def upload( - self, - files: Iterable[Path], - *, - search_space_id: int, - use_vision_llm: bool = False, - processing_mode: str = "basic", - ) -> FileUploadResult: - """Upload files to ``/api/v1/documents/fileupload``. - - ``files`` is materialised to a list because we may need to - re-read on retry. Caller is responsible for ensuring each path - exists and respects the per-file size cap (50 MB backend default). - """ - - materialised = [Path(p) for p in files] - if not materialised: - return FileUploadResult( - document_ids=[], - duplicate_document_ids=[], - total_files=0, - pending_files=0, - skipped_duplicates=0, - message="No files supplied", - ) - - opened: list[tuple[str, Any]] = [] - try: - for path in materialised: - # ``open`` directly — httpx wraps it in MultipartStream. - file_obj = path.open("rb") - mime, _ = mimetypes.guess_type(path.name) - opened.append( - ( - "files", - (path.name, file_obj, mime or "application/octet-stream"), - ) - ) - - response = await self._http.post( - f"{self._base}/api/v1/documents/fileupload", - data={ - "search_space_id": str(search_space_id), - "use_vision_llm": "true" if use_vision_llm else "false", - "processing_mode": processing_mode, - }, - files=opened, - # Multipart uploads can be slow for big PDFs; bump per-call. - timeout=httpx.Timeout(120.0, connect=10.0), - ) - finally: - for _, (_, file_obj, _) in opened: - with contextlib.suppress(Exception): - file_obj.close() - - response.raise_for_status() - return FileUploadResult.from_payload(response.json()) - - # ------------------------------------------------------------------ - # status polling - # ------------------------------------------------------------------ - - async def get_status( - self, *, search_space_id: int, document_ids: Sequence[int] - ) -> list[DocumentStatus]: - if not document_ids: - return [] - response = await self._http.get( - f"{self._base}/api/v1/documents/status", - params={ - "search_space_id": search_space_id, - "document_ids": ",".join(str(d) for d in document_ids), - }, - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - payload = response.json() - return [ - DocumentStatus( - document_id=int(item["id"]), - title=str(item.get("title", "")), - document_type=str(item.get("document_type", "")), - state=str((item.get("status") or {}).get("state", "ready")), - reason=(item.get("status") or {}).get("reason"), - ) - for item in payload.get("items", []) - ] - - async def wait_until_ready( - self, - *, - search_space_id: int, - document_ids: Sequence[int], - timeout_s: float = 300.0, - initial_poll_s: float = 1.0, - max_poll_s: float = 10.0, - ) -> list[DocumentStatus]: - """Poll ``GET /documents/status`` until every doc is ``ready``. - - Exponential backoff from ``initial_poll_s`` up to ``max_poll_s``. - Raises ``DocumentProcessingFailed`` if any doc lands in - ``failed`` (with the offending document ids), or - ``DocumentProcessingTimeout`` if the budget is exhausted. - """ - - if not document_ids: - return [] - deadline = asyncio.get_event_loop().time() + timeout_s - poll = initial_poll_s - while True: - statuses = await self.get_status( - search_space_id=search_space_id, document_ids=document_ids - ) - failed = [s for s in statuses if s.is_failed] - if failed: - raise DocumentProcessingFailed(failed) - ready = [s for s in statuses if s.is_ready] - if len(ready) == len(document_ids): - return statuses - now = asyncio.get_event_loop().time() - if now >= deadline: - pending = [s for s in statuses if not s.is_ready and not s.is_failed] - pending_ids = [s.document_id for s in pending] - raise DocumentProcessingTimeout( - f"Timed out after {timeout_s:.0f}s waiting for documents " - f"(still pending/processing: {pending_ids})" - ) - await asyncio.sleep(min(poll, max(0.1, deadline - now))) - poll = min(poll * 1.5, max_poll_s) - - # ------------------------------------------------------------------ - # chunks (chunk_id -> document_id map) - # ------------------------------------------------------------------ - - async def list_chunks(self, document_id: int, *, page_size: int = 100) -> list[ChunkRow]: - """Walk ``GET /documents/{id}/chunks`` until ``has_more=False``. - - Used by ingestion to materialise the ``chunk_id -> document_id`` - map needed for retrieval scoring (CUREv1). - """ - - rows: list[ChunkRow] = [] - page = 0 - while True: - response = await self._http.get( - f"{self._base}/api/v1/documents/{document_id}/chunks", - params={"page": page, "page_size": page_size}, - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - payload = response.json() - for item in payload.get("items", []): - rows.append( - ChunkRow( - id=int(item["id"]), - document_id=document_id, - content=str(item.get("content", "")), - raw=item, - ) - ) - if not payload.get("has_more"): - break - page += 1 - return rows diff --git a/surfsense_evals/src/surfsense_evals/core/clients/new_chat.py b/surfsense_evals/src/surfsense_evals/core/clients/new_chat.py deleted file mode 100644 index 397193bba5..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/clients/new_chat.py +++ /dev/null @@ -1,280 +0,0 @@ -"""Client for ``/api/v1/threads`` and ``/api/v1/new_chat`` (SSE). - -Verified against: - -* ``surfsense_backend/app/routes/new_chat_routes.py:793-848`` (POST /threads) -* ``surfsense_backend/app/routes/new_chat_routes.py:1073-1142`` (DELETE /threads/{id}) -* ``surfsense_backend/app/routes/new_chat_routes.py:1689-1800`` (POST /new_chat SSE) -* ``surfsense_backend/app/routes/new_chat_routes.py:191-220`` (THREAD_BUSY / TURN_CANCELLING 409) -* ``surfsense_backend/app/services/streaming/envelope/sse.py`` (wire framing) -* ``surfsense_backend/app/services/streaming/events/text.py`` (text-delta events) -* ``surfsense_backend/app/schemas/new_chat.py:234-288`` (NewChatRequest body) - -The wire format is "Vercel AI SDK"-flavoured SSE with one event per -``data: \n\n`` block (or the literal ``data: [DONE]\n\n`` -terminator). Text deltas arrive as ``{"type":"text-delta","id":...,"delta":...}`` -events; we accumulate them per ``id`` and emit the final concatenated -text plus parsed citations. -""" - -from __future__ import annotations - -import asyncio -import json -import logging -import time -from collections.abc import AsyncIterator, Sequence -from dataclasses import dataclass, field -from typing import Any - -import httpx - -from ..parse import iter_sse_events, parse_citations - -logger = logging.getLogger(__name__) - - -@dataclass -class StreamedAnswer: - """Result of a single ``/new_chat`` turn.""" - - text: str - raw_events: list[dict[str, Any]] = field(default_factory=list) - latency_ms: int = 0 - user_message_id: str | None = None - assistant_message_id: str | None = None - finished_normally: bool = False - - @property - def citations(self) -> list[dict[str, Any]]: - """Parsed citation tokens (lazy; small enough to recompute).""" - - return [token.to_dict() for token in parse_citations(self.text)] - - -class ThreadBusyError(RuntimeError): - """Raised after exhausting retries on a 409 ``THREAD_BUSY`` / ``TURN_CANCELLING``.""" - - def __init__(self, error_code: str, message: str) -> None: - super().__init__(f"{error_code}: {message}") - self.error_code = error_code - - -class NewChatClient: - """Thread create / delete / SSE ask.""" - - def __init__(self, http: httpx.AsyncClient, base_url: str) -> None: - self._http = http - self._base = base_url.rstrip("/") - - # ------------------------------------------------------------------ - # threads - # ------------------------------------------------------------------ - - async def create_thread( - self, - *, - search_space_id: int, - title: str = "eval", - archived: bool = False, - visibility: str = "PRIVATE", - ) -> int: - response = await self._http.post( - f"{self._base}/api/v1/threads", - json={ - "search_space_id": search_space_id, - "title": title, - "archived": archived, - "visibility": visibility, - }, - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - payload = response.json() - return int(payload["id"]) - - async def delete_thread(self, thread_id: int) -> None: - response = await self._http.delete( - f"{self._base}/api/v1/threads/{thread_id}", - headers={"Accept": "application/json"}, - ) - if response.status_code == 404: - return # idempotent - response.raise_for_status() - - # ------------------------------------------------------------------ - # /new_chat SSE - # ------------------------------------------------------------------ - - async def ask( - self, - *, - thread_id: int, - search_space_id: int, - user_query: str, - mentioned_document_ids: Sequence[int] | None = None, - disabled_tools: Sequence[str] | None = None, - max_busy_retries: int = 4, - timeout_s: float = 600.0, - ) -> StreamedAnswer: - """Stream a single turn and return the accumulated answer. - - Honours backend ``THREAD_BUSY`` / ``TURN_CANCELLING`` 409 - responses by sleeping for the ``Retry-After`` header (or the - ``retry-after-ms`` header if present) and replaying. Bounded - by ``max_busy_retries`` so a stuck thread never blocks the - whole run. - """ - - body: dict[str, Any] = { - "chat_id": thread_id, - "search_space_id": search_space_id, - "user_query": user_query, - } - if mentioned_document_ids: - body["mentioned_document_ids"] = list(mentioned_document_ids) - if disabled_tools: - body["disabled_tools"] = list(disabled_tools) - - attempt = 0 - while True: - try: - return await self._stream_once(body=body, timeout_s=timeout_s) - except ThreadBusyError as exc: - attempt += 1 - if attempt > max_busy_retries: - raise - # Cap wait at 30s; backend retry hint is exponential anyway. - wait = min(30.0, 0.5 * (2**attempt)) - logger.info( - "thread_id=%s busy (%s); retry %d/%d after %.1fs", - thread_id, - exc.error_code, - attempt, - max_busy_retries, - wait, - ) - await asyncio.sleep(wait) - - async def _stream_once( - self, - *, - body: dict[str, Any], - timeout_s: float, - ) -> StreamedAnswer: - # Per-call timeout — the connect should be quick, the read needs - # to outlive the longest LLM completion. - timeout = httpx.Timeout(timeout_s, connect=10.0) - started = time.monotonic() - async with self._http.stream( - "POST", - f"{self._base}/api/v1/new_chat", - json=body, - headers={"Accept": "text/event-stream"}, - timeout=timeout, - ) as response: - if response.status_code == 409: - detail = await self._extract_busy_detail(response) - raise ThreadBusyError( - error_code=detail.get("errorCode", "THREAD_BUSY"), - message=detail.get("message", "Thread is busy"), - ) - response.raise_for_status() - answer = await self._consume_sse(response) - answer.latency_ms = int((time.monotonic() - started) * 1000) - return answer - - @staticmethod - async def _extract_busy_detail(response: httpx.Response) -> dict[str, Any]: - try: - payload = json.loads(await response.aread()) - except (json.JSONDecodeError, ValueError): - return {"errorCode": "THREAD_BUSY", "message": response.text} - if isinstance(payload, dict) and isinstance(payload.get("detail"), dict): - return payload["detail"] - return payload if isinstance(payload, dict) else {} - - @staticmethod - async def _consume_sse(response: httpx.Response) -> StreamedAnswer: - """Walk SSE events, accumulate text-delta payloads. - - Backend events of interest: - - * ``{"type": "text-start", "id": ...}`` - * ``{"type": "text-delta", "id": ..., "delta": ...}`` - * ``{"type": "text-end", "id": ...}`` - * ``{"type": "start", "messageId": ...}`` (top-level message id) - * ``{"type": "finish"}`` - * literal ``[DONE]`` sentinel - - Multiple ``text-start`` blocks can interleave — each gets its - own ``id`` and we concatenate them in arrival order. That - mirrors the AI SDK client behaviour: one continuous assistant - message visible to the user. - """ - - ordered_text_ids: list[str] = [] - text_buffers: dict[str, list[str]] = {} - raw_events: list[dict[str, Any]] = [] - user_message_id: str | None = None - assistant_message_id: str | None = None - finished = False - - async for event in iter_sse_events(_aiter_lines(response)): - data = event.data - if data == "[DONE]": - finished = True - continue - try: - payload = json.loads(data) - except (json.JSONDecodeError, ValueError): - logger.debug("Skipping non-JSON SSE payload: %r", data[:120]) - continue - if not isinstance(payload, dict): - continue - raw_events.append(payload) - ev_type = payload.get("type") - if ev_type == "text-delta": - tid = str(payload.get("id", "")) - delta = payload.get("delta", "") - if not isinstance(delta, str): - continue - if tid not in text_buffers: - text_buffers[tid] = [] - ordered_text_ids.append(tid) - text_buffers[tid].append(delta) - elif ev_type == "text-start": - tid = str(payload.get("id", "")) - if tid and tid not in text_buffers: - text_buffers[tid] = [] - ordered_text_ids.append(tid) - elif ev_type == "start": - msg_id = payload.get("messageId") - if isinstance(msg_id, str): - user_message_id = user_message_id or msg_id - elif ev_type == "data-user-message-id": - msg_id = (payload.get("data") or {}).get("id") or payload.get("id") - if isinstance(msg_id, str): - user_message_id = msg_id - elif ev_type == "data-assistant-message-id": - msg_id = (payload.get("data") or {}).get("id") or payload.get("id") - if isinstance(msg_id, str): - assistant_message_id = msg_id - elif ev_type == "finish": - finished = True - - text = "".join("".join(text_buffers.get(tid, [])) for tid in ordered_text_ids) - return StreamedAnswer( - text=text, - raw_events=raw_events, - user_message_id=user_message_id, - assistant_message_id=assistant_message_id, - finished_normally=finished, - ) - - -async def _aiter_lines(response: httpx.Response) -> AsyncIterator[str]: - """Adapter so the parser can consume any line iterator (mockable in tests).""" - - async for line in response.aiter_lines(): - yield line diff --git a/surfsense_evals/src/surfsense_evals/core/clients/search_space.py b/surfsense_evals/src/surfsense_evals/core/clients/search_space.py deleted file mode 100644 index 19486aca15..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/clients/search_space.py +++ /dev/null @@ -1,188 +0,0 @@ -"""Client for ``/api/v1/searchspaces`` and model-role endpoints. - -Verified against: - -* ``surfsense_backend/app/routes/search_spaces_routes.py:116`` (POST create) -* ``surfsense_backend/app/routes/search_spaces_routes.py:234`` (GET by id) -* ``surfsense_backend/app/routes/search_spaces_routes.py:422`` (DELETE soft-delete) -* ``surfsense_backend/app/routes/model_connections_routes.py`` (GET/PUT model roles) -* ``surfsense_backend/app/schemas/search_space.py:14`` (SearchSpaceCreate body) - -Note the inconsistent pluralisation in the backend: ``/searchspaces`` -(no hyphen) for CRUD, but ``/search-spaces`` (hyphenated) for model-role -sub-resources. Both are mirrored verbatim here. -""" - -from __future__ import annotations - -from dataclasses import dataclass -from typing import Any - -import httpx - - -@dataclass -class SearchSpaceRow: - """Subset of the SearchSpace row we care about.""" - - id: int - name: str - description: str | None - user_id: str - citations_enabled: bool - qna_custom_instructions: str | None - - @classmethod - def from_payload(cls, payload: dict[str, Any]) -> SearchSpaceRow: - return cls( - id=int(payload["id"]), - name=str(payload["name"]), - description=payload.get("description"), - user_id=str(payload.get("user_id", "")), - citations_enabled=bool(payload.get("citations_enabled", True)), - qna_custom_instructions=payload.get("qna_custom_instructions"), - ) - - -@dataclass -class VisionModelEntry: - """Subset of one GLOBAL model-connection model with image input support.""" - - id: int - name: str - provider: str - model_name: str - is_auto_mode: bool - raw: dict[str, Any] - - @classmethod - def from_payload(cls, payload: dict[str, Any]) -> VisionModelEntry: - return cls( - id=int(payload.get("id", 0)), - name=str(payload.get("display_name") or payload.get("model_id") or ""), - provider=str(payload.get("provider", "")).upper(), - model_name=str(payload.get("model_id", "")), - is_auto_mode=False, - raw=payload, - ) - - -@dataclass -class ModelRoles: - """Model role ids for a search space.""" - - chat_model_id: int | None - image_gen_model_id: int | None - vision_model_id: int | None - raw: dict[str, Any] - - @classmethod - def from_payload(cls, payload: dict[str, Any]) -> ModelRoles: - return cls( - chat_model_id=payload.get("chat_model_id"), - image_gen_model_id=payload.get("image_gen_model_id"), - vision_model_id=payload.get("vision_model_id"), - raw=payload, - ) - - -class SearchSpaceClient: - """Thin wrapper around the SearchSpace + model role endpoints.""" - - def __init__(self, http: httpx.AsyncClient, base_url: str) -> None: - self._http = http - self._base = base_url.rstrip("/") - - async def create(self, name: str, *, description: str | None = None) -> SearchSpaceRow: - body: dict[str, Any] = {"name": name} - if description is not None: - body["description"] = description - # citations_enabled defaults to True backend-side; keep that default. - response = await self._http.post( - f"{self._base}/api/v1/searchspaces", - json=body, - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - return SearchSpaceRow.from_payload(response.json()) - - async def get(self, search_space_id: int) -> SearchSpaceRow: - response = await self._http.get( - f"{self._base}/api/v1/searchspaces/{search_space_id}", - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - return SearchSpaceRow.from_payload(response.json()) - - async def delete(self, search_space_id: int) -> None: - """Soft-delete: backend prefixes name with ``[DELETING]`` and dispatches a Celery cascade.""" - - response = await self._http.delete( - f"{self._base}/api/v1/searchspaces/{search_space_id}", - headers={"Accept": "application/json"}, - ) - # 404 means it's already gone — treat as success (idempotent teardown). - if response.status_code == 404: - return - response.raise_for_status() - - async def get_model_roles(self, search_space_id: int) -> ModelRoles: - response = await self._http.get( - f"{self._base}/api/v1/search-spaces/{search_space_id}/model-roles", - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - return ModelRoles.from_payload(response.json()) - - async def set_model_roles( - self, - search_space_id: int, - *, - chat_model_id: int | None = None, - image_gen_model_id: int | None = None, - vision_model_id: int | None = None, - ) -> ModelRoles: - """PUT a partial update to ``/search-spaces/{id}/model-roles``. - - Backend uses ``model_dump(exclude_unset=True)`` so omitted fields - are left unchanged. - """ - - body: dict[str, Any] = {} - if chat_model_id is not None: - body["chat_model_id"] = chat_model_id - if image_gen_model_id is not None: - body["image_gen_model_id"] = image_gen_model_id - if vision_model_id is not None: - body["vision_model_id"] = vision_model_id - response = await self._http.put( - f"{self._base}/api/v1/search-spaces/{search_space_id}/model-roles", - json=body, - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - return ModelRoles.from_payload(response.json()) - - async def list_global_vision_models(self) -> list[VisionModelEntry]: - """List registered GLOBAL models that can accept image input. - - Used by ``setup`` to resolve ``--vision-llm `` or auto-pick a - reproducible ingest-time vision model. - """ - - response = await self._http.get( - f"{self._base}/api/v1/model-connections/global", - headers={"Accept": "application/json"}, - ) - response.raise_for_status() - payload = response.json() - if not isinstance(payload, list): - raise RuntimeError(f"Unexpected /model-connections/global payload: {payload!r}") - entries: list[VisionModelEntry] = [] - for connection in payload: - provider = str(connection.get("provider", "")) - for model in connection.get("models") or []: - if not model.get("enabled", True) or not model.get("supports_image_input"): - continue - entries.append(VisionModelEntry.from_payload({**model, "provider": provider})) - return entries diff --git a/surfsense_evals/src/surfsense_evals/core/config.py b/surfsense_evals/src/surfsense_evals/core/config.py deleted file mode 100644 index 80002157e9..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/config.py +++ /dev/null @@ -1,280 +0,0 @@ -"""Environment + filesystem configuration for the harness. - -Two responsibilities: - -1. Load env vars (with sensible defaults) into a single immutable ``Config`` - so that every other module reads it from one place. -2. Read / write ``data/state.json``. State is keyed by suite name so multiple - suites can be set up in parallel and torn down independently. - -The pinned ``search_space_id`` lives in ``state.json`` (not env) so re-runs -are idempotent without forcing the operator to remember an integer. -""" - -from __future__ import annotations - -import json -import os -from collections.abc import Mapping -from dataclasses import dataclass, field -from datetime import UTC, datetime -from pathlib import Path -from typing import Any - -from dotenv import load_dotenv - -# Resolve once at import time. ``find_dotenv`` walks up; an explicit ``.env`` -# at the package root or in CWD wins. Silent-no-op if neither exists. -load_dotenv() - - -_PROJECT_ROOT = Path(__file__).resolve().parents[3] -"""Resolves to ``surfsense_evals/`` (the package root, not ``src/``).""" - - -def _project_root() -> Path: - """Return the ``surfsense_evals/`` project root. - - Computed from this file's path: ``src/surfsense_evals/core/config.py`` → - walk up four levels. Kept as a function so tests can monkeypatch. - """ - - return _PROJECT_ROOT - - -@dataclass(frozen=True) -class Config: - """Immutable runtime configuration.""" - - surfsense_api_base: str - openrouter_api_key: str | None - openrouter_base_url: str - - # Credentials — exactly ONE mode must be supplied. - surfsense_jwt: str | None - surfsense_refresh_token: str | None - surfsense_user_email: str | None - surfsense_user_password: str | None - - # Filesystem paths. - data_dir: Path - reports_dir: Path - - @property - def state_path(self) -> Path: - return self.data_dir / "state.json" - - def has_jwt_mode(self) -> bool: - return bool(self.surfsense_jwt) - - def has_local_mode(self) -> bool: - return bool(self.surfsense_user_email and self.surfsense_user_password) - - def credential_mode(self) -> str: - """Return ``"jwt"``, ``"local"``, or ``"none"`` (no credentials supplied).""" - - if self.has_jwt_mode(): - return "jwt" - if self.has_local_mode(): - return "local" - return "none" - - def suite_data_dir(self, suite: str) -> Path: - return self.data_dir / suite - - def suite_reports_dir(self, suite: str) -> Path: - return self.reports_dir / suite - - def suite_runs_dir(self, suite: str) -> Path: - return self.suite_data_dir(suite) / "runs" - - def suite_maps_dir(self, suite: str) -> Path: - return self.suite_data_dir(suite) / "maps" - - -def load_config() -> Config: - """Read the current process env into a ``Config``. - - No validation is performed here; callers (e.g. ``auth.acquire_token``, - ``cli`` subcommands) decide which fields they require. This keeps - ``models list`` and ``suites list`` runnable without OpenRouter creds. - """ - - project_root = _project_root() - data_dir = Path(os.environ.get("EVAL_DATA_DIR") or (project_root / "data")).resolve() - reports_dir = Path(os.environ.get("EVAL_REPORTS_DIR") or (project_root / "reports")).resolve() - return Config( - surfsense_api_base=os.environ.get("SURFSENSE_API_BASE", "http://localhost:8000").rstrip( - "/" - ), - openrouter_api_key=os.environ.get("OPENROUTER_API_KEY") or None, - openrouter_base_url=os.environ.get( - "OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1" - ).rstrip("/"), - surfsense_jwt=os.environ.get("SURFSENSE_JWT") or None, - surfsense_refresh_token=os.environ.get("SURFSENSE_REFRESH_TOKEN") or None, - surfsense_user_email=os.environ.get("SURFSENSE_USER_EMAIL") or None, - surfsense_user_password=os.environ.get("SURFSENSE_USER_PASSWORD") or None, - data_dir=data_dir, - reports_dir=reports_dir, - ) - - -# --------------------------------------------------------------------------- -# state.json — per-suite slots -# --------------------------------------------------------------------------- - - -# Scenario names — chosen at ``setup`` time, persisted in ``state.json``. -# -# * ``head-to-head`` (default, current behaviour): both arms answer with the -# SAME slug pinned via ``--provider-model``. Vision LLM at ingest is -# optional but recommended for image-bearing benchmarks. -# * ``symmetric-cheap``: both arms answer with the SAME (cheap, text-only) -# slug; SurfSense pre-extracted images at ingest with a vision LLM. -# Measures whether vision-RAG ingestion lets a cheap downstream model -# match a vision one. Native arm structurally loses on image questions — -# that's the point, and the report labels it accordingly. -# * ``cost-arbitrage``: native arm answers with an EXPENSIVE vision slug -# (``--native-arm-model``), SurfSense answers with a CHEAP text-only slug -# (``--provider-model``) over chunks the vision LLM already extracted at -# ingest. Measures how close SurfSense gets to native at a fraction of -# the per-query cost. The most compelling "shines" framing. -SCENARIOS: tuple[str, ...] = ("head-to-head", "symmetric-cheap", "cost-arbitrage") -DEFAULT_SCENARIO: str = "head-to-head" - - -@dataclass -class SuiteState: - """Per-suite persisted state. - - ``provider_model`` is the slug pinned to the SearchSpace's - ``chat_model_id`` — what answers SurfSense queries (and what the native - arm uses too, unless ``native_arm_model`` is set for cost-arbitrage). - - ``vision_provider_model`` is the slug of the OpenRouter vision model - attached to the SearchSpace's ``vision_model_id`` — what - SurfSense uses to extract image content at ingest time when - ``use_vision_llm=True``. ``None`` means no vision config was attached - at setup (legacy or text-only suite). - """ - - search_space_id: int - chat_model_id: int - provider_model: str - created_at: str - ingestion_maps: dict[str, str] = field(default_factory=dict) - scenario: str = DEFAULT_SCENARIO - vision_model_id: int | None = None - vision_provider_model: str | None = None - native_arm_model: str | None = None - - def to_dict(self) -> dict[str, Any]: - return { - "search_space_id": self.search_space_id, - "chat_model_id": self.chat_model_id, - "provider_model": self.provider_model, - "created_at": self.created_at, - "ingestion_maps": dict(self.ingestion_maps), - "scenario": self.scenario, - "vision_model_id": self.vision_model_id, - "vision_provider_model": self.vision_provider_model, - "native_arm_model": self.native_arm_model, - } - - @classmethod - def from_dict(cls, payload: Mapping[str, Any]) -> SuiteState: - # ``scenario`` / vision / native fields default for back-compat with - # ``state.json`` written before scenarios shipped. - scenario = str(payload.get("scenario") or DEFAULT_SCENARIO) - if scenario not in SCENARIOS: - scenario = DEFAULT_SCENARIO - raw_chat_id = payload.get("chat_model_id") - raw_vision_id = payload.get("vision_model_id") - return cls( - search_space_id=int(payload["search_space_id"]), - chat_model_id=int(raw_chat_id), - provider_model=str(payload["provider_model"]), - created_at=str(payload.get("created_at") or ""), - ingestion_maps=dict(payload.get("ingestion_maps") or {}), - scenario=scenario, - vision_model_id=int(raw_vision_id) if raw_vision_id is not None else None, - vision_provider_model=( - str(payload["vision_provider_model"]) - if payload.get("vision_provider_model") - else None - ), - native_arm_model=( - str(payload["native_arm_model"]) if payload.get("native_arm_model") else None - ), - ) - - @property - def effective_native_arm_model(self) -> str: - """Slug the native arm should use; falls back to ``provider_model``.""" - - return self.native_arm_model or self.provider_model - - -def _load_state(config: Config) -> dict[str, Any]: - if not config.state_path.exists(): - return {"suites": {}} - try: - with config.state_path.open("r", encoding="utf-8") as fh: - data = json.load(fh) - except (OSError, json.JSONDecodeError) as exc: - raise RuntimeError( - f"Failed to read state file {config.state_path}: {exc!s}. " - "Delete it if you want to start fresh." - ) from exc - if not isinstance(data, dict) or "suites" not in data: - return {"suites": {}} - return data - - -def _write_state(config: Config, payload: Mapping[str, Any]) -> None: - config.data_dir.mkdir(parents=True, exist_ok=True) - tmp = config.state_path.with_suffix(".json.tmp") - with tmp.open("w", encoding="utf-8") as fh: - json.dump(dict(payload), fh, indent=2, sort_keys=True) - fh.write("\n") - tmp.replace(config.state_path) - - -def get_suite_state(config: Config, suite: str) -> SuiteState | None: - """Return ``SuiteState`` for ``suite`` or ``None`` if not set up.""" - - state = _load_state(config) - raw = (state.get("suites") or {}).get(suite) - if not raw: - return None - return SuiteState.from_dict(raw) - - -def set_suite_state(config: Config, suite: str, suite_state: SuiteState) -> None: - """Persist ``suite_state`` under the suite slot. Other suites are untouched.""" - - state = _load_state(config) - suites = dict(state.get("suites") or {}) - suites[suite] = suite_state.to_dict() - state["suites"] = suites - _write_state(config, state) - - -def clear_suite_state(config: Config, suite: str) -> bool: - """Remove the slot for ``suite``. Returns ``True`` if removal happened.""" - - state = _load_state(config) - suites = dict(state.get("suites") or {}) - if suite not in suites: - return False - del suites[suite] - state["suites"] = suites - _write_state(config, state) - return True - - -def utc_iso_timestamp() -> str: - """Filesystem-safe UTC ISO timestamp, e.g. ``2026-05-11T20-30-00Z``.""" - - return datetime.now(UTC).strftime("%Y-%m-%dT%H-%M-%SZ") diff --git a/surfsense_evals/src/surfsense_evals/core/ingest_settings.py b/surfsense_evals/src/surfsense_evals/core/ingest_settings.py deleted file mode 100644 index 216ae36a4f..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/ingest_settings.py +++ /dev/null @@ -1,284 +0,0 @@ -"""Per-upload ingestion settings shared across every benchmark. - -The SurfSense ``POST /api/v1/documents/fileupload`` endpoint exposes -exactly three knobs (verified at -``surfsense_backend/app/routes/documents_routes.py`` and -``surfsense_backend/app/etl_pipeline/etl_document.py``): - -* ``processing_mode`` — ``"basic"`` (default) | ``"premium"`` -* ``use_vision_llm`` — ``bool`` (run vision LLM during ingest to - extract image content / captions / tables) - -This module gives every benchmark a uniform way to: - -1. Receive sensible per-benchmark defaults (text-only benchmarks - default vision off; image-bearing benchmarks default vision on). -2. Accept CLI overrides (``--use-vision-llm`` / ``--no-vision-llm``, - ``--processing-mode {basic,premium}``). -3. Persist the *actual* settings used into the doc-map manifest and - the run artifact so reports can show "vision=ON, mode=premium → - 65% accuracy" head-to-head with "vision=OFF, mode=basic → 52%". - -A/B testing on the same corpus ------------------------------- - -SurfSense dedupes uploads by ``(filename, search_space_id)`` — NOT by -content hash and NOT by ingestion settings. Re-uploading the same -filename to the same SearchSpace with a different ``use_vision_llm`` -flag will hit the duplicate branch and *not* re-process. To compare -two settings combos head-to-head on the same corpus you must give -each combo its own SearchSpace, which today means: - - teardown --suite - setup --suite ... - ingest --no-vision-llm # baseline run - run - teardown --suite - setup --suite ... - ingest --use-vision-llm # vision arm - run - -The runs land in different timestamped subdirectories under -``data//runs/`` and ``report --suite `` aggregates whichever -manifest is currently latest per benchmark. -""" - -from __future__ import annotations - -import argparse -import json -from collections.abc import Mapping -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -# Keep the constant list of valid processing modes here so benchmarks -# don't have to re-import from the backend (they don't have access to -# the backend package anyway). -PROCESSING_MODES: tuple[str, ...] = ("basic", "premium") - - -@dataclass(frozen=True) -class IngestSettings: - """Resolved per-upload knobs handed to ``DocumentsClient.upload``. - - Use ``IngestSettings(...)`` directly to define benchmark defaults, - or ``IngestSettings.merge(defaults, opts)`` to apply CLI overrides - on top of those defaults. - """ - - use_vision_llm: bool = False - processing_mode: str = "basic" - - def to_dict(self) -> dict[str, Any]: - return { - "use_vision_llm": self.use_vision_llm, - "processing_mode": self.processing_mode, - } - - @classmethod - def merge(cls, defaults: IngestSettings, opts: Mapping[str, Any]) -> IngestSettings: - """Apply CLI overrides on top of ``defaults``. - - ``opts`` is the kwargs dict built by ``core.cli`` from the - argparse namespace (see ``_cmd_ingest`` / ``_cmd_run``). Keys - we look for: ``use_vision_llm`` (bool or None), ``processing_mode`` - (str or None). Anything - else is ignored so benchmarks can pass through their own opts. - """ - - return cls( - use_vision_llm=_coerce_bool(opts.get("use_vision_llm"), defaults.use_vision_llm), - processing_mode=_coerce_mode(opts.get("processing_mode"), defaults.processing_mode), - ) - - def render_label(self) -> str: - """Human-readable single-line label for reports / log lines.""" - - return f"vision={'on' if self.use_vision_llm else 'off'}, mode={self.processing_mode}" - - -def _coerce_bool(value: Any, default: bool) -> bool: - """Argparse with ``BooleanOptionalAction`` yields True/False/None. - - ``None`` means the operator didn't pass the flag → fall back to - the benchmark default. - """ - - if value is None: - return default - if isinstance(value, bool): - return value - if isinstance(value, str): - return value.strip().lower() in {"1", "true", "yes", "on"} - return bool(value) - - -def _coerce_mode(value: Any, default: str) -> str: - if value is None or value == "": - return default - val = str(value).strip().lower() - if val not in PROCESSING_MODES: - raise ValueError(f"Invalid processing_mode {val!r}; must be one of {PROCESSING_MODES}") - return val - - -# --------------------------------------------------------------------------- -# Argparse helper -# --------------------------------------------------------------------------- - - -def _add_bool_pair( - parser: argparse.ArgumentParser, - *, - dest: str, - on_flag: str, - off_flag: str, - on_help: str, - off_help: str, -) -> None: - """Add a mutually exclusive ``--foo`` / ``--no-foo`` pair. - - We don't use ``argparse.BooleanOptionalAction`` because it would - auto-generate ``--no-use-vision-llm`` rather than the friendlier - ``--no-vision-llm`` that operators reach for. Default is ``None`` - so ``IngestSettings.merge`` can distinguish "silent" from - "explicit false". - """ - - group = parser.add_mutually_exclusive_group() - group.add_argument( - on_flag, - dest=dest, - action="store_true", - default=None, - help=on_help, - ) - group.add_argument( - off_flag, - dest=dest, - action="store_false", - default=None, - help=off_help, - ) - - -def add_ingest_settings_args( - parser: argparse.ArgumentParser, - *, - defaults: IngestSettings, -) -> None: - """Attach ingest-settings flags to ``parser``. - - The vision bool exposes a mutually exclusive ``--foo`` / ``--no-foo`` - pair so an operator can flip either direction without restating every - flag. Default is ``None`` so that "operator didn't pass the flag" is - distinguishable from "operator explicitly passed false" — - ``IngestSettings.merge`` then folds in the benchmark default only when - the operator was silent. - """ - - settings_group = parser.add_argument_group( - "ingest settings", - f"Per-upload knobs (forwarded to /documents/fileupload). " - f"Defaults for this benchmark: {defaults.render_label()}.", - ) - _add_bool_pair( - settings_group, - dest="use_vision_llm", - on_flag="--use-vision-llm", - off_flag="--no-vision-llm", - on_help=( - "Run vision LLM during ingest to extract image content " - f"(default for this benchmark: " - f"{'on' if defaults.use_vision_llm else 'off'})." - ), - off_help="Skip vision LLM during ingest (text-only ETL).", - ) - settings_group.add_argument( - "--processing-mode", - dest="processing_mode", - choices=PROCESSING_MODES, - default=None, - help=( - "SurfSense ETL processing mode (premium uses a 10x page " - f"multiplier and typically routes to a stronger ETL). " - f"Default for this benchmark: {defaults.processing_mode!r}." - ), - ) - - -# --------------------------------------------------------------------------- -# Doc-map manifest helpers -# --------------------------------------------------------------------------- -# -# Every benchmark writes a doc-map JSONL under ``data//maps/`` that -# pairs source identifiers (case_id, snippet_id, doc_path, …) to the -# SurfSense document_ids returned by the upload. To make the report -# self-describing we also write a header line: -# -# {"__settings__": {"use_vision_llm": ..., "processing_mode": ..., ...}} -# -# These two helpers centralise that protocol so each benchmark only has to -# call ``write_settings_header`` and ``read_settings_header``. - -SETTINGS_HEADER_KEY = "__settings__" - - -def settings_header_line(settings: IngestSettings) -> str: - """Return the JSON-serialised header line (no trailing newline).""" - - return json.dumps({SETTINGS_HEADER_KEY: settings.to_dict()}) - - -def is_settings_header(row: Mapping[str, Any]) -> bool: - return SETTINGS_HEADER_KEY in row - - -def read_settings_header(map_path: Path) -> dict[str, Any]: - """Read the ``__settings__`` header out of a doc-map JSONL. - - Returns ``{}`` on a missing file, an empty file, an unreadable - file, or a file whose first non-blank line is not a settings - header (e.g. a corpus ingested before this feature existed). - Callers use this purely to surface settings in the report; it - must never fail the run. - """ - - if not map_path.exists(): - return {} - try: - with map_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - if isinstance(row, dict) and SETTINGS_HEADER_KEY in row: - return dict(row[SETTINGS_HEADER_KEY]) - return {} - except (OSError, json.JSONDecodeError): - return {} - return {} - - -def format_ingest_settings_md(settings: Any) -> str: - """Render the resolved settings as a single Markdown bullet line.""" - - if not isinstance(settings, Mapping) or not settings: - return "- SurfSense ingest settings: (not recorded — re-ingest to capture)" - vision = "on" if settings.get("use_vision_llm") else "off" - mode = settings.get("processing_mode") or "basic" - return f"- SurfSense ingest settings: vision_llm=`{vision}`, processing_mode=`{mode}`" - - -__all__ = [ - "PROCESSING_MODES", - "SETTINGS_HEADER_KEY", - "IngestSettings", - "add_ingest_settings_args", - "format_ingest_settings_md", - "is_settings_header", - "read_settings_header", - "settings_header_line", -] diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/__init__.py b/surfsense_evals/src/surfsense_evals/core/metrics/__init__.py deleted file mode 100644 index bd0e6aafb1..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/metrics/__init__.py +++ /dev/null @@ -1,50 +0,0 @@ -"""Pure-function metric primitives. Lazy imports.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: # pragma: no cover - from .comparison import McnemarResult, bootstrap_delta_ci, mcnemar_test, paired_aggregate - from .mc_accuracy import AccuracyResult, accuracy_with_wilson_ci, wilson_ci - from .retrieval import RetrievalScores, mrr, ndcg_at_k, recall_at_k, score_run - -__all__ = [ - "AccuracyResult", - "McnemarResult", - "RetrievalScores", - "accuracy_with_wilson_ci", - "bootstrap_delta_ci", - "mcnemar_test", - "mrr", - "ndcg_at_k", - "paired_aggregate", - "recall_at_k", - "score_run", - "wilson_ci", -] - - -_MODULE_FOR = { - "AccuracyResult": "mc_accuracy", - "accuracy_with_wilson_ci": "mc_accuracy", - "wilson_ci": "mc_accuracy", - "RetrievalScores": "retrieval", - "mrr": "retrieval", - "ndcg_at_k": "retrieval", - "recall_at_k": "retrieval", - "score_run": "retrieval", - "McnemarResult": "comparison", - "bootstrap_delta_ci": "comparison", - "mcnemar_test": "comparison", - "paired_aggregate": "comparison", -} - - -def __getattr__(name: str): - if name in _MODULE_FOR: - from importlib import import_module - - mod = import_module(f".{_MODULE_FOR[name]}", __name__) - return getattr(mod, name) - raise AttributeError(f"module 'surfsense_evals.core.metrics' has no attribute {name!r}") diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/comparison.py b/surfsense_evals/src/surfsense_evals/core/metrics/comparison.py deleted file mode 100644 index 3325358717..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/metrics/comparison.py +++ /dev/null @@ -1,252 +0,0 @@ -"""Paired comparison statistics for head-to-head benchmarks. - -In every head-to-head benchmark (currently MedXpertQA-MM and -MMLongBench-Doc) each question is answered by both arms (Native PDF -and SurfSense). That makes per-question outcomes paired, so -``McNemar's test`` on the discordant pairs is the right significance -test for "are the two arms different?". We also expose a bootstrap -delta CI for visualising effect size. - -Aggregate cost / latency / token deltas are mean-based; the runner -slices them by arm before passing them in. -""" - -from __future__ import annotations - -import math -import statistics -from collections.abc import Sequence -from dataclasses import dataclass - -import numpy as np - - -@dataclass(frozen=True) -class McnemarResult: - """Discordant pair counts + the test statistics.""" - - n_total: int - b: int # native correct, surfsense wrong - c: int # native wrong, surfsense correct - statistic: float - p_value: float - method: str - - def to_dict(self) -> dict[str, float | int | str]: - return { - "n_total": self.n_total, - "b_native_correct_only": self.b, - "c_surfsense_correct_only": self.c, - "statistic": self.statistic, - "p_value": self.p_value, - "method": self.method, - } - - -def mcnemar_test( - arm_a_correct: Sequence[bool], - arm_b_correct: Sequence[bool], - *, - use_exact_below: int = 11, -) -> McnemarResult: - """Paired McNemar's test on per-question correctness. - - ``arm_a_correct`` is treated as the reference arm (typically the - "native" arm); ``arm_b_correct`` is the challenger (typically - "surfsense"). The test statistic only depends on discordant pairs. - - Default switch-over (``b + c < 11``): for very small discordant - samples the exact binomial test is preferred; above that the - continuity-corrected chi-square is well-behaved (Edwards 1948). - Callers can raise ``use_exact_below`` if they prefer the more - conservative ``b + c < 25`` rule. - - No external statistical package is required: scipy is a heavy dep - and we only need binomial CDFs / chi-square sf, both implementable - in stdlib + numpy without surprises. - """ - - if len(arm_a_correct) != len(arm_b_correct): - raise ValueError(f"Length mismatch: arm_a={len(arm_a_correct)}, arm_b={len(arm_b_correct)}") - n = len(arm_a_correct) - b = sum(1 for a, c in zip(arm_a_correct, arm_b_correct, strict=False) if a and not c) - c = sum(1 for a, cc in zip(arm_a_correct, arm_b_correct, strict=False) if (not a) and cc) - discordant = b + c - if discordant == 0: - return McnemarResult(n_total=n, b=b, c=c, statistic=0.0, p_value=1.0, method="degenerate") - - if discordant < use_exact_below: - # Exact binomial: under H0 each discordant pair is a Bernoulli(0.5). - # p-value = 2 * P(X <= min(b,c) | n=discordant, p=0.5), capped at 1. - k = min(b, c) - cdf = sum(_binom_pmf(discordant, i) for i in range(k + 1)) - p_value = min(1.0, 2.0 * cdf) - return McnemarResult( - n_total=n, b=b, c=c, statistic=float(k), p_value=p_value, method="exact" - ) - - # Chi-square with continuity correction (McNemar-Edwards). - chi = ((abs(b - c) - 1) ** 2) / discordant - p_value = _chi2_sf(chi, df=1) - return McnemarResult(n_total=n, b=b, c=c, statistic=chi, p_value=p_value, method="chi2_cc") - - -def _binom_pmf(n: int, k: int) -> float: - return math.comb(n, k) * (0.5**n) - - -def _chi2_sf(x: float, *, df: int) -> float: - """Survival function (1 - CDF) of chi-square; df=1 closed form.""" - - if x <= 0: - return 1.0 - if df == 1: - # Chi^2(1) = N(0,1)^2; sf(x) = 2 * Phi_complement(sqrt(x)) - return math.erfc(math.sqrt(x / 2.0)) - # General fallback via regularized upper incomplete gamma. - a = df / 2.0 - z = x / 2.0 - return _gammaincc(a, z) - - -def _gammaincc(a: float, x: float, *, max_iter: int = 200, tol: float = 1e-12) -> float: - """Regularised upper incomplete gamma Q(a, x). Series + continued fraction.""" - - if x < 0 or a <= 0: - return float("nan") - if x == 0: - return 1.0 - if x < a + 1.0: - # Series for P(a, x); subtract from 1. - p_series = _gammainc_series(a, x, max_iter=max_iter, tol=tol) - return 1.0 - p_series - return _gammaincc_cf(a, x, max_iter=max_iter, tol=tol) - - -def _gammainc_series(a: float, x: float, *, max_iter: int, tol: float) -> float: - term = 1.0 / a - summation = term - for n in range(1, max_iter): - term *= x / (a + n) - summation += term - if abs(term) < abs(summation) * tol: - break - log_pre = -x + a * math.log(x) - math.lgamma(a) - return summation * math.exp(log_pre) - - -def _gammaincc_cf(a: float, x: float, *, max_iter: int, tol: float) -> float: - b = x + 1.0 - a - c_val = 1.0 / 1e-300 - d = 1.0 / b - h = d - for i in range(1, max_iter): - an = -i * (i - a) - b += 2.0 - d = an * d + b - if abs(d) < 1e-300: - d = 1e-300 - c_val = b + an / c_val - if abs(c_val) < 1e-300: - c_val = 1e-300 - d = 1.0 / d - delta = d * c_val - h *= delta - if abs(delta - 1.0) < tol: - break - log_pre = -x + a * math.log(x) - math.lgamma(a) - return h * math.exp(log_pre) - - -# --------------------------------------------------------------------------- -# Bootstrap delta CI -# --------------------------------------------------------------------------- - - -@dataclass(frozen=True) -class BootstrapDelta: - delta: float - ci_low: float - ci_high: float - n_resamples: int - - def to_dict(self) -> dict[str, float | int]: - return { - "delta": self.delta, - "ci_low": self.ci_low, - "ci_high": self.ci_high, - "n_resamples": self.n_resamples, - } - - -def bootstrap_delta_ci( - arm_a_correct: Sequence[bool], - arm_b_correct: Sequence[bool], - *, - n_resamples: int = 5000, - level: float = 0.95, - random_state: int | None = 0, -) -> BootstrapDelta: - """Paired-sample bootstrap CI for ``mean(arm_b) - mean(arm_a)``. - - Resamples *paired indices* with replacement so the dependency - between arms is preserved. - """ - - if len(arm_a_correct) != len(arm_b_correct): - raise ValueError("paired arms must have the same length") - n = len(arm_a_correct) - if n == 0: - return BootstrapDelta(0.0, 0.0, 0.0, 0) - a = np.asarray(arm_a_correct, dtype=np.int8) - b = np.asarray(arm_b_correct, dtype=np.int8) - delta = float(b.mean() - a.mean()) - - rng = np.random.default_rng(random_state) - deltas = np.empty(n_resamples, dtype=np.float64) - for i in range(n_resamples): - idx = rng.integers(0, n, size=n) - deltas[i] = b[idx].mean() - a[idx].mean() - alpha = (1.0 - level) / 2.0 - ci_low, ci_high = float(np.quantile(deltas, alpha)), float(np.quantile(deltas, 1 - alpha)) - return BootstrapDelta(delta=delta, ci_low=ci_low, ci_high=ci_high, n_resamples=n_resamples) - - -# --------------------------------------------------------------------------- -# Simple aggregate helpers (cost / latency / tokens) -# --------------------------------------------------------------------------- - - -@dataclass(frozen=True) -class Aggregate: - mean: float - median: float - p95: float - n: int - - def to_dict(self) -> dict[str, float | int]: - return {"mean": self.mean, "median": self.median, "p95": self.p95, "n": self.n} - - -def paired_aggregate(values: Sequence[float]) -> Aggregate: - """Mean / median / p95 of a list of numbers (e.g. cost-per-question).""" - - if not values: - return Aggregate(0.0, 0.0, 0.0, 0) - arr = np.asarray(values, dtype=np.float64) - return Aggregate( - mean=float(arr.mean()), - median=float(statistics.median(values)), - p95=float(np.quantile(arr, 0.95)), - n=len(values), - ) - - -__all__ = [ - "Aggregate", - "BootstrapDelta", - "McnemarResult", - "bootstrap_delta_ci", - "mcnemar_test", - "paired_aggregate", -] diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py b/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py deleted file mode 100644 index 958f626003..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Multiple-choice accuracy + Wilson 95% confidence intervals. - -Wilson CI is preferred over normal-approximation because MIRAGE's -per-task subsets can be small (PubMedQA* and BioASQ-Y/N have a few -hundred questions each) and Wilson handles n→0 / p→{0,1} edges -gracefully. - -Reference for the closed form: Wilson (1927); identical to the -``statsmodels.stats.proportion.proportion_confint(method='wilson')`` -output and what scikit-learn implements internally for its bounded -estimators. -""" - -from __future__ import annotations - -import math -from collections.abc import Mapping, Sequence -from dataclasses import dataclass - - -@dataclass(frozen=True) -class AccuracyResult: - """Per-task accuracy with Wilson CI.""" - - n_correct: int - n_total: int - accuracy: float - ci_low: float - ci_high: float - - def to_dict(self) -> dict[str, float | int]: - return { - "n_correct": self.n_correct, - "n_total": self.n_total, - "accuracy": self.accuracy, - "ci_low": self.ci_low, - "ci_high": self.ci_high, - } - - -# Two-sided Wilson z values. 1.959964 ≈ z_{0.975}. -_Z_FOR_LEVEL: dict[float, float] = { - 0.90: 1.6448536269514722, - 0.95: 1.959963984540054, - 0.99: 2.5758293035489004, -} - - -def wilson_ci(n_correct: int, n_total: int, *, level: float = 0.95) -> tuple[float, float]: - """Two-sided Wilson score confidence interval for a proportion. - - Returns ``(low, high)``. ``n_total == 0`` returns ``(0.0, 1.0)`` — - the maximally uncertain interval. - """ - - if n_total <= 0: - return 0.0, 1.0 - if level not in _Z_FOR_LEVEL: - raise ValueError(f"Unsupported confidence level {level!r}") - z = _Z_FOR_LEVEL[level] - p = n_correct / n_total - n = n_total - denom = 1.0 + (z * z) / n - centre = (p + (z * z) / (2 * n)) / denom - half = (z / denom) * math.sqrt((p * (1 - p) / n) + (z * z) / (4 * n * n)) - low = max(0.0, centre - half) - high = min(1.0, centre + half) - return low, high - - -def accuracy_with_wilson_ci(n_correct: int, n_total: int, *, level: float = 0.95) -> AccuracyResult: - if n_total < 0: - raise ValueError(f"n_total must be >= 0, got {n_total}") - if n_correct < 0 or n_correct > n_total: - raise ValueError( - f"n_correct must be in [0, n_total]; got n_correct={n_correct}, n_total={n_total}" - ) - accuracy = (n_correct / n_total) if n_total > 0 else 0.0 - low, high = wilson_ci(n_correct, n_total, level=level) - return AccuracyResult( - n_correct=n_correct, - n_total=n_total, - accuracy=accuracy, - ci_low=low, - ci_high=high, - ) - - -def per_task_accuracy( - rows: Sequence[Mapping[str, object]], - *, - task_key: str = "task", - correct_key: str = "is_correct", - level: float = 0.95, -) -> dict[str, AccuracyResult]: - """Group ``rows`` by ``task_key`` and compute per-task ``AccuracyResult``. - - ``rows[i][correct_key]`` must be truthy iff the answer was correct. - """ - - counts: dict[str, list[int]] = {} - for row in rows: - task = str(row.get(task_key, "")) - bucket = counts.setdefault(task, [0, 0]) - bucket[1] += 1 - if row.get(correct_key): - bucket[0] += 1 - return {task: accuracy_with_wilson_ci(c[0], c[1], level=level) for task, c in counts.items()} - - -def macro_accuracy(per_task: Mapping[str, AccuracyResult]) -> float: - if not per_task: - return 0.0 - return sum(r.accuracy for r in per_task.values()) / len(per_task) - - -__all__ = [ - "AccuracyResult", - "accuracy_with_wilson_ci", - "macro_accuracy", - "per_task_accuracy", - "wilson_ci", -] diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py b/surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py deleted file mode 100644 index 3fd25f6345..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py +++ /dev/null @@ -1,134 +0,0 @@ -"""Retrieval metrics: Recall@k, MRR, nDCG@k. - -Used by CUREv1's runner to score the SurfSense arm against the -benchmark's qrels. ``corpus_id`` is the canonical CUREv1 passage id -(string); the runner maps SurfSense ``chunk_id`` → ``document_id`` → -``corpus_id`` before calling these. - -Graded relevance (CUREv1 uses 0/1/2 grades) is honoured by ``ndcg_at_k``; -``recall_at_k`` and ``mrr`` flatten anything > 0 to "relevant". -""" - -from __future__ import annotations - -import math -from collections.abc import Iterable, Mapping, Sequence -from dataclasses import dataclass - - -@dataclass(frozen=True) -class RetrievalScores: - """Aggregated retrieval scores.""" - - recall_at_k: dict[int, float] - mrr: float - ndcg_at_10: float - n_queries: int - - def to_dict(self) -> dict: - return { - "recall_at_k": dict(self.recall_at_k), - "mrr": self.mrr, - "ndcg_at_10": self.ndcg_at_10, - "n_queries": self.n_queries, - } - - -def recall_at_k(retrieved: Sequence[str], relevant: Iterable[str], k: int) -> float: - """Fraction of ``relevant`` documents found in ``retrieved[:k]``.""" - - if not relevant: - return 0.0 - relevant_set = set(relevant) - if not relevant_set: - return 0.0 - top_k = list(retrieved)[:k] - hits = sum(1 for doc in top_k if doc in relevant_set) - return hits / len(relevant_set) - - -def mrr(retrieved: Sequence[str], relevant: Iterable[str]) -> float: - """Reciprocal rank of the first relevant doc, 0 if none found.""" - - relevant_set = set(relevant) - for rank, doc in enumerate(retrieved, start=1): - if doc in relevant_set: - return 1.0 / rank - return 0.0 - - -def _dcg_at_k(grades: Sequence[float], k: int) -> float: - s = 0.0 - for i, grade in enumerate(grades[:k], start=1): - # Standard log-base-2 discount; gain = 2^grade - 1 for graded relevance. - s += (2.0**grade - 1.0) / math.log2(i + 1) - return s - - -def ndcg_at_k( - retrieved: Sequence[str], - qrels: Mapping[str, float], - k: int, -) -> float: - """nDCG@k against graded ``qrels`` (``{doc_id: grade}``). - - Unjudged documents in ``retrieved`` contribute zero gain. The - ideal ordering is ``qrels`` sorted by grade descending. - """ - - if not qrels: - return 0.0 - grades = [float(qrels.get(doc, 0.0)) for doc in retrieved] - dcg = _dcg_at_k(grades, k) - ideal = sorted(qrels.values(), reverse=True) - idcg = _dcg_at_k([float(g) for g in ideal], k) - if idcg == 0.0: - return 0.0 - return dcg / idcg - - -def score_run( - *, - per_query_retrieved: Mapping[str, Sequence[str]], - per_query_qrels: Mapping[str, Mapping[str, float]], - ks: Sequence[int] = (1, 5, 10, 32), - ndcg_k: int = 10, -) -> RetrievalScores: - """Aggregate Recall@k, MRR, nDCG@k across a run. - - ``per_query_retrieved`` maps ``query_id -> ordered list of doc ids``. - ``per_query_qrels`` maps ``query_id -> {doc_id: grade}`` (grade > 0 - is relevant). - - Queries present in retrieved but not in qrels are skipped. Queries - in qrels but missing from retrieved contribute zeros. - """ - - qids = set(per_query_qrels.keys()) & set(per_query_retrieved.keys()) - if not qids: - return RetrievalScores( - recall_at_k={k: 0.0 for k in ks}, mrr=0.0, ndcg_at_10=0.0, n_queries=0 - ) - - recall_totals = {k: 0.0 for k in ks} - mrr_total = 0.0 - ndcg_total = 0.0 - for qid in qids: - retrieved = list(per_query_retrieved[qid]) - qrels = per_query_qrels[qid] - relevant_docs = [d for d, g in qrels.items() if g > 0] - for k in ks: - recall_totals[k] += recall_at_k(retrieved, relevant_docs, k) - mrr_total += mrr(retrieved, relevant_docs) - ndcg_total += ndcg_at_k(retrieved, qrels, ndcg_k) - - n = len(qids) - return RetrievalScores( - recall_at_k={k: v / n for k, v in recall_totals.items()}, - mrr=mrr_total / n, - ndcg_at_10=ndcg_total / n, - n_queries=n, - ) - - -__all__ = ["RetrievalScores", "mrr", "ndcg_at_k", "recall_at_k", "score_run"] diff --git a/surfsense_evals/src/surfsense_evals/core/parse/__init__.py b/surfsense_evals/src/surfsense_evals/core/parse/__init__.py deleted file mode 100644 index 8b90de78b7..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parse/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Parsers shared across suites: citations, MCQ envelopes, AI-SDK SSE.""" - -from __future__ import annotations - -from .answer_letter import AnswerLetterResult, extract_answer_letter -from .citations import CITATION_REGEX, ChunkCitation, CitationToken, UrlCitation, parse_citations -from .freeform_answer import extract_freeform_answer -from .sse import SseEvent, iter_sse_events - -__all__ = [ - "CITATION_REGEX", - "CitationToken", - "ChunkCitation", - "UrlCitation", - "parse_citations", - "AnswerLetterResult", - "extract_answer_letter", - "extract_freeform_answer", - "SseEvent", - "iter_sse_events", -] diff --git a/surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py b/surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py deleted file mode 100644 index 8cf23869bb..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py +++ /dev/null @@ -1,122 +0,0 @@ -"""Robust extractor for MCQ answer letters. - -Handles three answer shapes seen in the wild: - -1. **MedRAG envelope** — ``{"step_by_step_thinking": "...", "answer_choice": "A"}`` - embedded somewhere in the assistant message (often inside ```` ```json ```` / - ``` ``` ``` fences). The regex grabs the JSON object and reads the - ``answer_choice`` field. - -2. **Final-line letter** — e.g. ``Answer: B`` or ``The correct answer is (C).``. - Falls back to a permissive regex over the last few lines. - -3. **Bare letter** — single uppercase letter at the end of the message. - -The function returns the parsed letter (uppercased) plus a discriminator -of which strategy fired so the runner / report can flag suspicious -parses (typically zero-confidence parses indicate the model didn't -follow the prompt). -""" - -from __future__ import annotations - -import json -import re -from dataclasses import dataclass -from typing import Literal - -ParserStrategy = Literal["json_envelope", "answer_line", "bare_letter", "none"] - - -@dataclass(frozen=True) -class AnswerLetterResult: - letter: str | None - strategy: ParserStrategy - - @property - def found(self) -> bool: - return self.letter is not None - - -# --------------------------------------------------------------------------- -# Strategies -# --------------------------------------------------------------------------- - - -_JSON_BLOCK = re.compile(r"\{[^{}]*\"answer_choice\"\s*:\s*\"([A-Za-z])\"[^{}]*\}", re.DOTALL) -_FENCED_JSON = re.compile(r"```(?:json)?\s*(\{.*?\})\s*```", re.DOTALL | re.IGNORECASE) -_ANSWER_LINE = re.compile( - r"(?:final\s*answer|answer\s*choice|the\s+correct\s+answer\s+is|answer)\s*[:=\-]?\s*" - r"\(?\s*([A-Za-z])\s*[\)\.]*\s*$", - re.IGNORECASE | re.MULTILINE, -) -_BARE_LETTER = re.compile(r"^\s*\(?\s*([A-Za-z])\s*[\)\.]*\s*$", re.MULTILINE) - - -def _from_json_envelope(text: str) -> str | None: - # Try fenced code blocks first (most likely to contain the JSON). - for fence in _FENCED_JSON.finditer(text): - try: - obj = json.loads(fence.group(1)) - except (json.JSONDecodeError, ValueError): - continue - if isinstance(obj, dict): - choice = obj.get("answer_choice") - if isinstance(choice, str) and choice.strip(): - return choice.strip()[:1].upper() - - # Fall back to a tolerant regex over the whole text (handles - # responses that drop the fences). - match = _JSON_BLOCK.search(text) - if match: - return match.group(1).upper() - return None - - -def _from_answer_line(text: str) -> str | None: - # Walk lines bottom-up; the answer is almost always near the end. - for match in reversed(list(_ANSWER_LINE.finditer(text))): - letter = match.group(1).upper() - if letter.isalpha(): - return letter - return None - - -def _from_bare_letter(text: str) -> str | None: - # Inspect only the final non-empty lines (avoid grabbing in-prose - # mentions of "A" or "I"). - lines = [ln.strip() for ln in text.splitlines() if ln.strip()] - for ln in reversed(lines[-3:]): - match = _BARE_LETTER.match(ln) - if match: - return match.group(1).upper() - return None - - -def extract_answer_letter(text: str) -> AnswerLetterResult: - """Run strategies in order and return the first hit. - - Order: JSON envelope → final-answer-line regex → bare-letter - fallback. Empty / whitespace-only text returns - ``AnswerLetterResult(None, "none")``. - """ - - if not text or not text.strip(): - return AnswerLetterResult(None, "none") - - letter = _from_json_envelope(text) - if letter: - return AnswerLetterResult(letter, "json_envelope") - - letter = _from_answer_line(text) - if letter: - return AnswerLetterResult(letter, "answer_line") - - letter = _from_bare_letter(text) - if letter: - return AnswerLetterResult(letter, "bare_letter") - - return AnswerLetterResult(None, "none") - - -__all__ = ["AnswerLetterResult", "ParserStrategy", "extract_answer_letter"] diff --git a/surfsense_evals/src/surfsense_evals/core/parse/citations.py b/surfsense_evals/src/surfsense_evals/core/parse/citations.py deleted file mode 100644 index 38bd16d317..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parse/citations.py +++ /dev/null @@ -1,110 +0,0 @@ -"""Python port of the canonical citation parser. - -Source of truth: ``surfsense_web/lib/citations/citation-parser.ts:20-21``. -The pattern is byte-for-byte identical to the TS export ``CITATION_REGEX`` -so a SurfSense user reading the web client and a CUREv1 retrieval scorer -running here see the same chunk_ids extracted from the same answer. - -The TS reference also handles a ``urlcite{N}`` placeholder produced by -``preprocessCitationMarkdown`` — that pre-processing step is web-only -(GFM autolink workaround), so the harness sees raw ``[citation:URL]`` -tokens and ``parse_citations`` returns them as ``UrlCitation`` directly. -""" - -from __future__ import annotations - -import re -from dataclasses import dataclass -from typing import Any - -# Pattern preserves the TS source verbatim: -# /[\[【]\u200B?citation:\s*(https?:\/\/[^\]】\u200B]+|urlcite\d+|(?:doc-)?-?\d+(?:\s*,\s*(?:doc-)?-?\d+)*)\s*\u200B?[\]】]/g -# -# Notes: -# * Matches both ASCII ``[]`` and Chinese fullwidth ``【】`` brackets. -# * Allows an optional ZWSP (``\u200B``) just inside each bracket. -# * ``citation:`` then EITHER a URL (anything not ``]``, ``】``, or ZWSP), -# OR a ``urlcite\d+`` placeholder, OR one or more comma-separated -# chunk ids (each optionally prefixed with ``doc-`` and optionally -# negative). -# * URL char class deliberately excludes the closing brackets so a -# ``[citation:https://x.com]`` doesn't swallow the ``]``. -# The ZWSP must be the actual code-point — the original TS source uses -# the regex literal ``\u200B`` which the JS engine interprets as the -# character. Python's ``re`` doesn't process the ``\u`` escape inside -# the pattern source, so we splice the literal character in via an -# f-string. This keeps our pattern functionally identical to the TS -# reference and lets ``"\u200B" in CITATION_REGEX.pattern`` succeed. -_ZWSP = "\u200b" -CITATION_REGEX = re.compile( - rf"[\[【]{_ZWSP}?citation:\s*(" - rf"https?://[^\]】{_ZWSP}]+|urlcite\d+|(?:doc-)?-?\d+(?:\s*,\s*(?:doc-)?-?\d+)*" - rf")\s*{_ZWSP}?[\]】]" -) - - -@dataclass(frozen=True) -class ChunkCitation: - chunk_id: int - is_docs_chunk: bool - - def to_dict(self) -> dict[str, Any]: - return { - "kind": "chunk", - "chunk_id": self.chunk_id, - "is_docs_chunk": self.is_docs_chunk, - } - - -@dataclass(frozen=True) -class UrlCitation: - url: str - - def to_dict(self) -> dict[str, Any]: - return {"kind": "url", "url": self.url} - - -CitationToken = ChunkCitation | UrlCitation - - -def parse_citations(text: str, *, url_map: dict[str, str] | None = None) -> list[CitationToken]: - """Return the citation tokens found in ``text`` in document order. - - ``url_map`` is the optional ``urlciteN -> URL`` lookup that the web - client builds in its preprocessing step. The harness ordinarily - doesn't preprocess (we don't render the markdown, we score it), so - the default empty map means ``urlciteN`` placeholders are dropped - rather than mis-resolved to a missing URL. - - Multi-id payloads like ``[citation:1, doc-2, -3]`` are flattened - into separate ``ChunkCitation`` entries — same as the TS reference. - """ - - out: list[CitationToken] = [] - for match in CITATION_REGEX.finditer(text): - captured = match.group(1) - if captured.startswith("http://") or captured.startswith("https://"): - out.append(UrlCitation(url=captured.strip())) - continue - if captured.startswith("urlcite"): - if url_map and captured in url_map: - out.append(UrlCitation(url=url_map[captured])) - continue - for raw_id in (s.strip() for s in captured.split(",")): - is_docs_chunk = raw_id.startswith("doc-") - number_part = raw_id[4:] if is_docs_chunk else raw_id - try: - chunk_id = int(number_part) - except ValueError: - continue - out.append(ChunkCitation(chunk_id=chunk_id, is_docs_chunk=is_docs_chunk)) - return out - - -__all__ = [ - "CITATION_REGEX", - "ChunkCitation", - "UrlCitation", - "CitationToken", - "parse_citations", -] diff --git a/surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py b/surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py deleted file mode 100644 index 104176d09e..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Extract free-form answers from open-ended LLM responses. - -Used by benchmarks that don't have a fixed letter set (MMLongBench-Doc, -DocVQA-style benchmarks, future legal/finance suites). The contract: - -* Strip leading "Answer:" / "Final answer:" markers if present. -* Drop fenced code blocks if the model wrapped its answer in one. -* Trim leading/trailing whitespace. -* Return the *last* meaningful chunk — models often think out loud - before stating the answer. - -If the message is empty or only contains a fence, return ``""``. -""" - -from __future__ import annotations - -import re - -_ANSWER_PREFIX = re.compile( - r"^\s*(?:final\s*answer|the\s+answer\s+is|answer)\s*[:=\-]\s*", - re.IGNORECASE, -) -# Marker-only regex (no capture group) used to find every "Answer:" -# token position. We then slice from the LAST marker's end to the -# next newline ourselves — robust to multiple inline answers because -# we never let the engine greedy-capture across markers. -_ANSWER_MARKER = re.compile( - r"(?:final\s*answer|the\s+answer\s+is|answer)\s*[:=\-]\s*", - re.IGNORECASE, -) -_FENCED_BLOCK = re.compile(r"```[a-zA-Z0-9]*\s*([\s\S]*?)\s*```") - - -def extract_freeform_answer(text: str) -> str: - """Pull the model's final answer out of a possibly-verbose response.""" - - if not text or not text.strip(): - return "" - - # 1. Find the last line that starts with an Answer: marker. If - # nothing matches, walk back to the last non-empty line. - lines = [ln.rstrip() for ln in text.strip().splitlines()] - candidate = "" - for ln in reversed(lines): - if not ln.strip(): - continue - if _ANSWER_PREFIX.search(ln): - candidate = _ANSWER_PREFIX.sub("", ln, count=1).strip() - break - - if not candidate: - # 2. Inline match: find every "Answer:" marker position and - # slice from the LAST marker's end to the next newline. Robust - # to "preamble.Answer: 42" one-liners and multiple inline - # markers (we always pick the final, freshest one). - marker_matches = list(_ANSWER_MARKER.finditer(text)) - if marker_matches: - last = marker_matches[-1] - tail = text[last.end() :] - nl = tail.find("\n") - if nl >= 0: - tail = tail[:nl] - candidate = tail.strip() - - if not candidate: - # 3. No "Answer:" marker — try fenced blocks. - fences = _FENCED_BLOCK.findall(text) - if fences: - candidate = fences[-1].strip() - else: - # Last non-empty line as a fallback. - for ln in reversed(lines): - if ln.strip(): - candidate = ln.strip() - break - - # 2. Strip wrapping quotes / parens / trailing punctuation that - # confuse the grader without changing meaning. - candidate = candidate.strip().strip("`").strip() - if candidate.startswith(('"', "'")) and candidate.endswith(('"', "'")): - candidate = candidate[1:-1].strip() - return candidate - - -__all__ = ["extract_freeform_answer"] diff --git a/surfsense_evals/src/surfsense_evals/core/parse/sse.py b/surfsense_evals/src/surfsense_evals/core/parse/sse.py deleted file mode 100644 index 76ded2d138..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parse/sse.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Minimal SSE consumer compatible with SurfSense's wire format. - -SurfSense uses ``app/services/streaming/envelope/sse.py`` to frame events: - -* ``data: \\n\\n`` -* ``data: \\n\\n`` (most events) -* ``data: [DONE]\\n\\n`` (terminator) - -There is no ``event:``, ``id:``, or ``retry:`` framing in production — -``format_sse(payload)`` only emits the ``data:`` line. This implementation -is therefore intentionally smaller than ``httpx-sse`` (which we still -list as a dep so callers who want richer parsing can opt in): one event -per ``data:`` line, separated by blank lines. - -We accept any line iterator (an ``httpx.Response.aiter_lines`` adapter -in production, a list in tests) so this is unit-testable without a -network mock. -""" - -from __future__ import annotations - -from collections.abc import AsyncIterator -from dataclasses import dataclass - - -@dataclass(frozen=True) -class SseEvent: - """A parsed SSE event. Only the ``data`` field is populated. - - Multi-line payloads (``data: a\\ndata: b``) are joined with ``\\n`` - per the SSE spec, even though SurfSense doesn't currently emit them. - """ - - data: str - - -async def iter_sse_events(lines: AsyncIterator[str]) -> AsyncIterator[SseEvent]: - """Yield one ``SseEvent`` per blank-line-terminated frame. - - Lines that are empty or whitespace flush the buffer. ``data:`` lines - are accumulated into the buffer; everything else is ignored - (matches the lenient browser EventSource behaviour). - """ - - buffer: list[str] = [] - async for raw in lines: - if raw is None: - continue - line = raw.rstrip("\r") - if line == "": - if buffer: - yield SseEvent(data="\n".join(buffer)) - buffer.clear() - continue - if line.startswith(":"): - # comment / heartbeat - continue - if line.startswith("data:"): - # spec: optional single space after the colon. - payload = line[5:] - if payload.startswith(" "): - payload = payload[1:] - buffer.append(payload) - continue - # Any other field (event:, id:, retry:) is currently unused. - continue - - if buffer: - yield SseEvent(data="\n".join(buffer)) - - -__all__ = ["SseEvent", "iter_sse_events"] diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/__init__.py b/surfsense_evals/src/surfsense_evals/core/parsers/__init__.py deleted file mode 100644 index 6a8e6c4ce5..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parsers/__init__.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Direct parser invocations for the parser_compare benchmark. - -The SurfSense backend exposes a single ``ETL_SERVICE`` env var that -picks one parser globally; per-ingestion overrides are not on the -public API. To drive the four (Azure DI x basic/premium, LlamaCloud x -basic/premium) extractions we need for ``multimodal_doc/parser_compare`` -we therefore call the Azure DI and LlamaCloud SDKs directly from the -eval harness, mirroring the production code path in -``surfsense_backend/app/etl_pipeline/parsers/``. - -Two design rules: - -* No backend imports — the eval harness cannot pull in the FastAPI - app's config layer (it would require the full backend ``.env`` plus a - reachable Postgres). We re-read keys from our own environment instead. -* Same wire shape as the backend's parsers (Azure ``prebuilt-read`` / - ``prebuilt-layout`` selected by ``processing_mode``; LlamaCloud - ``parse_page_with_llm`` / ``parse_page_with_agent`` selected by - ``processing_mode``) so any quality conclusions transfer back to - production behaviour. -""" - -from __future__ import annotations - -from .azure_di import AzureDIError, parse_with_azure_di -from .llamacloud import LlamaCloudError, parse_with_llamacloud -from .pdf_pages import count_pdf_pages - -__all__ = [ - "AzureDIError", - "LlamaCloudError", - "count_pdf_pages", - "parse_with_azure_di", - "parse_with_llamacloud", -] diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py b/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py deleted file mode 100644 index 16e618db47..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py +++ /dev/null @@ -1,148 +0,0 @@ -"""Azure Document Intelligence parser — eval-side mirror of the backend. - -Calls ``DocumentIntelligenceClient.begin_analyze_document`` with one -of two ``model_id`` slugs depending on ``processing_mode``: - -* ``basic`` → ``prebuilt-read`` (text OCR only, cheaper, faster) -* ``premium`` → ``prebuilt-layout`` (text + tables + structure; - produces real markdown headings, - pipe-tables, etc.) - -These are the same model selections the production -``surfsense_backend/app/etl_pipeline/parsers/azure_doc_intelligence.py`` -makes per ``processing_mode``. Output format is forced to Markdown -(``DocumentContentFormat.MARKDOWN``) so the long-context arm can stuff -it into a prompt verbatim. - -Retry policy is intentionally light here (the eval harness re-runs -the whole batch on top-level failure); we do one synchronous attempt -plus exponential backoff on transient transport errors. -""" - -from __future__ import annotations - -import asyncio -import logging -import os -import random -from pathlib import Path - -logger = logging.getLogger(__name__) - - -_AZURE_MODEL_BY_MODE = { - "basic": "prebuilt-read", - "premium": "prebuilt-layout", -} - -_MAX_RETRIES = 4 -_BASE_DELAY = 5.0 -_MAX_DELAY = 60.0 - - -class AzureDIError(RuntimeError): - """Raised when Azure DI fails after all retries.""" - - -async def parse_with_azure_di( - file_path: str | os.PathLike, - *, - processing_mode: str = "basic", - endpoint: str | None = None, - api_key: str | None = None, -) -> str: - """Run Azure DI on ``file_path`` and return the markdown content. - - ``endpoint`` / ``api_key`` default to ``AZURE_DI_ENDPOINT`` and - ``AZURE_DI_KEY`` env vars (set in ``surfsense_evals/.env``). - - Raises ``AzureDIError`` after exhausting retries; ``ValueError`` if - credentials are missing. - """ - - endpoint = endpoint or os.environ.get("AZURE_DI_ENDPOINT") - api_key = api_key or os.environ.get("AZURE_DI_KEY") - if not endpoint or not api_key: - raise ValueError( - "AZURE_DI_ENDPOINT and AZURE_DI_KEY must be set (see surfsense_evals/.env)." - ) - - model_id = _AZURE_MODEL_BY_MODE.get(processing_mode, "prebuilt-read") - - # Lazy imports — surfsense_evals shouldn't pay the azure-sdk - # import cost on every CLI invocation that doesn't touch - # parser_compare. - from azure.ai.documentintelligence.aio import DocumentIntelligenceClient - from azure.ai.documentintelligence.models import DocumentContentFormat - from azure.core.credentials import AzureKeyCredential - from azure.core.exceptions import ( - ClientAuthenticationError, - HttpResponseError, - ServiceRequestError, - ServiceResponseError, - ) - - file_size_mb = await asyncio.to_thread(os.path.getsize, file_path) / (1024 * 1024) - logger.info( - "Azure DI parsing %s (mode=%s, model=%s, size=%.1fMB)", - file_path, - processing_mode, - model_id, - file_size_mb, - ) - - last_exc: Exception | None = None - for attempt in range(1, _MAX_RETRIES + 1): - try: - client = DocumentIntelligenceClient( - endpoint=endpoint, - credential=AzureKeyCredential(api_key), - ) - async with client: - body = await asyncio.to_thread(Path(file_path).read_bytes) - poller = await client.begin_analyze_document( - model_id, - body=body, - output_content_format=DocumentContentFormat.MARKDOWN, - ) - result = await poller.result() - content = (result.content or "").strip() - if not content: - raise AzureDIError(f"Azure DI returned empty content for {file_path}") - logger.info( - "Azure DI OK: %s (%s) -> %d chars", - file_path, - model_id, - len(content), - ) - return content - - except ClientAuthenticationError: - raise - except HttpResponseError as exc: - # 4xx that's not auth: don't retry, the request itself is broken. - if exc.status_code and 400 <= exc.status_code < 500: - raise AzureDIError(f"Azure DI {exc.status_code} on {file_path}: {exc}") from exc - last_exc = exc - except (ServiceRequestError, ServiceResponseError) as exc: - last_exc = exc - - if attempt < _MAX_RETRIES: - delay = min(_BASE_DELAY * (2 ** (attempt - 1)), _MAX_DELAY) - jitter = delay * 0.25 * (2 * random.random() - 1) - sleep_for = delay + jitter - logger.warning( - "Azure DI attempt %d/%d failed (%s); retrying in %.1fs", - attempt, - _MAX_RETRIES, - type(last_exc).__name__, - sleep_for, - ) - await asyncio.sleep(sleep_for) - - raise AzureDIError( - f"Azure DI failed after {_MAX_RETRIES} attempts on {file_path}" - ) from last_exc - - -__all__ = ["AzureDIError", "parse_with_azure_di"] diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py b/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py deleted file mode 100644 index 32fd97e475..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py +++ /dev/null @@ -1,174 +0,0 @@ -"""LlamaParse (LlamaCloud) parser — eval-side mirror of the backend. - -Calls ``LlamaParse.aparse`` with one of two ``parse_mode`` slugs -depending on ``processing_mode``: - -* ``basic`` → ``parse_page_with_llm`` (cheap, single-LLM-call/page) -* ``premium`` → ``parse_page_with_agent`` (multi-step agent per page; - handles tables / figures - substantially better) - -These are the exact mappings from production -``surfsense_backend/app/etl_pipeline/parsers/llamacloud.py``. We keep -``num_workers=1`` and language=``"en"`` to match production. - -The result is materialised via ``get_markdown_documents(split_by_page=False)`` -which concatenates every page into a single markdown string, exactly -the shape we need for long-context stuffing. -""" - -from __future__ import annotations - -import asyncio -import logging -import os -import random - -import httpx - -logger = logging.getLogger(__name__) - -_LLAMA_PARSE_MODE_MAP = { - "basic": "parse_page_with_llm", - "premium": "parse_page_with_agent", -} - -_MAX_RETRIES = 3 -_BASE_DELAY = 10.0 -_MAX_DELAY = 90.0 - - -class LlamaCloudError(RuntimeError): - """Raised when LlamaCloud parse fails after all retries.""" - - -def _extract_markdown(result) -> str: - """Pull markdown out of whatever object LlamaParse.aparse returns. - - Mirrors backend's tolerant extraction: the SDK has gone through - several response shapes; we accept all of them so a minor SDK bump - doesn't silently zero the eval. - """ - - if hasattr(result, "get_markdown_documents"): - docs = result.get_markdown_documents(split_by_page=False) - if docs and hasattr(docs[0], "text"): - return docs[0].text - if hasattr(result, "pages") and result.pages: - return "\n\n".join(p.md for p in result.pages if hasattr(p, "md") and p.md) - - if isinstance(result, list): - if result and hasattr(result[0], "text"): - return result[0].text - return "\n\n".join( - doc.page_content if hasattr(doc, "page_content") else str(doc) for doc in result - ) - - return str(result) - - -async def parse_with_llamacloud( - file_path: str | os.PathLike, - *, - processing_mode: str = "basic", - estimated_pages: int = 50, - api_key: str | None = None, -) -> str: - """Run LlamaParse on ``file_path`` and return the markdown content. - - ``api_key`` defaults to the ``LLAMA_CLOUD_API_KEY`` env var (set - in ``surfsense_evals/.env``). - - Raises ``LlamaCloudError`` after exhausting retries; ``ValueError`` - if the API key is missing. - """ - - api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY") - if not api_key: - raise ValueError("LLAMA_CLOUD_API_KEY must be set (see surfsense_evals/.env).") - - parse_mode = _LLAMA_PARSE_MODE_MAP.get(processing_mode, "parse_page_with_llm") - - # Lazy import: llama-cloud pulls llama-index-core (~50 MB) on first - # touch; defer until the parser actually runs. - from llama_cloud_services import LlamaParse - from llama_cloud_services.parse.base import JobFailedException - from llama_cloud_services.parse.utils import ResultType - - file_size_mb = await asyncio.to_thread(os.path.getsize, file_path) / (1024 * 1024) - # Match backend's per-page timeout heuristic so big PDFs don't drop - # mid-job: 60s baseline + 30s/page (premium agent runs longer than - # basic; both fit comfortably here). - job_timeout = max(180.0, 60.0 + 30.0 * estimated_pages) - upload_timeout = max(120.0, 30.0 * file_size_mb) - - logger.info( - "LlamaCloud parsing %s (mode=%s, parse_mode=%s, %.1fMB, job_timeout=%.0fs)", - file_path, - processing_mode, - parse_mode, - file_size_mb, - job_timeout, - ) - - custom_timeout = httpx.Timeout( - connect=120.0, - read=upload_timeout, - write=upload_timeout, - pool=120.0, - ) - - last_exc: Exception | None = None - for attempt in range(1, _MAX_RETRIES + 1): - try: - async with httpx.AsyncClient(timeout=custom_timeout) as client: - parser = LlamaParse( - api_key=api_key, - num_workers=1, - verbose=False, - language="en", - result_type=ResultType.MD, - parse_mode=parse_mode, - ignore_errors=False, - max_timeout=int(max(2000.0, job_timeout + upload_timeout)), - job_timeout_in_seconds=job_timeout, - job_timeout_extra_time_per_page_in_seconds=60, - custom_client=client, - ) - result = await parser.aparse(str(file_path)) - content = _extract_markdown(result).strip() - if not content: - raise LlamaCloudError(f"LlamaCloud returned empty content for {file_path}") - logger.info( - "LlamaCloud OK: %s (%s) -> %d chars", - file_path, - parse_mode, - len(content), - ) - return content - - except ( - httpx.HTTPError, - JobFailedException, - RuntimeError, - ) as exc: - last_exc = exc - if attempt < _MAX_RETRIES: - delay = min(_BASE_DELAY * (2 ** (attempt - 1)), _MAX_DELAY) - jitter = delay * 0.25 * (2 * random.random() - 1) - sleep_for = delay + jitter - logger.warning( - "LlamaCloud attempt %d/%d failed (%s); retrying in %.1fs", - attempt, - _MAX_RETRIES, - type(last_exc).__name__, - sleep_for, - ) - await asyncio.sleep(sleep_for) - - raise LlamaCloudError( - f"LlamaCloud failed after {_MAX_RETRIES} attempts on {file_path}" - ) from last_exc - - -__all__ = ["LlamaCloudError", "parse_with_llamacloud"] diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py b/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py deleted file mode 100644 index e3691cb734..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Tiny pypdf wrapper for "how many pages does this PDF have?". - -Used by ``parser_compare`` to: - -* Decide LlamaCloud's per-page job timeout. -* Compute the SurfSense preprocessing dollar cost - (``$1 / 1k pages`` for basic, ``$10 / 1k pages`` for premium) so the - report can show "ingest + LLM" total cost per arm. - -Returns ``0`` (and logs) on parse failure rather than raising — costs -shown as ``?`` are always better than a benchmark that crashes mid-run. -""" - -from __future__ import annotations - -import logging -from pathlib import Path - -logger = logging.getLogger(__name__) - - -def count_pdf_pages(path: Path) -> int: - """Return the page count for ``path``; ``0`` if pypdf can't open it.""" - - try: - from pypdf import PdfReader - - reader = PdfReader(str(path)) - return len(reader.pages) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to count pages for %s: %s", path, exc) - return 0 - - -__all__ = ["count_pdf_pages"] diff --git a/surfsense_evals/src/surfsense_evals/core/pdf/__init__.py b/surfsense_evals/src/surfsense_evals/core/pdf/__init__.py deleted file mode 100644 index e03fa34c93..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/pdf/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Domain-agnostic PDF rendering helper. Lazy import.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: # pragma: no cover - from .render import ( - PdfImage, - render_pdf, - render_pdf_with_images, - render_text_files_to_pdf, - ) - -__all__ = [ - "PdfImage", - "render_pdf", - "render_pdf_with_images", - "render_text_files_to_pdf", -] - - -_LAZY = {"PdfImage", "render_pdf", "render_pdf_with_images", "render_text_files_to_pdf"} - - -def __getattr__(name: str): - if name in _LAZY: - from . import render as _mod - - return getattr(_mod, name) - raise AttributeError(f"module 'surfsense_evals.core.pdf' has no attribute {name!r}") diff --git a/surfsense_evals/src/surfsense_evals/core/pdf/render.py b/surfsense_evals/src/surfsense_evals/core/pdf/render.py deleted file mode 100644 index 21866f3e53..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/pdf/render.py +++ /dev/null @@ -1,347 +0,0 @@ -"""Deterministic ``.txt`` / ``.md`` → single PDF via reportlab. - -Used wherever a benchmark needs the same source bytes fed to both the -native-PDF arm and the SurfSense ingestion arm. The head-to-head -comparison is fair only if the *same* PDF is the input to both arms, -which is why we go to lengths to make the rendering deterministic. - -Determinism notes: - -* We pin the PDF metadata to a fixed creation date and producer - (``reportlab`` accepts neither directly, but ``Canvas.setAuthor`` and - the absence of an ``info`` mutator means the bytes only differ by - ``CreationDate`` / ``ModDate``). We post-process the PDF to scrub - those if ``deterministic=True`` is passed. -* Page size, font, margins, and tab handling are fixed in code so the - same input yields the same byte output across machines. -* PDF/A is overkill for our use; basic PDF 1.4 is what every model - expects. -""" - -from __future__ import annotations - -import io -import re -from collections.abc import Iterable, Sequence -from dataclasses import dataclass -from datetime import UTC, datetime -from pathlib import Path - -from reportlab.lib.pagesizes import LETTER -from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet -from reportlab.lib.units import inch -from reportlab.lib.utils import ImageReader -from reportlab.platypus import ( - Image, - KeepTogether, - PageBreak, - Paragraph, - SimpleDocTemplate, - Spacer, -) - - -@dataclass -class RenderedPdf: - path: Path - n_pages_estimate: int - n_chars: int - - -_PDF_DATE_KEY = re.compile(rb"/(?:CreationDate|ModDate)\s*\(D:[^)]*\)") -# reportlab also writes a `/ID []` trailer entry that -# embeds a per-run hash. Scrub it so two renders of the same input -# produce the same bytes. -_PDF_ID_ARRAY = re.compile(rb"/ID\s*\[\s*<[^>]*>\s*<[^>]*>\s*\]") - - -def _scrub_dates(pdf_bytes: bytes) -> bytes: - """Remove ``CreationDate`` / ``ModDate`` / trailer ``/ID`` so the - file is byte-deterministic across runs.""" - - pdf_bytes = _PDF_DATE_KEY.sub(b"/CreationDate (D:19700101000000Z)", pdf_bytes) - pdf_bytes = _PDF_ID_ARRAY.sub(b"/ID [<00><00>]", pdf_bytes) - return pdf_bytes - - -_DEFAULT_STYLES = getSampleStyleSheet() - - -def _build_body_style() -> ParagraphStyle: - base = _DEFAULT_STYLES["BodyText"] - style = ParagraphStyle( - "EvalBody", - parent=base, - fontName="Helvetica", - fontSize=10.5, - leading=14, - spaceAfter=6, - spaceBefore=0, - ) - return style - - -def _build_heading_style() -> ParagraphStyle: - base = _DEFAULT_STYLES["Heading2"] - style = ParagraphStyle( - "EvalHeading", - parent=base, - fontName="Helvetica-Bold", - fontSize=14, - leading=18, - spaceAfter=10, - spaceBefore=8, - ) - return style - - -def _normalise_paragraphs(text: str) -> list[str]: - """Split a text blob into paragraphs while preserving blank-line structure.""" - - blocks: list[list[str]] = [[]] - for line in text.splitlines(): - stripped = line.rstrip() - if stripped == "": - if blocks[-1]: - blocks.append([]) - continue - blocks[-1].append(stripped) - paragraphs: list[str] = [] - for block in blocks: - if not block: - continue - # Join lines within a paragraph with spaces (text-from-PDF style). - paragraphs.append(" ".join(block)) - return paragraphs - - -def _escape_html(text: str) -> str: - return text.replace("&", "&").replace("<", "<").replace(">", ">") - - -def render_pdf( - *, - title: str, - sections: Sequence[tuple[str | None, str]], - output_path: Path, - deterministic: bool = True, -) -> RenderedPdf: - """Render one PDF from a list of ``(section_heading, section_text)`` tuples. - - ``section_heading`` may be ``None`` for an unnamed section. Each - section is followed by a page break so the model's PDF parser sees - a clean structural boundary between source files. - """ - - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - buffer = io.BytesIO() - doc = SimpleDocTemplate( - buffer, - pagesize=LETTER, - leftMargin=0.75 * inch, - rightMargin=0.75 * inch, - topMargin=0.75 * inch, - bottomMargin=0.75 * inch, - title=title, - author="surfsense-evals", - subject="Eval input", - creator="surfsense-evals", - ) - - body_style = _build_body_style() - heading_style = _build_heading_style() - title_style = ParagraphStyle( - "EvalTitle", - parent=_DEFAULT_STYLES["Title"], - fontName="Helvetica-Bold", - fontSize=18, - leading=22, - spaceAfter=14, - ) - - flow: list = [Paragraph(_escape_html(title), title_style)] - total_chars = 0 - for index, (heading, text) in enumerate(sections): - if index > 0: - flow.append(PageBreak()) - if heading: - flow.append(Paragraph(_escape_html(heading), heading_style)) - for paragraph in _normalise_paragraphs(text): - total_chars += len(paragraph) - flow.append(Paragraph(_escape_html(paragraph), body_style)) - flow.append(Spacer(1, 4)) - - doc.build(flow) - pdf_bytes = buffer.getvalue() - if deterministic: - pdf_bytes = _scrub_dates(pdf_bytes) - output_path.write_bytes(pdf_bytes) - - # Conservative page estimate: ~3000 chars per LETTER page at 10.5pt. - n_pages = max(1, total_chars // 3000 + len(sections)) - return RenderedPdf(path=output_path, n_pages_estimate=n_pages, n_chars=total_chars) - - -@dataclass -class PdfImage: - """One image to embed inside a section. - - ``caption`` is rendered below the image (italic). ``max_width_in`` - caps the rendered width in inches; height auto-scales to preserve - aspect ratio (read with PIL). - """ - - path: Path - caption: str = "" - max_width_in: float = 5.5 # default leaves margin for LETTER 8.5" - - -def _make_image_flowable(image: PdfImage) -> Image: - """Build a reportlab Image flowable scaled to fit page width.""" - - reader = ImageReader(str(image.path)) - iw, ih = reader.getSize() - if iw <= 0 or ih <= 0: - raise ValueError(f"Invalid image dimensions for {image.path}: {iw}x{ih}") - target_w = image.max_width_in * inch - target_h = target_w * (ih / iw) - # Cap height too — some medical images are extreme portrait. - max_h = 7.0 * inch - if target_h > max_h: - target_h = max_h - target_w = target_h * (iw / ih) - return Image(str(image.path), width=target_w, height=target_h) - - -def render_pdf_with_images( - *, - title: str, - sections: Sequence[tuple[str | None, str, Sequence[PdfImage] | None]], - output_path: Path, - deterministic: bool = True, - page_break_between_sections: bool = False, -) -> RenderedPdf: - """Render a PDF that mixes text and embedded images. - - Each section is ``(heading, body_text, images)``. Images render - inline after the body text, each followed by an italic caption. - Set ``page_break_between_sections=True`` if you want explicit - structural boundaries (mostly useful for multi-case PDFs); the - default keeps everything on one page when possible (so a single - MedXpertQA case is one PDF page with case + images + options). - """ - - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - buffer = io.BytesIO() - doc = SimpleDocTemplate( - buffer, - pagesize=LETTER, - leftMargin=0.75 * inch, - rightMargin=0.75 * inch, - topMargin=0.75 * inch, - bottomMargin=0.75 * inch, - title=title, - author="surfsense-evals", - subject="Eval input", - creator="surfsense-evals", - ) - - body_style = _build_body_style() - heading_style = _build_heading_style() - caption_style = ParagraphStyle( - "EvalCaption", - parent=body_style, - fontSize=9, - leading=11, - textColor="#444", - spaceBefore=2, - spaceAfter=10, - ) - title_style = ParagraphStyle( - "EvalTitle", - parent=_DEFAULT_STYLES["Title"], - fontName="Helvetica-Bold", - fontSize=18, - leading=22, - spaceAfter=14, - ) - - flow: list = [Paragraph(_escape_html(title), title_style)] - total_chars = 0 - for index, (heading, text, images) in enumerate(sections): - if index > 0 and page_break_between_sections: - flow.append(PageBreak()) - if heading: - flow.append(Paragraph(_escape_html(heading), heading_style)) - for paragraph in _normalise_paragraphs(text): - total_chars += len(paragraph) - flow.append(Paragraph(_escape_html(paragraph), body_style)) - flow.append(Spacer(1, 4)) - for image in images or []: - try: - img_flow = _make_image_flowable(image) - except Exception: # noqa: BLE001 — bad image shouldn't kill PDF - continue - grouped = [img_flow] - if image.caption: - grouped.append(Paragraph(_escape_html(image.caption), caption_style)) - else: - grouped.append(Spacer(1, 8)) - flow.append(KeepTogether(grouped)) - - doc.build(flow) - pdf_bytes = buffer.getvalue() - if deterministic: - pdf_bytes = _scrub_dates(pdf_bytes) - output_path.write_bytes(pdf_bytes) - - n_pages = max(1, total_chars // 3000 + len(sections)) - return RenderedPdf(path=output_path, n_pages_estimate=n_pages, n_chars=total_chars) - - -def render_text_files_to_pdf( - *, - title: str, - files: Iterable[Path], - output_path: Path, - deterministic: bool = True, -) -> RenderedPdf: - """Convenience wrapper: read a list of text files, render to one PDF. - - The heading of each section is the file's name (no extension), so - e.g. ``admission_note.txt`` becomes a section header ``admission_note`` - in the rendered PDF. Useful for any text-only benchmark that ships - a corpus as separate ``.txt`` / ``.md`` shards per logical document. - """ - - sections: list[tuple[str | None, str]] = [] - for path in files: - path = Path(path) - text = path.read_text(encoding="utf-8") - sections.append((path.stem, text)) - return render_pdf( - title=title, - sections=sections, - output_path=output_path, - deterministic=deterministic, - ) - - -# Tiny self-check — handy when debugging. -def _self_test() -> None: # pragma: no cover - out = Path("./_render_self_test.pdf") - sections = [ - ("intro", "Hello world.\n\nThis is a test."), - ("body", "Line one.\nLine two."), - ] - rendered = render_pdf(title="Self test", sections=sections, output_path=out) - print(f"wrote {rendered.path} ({rendered.n_chars} chars)") - - -# Importing ``datetime`` keeps the timezone helper handy if a future -# benchmark wants to embed a real timestamp without losing determinism. -_NOW_FROZEN = datetime(2026, 5, 11, tzinfo=UTC) diff --git a/surfsense_evals/src/surfsense_evals/core/providers/__init__.py b/surfsense_evals/src/surfsense_evals/core/providers/__init__.py deleted file mode 100644 index fa82bcbf26..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/providers/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""External LLM providers (used by the native arm). - -Lazy imports so the SurfSense-only path doesn't transitively load the -OpenRouter client until something actually constructs ``OpenRouterPdfProvider``. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: # pragma: no cover - from .openrouter_pdf import OpenRouterPdfProvider, OpenRouterResponse - -__all__ = ["OpenRouterPdfProvider", "OpenRouterResponse"] - - -def __getattr__(name: str): - if name in {"OpenRouterPdfProvider", "OpenRouterResponse"}: - from . import openrouter_pdf as _mod - - return getattr(_mod, name) - raise AttributeError(f"module 'surfsense_evals.core.providers' has no attribute {name!r}") diff --git a/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py b/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py deleted file mode 100644 index 208fbb8655..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py +++ /dev/null @@ -1,118 +0,0 @@ -"""Bare OpenRouter ``chat/completions`` provider — no PDF, no plugins. - -Used by ``BareLlmArm`` to measure "what does the model answer with -zero retrieval context?". Same wire shape as ``OpenRouterPdfProvider`` -minus the file-parser plugin and the ``file`` content part: - -```json -{ - "model": "openai/gpt-5.4-mini", - "messages": [ - {"role": "system", "content": ""}, - {"role": "user", "content": ""} - ] -} -``` - -The response shape is identical to the PDF provider's, so we re-use -``_parse_chat_completion`` from ``openrouter_pdf`` and only specialise -the request builder. That keeps cost-extraction, token-counting, and -content-array handling in one place. -""" - -from __future__ import annotations - -import logging -import time -from typing import Any - -import httpx - -from .openrouter_pdf import ( - _DEFAULT_HEADERS, - OpenRouterResponse, - _parse_chat_completion, -) - -logger = logging.getLogger(__name__) - - -class OpenRouterChatProvider: - """Stateless bare-chat client. No PDF, no file-parser plugin.""" - - def __init__( - self, - *, - api_key: str, - base_url: str = "https://openrouter.ai/api/v1", - model: str, - timeout_s: float = 600.0, - ) -> None: - if not api_key: - raise ValueError("OPENROUTER_API_KEY is required for the bare-LLM arm.") - self._api_key = api_key - self._base = base_url.rstrip("/") - self._model = model - self._timeout = httpx.Timeout(timeout_s, connect=15.0) - - @property - def model(self) -> str: - return self._model - - def _build_payload( - self, - *, - prompt: str, - system_prompt: str | None, - max_tokens: int | None, - ) -> dict[str, Any]: - messages: list[dict[str, Any]] = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - messages.append({"role": "user", "content": prompt}) - body: dict[str, Any] = {"model": self._model, "messages": messages} - if max_tokens: - body["max_tokens"] = max_tokens - return body - - async def complete( - self, - *, - prompt: str, - system_prompt: str | None = None, - max_tokens: int | None = None, - http: httpx.AsyncClient | None = None, - ) -> OpenRouterResponse: - """Single chat completion. Errors are raised verbatim — caller decides retries.""" - - payload = self._build_payload( - prompt=prompt, - system_prompt=system_prompt, - max_tokens=max_tokens, - ) - headers = { - "Authorization": f"Bearer {self._api_key}", - "Content-Type": "application/json", - "Accept": "application/json", - **_DEFAULT_HEADERS, - } - url = f"{self._base}/chat/completions" - started = time.monotonic() - if http is not None: - response = await http.post(url, json=payload, headers=headers, timeout=self._timeout) - else: - async with httpx.AsyncClient(timeout=self._timeout) as client: - response = await client.post( - url, json=payload, headers=headers, timeout=self._timeout - ) - latency_ms = int((time.monotonic() - started) * 1000) - if response.status_code >= 400: - raise httpx.HTTPStatusError( - f"OpenRouter HTTP {response.status_code}: {response.text[:300]}", - request=response.request, - response=response, - ) - return _parse_chat_completion(response.json(), latency_ms=latency_ms) - - -__all__ = ["OpenRouterChatProvider"] diff --git a/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py b/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py deleted file mode 100644 index 5cd47b04e8..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py +++ /dev/null @@ -1,229 +0,0 @@ -"""Native-PDF arm provider: OpenRouter ``chat/completions`` with PDF input. - -Per ``__ the wire -shape is OpenAI-compatible with one PDF-specific extra: - -```json -{ - "model": "anthropic/claude-sonnet-4.5", - "messages": [{ - "role": "user", - "content": [ - {"type": "file", "file": {"filename": "case.pdf", - "file_data": "data:application/pdf;base64,"}}, - {"type": "text", "text": ""} - ] - }], - "plugins": [{"id": "file-parser", "pdf": {"engine": "native"}}] -} -``` - -``engine: "native"`` is the only engine that doesn't pre-OCR the -PDF — it forwards raw bytes to PDF-native models (Claude, Gemini), -matching what a human user does when "dropping the PDF into Claude". -``mistral-ocr`` and ``cloudflare-ai`` are exposed as enum options for -non-native models. - -Headers ``HTTP-Referer`` and ``X-Title`` make spend show up cleanly on -the OpenRouter dashboard. -""" - -from __future__ import annotations - -import base64 -import logging -import time -from dataclasses import dataclass -from enum import StrEnum -from pathlib import Path -from typing import Any - -import httpx - -logger = logging.getLogger(__name__) - - -class PdfEngine(StrEnum): - NATIVE = "native" - MISTRAL_OCR = "mistral-ocr" - CLOUDFLARE_AI = "cloudflare-ai" - - -@dataclass -class OpenRouterResponse: - """Subset of the OpenRouter response we care about for scoring.""" - - text: str - input_tokens: int - output_tokens: int - total_tokens: int - cost_micros: int - latency_ms: int - finish_reason: str | None - raw: dict[str, Any] - - -_DEFAULT_HEADERS = { - "HTTP-Referer": "https://github.com/MODSetter/SurfSense", - "X-Title": "SurfSense-evals", -} - - -class OpenRouterPdfProvider: - """Thin httpx-based client. Stateless; safe to reuse per arm instance.""" - - def __init__( - self, - *, - api_key: str, - base_url: str = "https://openrouter.ai/api/v1", - model: str, - engine: PdfEngine = PdfEngine.NATIVE, - timeout_s: float = 600.0, - ) -> None: - if not api_key: - raise ValueError("OPENROUTER_API_KEY is required for the native arm.") - self._api_key = api_key - self._base = base_url.rstrip("/") - self._model = model - self._engine = engine - self._timeout = httpx.Timeout(timeout_s, connect=15.0) - - @property - def model(self) -> str: - return self._model - - @property - def engine(self) -> PdfEngine: - return self._engine - - def _build_payload( - self, - *, - prompt: str, - pdf_path: Path, - max_tokens: int | None, - extra_messages: list[dict[str, Any]] | None, - ) -> dict[str, Any]: - b64 = base64.b64encode(pdf_path.read_bytes()).decode("ascii") - user_content: list[dict[str, Any]] = [ - { - "type": "file", - "file": { - "filename": pdf_path.name, - "file_data": f"data:application/pdf;base64,{b64}", - }, - }, - {"type": "text", "text": prompt}, - ] - messages: list[dict[str, Any]] = list(extra_messages or []) - messages.append({"role": "user", "content": user_content}) - body: dict[str, Any] = { - "model": self._model, - "messages": messages, - "plugins": [{"id": "file-parser", "pdf": {"engine": self._engine.value}}], - } - if max_tokens: - body["max_tokens"] = max_tokens - return body - - async def complete( - self, - *, - prompt: str, - pdf_path: Path, - max_tokens: int | None = None, - extra_messages: list[dict[str, Any]] | None = None, - http: httpx.AsyncClient | None = None, - ) -> OpenRouterResponse: - """Single chat completion. Errors are raised verbatim — runner decides retries.""" - - payload = self._build_payload( - prompt=prompt, - pdf_path=pdf_path, - max_tokens=max_tokens, - extra_messages=extra_messages, - ) - headers = { - "Authorization": f"Bearer {self._api_key}", - "Content-Type": "application/json", - "Accept": "application/json", - **_DEFAULT_HEADERS, - } - url = f"{self._base}/chat/completions" - started = time.monotonic() - if http is not None: - response = await http.post(url, json=payload, headers=headers, timeout=self._timeout) - else: - async with httpx.AsyncClient(timeout=self._timeout) as client: - response = await client.post( - url, json=payload, headers=headers, timeout=self._timeout - ) - latency_ms = int((time.monotonic() - started) * 1000) - if response.status_code >= 400: - raise httpx.HTTPStatusError( - f"OpenRouter HTTP {response.status_code}: {response.text[:300]}", - request=response.request, - response=response, - ) - data = response.json() - return _parse_chat_completion(data, latency_ms=latency_ms) - - -def _parse_chat_completion(payload: dict[str, Any], *, latency_ms: int) -> OpenRouterResponse: - """Tolerant parser for OpenRouter / OpenAI chat-completions JSON. - - OpenRouter passes through any provider-specific extras, but the - canonical shape is ``choices[0].message.content`` (string OR array - of content parts) and ``usage.prompt_tokens / completion_tokens / total_tokens``. - Cost lives at the top level (``payload["usage"]["cost"]`` or - ``payload["x-or-cost"]``) depending on routing. - """ - - text = "" - finish_reason: str | None = None - choices = payload.get("choices") or [] - if choices: - message = (choices[0] or {}).get("message") or {} - content = message.get("content") - if isinstance(content, str): - text = content - elif isinstance(content, list): - chunks: list[str] = [] - for part in content: - if isinstance(part, dict) and part.get("type") in {"text", "output_text"}: - chunks.append(str(part.get("text", ""))) - text = "".join(chunks) - finish_reason = (choices[0] or {}).get("finish_reason") or None - - usage = payload.get("usage") or {} - input_tokens = int(usage.get("prompt_tokens") or 0) - output_tokens = int(usage.get("completion_tokens") or 0) - total_tokens = int(usage.get("total_tokens") or (input_tokens + output_tokens)) - - # OpenRouter exposes cost in dollars on `usage.cost` or `cost`. We - # convert to integer micros to avoid float-summing surprises across - # 7,663 MIRAGE questions. - raw_cost = usage.get("cost") - if raw_cost is None: - raw_cost = payload.get("cost") - cost_micros = 0 - if raw_cost is not None: - try: - cost_micros = int(round(float(raw_cost) * 1_000_000)) - except (TypeError, ValueError): - cost_micros = 0 - - return OpenRouterResponse( - text=text, - input_tokens=input_tokens, - output_tokens=output_tokens, - total_tokens=total_tokens, - cost_micros=cost_micros, - latency_ms=latency_ms, - finish_reason=finish_reason, - raw=payload, - ) - - -__all__ = ["OpenRouterPdfProvider", "OpenRouterResponse", "PdfEngine"] diff --git a/surfsense_evals/src/surfsense_evals/core/registry.py b/surfsense_evals/src/surfsense_evals/core/registry.py deleted file mode 100644 index 7fb64c36fb..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/registry.py +++ /dev/null @@ -1,265 +0,0 @@ -"""Suite + Benchmark protocols and the global registry. - -The extensibility seam: ``core.cli`` walks ``surfsense_evals.suites`` on -import, which auto-imports every benchmark subpackage, which calls -``register()`` at module bottom. The CLI then iterates the -populated registry to build subcommand groups dynamically. - -Adding a new domain = drop a folder under ``suites///`` -that ends in ``register(MyBenchmark())``. No edits anywhere in -``core/`` are required. -""" - -from __future__ import annotations - -import argparse -from collections.abc import Mapping -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any, Protocol, runtime_checkable - -import httpx - -from .clients import DocumentsClient, NewChatClient, SearchSpaceClient -from .config import Config, SuiteState - -# --------------------------------------------------------------------------- -# Run context — what every benchmark.ingest/run receives -# --------------------------------------------------------------------------- - - -@dataclass -class RunContext: - """Per-invocation environment threaded into ``ingest`` and ``run``. - - A benchmark uses this to read pinned suite state, build new HTTP - clients on the shared ``http`` session, find the right data / - reports paths, and discover the active OpenRouter model + key. - - ``http`` is the authenticated SurfSense client (auth event hook - attached). It is **not** an OpenRouter client — providers create - their own short-lived clients because OpenRouter doesn't share the - SurfSense bearer. - """ - - suite: str - benchmark: str - config: Config - suite_state: SuiteState - http: httpx.AsyncClient - - @property - def search_space_id(self) -> int: - return self.suite_state.search_space_id - - @property - def chat_model_id(self) -> int: - return self.suite_state.chat_model_id - - @property - def provider_model(self) -> str: - """Slug used by the SurfSense agent (and the native arm by default). - - For ``cost-arbitrage`` scenarios this is the *cheap, text-only* - slug — SurfSense answers from the chunks the vision LLM already - extracted at ingest. The native arm should use - ``native_arm_model`` instead in that scenario. - """ - - return self.suite_state.provider_model - - @property - def native_arm_model(self) -> str: - """Slug the native_pdf arm should use. - - Defaults to ``provider_model`` (head-to-head / symmetric-cheap); - for ``cost-arbitrage`` it returns the explicit - ``--native-arm-model`` so the native arm can fairly answer - image-bearing questions. - """ - - return self.suite_state.effective_native_arm_model - - @property - def vision_provider_model(self) -> str | None: - """Slug of the OpenRouter vision LLM SurfSense used at ingest. - - ``None`` if no vision config was attached at setup (legacy or - text-only suite). Used by runners purely to record what was - actually used in ``RunArtifact.extra`` and to label reports. - """ - - return self.suite_state.vision_provider_model - - @property - def scenario(self) -> str: - """Scenario name pinned at setup time (see ``config.SCENARIOS``).""" - - return self.suite_state.scenario - - def search_space_client(self) -> SearchSpaceClient: - return SearchSpaceClient(self.http, self.config.surfsense_api_base) - - def documents_client(self) -> DocumentsClient: - return DocumentsClient(self.http, self.config.surfsense_api_base) - - def new_chat_client(self) -> NewChatClient: - return NewChatClient(self.http, self.config.surfsense_api_base) - - def maps_dir(self) -> Path: - path = self.config.suite_maps_dir(self.suite) - path.mkdir(parents=True, exist_ok=True) - return path - - def runs_dir(self, *, run_timestamp: str) -> Path: - path = self.config.suite_runs_dir(self.suite) / run_timestamp / self.benchmark - path.mkdir(parents=True, exist_ok=True) - return path - - def benchmark_data_dir(self) -> Path: - path = self.config.suite_data_dir(self.suite) / self.benchmark - path.mkdir(parents=True, exist_ok=True) - return path - - -# --------------------------------------------------------------------------- -# Run artifact + report section -# --------------------------------------------------------------------------- - - -@dataclass -class RunArtifact: - """Everything a runner persists for the report writer to consume. - - ``raw_path`` points at the JSONL of per-question ``ArmResult`` - rows. ``metrics`` is a free-form dict the benchmark fills in (e.g. - ``{"native": {...}, "surfsense": {...}, "delta": {...}}``). - """ - - suite: str - benchmark: str - run_timestamp: str - raw_path: Path - metrics: dict[str, Any] = field(default_factory=dict) - extra: dict[str, Any] = field(default_factory=dict) - - -@dataclass -class ReportSection: - """One benchmark's slice of the final summary.""" - - title: str - headline: bool - body_md: str - body_json: dict[str, Any] = field(default_factory=dict) - - -# --------------------------------------------------------------------------- -# Benchmark protocol + registry -# --------------------------------------------------------------------------- - - -@runtime_checkable -class Benchmark(Protocol): - """The contract every benchmark module ends with ``register()``.""" - - suite: str - name: str - headline: bool - description: str - - async def ingest(self, ctx: RunContext, **opts: Any) -> None: # pragma: no cover - protocol - ... - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: # pragma: no cover - protocol - ... - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: # pragma: no cover - protocol - """Add benchmark-specific flags to ``run ``.""" - - def report_section( - self, artifacts: list[RunArtifact] - ) -> ReportSection: # pragma: no cover - protocol - ... - - -# --------------------------------------------------------------------------- -# Registry storage -# --------------------------------------------------------------------------- - - -_REGISTRY: dict[tuple[str, str], Benchmark] = {} - - -def register(benchmark: Benchmark) -> None: - """Add ``benchmark`` to the registry. Last-wins on duplicate keys. - - Duplicate registrations log a warning rather than raising so a - benchmark module imported twice (once via auto-discovery, once via - a test directly importing it) doesn't blow up the CLI. - """ - - key = (benchmark.suite, benchmark.name) - if key in _REGISTRY: - import logging - - logging.getLogger(__name__).warning( - "Benchmark %s/%s re-registered (overwriting prior)", *key - ) - _REGISTRY[key] = benchmark - - -def unregister(suite: str, name: str) -> None: - """Test helper: drop a single benchmark from the registry.""" - - _REGISTRY.pop((suite, name), None) - - -def reset() -> None: - """Test helper: wipe the registry (use with monkeypatched discovery).""" - - _REGISTRY.clear() - - -def get(suite: str, name: str) -> Benchmark: - try: - return _REGISTRY[(suite, name)] - except KeyError as exc: - available = ", ".join(f"{s}/{n}" for s, n in sorted(_REGISTRY)) or "" - raise KeyError(f"Unknown benchmark '{suite}/{name}'. Registered: {available}") from exc - - -def list_suites() -> list[str]: - return sorted({s for s, _ in _REGISTRY}) - - -def list_benchmarks(suite: str | None = None) -> list[Benchmark]: - if suite is None: - return [_REGISTRY[k] for k in sorted(_REGISTRY)] - return [_REGISTRY[k] for k in sorted(_REGISTRY) if k[0] == suite] - - -def snapshot() -> Mapping[tuple[str, str], Benchmark]: - """Read-only view for diagnostics (e.g. ``benchmarks list`` rendering).""" - - return dict(_REGISTRY) - - -__all__ = [ - "Arm", - "Benchmark", - "ReportSection", - "RunArtifact", - "RunContext", - "get", - "list_benchmarks", - "list_suites", - "register", - "reset", - "snapshot", - "unregister", -] - - -# Re-export Arm from arms.base so suites can `from core.registry import Arm`. -from .arms.base import Arm # noqa: E402, F401 (deliberate re-export at bottom) diff --git a/surfsense_evals/src/surfsense_evals/core/report/__init__.py b/surfsense_evals/src/surfsense_evals/core/report/__init__.py deleted file mode 100644 index c5ccbc64c4..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/report/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Report writer + section composition primitives. Lazy import.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: # pragma: no cover - from .writer import write_report - -__all__ = ["write_report"] - - -def __getattr__(name: str): - if name == "write_report": - from .writer import write_report - - return write_report - raise AttributeError(f"module 'surfsense_evals.core.report' has no attribute {name!r}") diff --git a/surfsense_evals/src/surfsense_evals/core/report/writer.py b/surfsense_evals/src/surfsense_evals/core/report/writer.py deleted file mode 100644 index 8d1ffa07a1..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/report/writer.py +++ /dev/null @@ -1,89 +0,0 @@ -"""Report writer — composes per-benchmark sections into one summary. - -Output: - -* ``reports///summary.md`` — human-readable. - Bullet lists only (no tables) per project's coding-standards. -* ``reports///summary.json`` — same content as - structured JSON for downstream tooling (CI dashboards, regressions). - -Headline benchmarks come first in both outputs. -""" - -from __future__ import annotations - -import json -from collections.abc import Iterable -from pathlib import Path - -from ..config import Config -from ..registry import ReportSection - - -def write_report( - *, - config: Config, - suite: str, - sections: Iterable[ReportSection], - run_timestamp: str, -) -> Path: - """Write ``summary.md`` + ``summary.json``. Returns the path of the .md file.""" - - sections_list = list(sections) - sections_list.sort(key=lambda s: (not s.headline, s.title.lower())) - - out_dir = config.suite_reports_dir(suite) / run_timestamp - out_dir.mkdir(parents=True, exist_ok=True) - - md_path = out_dir / "summary.md" - json_path = out_dir / "summary.json" - - md_lines: list[str] = [ - f"# SurfSense evals — suite `{suite}`", - "", - f"- Run timestamp: `{run_timestamp}`", - f"- Sections: {len(sections_list)}", - "", - ] - headline = [s for s in sections_list if s.headline] - secondary = [s for s in sections_list if not s.headline] - if headline: - md_lines.append("## Headline") - md_lines.append("") - for section in headline: - md_lines.append(f"### {section.title}") - md_lines.append("") - md_lines.append(section.body_md.rstrip()) - md_lines.append("") - if secondary: - md_lines.append("## Secondary measurements") - md_lines.append("") - for section in secondary: - md_lines.append(f"### {section.title}") - md_lines.append("") - md_lines.append(section.body_md.rstrip()) - md_lines.append("") - - md_path.write_text("\n".join(md_lines).rstrip() + "\n", encoding="utf-8") - - json_payload = { - "suite": suite, - "run_timestamp": run_timestamp, - "sections": [ - { - "title": s.title, - "headline": s.headline, - "body_md": s.body_md, - "body_json": s.body_json, - } - for s in sections_list - ], - } - json_path.write_text( - json.dumps(json_payload, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - return md_path - - -__all__ = ["ReportSection", "write_report"] diff --git a/surfsense_evals/src/surfsense_evals/core/scenarios.py b/surfsense_evals/src/surfsense_evals/core/scenarios.py deleted file mode 100644 index fefdc6865e..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/scenarios.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Shared scenario formatting helpers for head-to-head benchmark reports. - -The scenario chosen at ``setup`` time (``head-to-head``, ``symmetric-cheap``, -``cost-arbitrage``) materially changes how a head-to-head report should be -read. This module produces the one-bullet summary every head-to-head -runner stamps near the top of its ``report_section`` body so reviewers -immediately see the framing — no need to dig into ``run_artifact.json``. -""" - -from __future__ import annotations - -from collections.abc import Mapping -from typing import Any - - -def format_scenario_md(extra: Mapping[str, Any] | None) -> str: - """Render a scenario-aware bullet for a benchmark report. - - Reads ``extra["scenario"]`` plus the runtime LLM slugs the runner - recorded. Falls back to a sensible "head-to-head" line if the artifact - pre-dates scenarios so old runs still render cleanly. - """ - - extra = dict(extra or {}) - scenario = str(extra.get("scenario") or "head-to-head") - surf_slug = str(extra.get("provider_model") or "?") - native_slug = str(extra.get("native_arm_model") or surf_slug) - vision_slug = extra.get("vision_provider_model") - - if scenario == "cost-arbitrage": - body = ( - f"- Scenario: **cost-arbitrage** — native arm answers with " - f"`{native_slug}` (vision); SurfSense answers with `{surf_slug}` " - f"over chunks vision-extracted at ingest" - f"{f' by `{vision_slug}`' if vision_slug else ''}. " - "Measures how close SurfSense gets to native at a fraction of " - "the per-query cost." - ) - elif scenario == "symmetric-cheap": - body = ( - f"- Scenario: **symmetric-cheap** — both arms answer with " - f"`{surf_slug}`; SurfSense pre-extracted images at ingest" - f"{f' via `{vision_slug}`' if vision_slug else ''}. " - "Native arm structurally loses on image-bearing questions " - "(text-only model can't see images) — that's the point." - ) - else: - body = f"- Scenario: head-to-head — both arms answer with `{surf_slug}` via OpenRouter." - if vision_slug: - body += f" SurfSense ingest VLM: `{vision_slug}`." - - return body - - -__all__ = ["format_scenario_md"] diff --git a/surfsense_evals/src/surfsense_evals/core/vision_llm.py b/surfsense_evals/src/surfsense_evals/core/vision_llm.py deleted file mode 100644 index 5d5e2c6d1a..0000000000 --- a/surfsense_evals/src/surfsense_evals/core/vision_llm.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Vision LLM resolution + auto-pick logic for the harness's ``setup`` command. - -Two responsibilities: - -1. Resolve an explicit ``--vision-llm `` to a global OpenRouter - vision-capable model id that ``set_model_roles(vision_model_id=...)`` can - accept. -2. Auto-pick the strongest registered vision config when the operator - doesn't pass ``--vision-llm`` but the scenario / benchmark needs one. - -The priority list mirrors the recommended slugs in the README so the -auto-pick is deterministic and reviewable. -""" - -from __future__ import annotations - -from collections.abc import Iterable -from dataclasses import dataclass - -from .clients.search_space import VisionLlmConfigEntry - -# Order matters — first match wins when auto-picking. Keep these in sync -# with the "Recommended vision slugs" table in the README so the -# auto-pick story is the same one users read about. -RECOMMENDED_VISION_PRIORITY: tuple[str, ...] = ( - "anthropic/claude-sonnet-4.5", - "anthropic/claude-opus-4.7", - "openai/gpt-5", - "google/gemini-2.5-pro", -) - - -class VisionConfigError(RuntimeError): - """Raised when no vision config can be resolved (explicit or auto).""" - - -@dataclass(frozen=True) -class ResolvedVisionConfig: - """Result of ``resolve_vision_llm`` — what to attach + a label for logs.""" - - config_id: int - provider_model: str - selected_via: str # "explicit" | "auto-priority" | "auto-fallback" - - -def _openrouter_only(entries: Iterable[VisionLlmConfigEntry]) -> list[VisionLlmConfigEntry]: - return [e for e in entries if e.provider == "OPENROUTER" and not e.is_auto_mode] - - -def resolve_vision_llm( - candidates: list[VisionLlmConfigEntry], - *, - explicit_slug: str | None, -) -> ResolvedVisionConfig: - """Resolve a vision LLM config id from a slug or by auto-picking. - - * If ``explicit_slug`` is given: must match exactly one OpenRouter - vision config's ``model_name``. Raises ``VisionConfigError`` with a - friendly listing if zero / many match. - * Otherwise: walk ``RECOMMENDED_VISION_PRIORITY`` in order and return - the first registered one. If none of the recommended slugs are - registered, fall back to the first OpenRouter vision config in the - list (deterministic by listing order). Raises ``VisionConfigError`` - if zero are registered at all. - """ - - or_vision = _openrouter_only(candidates) - - if explicit_slug is not None: - matches = [e for e in or_vision if e.model_name == explicit_slug] - if not matches: - sample = ", ".join(e.model_name for e in or_vision[:8]) or "" - raise VisionConfigError( - f"No OpenRouter vision config found for slug '{explicit_slug}'. " - "Make sure `openrouter_integration.vision_enabled: true` in " - "global_llm_config.yaml and that the Celery worker has finished " - "its first refresh. " - f"Available OpenRouter vision slugs (sample): {sample}." - ) - if len(matches) > 1: - listing = "\n".join(f" id={e.id} name={e.name!r}" for e in matches) - raise VisionConfigError( - f"Multiple OpenRouter vision configs match '{explicit_slug}':\n{listing}" - ) - only = matches[0] - return ResolvedVisionConfig( - config_id=only.id, - provider_model=only.model_name, - selected_via="explicit", - ) - - if not or_vision: - raise VisionConfigError( - "No OpenRouter vision LLM configs are registered with this " - "SurfSense backend. Either pass `--no-vision-llm` to the ingest " - "step (text-only ingestion), or enable " - "`openrouter_integration.vision_enabled: true` in " - "global_llm_config.yaml so the Celery worker syncs vision-capable " - "OpenRouter models on next refresh." - ) - - by_slug = {e.model_name: e for e in or_vision} - for preferred in RECOMMENDED_VISION_PRIORITY: - match = by_slug.get(preferred) - if match is not None: - return ResolvedVisionConfig( - config_id=match.id, - provider_model=match.model_name, - selected_via="auto-priority", - ) - - # Fallback: first registered OpenRouter vision config. Deterministic - # because the backend returns them in a stable order. - fallback = or_vision[0] - return ResolvedVisionConfig( - config_id=fallback.id, - provider_model=fallback.model_name, - selected_via="auto-fallback", - ) - - -__all__ = [ - "RECOMMENDED_VISION_PRIORITY", - "ResolvedVisionConfig", - "VisionConfigError", - "resolve_vision_llm", -] diff --git a/surfsense_evals/src/surfsense_evals/suites/__init__.py b/surfsense_evals/src/surfsense_evals/suites/__init__.py deleted file mode 100644 index f3d26f8659..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/__init__.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Suite registry auto-discovery. - -Importing ``surfsense_evals.suites`` walks every subpackage one level deep -(domain like ``medical``) AND its benchmark subpackages -(``medical/medxpertqa``, ``medical/mirage``, ``medical/cure``). Each -benchmark's ``__init__.py`` is expected to call -``core.registry.register()`` at module bottom; merely importing -the module is enough to populate the registry. - -Adding a new domain is therefore: drop a folder under ``suites/`` with the -right structure. No edits anywhere else. - -Subpackages whose name starts with ``_`` are skipped — that's reserved for -test fixtures (e.g. ``suites/_demo/``) so they don't accidentally show up -in ``benchmarks list``. -""" - -from __future__ import annotations - -import importlib -import logging -import pkgutil -from collections.abc import Iterable - -logger = logging.getLogger(__name__) - - -def _iter_subpackages(package) -> Iterable[str]: - """Yield fully-qualified subpackage names one level deep, skipping ``_*``.""" - - for module_info in pkgutil.iter_modules(package.__path__, prefix=f"{package.__name__}."): - if not module_info.ispkg: - continue - leaf = module_info.name.rsplit(".", 1)[-1] - if leaf.startswith("_"): - continue - yield module_info.name - - -def discover_suites() -> list[str]: - """Import every domain + benchmark subpackage so registrations fire. - - Returns the list of fully-qualified benchmark module names that were - successfully imported. Failures are logged (not raised) so a single - broken benchmark doesn't take down the whole CLI — the operator still - sees the working benchmarks via ``benchmarks list``. - """ - - import surfsense_evals.suites as _suites # self-import for __path__ - - imported: list[str] = [] - for domain_name in _iter_subpackages(_suites): - try: - domain_pkg = importlib.import_module(domain_name) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to import suite domain %s: %s", domain_name, exc) - continue - for benchmark_name in _iter_subpackages(domain_pkg): - try: - importlib.import_module(benchmark_name) - imported.append(benchmark_name) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to import benchmark %s: %s", benchmark_name, exc) - return imported diff --git a/surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py b/surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py deleted file mode 100644 index 9a8cd447e4..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -"""Test fixture suite — skipped by the auto-discovery walker (name starts with ``_``). - -Imported explicitly by ``tests/core/test_registry.py`` to prove the -register-on-import contract works without polluting the production -benchmark list. -""" - -from __future__ import annotations diff --git a/surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py b/surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py deleted file mode 100644 index 43dc51ac56..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Demo benchmark — registers on import, used only by the registry tests.""" - -from __future__ import annotations - -import argparse -from typing import Any - -from ....core.registry import ( - ReportSection, - RunArtifact, - RunContext, - register, -) - - -class HelloBenchmark: - suite: str = "_demo" - name: str = "hello" - headline: bool = False - description: str = "Demo benchmark used by the registry test." - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: - parser.add_argument("--echo", default="hi") - - async def ingest(self, ctx: RunContext, **_opts: Any) -> None: # pragma: no cover - return None - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: # pragma: no cover - return RunArtifact( - suite=self.suite, - benchmark=self.name, - run_timestamp="0", - raw_path=ctx.benchmark_data_dir() / "raw.jsonl", - metrics={"echo": opts.get("echo")}, - ) - - def report_section(self, artifacts: list[RunArtifact]) -> ReportSection: - return ReportSection( - title="Hello demo", - headline=False, - body_md="- runs: " + str(len(artifacts)), - ) - - -register(HelloBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/__init__.py deleted file mode 100644 index 9c0067e25c..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Medical RAG benchmarks (MedXpertQA-MM headline + MIRAGE/CUREv1 secondary). - -Subpackages register themselves with ``core.registry`` on import. The -``suites/__init__.py`` discovery walker imports them automatically. -""" - -from __future__ import annotations diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py deleted file mode 100644 index 7e9d9a07b9..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""CUREv1 — secondary single-arm SurfSense retrieval measurement. - -Source: https://huggingface.co/datasets/clinia/CUREv1 -Paper: https://arxiv.org/html/2412.06954v4 - -Pure retrieval benchmark — 10 medical disciplines, English/French/Spanish -queries, expert-curated qrels (graded 0/1/2). The harness ingests the -corpus, runs each query via SurfSense's ``/api/v1/new_chat``, parses -chunk citations, maps them back to CUREv1 ``corpus-id``, and scores -Recall@k / MRR / nDCG@10 against qrels. -""" - -from __future__ import annotations - -from ....core import registry as _registry -from .runner import CureBenchmark - -_registry.register(CureBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py b/surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py deleted file mode 100644 index 84108b4df2..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py +++ /dev/null @@ -1,230 +0,0 @@ -"""CUREv1 ingestion. - -For each (lang, discipline) requested, downloads the corpus split via -``datasets.load_dataset(path="clinia/CUREv1", name="corpus", split=)``, -batches passages into ~5 MB markdown bundles, uploads them to -SurfSense, polls until ``ready``, and persists the -``corpus_id -> document_id`` map under -``data/medical/maps/cure_corpus_map_.jsonl``. A union map -``cure_corpus_map.jsonl`` is also written so the runner can resolve -citations across disciplines without juggling per-file paths. -""" - -from __future__ import annotations - -import io -import json -import logging -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path - -from ....core.config import set_suite_state -from ....core.ingest_settings import IngestSettings, settings_header_line -from ....core.registry import RunContext - -logger = logging.getLogger(__name__) - - -_BATCH_SIZE_BYTES = 5 * 1024 * 1024 - -# 10 disciplines covered by the dataset card. We exhaustively list -# them so a smoke test can default to one. -DISCIPLINES = ( - "anesthesiology", - "cardiology", - "dermatology", - "endocrinology", - "gastroenterology", - "hematology", - "nephrology", - "neurology", - "obstetrics_gynecology", - "psychiatry", -) - - -@dataclass -class CorpusPassage: - corpus_id: str - title: str - text: str - - def to_markdown(self) -> str: - title = (self.title or "").strip() or "Untitled" - body = (self.text or "").strip() - return f"# {title}\n\n_id: `{self.corpus_id}`_\n\n{body}\n" - - -@dataclass -class PassageBatch: - path: Path - corpus_ids: list[str] - - -def _stream_corpus(discipline: str) -> Iterable[CorpusPassage]: - """Stream corpus rows for one discipline via the ``datasets`` library.""" - - from datasets import load_dataset # noqa: PLC0415 - - logger.info("Loading CUREv1 corpus for discipline=%s", discipline) - ds = load_dataset(path="clinia/CUREv1", name="corpus", split=discipline) - for row in ds: - cid = str(row.get("_id") or "") - if not cid: - continue - yield CorpusPassage( - corpus_id=cid, - title=str(row.get("title") or ""), - text=str(row.get("text") or ""), - ) - - -def _write_batches( - passages: Iterable[CorpusPassage], - *, - out_dir: Path, - discipline: str, - batch_bytes: int = _BATCH_SIZE_BYTES, -) -> list[PassageBatch]: - out_dir.mkdir(parents=True, exist_ok=True) - batches: list[PassageBatch] = [] - current_buffer = io.StringIO() - current_ids: list[str] = [] - current_bytes = 0 - batch_idx = 0 - - def _flush() -> None: - nonlocal current_buffer, current_ids, current_bytes, batch_idx - if not current_ids: - return - path = out_dir / f"cure_{discipline}_{batch_idx:04d}.md" - path.write_text(current_buffer.getvalue(), encoding="utf-8") - batches.append(PassageBatch(path=path, corpus_ids=current_ids)) - batch_idx += 1 - current_buffer = io.StringIO() - current_ids = [] - current_bytes = 0 - - for passage in passages: - chunk = passage.to_markdown() + "\n---\n\n" - chunk_bytes = len(chunk.encode("utf-8")) - if current_bytes + chunk_bytes > batch_bytes and current_ids: - _flush() - current_buffer.write(chunk) - current_ids.append(passage.corpus_id) - current_bytes += chunk_bytes - _flush() - return batches - - -async def run_ingest( - ctx: RunContext, - *, - disciplines: list[str] | None = None, - max_per_discipline: int | None = None, - settings: IngestSettings | None = None, -) -> None: - disciplines = disciplines or list(DISCIPLINES) - settings = settings or IngestSettings(use_vision_llm=False, processing_mode="basic") - bench_dir = ctx.benchmark_data_dir() - batches_root = bench_dir / "batches" - batches_root.mkdir(parents=True, exist_ok=True) - - docs_client = ctx.documents_client() - union_map_path = ctx.maps_dir() / "cure_corpus_map.jsonl" - union_map_fh = union_map_path.open("w", encoding="utf-8") - # Header row records the ingest-time settings so the runner can - # surface them in the report (see core/ingest_settings.py). - union_map_fh.write(settings_header_line(settings) + "\n") - try: - for discipline in disciplines: - try: - passages_iter = _stream_corpus(discipline) - if max_per_discipline is not None: - passages_iter = _take(passages_iter, max_per_discipline) - batches = _write_batches( - passages_iter, - out_dir=batches_root / discipline, - discipline=discipline, - ) - except Exception as exc: # noqa: BLE001 - logger.warning("Skipping discipline %s: %s", discipline, exc) - continue - if not batches: - logger.warning("Discipline %s produced 0 batches; skipping upload", discipline) - continue - logger.info("Uploading %d batches for discipline %s", len(batches), discipline) - upload_result = await docs_client.upload( - files=[b.path for b in batches], - search_space_id=ctx.search_space_id, - use_vision_llm=settings.use_vision_llm, - processing_mode=settings.processing_mode, - ) - new_doc_ids = list(upload_result.document_ids) - if new_doc_ids: - await docs_client.wait_until_ready( - search_space_id=ctx.search_space_id, - document_ids=new_doc_ids, - timeout_s=3600.0, - max_poll_s=15.0, - ) - statuses = await docs_client.get_status( - search_space_id=ctx.search_space_id, - document_ids=new_doc_ids + upload_result.duplicate_document_ids, - ) - title_to_doc = {s.title: s.document_id for s in statuses} - - per_discipline_path = ctx.maps_dir() / f"cure_corpus_map_{discipline}.jsonl" - with per_discipline_path.open("w", encoding="utf-8") as fh: - fh.write(settings_header_line(settings) + "\n") - for batch in batches: - doc_id = title_to_doc.get(batch.path.name) - if doc_id is None: - logger.warning("No document_id for batch %s", batch.path.name) - continue - for cid in batch.corpus_ids: - record = { - "corpus_id": cid, - "document_id": doc_id, - "discipline": discipline, - } - fh.write(json.dumps(record) + "\n") - union_map_fh.write(json.dumps(record) + "\n") - - chunks_map_path = ctx.maps_dir() / f"cure_chunk_map_{discipline}.jsonl" - with chunks_map_path.open("w", encoding="utf-8") as fh: - for doc_id in {title_to_doc.get(b.path.name) for b in batches} - {None}: - try: - chunks = await docs_client.list_chunks(int(doc_id)) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to list chunks for doc_id=%s: %s", doc_id, exc) - continue - for chunk in chunks: - fh.write( - json.dumps( - { - "chunk_id": chunk.id, - "document_id": doc_id, - "discipline": discipline, - } - ) - + "\n" - ) - finally: - union_map_fh.close() - - new_state = ctx.suite_state - new_state.ingestion_maps["cure"] = str(union_map_path) - set_suite_state(ctx.config, ctx.suite, new_state) - logger.info("CUREv1 ingestion complete; union map at %s", union_map_path) - - -def _take(it: Iterable, n: int) -> Iterable: - for i, x in enumerate(it): - if i >= n: - return - yield x - - -__all__ = ["DISCIPLINES", "CorpusPassage", "PassageBatch", "run_ingest"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py b/surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py deleted file mode 100644 index d2735c8d50..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py +++ /dev/null @@ -1,400 +0,0 @@ -"""CUREv1 runner — single-arm SurfSense retrieval scoring. - -For each query we ask SurfSense via ``/api/v1/new_chat`` (no -``mentioned_document_ids``) and parse chunk citations from the -streamed answer. Cited ``chunk_id`` → ``document_id`` (chunk map) → -``corpus_id`` (corpus map). The resulting ranked list is scored -against the dataset's qrels. - -The prompt nudges the model to surface its supporting passages via -SurfSense's standard ``[citation:CHUNK_ID]`` format (already required -by the agent system prompt), so we recover retrieval ordering from -the answer text without needing a separate retrieval API. -""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import logging -from collections import defaultdict -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from ....core.arms import ArmRequest, ArmResult, SurfSenseArm -from ....core.config import utc_iso_timestamp -from ....core.ingest_settings import ( - IngestSettings, - add_ingest_settings_args, - format_ingest_settings_md, - is_settings_header, - read_settings_header, -) -from ....core.metrics.retrieval import score_run -from ....core.registry import ( - ReportSection, - RunArtifact, - RunContext, -) - -logger = logging.getLogger(__name__) - - -_PROMPT = """\ -You are a medical literature retrieval assistant for the question -below. Identify the top passages from the knowledge base that best -answer it and cite each one in the standard format -[citation:CHUNK_ID]. List as many citations as are useful, ordered -from most to least relevant. Provide a one-sentence justification -for each citation. - -Query: {query} -""" - - -_DESCRIPTION = "CUREv1 retrieval (single-arm SurfSense): Recall@k / MRR / nDCG@10." - -# CUREv1 corpus is text-only markdown bundles; vision LLM at ingest -# is wasted by default but the operator can flip it via CLI for an -# A/B comparison. -_DEFAULT_INGEST_SETTINGS = IngestSettings( - use_vision_llm=False, - processing_mode="basic", -) - - -@dataclass -class CureQuery: - qid: str - text: str - discipline: str - - -def _load_chunk_map(maps_dir: Path) -> dict[int, int]: - """Union all ``cure_chunk_map_.jsonl`` into one dict.""" - - out: dict[int, int] = {} - for path in sorted(maps_dir.glob("cure_chunk_map_*.jsonl")): - with path.open("r", encoding="utf-8") as fh: - for line in fh: - if not line.strip(): - continue - row = json.loads(line) - if is_settings_header(row): - continue - try: - out[int(row["chunk_id"])] = int(row["document_id"]) - except (KeyError, TypeError, ValueError): - continue - return out - - -def _load_doc_to_corpus(maps_dir: Path) -> dict[int, list[str]]: - """Map ``document_id -> [corpus_id, ...]`` from the union map. - - Multiple corpus passages may live in one batched markdown - document, so each doc_id maps to a list. Citation ordering of the - first occurrence is preserved. - """ - - out: dict[int, list[str]] = defaultdict(list) - union_path = maps_dir / "cure_corpus_map.jsonl" - if not union_path.exists(): - return out - with union_path.open("r", encoding="utf-8") as fh: - for line in fh: - if not line.strip(): - continue - row = json.loads(line) - if is_settings_header(row): - continue - try: - out[int(row["document_id"])].append(str(row["corpus_id"])) - except (KeyError, TypeError, ValueError): - continue - return out - - -def _load_queries(*, lang: str, disciplines: list[str], sample_n: int | None) -> list[CureQuery]: - from datasets import load_dataset # noqa: PLC0415 - - out: list[CureQuery] = [] - for discipline in disciplines: - try: - ds = load_dataset(path="clinia/CUREv1", name=f"queries-{lang}", split=discipline) - except Exception as exc: # noqa: BLE001 - logger.warning("Skipping queries for %s/%s: %s", lang, discipline, exc) - continue - for row in ds: - qid = str(row.get("_id") or "") - text = str(row.get("text") or "") - if not qid or not text: - continue - out.append(CureQuery(qid=qid, text=text, discipline=discipline)) - out.sort(key=lambda q: (q.discipline, q.qid)) - if sample_n is not None and sample_n > 0: - # Stratified-by-discipline slice. - per_d = max(1, sample_n // max(1, len(disciplines))) - sliced: list[CureQuery] = [] - counter: dict[str, int] = defaultdict(int) - for q in out: - if counter[q.discipline] >= per_d: - continue - sliced.append(q) - counter[q.discipline] += 1 - if len(sliced) >= sample_n: - break - out = sliced - return out - - -def _load_qrels(*, disciplines: list[str]) -> dict[str, dict[str, float]]: - from datasets import load_dataset # noqa: PLC0415 - - out: dict[str, dict[str, float]] = defaultdict(dict) - for discipline in disciplines: - try: - ds = load_dataset(path="clinia/CUREv1", name="qrels", split=discipline) - except Exception as exc: # noqa: BLE001 - logger.warning("Skipping qrels for %s: %s", discipline, exc) - continue - for row in ds: - qid = str(row.get("query-id") or row.get("query_id") or "") - cid = str(row.get("corpus-id") or row.get("corpus_id") or "") - score = row.get("score") - if not qid or not cid or score is None: - continue - try: - out[qid][cid] = float(score) - except (TypeError, ValueError): - continue - return out - - -async def _gather_with_limit(coros, *, concurrency: int) -> list[Any]: - sem = asyncio.Semaphore(max(1, concurrency)) - - async def _wrap(c): - async with sem: - return await c - - return await asyncio.gather(*(_wrap(c) for c in coros)) - - -class CureBenchmark: - suite: str = "medical" - name: str = "cure" - headline: bool = False - description: str = _DESCRIPTION - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: - parser.add_argument("--lang", default="en", choices=("en", "es", "fr")) - parser.add_argument( - "--discipline", default=None, help="Restrict to one discipline (default: all ingested)." - ) - parser.add_argument("--n", dest="sample_n", type=int, default=None) - parser.add_argument("--concurrency", type=int, default=4) - parser.add_argument( - "--max-passages-per-discipline", - type=int, - default=None, - help="(ingest only) cap corpus rows per discipline for smoke testing.", - ) - # Per-upload knobs forwarded to /documents/fileupload at ingest; - # ignored at run-time (runner reads resolved settings from the - # union-map header). - add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS) - - async def ingest(self, ctx: RunContext, **opts: Any) -> None: - from .ingest import DISCIPLINES, run_ingest - - settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts) - await run_ingest( - ctx, - disciplines=list(DISCIPLINES), - max_per_discipline=opts.get("max_passages_per_discipline"), - settings=settings, - ) - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: - lang = opts.get("lang") or "en" - discipline_filter = opts.get("discipline") - sample_n = opts.get("sample_n") - concurrency = int(opts.get("concurrency") or 4) - - maps_dir = ctx.maps_dir() - chunk_to_doc = _load_chunk_map(maps_dir) - doc_to_corpus = _load_doc_to_corpus(maps_dir) - ingest_settings = read_settings_header(maps_dir / "cure_corpus_map.jsonl") - if not chunk_to_doc or not doc_to_corpus: - raise RuntimeError( - "CUREv1 not ingested for this suite. Run " - "`python -m surfsense_evals ingest medical cure` first." - ) - - # Disciplines to query are determined by the per-discipline maps - # actually present (either user-filtered or whatever was ingested). - ingested_disciplines = sorted( - { - row_disc - for path in maps_dir.glob("cure_corpus_map_*.jsonl") - for row_disc in [path.stem[len("cure_corpus_map_") :]] - } - ) - if discipline_filter: - disciplines = [discipline_filter] - else: - disciplines = ingested_disciplines or ["dermatology"] - - queries = _load_queries(lang=lang, disciplines=disciplines, sample_n=sample_n) - if not queries: - raise RuntimeError( - f"No CUREv1 queries matched lang={lang!r} disciplines={disciplines!r}." - ) - qrels = _load_qrels(disciplines=disciplines) - logger.info( - "CUREv1: %d queries / %d qrels across disciplines %s", - len(queries), - len(qrels), - disciplines, - ) - - arm = SurfSenseArm( - client=ctx.new_chat_client(), - search_space_id=ctx.search_space_id, - ephemeral_threads=True, - ) - - async def _ask(q: CureQuery) -> ArmResult: - return await arm.answer( - ArmRequest( - question_id=f"{q.discipline}::{q.qid}", - prompt=_PROMPT.format(query=q.text.strip()), - ) - ) - - results: list[ArmResult] = await _gather_with_limit( - (_ask(q) for q in queries), concurrency=concurrency - ) - - per_query_retrieved: dict[str, list[str]] = {} - for q, res in zip(queries, results, strict=False): - chunk_ids: list[int] = [] - seen: set[int] = set() - for citation in res.citations: - if citation.get("kind") != "chunk": - continue - cid = int(citation.get("chunk_id")) - if cid in seen: - continue - chunk_ids.append(cid) - seen.add(cid) - corpus_ids: list[str] = [] - seen_corpus: set[str] = set() - for cid in chunk_ids: - doc_id = chunk_to_doc.get(cid) - if doc_id is None: - continue - for corpus_id in doc_to_corpus.get(doc_id, []): - if corpus_id in seen_corpus: - continue - corpus_ids.append(corpus_id) - seen_corpus.add(corpus_id) - per_query_retrieved[q.qid] = corpus_ids - - scores = score_run( - per_query_retrieved=per_query_retrieved, - per_query_qrels=qrels, - ks=(1, 5, 10, 32), - ndcg_k=10, - ) - - run_timestamp = utc_iso_timestamp() - run_dir = ctx.runs_dir(run_timestamp=run_timestamp) - raw_path = run_dir / "raw.jsonl" - with raw_path.open("w", encoding="utf-8") as fh: - for q, res in zip(queries, results, strict=False): - fh.write( - json.dumps( - { - "discipline": q.discipline, - "qid": q.qid, - "lang": lang, - "retrieved_corpus_ids": per_query_retrieved.get(q.qid, []), - **res.to_jsonl(), - } - ) - + "\n" - ) - - metrics = scores.to_dict() - metrics["lang"] = lang - metrics["disciplines"] = disciplines - - artifact = RunArtifact( - suite=self.suite, - benchmark=self.name, - run_timestamp=run_timestamp, - raw_path=raw_path, - metrics=metrics, - extra={ - "n_queries": len(queries), - "lang": lang, - "disciplines": disciplines, - "concurrency": concurrency, - "provider_model": ctx.provider_model, - "ingest_settings": ingest_settings, - }, - ) - manifest_path = run_dir / "run_artifact.json" - manifest_path.write_text( - json.dumps( - { - "suite": self.suite, - "benchmark": self.name, - "raw_path": "raw.jsonl", - "metrics": metrics, - "extra": artifact.extra, - }, - indent=2, - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - return artifact - - def report_section(self, artifacts: list[RunArtifact]) -> ReportSection: - if not artifacts: - return ReportSection( - title="CUREv1 — single-arm SurfSense retrieval", - headline=False, - body_md="(no run artifacts found)", - body_json={}, - ) - latest = max(artifacts, key=lambda a: a.run_timestamp) - m = latest.metrics - recall = m.get("recall_at_k", {}) - lines: list[str] = [ - format_ingest_settings_md(latest.extra.get("ingest_settings")), - f"- Language: {m.get('lang', '?')}", - f"- Disciplines: {', '.join(m.get('disciplines', []) or ['?'])}", - f"- n_queries (after qrels intersection): {m.get('n_queries', 0)}", - ] - for k in (1, 5, 10, 32): - v = recall.get(str(k), recall.get(k)) - if v is not None: - lines.append(f"- Recall@{k}: {float(v):.3f}") - lines.append(f"- MRR: {float(m.get('mrr', 0.0)):.3f}") - lines.append(f"- nDCG@10: {float(m.get('ndcg_at_10', 0.0)):.3f}") - return ReportSection( - title="CUREv1 — single-arm SurfSense retrieval", - headline=False, - body_md="\n".join(lines), - body_json=m, - ) - - -__all__ = ["CureBenchmark", "CureQuery"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py deleted file mode 100644 index 3e803398d8..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""MedXpertQA-MM — multimodal medical exam head-to-head (medical suite headline). - -Source: https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA -Paper: https://arxiv.org/abs/2501.18362 (ICML 2025) - -* MM subset: ~2,000 expert-level exam questions with diverse medical - images (radiology, dermatology, pathology, ECGs, gross specimens, - fundus photos) and structured patient information embedded in the - question stem. -* 5 answer choices per MM question (A–E). -* USMLE / COMLEX / 17 specialty board sources; rigorously filtered - and reviewed by physicians. - -Real diagnostic images carry signal that text-only patient charts -cannot (e.g. CT scans, dermoscopy), so this benchmark exercises the -full vision RAG pipeline end-to-end against a vision-capable model -fed the same PDF natively. -""" - -from __future__ import annotations - -from ....core import registry as _registry -from .runner import MedXpertQAMMBenchmark - -_registry.register(MedXpertQAMMBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py deleted file mode 100644 index f50247acb3..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py +++ /dev/null @@ -1,409 +0,0 @@ -"""MedXpertQA-MM ingestion. - -Steps: - -1. Pull ``MM/test.jsonl`` (and optionally ``MM/dev.jsonl``) plus - ``images.zip`` from - ``hf://datasets/TsinghuaC3I/MedXpertQA``. Cache under - ``/medical/medxpertqa/``. -2. Extract ``images.zip`` once into ``/medical/medxpertqa/images/``. -3. Render one PDF per MM question (text question + structured patient - info embedded in the question stem + each image flowable + answer - options). Output: ``/medical/medxpertqa/pdfs/.pdf``. -4. Upload each PDF to SurfSense with ``use_vision_llm=True``; persist - ``id -> document_id`` in - ``/medical/maps/medxpertqa_doc_map.jsonl``. - -Both arms then receive byte-identical PDFs. The native arm sends the -PDF directly to OpenRouter; SurfSense ingests via its own vision -pipeline and the runner queries with ``mentioned_document_ids=[...]`` -to scope retrieval to the question's PDF. -""" - -from __future__ import annotations - -import json -import logging -import zipfile -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path - -from ....core.config import set_suite_state -from ....core.ingest_settings import IngestSettings, settings_header_line -from ....core.pdf import PdfImage, render_pdf_with_images -from ....core.registry import RunContext -from .prompt import format_options - -logger = logging.getLogger(__name__) - - -HF_REPO_ID = "TsinghuaC3I/MedXpertQA" -HF_REPO_TYPE = "dataset" - - -def _hf_hub_download(*args, **kwargs): - from huggingface_hub import hf_hub_download - - return hf_hub_download(*args, **kwargs) - - -# --------------------------------------------------------------------------- -# Question shape -# --------------------------------------------------------------------------- - - -@dataclass -class MedXpertQuestion: - qid: str # e.g. "MM-26" - question: str # full question text (case + ask) - options: dict[str, str] # A-E - label: str # "A".."E" - image_files: list[str] # filenames inside images.zip - medical_task: str - body_system: str - question_type: str - split: str # "test" or "dev" - - -def _load_jsonl(path: Path, *, split: str) -> list[MedXpertQuestion]: - out: list[MedXpertQuestion] = [] - with path.open("r", encoding="utf-8") as fh: - for raw_line in fh: - line = raw_line.strip() - if not line: - continue - row = json.loads(line) - qid = str(row.get("id") or "").strip() - question = str(row.get("question") or "").strip() - options = row.get("options") or {} - label = str(row.get("label") or "").strip().upper() - if not qid or not question or not isinstance(options, dict) or not label: - continue - opts = {str(k).strip().upper(): str(v).strip() for k, v in options.items()} - images = row.get("images") or [] - if not isinstance(images, list): - images = [] - out.append( - MedXpertQuestion( - qid=qid, - question=question, - options=opts, - label=label, - image_files=[str(x).strip() for x in images if str(x).strip()], - medical_task=str(row.get("medical_task") or "").strip(), - body_system=str(row.get("body_system") or "").strip(), - question_type=str(row.get("question_type") or "").strip(), - split=split, - ) - ) - return out - - -# --------------------------------------------------------------------------- -# Image archive helpers -# --------------------------------------------------------------------------- - - -def _ensure_images_extracted(images_zip: Path, images_dir: Path) -> None: - """Extract images.zip once, tolerantly handle re-runs.""" - - marker = images_dir / ".extracted_ok" - if marker.exists(): - return - images_dir.mkdir(parents=True, exist_ok=True) - logger.info("Extracting MedXpertQA images.zip -> %s", images_dir) - with zipfile.ZipFile(images_zip) as zf: - zf.extractall(images_dir) - marker.write_text("ok\n", encoding="utf-8") - - -def _resolve_image_path(image_filename: str, images_dir: Path) -> Path | None: - """Find a question's image in the (possibly nested) extract directory. - - The zip layout sometimes nests under ``images/`` and sometimes - flat — handle both. - """ - - direct = images_dir / image_filename - if direct.exists(): - return direct - nested = images_dir / "images" / image_filename - if nested.exists(): - return nested - # Last-ditch: glob recursively (slow but correct for unusual layouts). - matches = list(images_dir.rglob(image_filename)) - return matches[0] if matches else None - - -# --------------------------------------------------------------------------- -# PDF rendering -# --------------------------------------------------------------------------- - - -def _render_question_pdf( - q: MedXpertQuestion, - *, - images_dir: Path, - pdfs_dir: Path, -) -> tuple[Path, list[str]]: - """Render one MedXpertQA question into a PDF. - - Layout: - Title: MedXpertQA — (medical_task / body_system) - Section 1 (case): full question text - Section 1 images: each image flowable + caption - Section 2 (options): A) ... B) ... C) ... D) ... E) ... - - Returns (pdf_path, missing_images) so the caller can warn on - questions where some image files weren't found. - """ - - out_path = pdfs_dir / f"{q.qid}.pdf" - images: list[PdfImage] = [] - missing: list[str] = [] - for fname in q.image_files: - resolved = _resolve_image_path(fname, images_dir) - if resolved is None: - missing.append(fname) - continue - images.append(PdfImage(path=resolved, caption=f"Image: {fname}", max_width_in=5.5)) - - title_meta_parts = [] - if q.medical_task: - title_meta_parts.append(q.medical_task) - if q.body_system: - title_meta_parts.append(q.body_system) - if q.question_type: - title_meta_parts.append(q.question_type) - title_suffix = f" ({' / '.join(title_meta_parts)})" if title_meta_parts else "" - - sections = [ - ("Clinical case", q.question, images), - ("Answer choices", format_options(q.options), None), - ] - render_pdf_with_images( - title=f"MedXpertQA-MM {q.qid}{title_suffix}", - sections=sections, - output_path=out_path, - ) - return out_path, missing - - -# --------------------------------------------------------------------------- -# Upload helper -# --------------------------------------------------------------------------- - - -async def _upload_pdfs( - ctx: RunContext, - pdf_paths: Iterable[Path], - *, - batch_size: int, - settings: IngestSettings, -) -> dict[str, int]: - docs_client = ctx.documents_client() - name_to_id: dict[str, int] = {} - pdf_list = list(pdf_paths) - for batch_start in range(0, len(pdf_list), batch_size): - batch = pdf_list[batch_start : batch_start + batch_size] - result = await docs_client.upload( - files=batch, - search_space_id=ctx.search_space_id, - use_vision_llm=settings.use_vision_llm, - processing_mode=settings.processing_mode, - ) - all_ids = list(result.document_ids) + list(result.duplicate_document_ids) - if all_ids: - await docs_client.wait_until_ready( - search_space_id=ctx.search_space_id, - document_ids=result.document_ids, - timeout_s=1800.0, - ) - statuses = await docs_client.get_status( - search_space_id=ctx.search_space_id, - document_ids=all_ids, - ) - for s in statuses: - name_to_id[s.title] = s.document_id - logger.info( - "Uploaded MedXpertQA batch %d-%d: %d new, %d duplicate", - batch_start, - batch_start + len(batch), - len(result.document_ids), - len(result.duplicate_document_ids), - ) - return name_to_id - - -# --------------------------------------------------------------------------- -# Public entry point -# --------------------------------------------------------------------------- - - -async def run_ingest( - ctx: RunContext, - *, - split: str = "test", - max_questions: int | None = None, - upload_batch_size: int = 8, - skip_upload: bool = False, - include_dev: bool = False, - settings: IngestSettings | None = None, -) -> None: - """Ingest MedXpertQA-MM into the medical suite. - - Parameters - ---------- - split : 'test' (default), 'dev', or 'both' - Which subset to render + upload. - max_questions : int | None - Cap on number of questions ingested (handy for fast iteration). - upload_batch_size : int - PDFs per ``fileupload`` call. - skip_upload : bool - Render PDFs locally but don't push to SurfSense. - include_dev : bool - Convenience: equivalent to ``split='both'``. - """ - - settings = settings or IngestSettings(use_vision_llm=True, processing_mode="basic") - bench_dir = ctx.benchmark_data_dir() - images_zip_local = bench_dir / "images.zip" - images_dir = bench_dir / "images" - pdfs_dir = bench_dir / "pdfs" - pdfs_dir.mkdir(parents=True, exist_ok=True) - hf_cache = bench_dir / ".hf_cache" - hf_cache.mkdir(parents=True, exist_ok=True) - - # Step 1: download jsonl(s) - splits_to_load: list[str] = [] - if split == "both" or include_dev: - splits_to_load = ["dev", "test"] - elif split in {"dev", "test"}: - splits_to_load = [split] - else: - raise ValueError(f"Unknown split {split!r}; use 'test' / 'dev' / 'both'") - - questions: list[MedXpertQuestion] = [] - for sp in splits_to_load: - rel = f"MM/{sp}.jsonl" - local = _hf_hub_download( - repo_id=HF_REPO_ID, - filename=rel, - repo_type=HF_REPO_TYPE, - cache_dir=str(hf_cache), - ) - loaded = _load_jsonl(Path(local), split=sp) - questions.extend(loaded) - logger.info("Loaded %d MedXpertQA-MM questions from %s split", len(loaded), sp) - - if max_questions is not None and max_questions > 0: - questions = questions[:max_questions] - if not questions: - raise RuntimeError("No MedXpertQA-MM questions loaded; check the split argument.") - - # Step 2: download images.zip + extract once - if not images_zip_local.exists(): - local_zip = _hf_hub_download( - repo_id=HF_REPO_ID, - filename="images.zip", - repo_type=HF_REPO_TYPE, - cache_dir=str(hf_cache), - ) - # Materialise into bench_dir so the path is stable. - try: - from os import link as _link - - _link(local_zip, images_zip_local) - except OSError: - from shutil import copy2 - - copy2(local_zip, images_zip_local) - _ensure_images_extracted(images_zip_local, images_dir) - - # Step 3: render PDFs - pdf_paths: dict[str, Path] = {} - missing_image_count = 0 - for i, q in enumerate(questions, start=1): - try: - pdf, missing = _render_question_pdf(q, images_dir=images_dir, pdfs_dir=pdfs_dir) - pdf_paths[q.qid] = pdf - if missing: - missing_image_count += len(missing) - logger.debug("qid=%s missing %d images: %s", q.qid, len(missing), missing) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to render MedXpertQA PDF for %s: %s", q.qid, exc) - if i % 50 == 0: - logger.info(" ... rendered %d / %d PDFs", i, len(questions)) - if missing_image_count: - logger.warning( - "MedXpertQA: %d image references could not be resolved on disk " - "(rendered PDFs may be missing some images).", - missing_image_count, - ) - - # Step 4: upload - name_to_id: dict[str, int] = {} - if skip_upload: - logger.info("MedXpertQA: --skip-upload set; skipping SurfSense ingestion") - else: - logger.info("MedXpertQA upload settings: %s", settings.render_label()) - name_to_id = await _upload_pdfs( - ctx, - pdf_paths.values(), - batch_size=upload_batch_size, - settings=settings, - ) - - # Step 5: persist manifest + questions - questions_jsonl = bench_dir / "questions.jsonl" - with questions_jsonl.open("w", encoding="utf-8") as fh: - for q in questions: - fh.write( - json.dumps( - { - "qid": q.qid, - "question": q.question, - "options": q.options, - "label": q.label, - "image_files": q.image_files, - "medical_task": q.medical_task, - "body_system": q.body_system, - "question_type": q.question_type, - "split": q.split, - } - ) - + "\n" - ) - logger.info("Wrote %d MedXpertQA questions to %s", len(questions), questions_jsonl) - - map_path = ctx.maps_dir() / "medxpertqa_doc_map.jsonl" - with map_path.open("w", encoding="utf-8") as fh: - # Header line records the resolved ingest settings - # (see core/ingest_settings.py). - fh.write(settings_header_line(settings) + "\n") - for q in questions: - local = pdf_paths.get(q.qid) - if local is None: - continue - fh.write( - json.dumps( - { - "qid": q.qid, - "document_id": name_to_id.get(local.name), - "pdf_path": str(local), - "n_images": len(q.image_files), - "split": q.split, - } - ) - + "\n" - ) - logger.info("Wrote MedXpertQA doc map to %s", map_path) - - new_state = ctx.suite_state - new_state.ingestion_maps["medxpertqa"] = str(map_path) - set_suite_state(ctx.config, ctx.suite, new_state) - - -__all__ = ["MedXpertQuestion", "run_ingest"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py deleted file mode 100644 index 5c4a69916e..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py +++ /dev/null @@ -1,54 +0,0 @@ -"""MedXpertQA-MM prompt. - -Mirrors the upstream paper's evaluation prompt (Zuo et al., ICML 2025 -§3.4): present case + 5 options A-E, ask for a single letter answer. -We also instruct the model to use the embedded images explicitly, -since the whole point of the MM subset is that the answer depends on -visual evidence (radiology / dermoscopy / pathology / ECG, etc.). -""" - -from __future__ import annotations - -from collections.abc import Mapping - -ANSWER_LETTERS = ("A", "B", "C", "D", "E") - - -_PROMPT = """\ -You are a board-certified physician. The following exam question -includes a clinical case and one or more medical images (radiology, -dermatology, pathology, ECG, etc.). Use BOTH the text and the images -to choose the best answer. Do not rely on memorisation of the case; -read the images carefully — they often determine the correct answer. - -Case + question: -{question} - -Answer choices: -{options_block} - -Respond on a single line in the format `Answer: X` where X is one of -A, B, C, D, or E. -""" - - -def format_options(options: Mapping[str, str]) -> str: - """Render the ``A) ... E) ...`` options block.""" - - parts: list[str] = [] - for letter in ANSWER_LETTERS: - text = options.get(letter) - if text is None or str(text).strip() == "": - continue - parts.append(f"{letter}) {str(text).strip()}") - return "\n".join(parts) - - -def build_prompt(question: str, options: Mapping[str, str]) -> str: - return _PROMPT.format( - question=question.strip(), - options_block=format_options(options), - ) - - -__all__ = ["ANSWER_LETTERS", "build_prompt", "format_options"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py deleted file mode 100644 index f7a3331a9e..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py +++ /dev/null @@ -1,721 +0,0 @@ -"""MedXpertQA-MM runner — Native PDF (vision) vs SurfSense (vision RAG). - -Headline benchmark for the medical suite. - -* Native arm reads the rendered PDF (case + images + options) via - OpenRouter ``chat/completions`` + the file-parser plugin. -* SurfSense arm queries ``POST /api/v1/new_chat`` scoped via - ``mentioned_document_ids=[doc_id]`` to the same per-question PDF. - -Operational notes: - -* PDFs contain real images (radiology, dermoscopy, pathology, ECGs). - Operator must pin a vision-capable model via - ``setup --provider-model anthropic/claude-sonnet-4.5`` (or similar); - the runner emits a warning if a known text-only slug is pinned. -* MedXpertQA tags ``medical_task`` (Diagnosis / Treatment / Basic - Medicine) and ``body_system`` (Cardiovascular / Lymphatic / …) - directly on every row; we slice the report by both. -""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import logging -import os -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from ....core.arms import ArmRequest, ArmResult, NativePdfArm, SurfSenseArm -from ....core.config import utc_iso_timestamp -from ....core.ingest_settings import ( - IngestSettings, - add_ingest_settings_args, - format_ingest_settings_md, - is_settings_header, -) -from ....core.metrics.comparison import ( - bootstrap_delta_ci, - mcnemar_test, - paired_aggregate, -) -from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci -from ....core.providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine -from ....core.registry import ( - ReportSection, - RunArtifact, - RunContext, -) -from ....core.scenarios import format_scenario_md -from .prompt import ANSWER_LETTERS, build_prompt - -logger = logging.getLogger(__name__) - - -_TEXT_ONLY_HINTS = ("gpt-5.4-mini", "gpt-3.5", "text-only", "instruct-") - - -@dataclass -class MXQuestion: - qid: str - question: str - options: dict[str, str] - label: str - medical_task: str - body_system: str - question_type: str - split: str - n_images: int - pdf_path: Path - document_id: int | None - - -def _load_doc_map(map_path: Path) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]: - """Read the doc map JSONL. - - Returns ``(rows, settings)`` where ``settings`` is the - ``__settings__`` header blob (or ``{}`` for legacy maps). - """ - - rows: dict[str, dict[str, Any]] = {} - settings: dict[str, Any] = {} - with map_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - if is_settings_header(row): - settings = dict(row["__settings__"]) - continue - rows[str(row["qid"])] = row - return rows, settings - - -def _load_questions( - questions_jsonl: Path, - doc_map: dict[str, dict[str, Any]], - *, - split_filter: str | None, - task_filter: str | None, - body_filter: str | None, - require_images: bool, - sample_n: int | None, -) -> list[MXQuestion]: - out: list[MXQuestion] = [] - with questions_jsonl.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - qid = str(row.get("qid") or "").strip() - if not qid: - continue - if split_filter and split_filter != "all" and row.get("split") != split_filter: - continue - if task_filter and task_filter != "all" and row.get("medical_task") != task_filter: - continue - if body_filter and body_filter != "all" and row.get("body_system") != body_filter: - continue - map_row = doc_map.get(qid) - if map_row is None: - logger.debug("No doc-map entry for %s; skipping", qid) - continue - n_images = int(map_row.get("n_images", 0)) - if require_images and n_images <= 0: - continue - out.append( - MXQuestion( - qid=qid, - question=str(row.get("question") or ""), - options={str(k).upper(): str(v) for k, v in (row.get("options") or {}).items()}, - label=str(row.get("label") or "").strip().upper(), - medical_task=str(row.get("medical_task") or "").strip(), - body_system=str(row.get("body_system") or "").strip(), - question_type=str(row.get("question_type") or "").strip(), - split=str(row.get("split") or ""), - n_images=n_images, - pdf_path=Path(map_row["pdf_path"]), - document_id=map_row.get("document_id"), - ) - ) - out.sort(key=lambda q: (q.split, q.qid)) - if sample_n is not None and sample_n > 0: - out = out[:sample_n] - return out - - -async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]: - sem = asyncio.Semaphore(max(1, concurrency)) - - async def _wrap(coro): - async with sem: - return await coro - - return await asyncio.gather(*(_wrap(c) for c in coros)) - - -_DESCRIPTION = ( - "MedXpertQA-MM (~2,000 multimodal medical exam questions, 5 options, with images) — " - "Native PDF (vision) vs SurfSense (vision RAG) head-to-head." -) - -# MedXpertQA-MM PDFs embed clinical images; vision LLM at ingest is -# the whole point. Operators can flip ``--no-vision-llm`` to measure -# how much we degrade without it (likely material). -_DEFAULT_INGEST_SETTINGS = IngestSettings( - use_vision_llm=True, - processing_mode="basic", -) - - -class MedXpertQAMMBenchmark: - """Multimodal medical exam head-to-head.""" - - suite: str = "medical" - name: str = "medxpertqa" - headline: bool = True # The medical suite headline. - description: str = _DESCRIPTION - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: - parser.add_argument( - "--split", - default="test", - choices=["test", "dev", "all"], - help="Which MedXpertQA-MM split to run (default: test).", - ) - parser.add_argument( - "--task", - default="all", - help="Filter by medical_task value (e.g. Diagnosis, Treatment, Basic Medicine).", - ) - parser.add_argument( - "--body-system", - dest="body_filter", - default="all", - help="Filter by body_system value (e.g. Cardiovascular, Lymphatic).", - ) - parser.add_argument( - "--require-images", - dest="require_images", - action="store_true", - help="Skip rare MM rows that ended up with zero resolvable images.", - ) - parser.add_argument( - "--n", - dest="sample_n", - type=int, - default=None, - help="Run only the first N questions after filters apply.", - ) - parser.add_argument( - "--concurrency", type=int, default=4, help="Parallel question workers per arm." - ) - parser.add_argument( - "--no-mentions", - dest="no_mentions", - action="store_true", - help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).", - ) - parser.add_argument( - "--pdf-engine", - default="native", - choices=[e.value for e in PdfEngine], - help="OpenRouter file-parser engine for the native arm.", - ) - parser.add_argument( - "--max-output-tokens", - type=int, - default=512, - help="Cap on completion length for both arms.", - ) - # Ingest-only knobs (forwarded by the CLI to ingest.run_ingest). - parser.add_argument( - "--max-questions", - dest="max_questions", - type=int, - default=None, - help="(ingest only) cap on number of MM questions to render + upload.", - ) - parser.add_argument( - "--upload-batch-size", - dest="upload_batch_size", - type=int, - default=8, - help="(ingest only) PDFs per fileupload call.", - ) - parser.add_argument( - "--skip-upload", - dest="skip_upload", - action="store_true", - help="(ingest only) render PDFs locally but don't push to SurfSense.", - ) - parser.add_argument( - "--include-dev", - dest="include_dev", - action="store_true", - help="(ingest only) shorthand for --split all.", - ) - # Per-upload knobs forwarded to /documents/fileupload at ingest; - # ignored at run-time (runner reads the resolved settings out of - # the doc-map manifest header). - add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS) - - async def ingest(self, ctx: RunContext, **opts: Any) -> None: - from .ingest import run_ingest - - settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts) - await run_ingest( - ctx, - split=opts.get("split") or "test", - max_questions=opts.get("max_questions"), - upload_batch_size=int(opts.get("upload_batch_size") or 8), - skip_upload=bool(opts.get("skip_upload", False)), - include_dev=bool(opts.get("include_dev", False)), - settings=settings, - ) - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: - split_filter = opts.get("split") or "test" - task_filter = opts.get("task") or "all" - body_filter = opts.get("body_filter") or "all" - require_images = bool(opts.get("require_images")) - sample_n = opts.get("sample_n") - concurrency = int(opts.get("concurrency") or 4) - no_mentions = bool(opts.get("no_mentions")) - pdf_engine_name = opts.get("pdf_engine") or "native" - max_output_tokens = int(opts.get("max_output_tokens") or 512) - - bench_dir = ctx.benchmark_data_dir() - questions_jsonl = bench_dir / "questions.jsonl" - map_path = ctx.maps_dir() / "medxpertqa_doc_map.jsonl" - if not questions_jsonl.exists() or not map_path.exists(): - raise RuntimeError( - "MedXpertQA-MM not ingested for this suite. Run " - "`python -m surfsense_evals ingest medical medxpertqa` first." - ) - - doc_map, ingest_settings = _load_doc_map(map_path) - questions = _load_questions( - questions_jsonl, - doc_map, - split_filter=split_filter, - task_filter=task_filter if task_filter != "all" else None, - body_filter=body_filter if body_filter != "all" else None, - require_images=require_images, - sample_n=sample_n, - ) - if not questions: - raise RuntimeError( - "No MedXpertQA-MM questions matched the filters; broaden --split/--task/--body-system/--n." - ) - logger.info("MedXpertQA-MM: scheduled %d questions", len(questions)) - - api_key = os.environ.get("OPENROUTER_API_KEY") - if not api_key: - raise RuntimeError("OPENROUTER_API_KEY env var is required for the native arm.") - - # Native arm slug differs from SurfSense slug only in cost-arbitrage - # scenario; otherwise both arms answer with provider_model. - native_arm_model = ctx.native_arm_model - if any(hint in native_arm_model.lower() for hint in _TEXT_ONLY_HINTS): - if ctx.scenario == "symmetric-cheap": - logger.info( - "symmetric-cheap: native arm pinned to text-only %r as " - "intended; expect it to lose on image-bearing questions " - "(SurfSense answers from vision-extracted chunks).", - native_arm_model, - ) - else: - logger.warning( - "Native arm slug %r looks text-only; image content in " - "MedXpertQA PDFs will be ignored. Re-pin via " - "`setup --provider-model anthropic/claude-sonnet-4.5` " - "(or pass --native-arm-model and --scenario cost-arbitrage " - "to make this asymmetry explicit).", - native_arm_model, - ) - - provider = OpenRouterPdfProvider( - api_key=api_key, - base_url=ctx.config.openrouter_base_url, - model=native_arm_model, - engine=PdfEngine(pdf_engine_name), - ) - native_arm = NativePdfArm(provider=provider, max_output_tokens=max_output_tokens) - surf_arm = SurfSenseArm( - client=ctx.new_chat_client(), - search_space_id=ctx.search_space_id, - ephemeral_threads=True, - ) - - run_timestamp = utc_iso_timestamp() - run_dir = ctx.runs_dir(run_timestamp=run_timestamp) - raw_path = run_dir / "raw.jsonl" - - async def _native_one(q: MXQuestion) -> ArmResult: - return await native_arm.answer(_make_native_request(q, max_output_tokens)) - - async def _surf_one(q: MXQuestion) -> ArmResult: - return await surf_arm.answer(_make_surfsense_request(q, no_mentions=no_mentions)) - - native_results, surf_results = await asyncio.gather( - _gather_with_limit((_native_one(q) for q in questions), concurrency=concurrency), - _gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency), - ) - - with raw_path.open("w", encoding="utf-8") as fh: - for q, n_res, s_res in zip(questions, native_results, surf_results, strict=False): - meta = { - "qid": q.qid, - "split": q.split, - "medical_task": q.medical_task, - "body_system": q.body_system, - "question_type": q.question_type, - "n_images": q.n_images, - "correct": q.label, - "document_id": q.document_id, - } - fh.write(json.dumps({**meta, **n_res.to_jsonl()}) + "\n") - fh.write(json.dumps({**meta, **s_res.to_jsonl()}) + "\n") - - metrics = _compute_metrics(questions, native_results, surf_results) - artifact = RunArtifact( - suite=self.suite, - benchmark=self.name, - run_timestamp=run_timestamp, - raw_path=raw_path, - metrics=metrics, - extra={ - "n_questions": len(questions), - "concurrency": concurrency, - "split_filter": split_filter, - "task_filter": task_filter, - "body_filter": body_filter, - "require_images": require_images, - "no_mentions": no_mentions, - "pdf_engine": pdf_engine_name, - "scenario": ctx.scenario, - "provider_model": ctx.provider_model, - "native_arm_model": native_arm_model, - "vision_provider_model": ctx.vision_provider_model, - "chat_model_id": ctx.chat_model_id, - "ingest_settings": ingest_settings, - }, - ) - - manifest_path = run_dir / "run_artifact.json" - manifest_path.write_text( - json.dumps( - { - "suite": self.suite, - "benchmark": self.name, - "raw_path": "raw.jsonl", - "metrics": metrics, - "extra": artifact.extra, - }, - indent=2, - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - return artifact - - def report_section(self, artifacts: list[RunArtifact]) -> ReportSection: - if not artifacts: - return ReportSection( - title="MedXpertQA-MM — Native PDF (vision) vs SurfSense (vision RAG)", - headline=False, - body_md="(no run artifacts found)", - body_json={}, - ) - latest = max(artifacts, key=lambda a: a.run_timestamp) - m = latest.metrics - native = m.get("native", {}) - surf = m.get("surfsense", {}) - delta = m.get("delta", {}) - per_task = m.get("per_task", {}) - per_body = m.get("per_body_system", {}) - extra = latest.extra - - body_lines: list[str] = [] - body_lines.append( - f"- Sample size: {extra.get('n_questions', '?')} questions " - f"(split: `{extra.get('split_filter', 'test')}`, " - f"task: `{extra.get('task_filter', 'all')}`, " - f"body: `{extra.get('body_filter', 'all')}`, " - f"engine: `{extra.get('pdf_engine', 'native')}`)." - ) - body_lines.append(format_scenario_md(extra)) - body_lines.append(format_ingest_settings_md(extra.get("ingest_settings"))) - body_lines.append( - "- Native arm (OpenRouter `chat/completions` + file plugin, " - f"`{extra.get('native_arm_model') or extra.get('provider_model', '?')}`):" - ) - body_lines.append(_arm_summary_lines(native, indent=" ")) - body_lines.append( - "- SurfSense arm (`POST /api/v1/new_chat`, vision RAG over chunks, " - f"`{extra.get('provider_model', '?')}`):" - ) - body_lines.append(_arm_summary_lines(surf, indent=" ")) - body_lines.append("- Delta (paired):") - body_lines.append( - f" - Accuracy: SurfSense {_pp(delta.get('accuracy_pp'))} pp " - f"(McNemar p={_fmt(delta.get('mcnemar_p_value'), 4)}, " - f"method={delta.get('mcnemar_method')})" - ) - body_lines.append( - f" - Bootstrap 95% CI on delta: " - f"[{_pp(delta.get('bootstrap_ci_low'))}pp, {_pp(delta.get('bootstrap_ci_high'))}pp]" - ) - body_lines.append( - f" - Cost / question: native ${_dollars(native.get('cost_micros_mean'))}, " - f"surfsense ${_dollars(surf.get('cost_micros_mean'))} " - f"(SurfSense delta {_pct_change(delta.get('cost_micros_pct'))})" - ) - body_lines.append( - f" - Latency p50: native {_ms_to_s(native.get('latency_ms_median'))}, " - f"surfsense {_ms_to_s(surf.get('latency_ms_median'))} " - f"(SurfSense delta {_pct_change(delta.get('latency_ms_pct'))})" - ) - if per_task: - body_lines.append("- Per-medical_task split:") - for task_name, vals in sorted(per_task.items()): - body_lines.append( - f" - {task_name}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp " - f"(n={vals.get('n')})" - ) - if per_body: - body_lines.append("- Per-body_system split (top 5 by sample size):") - top = sorted(per_body.items(), key=lambda kv: -kv[1].get("n", 0))[:5] - for body_name, vals in top: - body_lines.append( - f" - {body_name}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp " - f"(n={vals.get('n')})" - ) - - return ReportSection( - title="MedXpertQA-MM — Native PDF (vision) vs SurfSense (vision RAG)", - headline=False, - body_md="\n".join(body_lines), - body_json=m, - ) - - -# --------------------------------------------------------------------------- -# Per-question helpers -# --------------------------------------------------------------------------- - - -def _make_native_request(q: MXQuestion, max_tokens: int) -> ArmRequest: - prompt = build_prompt(q.question, q.options) - return ArmRequest( - question_id=q.qid, - prompt=prompt, - pdf_paths=[q.pdf_path], - options={"max_tokens": max_tokens}, - ) - - -def _make_surfsense_request(q: MXQuestion, *, no_mentions: bool) -> ArmRequest: - prompt = build_prompt(q.question, q.options) - mentions: list[int] | None = None - if not no_mentions and q.document_id is not None: - mentions = [int(q.document_id)] - return ArmRequest( - question_id=q.qid, - prompt=prompt, - mentioned_document_ids=mentions, - ) - - -# --------------------------------------------------------------------------- -# Metrics -# --------------------------------------------------------------------------- - - -def _compute_metrics( - questions: list[MXQuestion], - native_results: list[ArmResult], - surf_results: list[ArmResult], -) -> dict[str, Any]: - native_correct: list[bool] = [] - surf_correct: list[bool] = [] - for q, n_res, s_res in zip(questions, native_results, surf_results, strict=False): - gold = q.label - n_ok = (n_res.answer_letter or "").upper() == gold and gold in ANSWER_LETTERS - s_ok = (s_res.answer_letter or "").upper() == gold and gold in ANSWER_LETTERS - native_correct.append(n_ok) - surf_correct.append(s_ok) - - native_costs = [float(r.cost_micros) for r in native_results] - surf_costs = [float(r.cost_micros) for r in surf_results] - native_lats = [float(r.latency_ms) for r in native_results] - surf_lats = [float(r.latency_ms) for r in surf_results] - native_in = [float(r.input_tokens) for r in native_results] - native_out = [float(r.output_tokens) for r in native_results] - - native_acc = accuracy_with_wilson_ci(sum(native_correct), len(native_correct)) - surf_acc = accuracy_with_wilson_ci(sum(surf_correct), len(surf_correct)) - mc = mcnemar_test(native_correct, surf_correct) - boot = bootstrap_delta_ci(native_correct, surf_correct, n_resamples=2000) - - native_cost_agg = paired_aggregate(native_costs) - surf_cost_agg = paired_aggregate(surf_costs) - native_lat_agg = paired_aggregate(native_lats) - surf_lat_agg = paired_aggregate(surf_lats) - - cost_pct = _safe_pct(surf_cost_agg.mean, native_cost_agg.mean) - lat_pct = _safe_pct(surf_lat_agg.median, native_lat_agg.median) - - per_task = _per_field( - questions, native_correct, surf_correct, key=lambda q: q.medical_task or "unknown" - ) - per_body = _per_field( - questions, native_correct, surf_correct, key=lambda q: q.body_system or "unknown" - ) - - return { - "native": { - **native_acc.to_dict(), - "cost_micros_mean": native_cost_agg.mean, - "cost_micros_median": native_cost_agg.median, - "latency_ms_mean": native_lat_agg.mean, - "latency_ms_median": native_lat_agg.median, - "latency_ms_p95": native_lat_agg.p95, - "input_tokens_mean": (sum(native_in) / len(native_in)) if native_in else 0.0, - "output_tokens_mean": (sum(native_out) / len(native_out)) if native_out else 0.0, - }, - "surfsense": { - **surf_acc.to_dict(), - "cost_micros_mean": surf_cost_agg.mean, - "cost_micros_median": surf_cost_agg.median, - "latency_ms_mean": surf_lat_agg.mean, - "latency_ms_median": surf_lat_agg.median, - "latency_ms_p95": surf_lat_agg.p95, - }, - "delta": { - "accuracy_pp": 100.0 * (surf_acc.accuracy - native_acc.accuracy), - "mcnemar_p_value": mc.p_value, - "mcnemar_method": mc.method, - "mcnemar_b_native_only": mc.b, - "mcnemar_c_surfsense_only": mc.c, - "bootstrap_ci_low": 100.0 * boot.ci_low, - "bootstrap_ci_high": 100.0 * boot.ci_high, - "cost_micros_pct": cost_pct, - "latency_ms_pct": lat_pct, - }, - "per_task": per_task, - "per_body_system": per_body, - } - - -def _per_field( - questions: list[MXQuestion], - native_correct: list[bool], - surf_correct: list[bool], - *, - key, -) -> dict[str, dict[str, Any]]: - bucket: dict[str, list[tuple[bool, bool]]] = {} - for q, n_ok, s_ok in zip(questions, native_correct, surf_correct, strict=False): - bucket.setdefault(key(q), []).append((n_ok, s_ok)) - out: dict[str, dict[str, Any]] = {} - for k, pairs in bucket.items(): - n_correct = [a for a, _ in pairs] - s_correct = [b for _, b in pairs] - out[k] = { - "n": len(pairs), - "native_accuracy": (sum(n_correct) / len(pairs)) if pairs else 0.0, - "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0, - "delta_accuracy_pp": ( - 100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs) if pairs else 0.0 - ), - } - return out - - -def _safe_pct(numerator: float, denominator: float) -> float | None: - if denominator == 0: - return None - return 100.0 * (numerator - denominator) / denominator - - -# --------------------------------------------------------------------------- -# Formatters -# --------------------------------------------------------------------------- - - -def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str: - if not d: - return f"{indent}(no data)" - acc = d.get("accuracy", 0.0) - low = d.get("ci_low", 0.0) - high = d.get("ci_high", 0.0) - lines = [ - f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)", - f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), " - f"${_dollars(d.get('cost_micros_median'))} (median)", - f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, " - f"p95 {_ms_to_s(d.get('latency_ms_p95'))}", - ] - if "input_tokens_mean" in d: - lines.append( - f"{indent}- Mean tokens / question: in {d.get('input_tokens_mean', 0):.0f}, " - f"out {d.get('output_tokens_mean', 0):.0f}" - ) - return "\n".join(lines) - - -def _dollars(micros: Any) -> str: - if micros is None: - return "?" - try: - return f"{(float(micros) / 1_000_000):.4f}" - except (TypeError, ValueError): - return "?" - - -def _ms_to_s(ms: Any) -> str: - if ms is None: - return "?" - try: - return f"{float(ms) / 1000:.1f}s" - except (TypeError, ValueError): - return "?" - - -def _pp(value: Any) -> str: - if value is None: - return "?" - try: - return f"{float(value):+.1f}" - except (TypeError, ValueError): - return "?" - - -def _pct_change(value: Any) -> str: - if value is None: - return "?" - try: - return f"{float(value):+.0f}%" - except (TypeError, ValueError): - return "?" - - -def _fmt(value: Any, ndigits: int) -> str: - if value is None: - return "?" - try: - return f"{float(value):.{ndigits}f}" - except (TypeError, ValueError): - return "?" - - -__all__ = ["MedXpertQAMMBenchmark", "MXQuestion"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py deleted file mode 100644 index 265dd62f7c..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""MIRAGE — secondary single-arm SurfSense MCQ measurement. - -Source: https://github.com/Teddy-XiongGZ/MIRAGE, paper -https://aclanthology.org/2024.findings-acl.372/. 7,663 questions -across MMLU-Med, MedQA-US, MedMCQA, PubMedQA*, BioASQ-Y/N. - -This is a SurfSense-only measurement (not a head-to-head); native -PDF-in-LLM doesn't apply because there is no per-question discrete -document — the corpus is millions of biomedical snippets. -""" - -from __future__ import annotations - -from ....core import registry as _registry -from .runner import MirageBenchmark - -_registry.register(MirageBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py deleted file mode 100644 index c4aa53fe25..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py +++ /dev/null @@ -1,550 +0,0 @@ -"""MIRAGE ingestion. - -Downloads: - -* ``benchmark.json`` (≈ 4 MB; questions for the 5 sub-tasks). -* ``retrieved_snippets_10k.zip`` (the union of top-10k snippet ids - retrieved by every retriever in the MedRAG paper, per task — a - recall ceiling that avoids needing the full 23.9M-doc PubMed mirror). - -Snippet *content* lives in the MedRAG HF mirrors -(``MedRAG/textbooks``, ``MedRAG/pubmed``, ``MedRAG/statpearls``, -``MedRAG/wikipedia``). We default to ``MedRAG/textbooks`` (212 MB, -125k snippets) which is the smallest and covers the majority of -``MedQA-US`` and the medical examination subsets. Operators can -opt into larger corpora with ``--corpus``. - -Each snippet is written as one markdown file then batched into -``~5 MB`` markdown bundles for SurfSense's file upload (smaller -than backend default ``MAX_FILE_SIZE_BYTES`` and avoids the per-call -overhead of one HTTP request per snippet). - -The ingestion produces two maps under ``data/medical/maps/``: - -* ``mirage_snippet_map.jsonl`` — ``{snippet_id, document_id, batch_path}`` -* ``mirage_chunk_map.jsonl`` — ``{chunk_id, document_id, snippet_id?}`` - (best-effort; chunk text is heuristically attributed to the - snippet it overlaps when the SurfSense chunker splits a batched - markdown). -""" - -from __future__ import annotations - -import asyncio -import io -import json -import logging -import zipfile -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path - -import httpx - -from ....core.config import set_suite_state -from ....core.ingest_settings import IngestSettings, settings_header_line -from ....core.registry import RunContext - -logger = logging.getLogger(__name__) - - -MIRAGE_BENCHMARK_URL = "https://raw.githubusercontent.com/Teddy-XiongGZ/MIRAGE/main/benchmark.json" -# Upstream only ships ONE zip — top-10k retrievals across 5 retrievers, -# ~16 GB. We default to skipping it (see `--skip-snippet-filter`) and -# ingesting the chosen corpus in full; this URL is only fetched when -# the operator explicitly opts in. -MIRAGE_SNIPPETS_ZIP_URL = ( - "https://virginia.box.com/shared/static/cxq17th6eisl2pn04vp0x723zczlvlzc.zip" -) - - -_DEFAULT_CORPUS = "MedRAG/textbooks" -_BATCH_SIZE_BYTES = 5 * 1024 * 1024 -# 2 GB safety cap. Anything larger requires --allow-large-download. -# Set high enough that ``benchmark.json`` and small zips pass through -# untouched but the 16 GB MIRAGE retrievals zip trips the guard. -_LARGE_DOWNLOAD_BYTES = 2 * 1024 * 1024 * 1024 -_DOWNLOAD_RETRIES = 5 -_RETRYABLE_NET_EXC: tuple[type[BaseException], ...] = ( - httpx.RemoteProtocolError, - httpx.ReadError, - httpx.ReadTimeout, - httpx.ConnectError, - httpx.ConnectTimeout, -) - - -@dataclass -class SnippetRow: - snippet_id: str - title: str - content: str - - def to_markdown(self) -> str: - title = (self.title or "").strip() or "Untitled" - body = (self.content or "").strip() - return f"# {title}\n\n_id: `{self.snippet_id}`_\n\n{body}\n" - - -# --------------------------------------------------------------------------- -# Download helpers -# --------------------------------------------------------------------------- - - -def _reuse_cached_dest(dest: Path, *, expect_zip: bool, label: str) -> Path | None: - """Return ``dest`` if a usable cache hit, else ``None`` (and delete corrupt zips).""" - - if not dest.exists(): - return None - if expect_zip and not _is_valid_zip(dest): - logger.warning( - "Cached %s at %s failed ZIP validation (size=%d B); deleting and re-downloading.", - label, - dest, - dest.stat().st_size, - ) - dest.unlink(missing_ok=True) - return None - logger.info("Using cached %s at %s", label, dest) - return dest - - -async def _fetch_to_path( - url: str, - *, - dest: Path, - label: str, - timeout_s: float = 600.0, - allow_large_download: bool = False, - expect_zip: bool = False, -) -> Path: - """Download ``url`` to ``dest`` with retry, atomic-rename, and - HTTP ``Range`` resume. - - Operational properties: - - * If ``dest`` already exists *and* (when ``expect_zip`` is True) the - cached file is a valid ZIP, returns it immediately. A corrupt ZIP - is removed and re-downloaded — this is the safety net for the - `box.com truncated 16 GB zip` failure mode where the previous - run wrote a half-completed file then exited with an exception. - * Bytes are written to ``.partial`` and renamed only after the - stream completes cleanly (and, for zips, only after a quick - central-directory check). A failure mid-download leaves the - ``.partial`` file in place so the next attempt can resume from - where it stopped via an HTTP ``Range`` header. - * Retries on transient network errors (``RemoteProtocolError``, - ``ReadError``, ``ReadTimeout``, ``ConnectError``, - ``ConnectTimeout``) with exponential backoff, up to - ``_DOWNLOAD_RETRIES``. - * Aborts before downloading if the ``Content-Length`` (or already- - downloaded ``.partial`` size) is over ``_LARGE_DOWNLOAD_BYTES`` - and ``allow_large_download`` is False, to keep an operator from - surprise-grabbing 16 GB on a slow link. - """ - - cached = _reuse_cached_dest(dest, expect_zip=expect_zip, label=label) - if cached is not None: - return cached - - dest.parent.mkdir(parents=True, exist_ok=True) - partial = dest.with_suffix(dest.suffix + ".partial") - last_exc: BaseException | None = None - - for attempt in range(1, _DOWNLOAD_RETRIES + 1): - existing_bytes = partial.stat().st_size if partial.exists() else 0 - headers: dict[str, str] = {} - if existing_bytes: - headers["Range"] = f"bytes={existing_bytes}-" - logger.info( - "Resuming %s from byte %d (attempt %d/%d)", - label, - existing_bytes, - attempt, - _DOWNLOAD_RETRIES, - ) - else: - logger.info( - "Downloading %s from %s (attempt %d/%d)", - label, - url, - attempt, - _DOWNLOAD_RETRIES, - ) - - try: - async with ( - httpx.AsyncClient( - timeout=httpx.Timeout(timeout_s, connect=20.0), - follow_redirects=True, - ) as client, - client.stream("GET", url, headers=headers) as response, - ): - if existing_bytes and response.status_code == 200: - logger.warning( - "Server ignored Range header for %s; restarting from 0.", - label, - ) - partial.unlink(missing_ok=True) - existing_bytes = 0 - elif response.status_code == 416: - # Range not satisfiable — the .partial is at or - # past the end. Treat as "already downloaded"; - # validate by closing and re-opening for atomic - # rename below. - logger.info( - "Server reports %s already complete (HTTP 416).", - label, - ) - elif response.status_code not in (200, 206): - response.raise_for_status() - - total_size = _planned_total_size(response, existing_bytes) - if ( - total_size is not None - and total_size > _LARGE_DOWNLOAD_BYTES - and not allow_large_download - ): - raise _LargeDownloadAbort(label, total_size) - - mode = "ab" if existing_bytes else "wb" - with partial.open(mode) as fh: - async for chunk in response.aiter_bytes(chunk_size=1 << 18): - fh.write(chunk) - # Optional content sanity check before promoting to dest. - if expect_zip and not _is_valid_zip(partial): - raise zipfile.BadZipFile( - f"{label} downloaded to {partial} but failed central-" - "directory check; will retry." - ) - partial.replace(dest) - return dest - except _LargeDownloadAbort: - raise - except _RETRYABLE_NET_EXC as exc: - last_exc = exc - wait = min(60.0, 2.0**attempt) - logger.warning( - "Network error fetching %s (%s: %s); retrying in %.0fs.", - label, - type(exc).__name__, - exc, - wait, - ) - await asyncio.sleep(wait) - except zipfile.BadZipFile as exc: - last_exc = exc - # Truncated body — drop the partial and retry from scratch. - partial.unlink(missing_ok=True) - wait = min(60.0, 2.0**attempt) - logger.warning( - "Truncated ZIP for %s; restarting from byte 0 in %.0fs.", - label, - wait, - ) - await asyncio.sleep(wait) - - raise RuntimeError( - f"Failed to download {label} after {_DOWNLOAD_RETRIES} attempts: {last_exc!s}" - ) - - -def _planned_total_size(response: httpx.Response, existing_bytes: int) -> int | None: - """Best-effort total size including any already-buffered .partial bytes.""" - - cl = response.headers.get("Content-Length") - if not cl: - return None - try: - remaining = int(cl) - except ValueError: - return None - return existing_bytes + remaining - - -def _is_valid_zip(path: Path) -> bool: - """Cheap ZIP validity check via central-directory parse.""" - - try: - with zipfile.ZipFile(path) as zf: - # ``namelist`` forces the central directory to be parsed. - zf.namelist() - return True - except (zipfile.BadZipFile, OSError): - return False - - -class _LargeDownloadAbort(RuntimeError): - """Raised when a download exceeds the safety threshold without opt-in.""" - - def __init__(self, label: str, size_bytes: int) -> None: - gb = size_bytes / (1024**3) - super().__init__( - f"{label} would download ~{gb:.1f} GB, above the {_LARGE_DOWNLOAD_BYTES / (1024**3):.0f} GB safety cap. " - "Re-run with `--allow-large-download` to acknowledge, or use " - "`--skip-snippet-filter` to bypass this download entirely and " - "ingest the full corpus instead." - ) - - -def _read_snippet_ids(zip_path: Path, *, tasks: list[str]) -> dict[str, set[str]]: - """Walk the ZIP for files whose path contains any task name. - - Each MedRAG retriever produces one JSON file per task in the zip; - we union all retrievers' top-K ids. The exact directory layout has - historically been ``/.json`` mapping - ``question_id -> [snippet_id, ...]``. - """ - - out: dict[str, set[str]] = {t: set() for t in tasks} - with zipfile.ZipFile(zip_path, "r") as zf: - for member in zf.namelist(): - if not member.lower().endswith(".json"): - continue - stem = Path(member).stem.lower() - for task in tasks: - if task.lower() in stem: - try: - with zf.open(member) as fh: - payload = json.loads(fh.read().decode("utf-8")) - except (json.JSONDecodeError, KeyError): - continue - for ids in payload.values(): - if isinstance(ids, list): - for sid in ids: - if isinstance(sid, str): - out[task].add(sid) - elif isinstance(sid, dict) and "id" in sid: - out[task].add(str(sid["id"])) - break - return out - - -def _load_corpus(corpus_name: str, snippet_ids: set[str] | None) -> Iterable[SnippetRow]: - """Stream rows from a MedRAG HF corpus. - - * ``snippet_ids=None`` → yield every row (full-corpus ingestion path). - * ``snippet_ids={...}`` → filter to the requested ids. - - Imported lazily — ``datasets`` is a heavyweight dep. - """ - - if snippet_ids is not None and not snippet_ids: - return iter(()) - from datasets import load_dataset # noqa: PLC0415 - - logger.info("Loading corpus %s (this may take a while)", corpus_name) - ds = load_dataset(corpus_name, split="train", streaming=True) - for row in ds: - sid = str(row.get("id") or "") - if snippet_ids is not None and sid not in snippet_ids: - continue - yield SnippetRow( - snippet_id=sid, - title=str(row.get("title") or ""), - content=str(row.get("content") or row.get("contents") or ""), - ) - - -# --------------------------------------------------------------------------- -# Batching + upload -# --------------------------------------------------------------------------- - - -@dataclass -class SnippetBatch: - path: Path - snippet_ids: list[str] - - -def _write_batches( - snippets: Iterable[SnippetRow], - *, - out_dir: Path, - batch_bytes: int = _BATCH_SIZE_BYTES, - prefix: str = "mirage", -) -> list[SnippetBatch]: - out_dir.mkdir(parents=True, exist_ok=True) - batches: list[SnippetBatch] = [] - current_buffer = io.StringIO() - current_ids: list[str] = [] - current_bytes = 0 - batch_idx = 0 - - def _flush() -> None: - nonlocal current_buffer, current_ids, current_bytes, batch_idx - if not current_ids: - return - path = out_dir / f"{prefix}_{batch_idx:04d}.md" - path.write_text(current_buffer.getvalue(), encoding="utf-8") - batches.append(SnippetBatch(path=path, snippet_ids=current_ids)) - batch_idx += 1 - current_buffer = io.StringIO() - current_ids = [] - current_bytes = 0 - - for snippet in snippets: - chunk = snippet.to_markdown() + "\n---\n\n" - chunk_bytes = len(chunk.encode("utf-8")) - if current_bytes + chunk_bytes > batch_bytes and current_ids: - _flush() - current_buffer.write(chunk) - current_ids.append(snippet.snippet_id) - current_bytes += chunk_bytes - _flush() - return batches - - -# --------------------------------------------------------------------------- -# Public entry point -# --------------------------------------------------------------------------- - - -async def run_ingest( - ctx: RunContext, - *, - tasks: list[str] | None = None, - corpus: str = _DEFAULT_CORPUS, - max_snippets_per_task: int | None = None, - skip_snippet_filter: bool = True, - allow_large_download: bool = False, - settings: IngestSettings | None = None, -) -> None: - """Ingest a MedRAG corpus into the suite SearchSpace. - - By default (``skip_snippet_filter=True``) we ingest the **entire** - chosen corpus and let SurfSense's own retriever do the work. The - upstream MIRAGE retrieval zip is ~16 GB and only useful when you - want to pre-filter the corpus to the set of snippets some other - retriever surfaced; for ``MedRAG/textbooks`` (212 MB / 125k snippets) - that pre-filter is unnecessary overhead and routinely fails to - download (box.com truncates the stream). Set - ``skip_snippet_filter=False`` (CLI: ``--use-snippet-filter``) only - if you specifically want the upstream filter — and budget the - 16 GB zip transfer. - """ - - tasks = tasks or ["mmlu", "medqa", "medmcqa", "pubmedqa", "bioasq"] - settings = settings or IngestSettings(use_vision_llm=False, processing_mode="basic") - - bench_path = ctx.benchmark_data_dir() / "benchmark.json" - await _fetch_to_path(MIRAGE_BENCHMARK_URL, dest=bench_path, label="MIRAGE benchmark.json") - - if skip_snippet_filter: - logger.info( - "Skipping retrieved_snippets_10k.zip (skip_snippet_filter=True); " - "ingesting entire corpus %s.", - corpus, - ) - snippets = list(_load_corpus(corpus, snippet_ids=None)) - else: - zip_path = ctx.benchmark_data_dir() / "retrieved_snippets_10k.zip" - await _fetch_to_path( - MIRAGE_SNIPPETS_ZIP_URL, - dest=zip_path, - label="MIRAGE retrieved_snippets_10k.zip", - allow_large_download=allow_large_download, - expect_zip=True, - ) - - by_task = _read_snippet_ids(zip_path, tasks=tasks) - if max_snippets_per_task is not None: - by_task = {k: set(list(v)[:max_snippets_per_task]) for k, v in by_task.items()} - - union_ids = set().union(*by_task.values()) - logger.info( - "MIRAGE: tasks=%s, snippet ids per task: %s, union=%d", - tasks, - {k: len(v) for k, v in by_task.items()}, - len(union_ids), - ) - if not union_ids: - raise RuntimeError( - f"No snippet ids parsed for tasks {tasks!r} from {zip_path}. " - "Check the zip layout (the upstream archive may have changed)." - ) - - snippets = list(_load_corpus(corpus, snippet_ids=union_ids)) - logger.info( - "Loaded %d / %d requested snippets from corpus %s", - len(snippets), - len(union_ids), - corpus, - ) - if not snippets: - raise RuntimeError( - f"Corpus {corpus} returned 0 matching rows. Either the snippet " - "ids reference a different corpus (e.g. PubMed) or the HF mirror " - "is unavailable. Pass --corpus to override." - ) - - batches_dir = ctx.benchmark_data_dir() / "batches" - batches = _write_batches(snippets, out_dir=batches_dir) - logger.info("Wrote %d snippet batches to %s", len(batches), batches_dir) - - docs_client = ctx.documents_client() - upload_result = await docs_client.upload( - files=[b.path for b in batches], - search_space_id=ctx.search_space_id, - use_vision_llm=settings.use_vision_llm, - processing_mode=settings.processing_mode, - ) - logger.info("MIRAGE upload settings: %s", settings.render_label()) - new_doc_ids = list(upload_result.document_ids) - if new_doc_ids: - await docs_client.wait_until_ready( - search_space_id=ctx.search_space_id, - document_ids=new_doc_ids, - timeout_s=3600.0, - max_poll_s=15.0, - ) - - statuses = await docs_client.get_status( - search_space_id=ctx.search_space_id, - document_ids=new_doc_ids + upload_result.duplicate_document_ids, - ) - title_to_doc = {s.title: s.document_id for s in statuses} - - snippet_map_path = ctx.maps_dir() / "mirage_snippet_map.jsonl" - chunk_map_path = ctx.maps_dir() / "mirage_chunk_map.jsonl" - with snippet_map_path.open("w", encoding="utf-8") as fh: - # Header line records the ingest-time settings (see - # core/ingest_settings.py for the protocol). - fh.write(settings_header_line(settings) + "\n") - for batch in batches: - doc_id = title_to_doc.get(batch.path.name) - if doc_id is None: - logger.warning("No document_id for batch %s", batch.path.name) - continue - for sid in batch.snippet_ids: - fh.write( - json.dumps( - { - "snippet_id": sid, - "document_id": doc_id, - "batch_path": str(batch.path), - } - ) - + "\n" - ) - - # Best-effort chunk map. SurfSense doesn't expose snippet attribution - # per chunk, so we just record (chunk_id -> document_id) here; the - # MIRAGE runner only needs document_id for accuracy scoring. - with chunk_map_path.open("w", encoding="utf-8") as fh: - for doc_id in {b.path.name and title_to_doc.get(b.path.name) for b in batches} - {None}: - try: - chunks = await docs_client.list_chunks(int(doc_id)) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to list chunks for doc_id=%s: %s", doc_id, exc) - continue - for chunk in chunks: - fh.write(json.dumps({"chunk_id": chunk.id, "document_id": doc_id}) + "\n") - - new_state = ctx.suite_state - new_state.ingestion_maps["mirage"] = str(snippet_map_path) - set_suite_state(ctx.config, ctx.suite, new_state) - logger.info("Wrote MIRAGE maps to %s and %s", snippet_map_path, chunk_map_path) - - -__all__ = ["run_ingest", "SnippetRow", "SnippetBatch"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py deleted file mode 100644 index 3e5192aaa3..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py +++ /dev/null @@ -1,43 +0,0 @@ -"""MedRAG ``{step_by_step_thinking, answer_choice}`` MCQ prompt. - -Mirrors the MedRAG paper's prompt format so accuracy numbers are -comparable to the published MIRAGE leaderboard. -""" - -from __future__ import annotations - -from collections.abc import Mapping - -_PROMPT_TEMPLATE = """\ -You are a helpful medical expert. Answer the following multiple-choice -question using the relevant medical knowledge available to you (and any -retrieved context, if provided). - -Respond with a JSON object on a single line: -{{"step_by_step_thinking": "", "answer_choice": ""}} - -Question: {question} - -Options: -{options_block} -""" - - -def _options_block(options: Mapping[str, str]) -> str: - parts: list[str] = [] - for letter in sorted(options.keys()): - text = options.get(letter) - if text is None or text == "": - continue - parts.append(f"{letter}) {text}") - return "\n".join(parts) - - -def build_prompt(question: str, options: Mapping[str, str]) -> str: - return _PROMPT_TEMPLATE.format( - question=question.strip(), - options_block=_options_block(options), - ) - - -__all__ = ["build_prompt"] diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py deleted file mode 100644 index 76e719f1fd..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py +++ /dev/null @@ -1,344 +0,0 @@ -"""MIRAGE runner: SurfSense-only per-task accuracy. - -The benchmark file format is one top-level dict per task (``mmlu``, -``medqa``, ``medmcqa``, ``pubmedqa``, ``bioasq``); each task value is -``{question_id: {question, options, answer}}``. - -We restrict retrieval to the suite SearchSpace's full corpus (no -``mentioned_document_ids`` — MIRAGE has no per-question ground-truth -document; retrieval *is* the test). Accuracy is paired against the -``answer`` letter from the dataset. -""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import logging -from dataclasses import dataclass -from typing import Any - -from ....core.arms import ArmRequest, ArmResult, SurfSenseArm -from ....core.config import utc_iso_timestamp -from ....core.ingest_settings import ( - IngestSettings, - add_ingest_settings_args, - format_ingest_settings_md, - read_settings_header, -) -from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci, macro_accuracy -from ....core.registry import ( - ReportSection, - RunArtifact, - RunContext, -) -from .prompt import build_prompt - -logger = logging.getLogger(__name__) - - -_TASKS = ("mmlu", "medqa", "medmcqa", "pubmedqa", "bioasq") -_DESCRIPTION = "MIRAGE (7,663 medical MCQs) — single-arm SurfSense per-task accuracy." - -# MIRAGE corpus is text-only (textbook + abstract markdown). Vision -# LLM at ingest is wasted compute by default; flip ``--use-vision-llm`` -# to measure cost. -_DEFAULT_INGEST_SETTINGS = IngestSettings( - use_vision_llm=False, - processing_mode="basic", -) - - -@dataclass -class MirageQuestion: - task: str - qid: str - question: str - options: dict[str, str] - correct: str - - @property - def question_id(self) -> str: - return f"{self.task}::{self.qid}" - - -def _load_questions( - benchmark: dict[str, Any], - *, - tasks: list[str], - sample_n: int | None, -) -> list[MirageQuestion]: - out: list[MirageQuestion] = [] - for task in tasks: - rows = benchmark.get(task) or {} - if not isinstance(rows, dict): - continue - for qid, raw in rows.items(): - if not isinstance(raw, dict): - continue - options = raw.get("options") or {} - if not isinstance(options, dict): - continue - answer_raw = str(raw.get("answer") or "").strip() - if not answer_raw: - continue - answer_letter = answer_raw[:1].upper() - out.append( - MirageQuestion( - task=task, - qid=str(qid), - question=str(raw.get("question", "")), - options={str(k): str(v) for k, v in options.items() if v}, - correct=answer_letter, - ) - ) - out.sort(key=lambda q: (q.task, q.qid)) - if sample_n is not None and sample_n > 0: - # Stratified-by-task slice so smoke runs cover every task. - per_task = max(1, sample_n // max(1, len(tasks))) - sliced: list[MirageQuestion] = [] - per_task_counter: dict[str, int] = {} - for q in out: - n = per_task_counter.get(q.task, 0) - if n >= per_task: - continue - sliced.append(q) - per_task_counter[q.task] = n + 1 - if len(sliced) >= sample_n: - break - out = sliced - return out - - -async def _gather_with_limit(coros, *, concurrency: int) -> list[Any]: - sem = asyncio.Semaphore(max(1, concurrency)) - - async def _wrap(c): - async with sem: - return await c - - return await asyncio.gather(*(_wrap(c) for c in coros)) - - -class MirageBenchmark: - suite: str = "medical" - name: str = "mirage" - headline: bool = False - description: str = _DESCRIPTION - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: - parser.add_argument( - "--task", - default="all", - choices=("all", *_TASKS), - help="Run a single task or all (default: all).", - ) - parser.add_argument( - "--n", - dest="sample_n", - type=int, - default=None, - help="Stratified sample size across tasks.", - ) - parser.add_argument("--concurrency", type=int, default=4) - parser.add_argument( - "--corpus", - default="MedRAG/textbooks", - help="HF MedRAG corpus to ingest from (default: MedRAG/textbooks).", - ) - parser.add_argument( - "--max-snippets-per-task", - type=int, - default=None, - help="Cap the per-task ingestion to N snippets (smoke).", - ) - # Mutually exclusive: by default we skip the upstream 16 GB - # retrievals zip and ingest the entire corpus. Operators who - # want the upstream pre-filter pass --use-snippet-filter (and, - # if their corpus mismatch warrants the 16 GB transfer, - # --allow-large-download). - snippet_group = parser.add_mutually_exclusive_group() - snippet_group.add_argument( - "--use-snippet-filter", - dest="use_snippet_filter", - action="store_true", - default=False, - help="Download retrieved_snippets_10k.zip (~16 GB) and " - "filter the corpus to those ids before ingest. " - "Default: skip and ingest entire corpus.", - ) - snippet_group.add_argument( - "--skip-snippet-filter", - dest="use_snippet_filter", - action="store_false", - help="(Default) Skip the 16 GB upstream zip; ingest entire corpus.", - ) - parser.add_argument( - "--allow-large-download", - action="store_true", - default=False, - help="Permit downloads larger than 2 GB (e.g. retrieved_snippets_10k.zip).", - ) - # Per-upload knobs; ignored at run-time (runner reads the - # resolved settings out of the snippet-map manifest header). - add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS) - - async def ingest(self, ctx: RunContext, **opts: Any) -> None: - from .ingest import run_ingest - - settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts) - await run_ingest( - ctx, - corpus=str(opts.get("corpus") or "MedRAG/textbooks"), - max_snippets_per_task=opts.get("max_snippets_per_task"), - skip_snippet_filter=not bool(opts.get("use_snippet_filter")), - allow_large_download=bool(opts.get("allow_large_download")), - settings=settings, - ) - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: - task_filter = opts.get("task") or "all" - tasks = list(_TASKS) if task_filter == "all" else [task_filter] - sample_n = opts.get("sample_n") - concurrency = int(opts.get("concurrency") or 4) - - bench_path = ctx.benchmark_data_dir() / "benchmark.json" - if not bench_path.exists(): - raise RuntimeError( - "MIRAGE benchmark.json missing. Run " - "`python -m surfsense_evals ingest medical mirage` first." - ) - benchmark = json.loads(bench_path.read_text(encoding="utf-8")) - ingest_settings = read_settings_header(ctx.maps_dir() / "mirage_snippet_map.jsonl") - questions = _load_questions(benchmark, tasks=tasks, sample_n=sample_n) - if not questions: - raise RuntimeError( - f"No MIRAGE questions matched task={task_filter!r} sample_n={sample_n!r}." - ) - logger.info("MIRAGE: scheduled %d questions across tasks %s", len(questions), tasks) - - arm = SurfSenseArm( - client=ctx.new_chat_client(), - search_space_id=ctx.search_space_id, - ephemeral_threads=True, - ) - - async def _ask(q: MirageQuestion) -> ArmResult: - request = ArmRequest( - question_id=q.question_id, - prompt=build_prompt(q.question, q.options), - ) - return await arm.answer(request) - - results: list[ArmResult] = await _gather_with_limit( - (_ask(q) for q in questions), concurrency=concurrency - ) - - run_timestamp = utc_iso_timestamp() - run_dir = ctx.runs_dir(run_timestamp=run_timestamp) - raw_path = run_dir / "raw.jsonl" - with raw_path.open("w", encoding="utf-8") as fh: - for q, res in zip(questions, results, strict=False): - fh.write( - json.dumps( - { - "task": q.task, - "qid": q.qid, - "correct": q.correct, - **res.to_jsonl(), - } - ) - + "\n" - ) - - per_task_acc: dict[str, dict[str, Any]] = {} - for task in tasks: - n_correct = 0 - n_total = 0 - for q, res in zip(questions, results, strict=False): - if q.task != task: - continue - n_total += 1 - if (res.answer_letter or "").upper() == q.correct: - n_correct += 1 - acc = accuracy_with_wilson_ci(n_correct, n_total) - per_task_acc[task] = acc.to_dict() - - macro = macro_accuracy( - { - t: accuracy_with_wilson_ci(d["n_correct"], d["n_total"]) - for t, d in per_task_acc.items() - } - ) - metrics = {"per_task": per_task_acc, "macro_accuracy": macro} - - artifact = RunArtifact( - suite=self.suite, - benchmark=self.name, - run_timestamp=run_timestamp, - raw_path=raw_path, - metrics=metrics, - extra={ - "n_questions": len(questions), - "task_filter": task_filter, - "concurrency": concurrency, - "provider_model": ctx.provider_model, - "ingest_settings": ingest_settings, - }, - ) - manifest_path = run_dir / "run_artifact.json" - manifest_path.write_text( - json.dumps( - { - "suite": self.suite, - "benchmark": self.name, - "raw_path": "raw.jsonl", - "metrics": metrics, - "extra": artifact.extra, - }, - indent=2, - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - return artifact - - def report_section(self, artifacts: list[RunArtifact]) -> ReportSection: - if not artifacts: - return ReportSection( - title="MIRAGE — single-arm SurfSense per-task accuracy", - headline=False, - body_md="(no run artifacts found)", - body_json={}, - ) - latest = max(artifacts, key=lambda a: a.run_timestamp) - per_task = latest.metrics.get("per_task", {}) - macro = latest.metrics.get("macro_accuracy", 0.0) - lines: list[str] = [] - lines.append(format_ingest_settings_md(latest.extra.get("ingest_settings"))) - for task in _TASKS: - row = per_task.get(task) - if not row: - continue - acc = row.get("accuracy", 0.0) - low = row.get("ci_low", 0.0) - high = row.get("ci_high", 0.0) - lines.append( - f"- {task}: {acc * 100:.1f}% " - f"(Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%, " - f"n={row.get('n_total', '?')})" - ) - if not lines: - lines.append("- (no per-task results)") - lines.append(f"- Macro accuracy: {macro * 100:.2f}%") - return ReportSection( - title="MIRAGE — single-arm SurfSense per-task accuracy", - headline=False, - body_md="\n".join(lines), - body_json=latest.metrics, - ) - - -__all__ = ["MirageBenchmark", "MirageQuestion"] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py deleted file mode 100644 index 22682ed3f7..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Multimodal long-document benchmarks (PDFs with embedded images/charts/tables). - -Distinct from the medical suite because these documents are domain-mixed -(research reports, financials, manuals, government, brochures, papers). -The hypothesis being tested here is *general*: does SurfSense's -chunking-based vision RAG preserve information that lives in pixels — -across long PDFs, across pages — versus feeding the same PDF directly -to a vision-capable model? - -Subpackages register themselves with ``core.registry`` on import. The -``suites/__init__.py`` discovery walker imports them automatically. -""" - -from __future__ import annotations diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py deleted file mode 100644 index 1c2bfa84c2..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""MMLongBench-Doc — head-to-head Native PDF (vision) vs SurfSense (vision RAG). - -Source: https://huggingface.co/datasets/yubo2333/MMLongBench-Doc -Paper: https://arxiv.org/abs/2407.01523 (NeurIPS 2024 D&B Track) - -* 135 long PDFs (avg 47 pages, multi-modal: text, images, charts, tables) -* 1,091 expert-annotated questions -* 33% require evidence from multiple pages -* ~22% intentionally unanswerable (tests hallucination resistance) -* 7 document types: research report, tutorial/workshop, academic paper, - financial report, brochure, government, manuals -""" - -from __future__ import annotations - -from ....core import registry as _registry -from .runner import MMLongBenchDocBenchmark - -_registry.register(MMLongBenchDocBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py deleted file mode 100644 index ecb5144e88..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py +++ /dev/null @@ -1,240 +0,0 @@ -"""Format-aware grader for MMLongBench-Doc answers. - -The dataset ships with five ``answer_format`` values per question: - -* ``Str`` — short factoid string -* ``Int`` — integer count / year -* ``Float`` — decimal number (often with units stripped) -* ``List`` — comma- or semicolon-separated bag of items -* ``None`` — gold answer is literally "Not answerable" (hallucination probe) - -The official MMLongBench-Doc paper grades with GPT-4 as judge. We -implement a *deterministic* rule-based grader as the default (so two -researchers running the same harness get the same number); an -LLM-judge mode is exposed via ``--judge gpt5`` and routed through the -same OpenRouter key the arms use, but is opt-in to keep cost down. - -Returned by every grading call: - -* ``correct: bool`` — final pass/fail used for accuracy + McNemar -* ``f1: float`` — token-level F1 (continuous credit, useful when - comparing arms that get *most* of a list right) -* ``method: str`` — which path graded the row (one of - ``str_norm`` / ``int_eq`` / ``float_tol`` / ``list_set`` / - ``none_match`` / ``llm_judge``). -""" - -from __future__ import annotations - -import re -import string -from collections import Counter -from dataclasses import dataclass - -# --------------------------------------------------------------------------- -# Public types -# --------------------------------------------------------------------------- - - -@dataclass -class GradeResult: - correct: bool - f1: float - method: str - normalised_pred: str = "" - normalised_gold: str = "" - - -# --------------------------------------------------------------------------- -# Normalisation helpers (shared) -# --------------------------------------------------------------------------- - -_PUNCT_TABLE = str.maketrans({c: " " for c in string.punctuation}) -_ARTICLES = re.compile(r"\b(a|an|the)\b", re.IGNORECASE) -_WS = re.compile(r"\s+") -_NOT_ANSWERABLE_TOKENS = { - "not answerable", - "cannot be answered", - "cannot answer", - "no answer", - "unknown", - "none", - "not specified", - "not mentioned", - "not provided", - "the answer is not in the document", -} - -# Abbreviations that should be matched literally on the lowercased -# prediction (because normalisation strips their punctuation and -# leaves them too short to be safe as substring tokens). -_NOT_ANSWERABLE_LITERAL = {"n/a", "na/", "n.a.", "n a"} - - -def _normalise_text(s: str) -> str: - """SQuAD-style normalisation: lowercase, drop punctuation/articles, squash whitespace.""" - - s = s.lower() - s = s.translate(_PUNCT_TABLE) - s = _ARTICLES.sub(" ", s) - s = _WS.sub(" ", s).strip() - return s - - -# --------------------------------------------------------------------------- -# Per-format graders -# --------------------------------------------------------------------------- - - -def _grade_str(pred: str, gold: str) -> GradeResult: - p = _normalise_text(pred) - g = _normalise_text(gold) - if not p: - return GradeResult(False, 0.0, "str_norm", p, g) - if p == g: - return GradeResult(True, 1.0, "str_norm", p, g) - # Substring match in either direction = correct (handles the common - # "model emits a fuller sentence containing the gold" case). - if g and (g in p or p in g): - return GradeResult(True, _f1_tokens(p, g), "str_norm", p, g) - return GradeResult(False, _f1_tokens(p, g), "str_norm", p, g) - - -_INT_RE = re.compile(r"-?\d[\d,]*") - - -def _grade_int(pred: str, gold: str) -> GradeResult: - g_match = _INT_RE.search(gold) - if g_match is None: - return _grade_str(pred, gold) - g_val = int(g_match.group(0).replace(",", "")) - p_match = _INT_RE.search(pred) - if p_match is None: - return GradeResult(False, 0.0, "int_eq", str(p_match), str(g_val)) - p_val = int(p_match.group(0).replace(",", "")) - return GradeResult( - p_val == g_val, 1.0 if p_val == g_val else 0.0, "int_eq", str(p_val), str(g_val) - ) - - -_FLOAT_RE = re.compile(r"-?\d+(?:[.,]\d+)?") - - -def _grade_float(pred: str, gold: str, *, rel_tol: float = 1e-2) -> GradeResult: - g_match = _FLOAT_RE.search(gold) - if g_match is None: - return _grade_str(pred, gold) - g_val = float(g_match.group(0).replace(",", ".")) - p_match = _FLOAT_RE.search(pred) - if p_match is None: - return GradeResult(False, 0.0, "float_tol", "", str(g_val)) - p_val = float(p_match.group(0).replace(",", ".")) - # Tolerance: 1% relative or 0.01 absolute, whichever is looser. - abs_diff = abs(p_val - g_val) - tol = max(abs(g_val) * rel_tol, 0.01) - ok = abs_diff <= tol - return GradeResult(ok, 1.0 if ok else 0.0, "float_tol", str(p_val), str(g_val)) - - -_LIST_SPLIT = re.compile(r"[;,\n]") - - -def _grade_list(pred: str, gold: str) -> GradeResult: - g_items = {_normalise_text(x) for x in _LIST_SPLIT.split(gold) if x.strip()} - p_items = {_normalise_text(x) for x in _LIST_SPLIT.split(pred) if x.strip()} - if not g_items: - return _grade_str(pred, gold) - inter = g_items & p_items - if not inter: - return GradeResult( - False, 0.0, "list_set", ", ".join(sorted(p_items)), ", ".join(sorted(g_items)) - ) - precision = len(inter) / len(p_items) if p_items else 0.0 - recall = len(inter) / len(g_items) - f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0 - return GradeResult( - f1 >= 0.999, f1, "list_set", ", ".join(sorted(p_items)), ", ".join(sorted(g_items)) - ) - - -def _grade_none(pred: str, gold: str) -> GradeResult: - """Gold == 'Not answerable'. The arm earns credit if its prediction - expresses inability to answer. - - Two passes: - - 1. Literal-substring check on the lowercased+stripped pred for - ambiguous abbreviations like ``n/a`` (since normalisation - strips the punctuation and would over-match). - 2. Word-boundary substring check on the normalised pred for the - multi-word phrases (``cannot answer``, ``not specified`` etc.). - """ - - raw_lower = (pred or "").strip().lower() - p = _normalise_text(pred) - expressed_unknown = False - - # Pass 1: literal abbreviation hits on the raw lowercased text. - if any(lit in raw_lower for lit in _NOT_ANSWERABLE_LITERAL): - expressed_unknown = True - - # Pass 2: word-boundary check on normalised tokens. - if not expressed_unknown: - p_padded = f" {p} " - for tok_raw in _NOT_ANSWERABLE_TOKENS: - tok = _normalise_text(tok_raw) - if not tok or len(tok) < 3: - continue - if f" {tok} " in p_padded: - expressed_unknown = True - break - return GradeResult( - expressed_unknown, - 1.0 if expressed_unknown else 0.0, - "none_match", - p, - _normalise_text(gold), - ) - - -def _f1_tokens(pred: str, gold: str) -> float: - p_tok = pred.split() - g_tok = gold.split() - if not p_tok or not g_tok: - return 0.0 - common = Counter(p_tok) & Counter(g_tok) - overlap = sum(common.values()) - if overlap == 0: - return 0.0 - precision = overlap / len(p_tok) - recall = overlap / len(g_tok) - return 2 * precision * recall / (precision + recall) - - -# --------------------------------------------------------------------------- -# Public dispatcher -# --------------------------------------------------------------------------- - - -_FORMAT_DISPATCH = { - "str": _grade_str, - "int": _grade_int, - "float": _grade_float, - "list": _grade_list, - "none": _grade_none, -} - - -def grade(*, pred: str, gold: str, answer_format: str) -> GradeResult: - """Grade a single (prediction, gold) pair. - - ``answer_format`` is the dataset's ``answer_format`` column value. - Unknown / blank values fall through to string grading. - """ - - fmt = (answer_format or "").strip().lower() - fn = _FORMAT_DISPATCH.get(fmt, _grade_str) - return fn(pred or "", gold or "") - - -__all__ = ["GradeResult", "grade"] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py deleted file mode 100644 index 3c736756a3..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py +++ /dev/null @@ -1,379 +0,0 @@ -"""MMLongBench-Doc ingestion. - -Steps: - -1. Pull the questions parquet from - ``hf://datasets/yubo2333/MMLongBench-Doc/data/`` and cache locally. -2. Resolve the unique set of ``doc_id`` referenced by questions, and - download each PDF from - ``hf://datasets/yubo2333/MMLongBench-Doc/documents/``. - ``huggingface_hub.hf_hub_download`` is resumable + content-hash - verifying; we cache PDFs under ``/multimodal_doc/mmlongbench/pdfs/``. -3. Upload every PDF to SurfSense via ``DocumentsClient.upload`` with - ``use_vision_llm=True`` so SurfSense's Pillow + LiteLLM vision - pipeline extracts captions / OCR for embedded images, charts, and - tables. -4. Wait for ``processed`` status and persist - ``doc_id -> document_id`` in - ``/multimodal_doc/maps/mmlongbench_doc_map.jsonl``. - -By default we ingest **all** 135 PDFs (~660 MB, totally manageable). -Operators can scope to a subset with ``--max-docs N`` if iterating on -a slow vision pipeline. -""" - -from __future__ import annotations - -import json -import logging -import os -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path - -from ....core.config import set_suite_state -from ....core.ingest_settings import IngestSettings, settings_header_line -from ....core.registry import RunContext - -logger = logging.getLogger(__name__) - - -HF_REPO_ID = "yubo2333/MMLongBench-Doc" -HF_REPO_TYPE = "dataset" - - -# Lazy import: huggingface_hub + pyarrow are heavyweight; keep the -# benchmark module importable on machines that have only the core -# install (e.g. CI lint jobs). -def _hf_hub_download(*args, **kwargs): - from huggingface_hub import hf_hub_download - - return hf_hub_download(*args, **kwargs) - - -def _list_repo_files() -> list[str]: - from huggingface_hub import list_repo_files - - return list_repo_files(repo_id=HF_REPO_ID, repo_type=HF_REPO_TYPE) - - -# --------------------------------------------------------------------------- -# Question parquet -> Python rows -# --------------------------------------------------------------------------- - - -@dataclass -class MMLongBenchQuestion: - doc_id: str # filename inside the documents/ folder - doc_type: str - question: str - answer: str - answer_format: str # Str / Int / Float / List / None - evidence_pages: list[int] - evidence_sources: list[str] - - -def _load_questions_from_parquet(parquet_path: Path) -> list[MMLongBenchQuestion]: - import pyarrow.parquet as pq - - table = pq.read_table(parquet_path) - rows = table.to_pylist() - out: list[MMLongBenchQuestion] = [] - for row in rows: - doc_id = str(row.get("doc_id") or "").strip() - if not doc_id: - continue - question = str(row.get("question") or "").strip() - if not question: - continue - out.append( - MMLongBenchQuestion( - doc_id=doc_id, - doc_type=str(row.get("doc_type") or "").strip(), - question=question, - answer=str(row.get("answer") or "").strip(), - answer_format=str(row.get("answer_format") or "").strip(), - evidence_pages=_parse_int_list(row.get("evidence_pages")), - evidence_sources=_parse_str_list(row.get("evidence_sources")), - ) - ) - return out - - -def _parse_int_list(raw) -> list[int]: - if raw is None: - return [] - if isinstance(raw, list): - out = [] - for x in raw: - try: - out.append(int(x)) - except (TypeError, ValueError): - continue - return out - text = str(raw).strip().strip("[]") - if not text: - return [] - out: list[int] = [] - for tok in text.split(","): - tok = tok.strip().strip("'\"") - if tok.isdigit(): - out.append(int(tok)) - return out - - -def _parse_str_list(raw) -> list[str]: - if raw is None: - return [] - if isinstance(raw, list): - return [str(x).strip().strip("'\"") for x in raw if str(x).strip()] - text = str(raw).strip().strip("[]") - if not text: - return [] - return [tok.strip().strip("'\"") for tok in text.split(",") if tok.strip()] - - -# --------------------------------------------------------------------------- -# Download helpers -# --------------------------------------------------------------------------- - - -def _download_questions_parquet(cache_dir: Path) -> Path: - """Download every parquet under ``data/`` and concatenate. - - The HF dataset usually publishes a single ``train`` split, but we - enumerate to be robust to repo restructuring. - """ - - parquet_paths: list[Path] = [] - files = _list_repo_files() - data_files = [f for f in files if f.startswith("data/") and f.endswith(".parquet")] - if not data_files: - raise RuntimeError( - f"No parquet files found under data/ in {HF_REPO_ID}; " - f"upstream repo may have been restructured." - ) - for rel in sorted(data_files): - local = _hf_hub_download( - repo_id=HF_REPO_ID, - filename=rel, - repo_type=HF_REPO_TYPE, - cache_dir=str(cache_dir), - ) - parquet_paths.append(Path(local)) - logger.info("Cached MMLongBench parquet shard %s -> %s", rel, local) - return ( - parquet_paths[0] if len(parquet_paths) == 1 else _merge_parquets(parquet_paths, cache_dir) - ) - - -def _merge_parquets(paths: list[Path], cache_dir: Path) -> Path: - """Combine multiple parquet shards into one (rare branch, but correct).""" - - import pyarrow as pa - import pyarrow.parquet as pq - - tables = [pq.read_table(p) for p in paths] - merged = pa.concat_tables(tables, promote_options="default") - out = cache_dir / "merged_questions.parquet" - pq.write_table(merged, out) - return out - - -def _download_pdf(doc_id: str, cache_dir: Path, pdfs_dir: Path) -> Path: - """Download a single PDF (resumable via huggingface_hub cache).""" - - rel = f"documents/{doc_id}" - local = _hf_hub_download( - repo_id=HF_REPO_ID, - filename=rel, - repo_type=HF_REPO_TYPE, - cache_dir=str(cache_dir), - ) - # Materialise to a stable path inside our data/ tree so the runner - # has a deterministic location regardless of HF cache internals. - dest = pdfs_dir / doc_id - if not dest.exists() or dest.stat().st_size != Path(local).stat().st_size: - # Use a hardlink when possible (cheap), fall back to copy. - try: - if dest.exists(): - dest.unlink() - os.link(local, dest) - except OSError: - from shutil import copy2 - - copy2(local, dest) - return dest - - -# --------------------------------------------------------------------------- -# Upload helpers -# --------------------------------------------------------------------------- - - -async def _upload_pdfs( - ctx: RunContext, - pdf_paths: Iterable[Path], - *, - batch_size: int, - settings: IngestSettings, -) -> dict[str, int]: - """Upload PDFs in batches, return ``filename -> document_id`` map.""" - - docs_client = ctx.documents_client() - name_to_id: dict[str, int] = {} - pdf_list = list(pdf_paths) - for batch_start in range(0, len(pdf_list), batch_size): - batch = pdf_list[batch_start : batch_start + batch_size] - result = await docs_client.upload( - files=batch, - search_space_id=ctx.search_space_id, - use_vision_llm=settings.use_vision_llm, - processing_mode=settings.processing_mode, - ) - all_ids = list(result.document_ids) + list(result.duplicate_document_ids) - if all_ids: - await docs_client.wait_until_ready( - search_space_id=ctx.search_space_id, - document_ids=result.document_ids, # only newly added need polling - timeout_s=1800.0, # vision pipeline is slow on long PDFs - ) - statuses = await docs_client.get_status( - search_space_id=ctx.search_space_id, - document_ids=all_ids, - ) - for s in statuses: - name_to_id[s.title] = s.document_id - logger.info( - "Uploaded MMLongBench batch %d-%d: %d new, %d duplicate", - batch_start, - batch_start + len(batch), - len(result.document_ids), - len(result.duplicate_document_ids), - ) - return name_to_id - - -# --------------------------------------------------------------------------- -# Public entry point -# --------------------------------------------------------------------------- - - -async def run_ingest( - ctx: RunContext, - *, - max_docs: int | None = None, - upload_batch_size: int = 8, - skip_upload: bool = False, - settings: IngestSettings | None = None, -) -> None: - """Ingest MMLongBench-Doc into the multimodal_doc suite. - - Parameters - ---------- - max_docs : int | None - Cap the number of PDFs to download + upload. ``None`` = all 135. - Useful when iterating on the runner without paying for the full - vision pipeline pass each time. - upload_batch_size : int - How many PDFs to send per ``fileupload`` call. Smaller batches - recover faster from individual failures; larger batches reduce - round-trip overhead. - skip_upload : bool - Download + cache PDFs locally but skip SurfSense ingestion. - Useful for testing the native arm in isolation. - """ - - settings = settings or IngestSettings(use_vision_llm=True, processing_mode="basic") - bench_dir = ctx.benchmark_data_dir() - pdfs_dir = bench_dir / "pdfs" - pdfs_dir.mkdir(parents=True, exist_ok=True) - hf_cache = bench_dir / ".hf_cache" - hf_cache.mkdir(parents=True, exist_ok=True) - - # Step 1: questions - parquet_path = _download_questions_parquet(hf_cache) - questions = _load_questions_from_parquet(parquet_path) - if not questions: - raise RuntimeError( - "MMLongBench-Doc parquet contains no parseable questions. " - "Upstream may have changed schema." - ) - - # Persist a copy alongside the PDFs so the runner has one place to read. - questions_jsonl = bench_dir / "questions.jsonl" - with questions_jsonl.open("w", encoding="utf-8") as fh: - for q in questions: - fh.write( - json.dumps( - { - "doc_id": q.doc_id, - "doc_type": q.doc_type, - "question": q.question, - "answer": q.answer, - "answer_format": q.answer_format, - "evidence_pages": q.evidence_pages, - "evidence_sources": q.evidence_sources, - } - ) - + "\n" - ) - logger.info("Wrote %d MMLongBench questions to %s", len(questions), questions_jsonl) - - # Step 2: download unique PDFs - unique_doc_ids = sorted({q.doc_id for q in questions}) - if max_docs is not None and max_docs > 0: - unique_doc_ids = unique_doc_ids[:max_docs] - logger.info("MMLongBench: downloading %d unique PDFs", len(unique_doc_ids)) - - pdf_paths: dict[str, Path] = {} - for i, doc_id in enumerate(unique_doc_ids, start=1): - try: - pdf_paths[doc_id] = _download_pdf(doc_id, hf_cache, pdfs_dir) - if i % 10 == 0: - logger.info(" ... %d / %d PDFs cached", i, len(unique_doc_ids)) - except Exception as exc: # noqa: BLE001 - logger.warning("Failed to download MMLongBench PDF %s: %s", doc_id, exc) - - # Step 3: upload to SurfSense - name_to_id: dict[str, int] = {} - if skip_upload: - logger.info("MMLongBench: --skip-upload set; skipping SurfSense ingestion") - else: - logger.info("MMLongBench upload settings: %s", settings.render_label()) - name_to_id = await _upload_pdfs( - ctx, - pdf_paths.values(), - batch_size=upload_batch_size, - settings=settings, - ) - - # Step 4: persist doc_id -> document_id manifest - map_path = ctx.maps_dir() / "mmlongbench_doc_map.jsonl" - with map_path.open("w", encoding="utf-8") as fh: - # Header line records the resolved ingest settings - # (see core/ingest_settings.py). - fh.write(settings_header_line(settings) + "\n") - for doc_id in unique_doc_ids: - local = pdf_paths.get(doc_id) - if local is None: - continue - fh.write( - json.dumps( - { - "doc_id": doc_id, - "document_id": name_to_id.get(local.name), - "pdf_path": str(local), - "n_questions": sum(1 for q in questions if q.doc_id == doc_id), - } - ) - + "\n" - ) - logger.info("Wrote MMLongBench doc map to %s", map_path) - - new_state = ctx.suite_state - new_state.ingestion_maps["mmlongbench"] = str(map_path) - set_suite_state(ctx.config, ctx.suite, new_state) - - -__all__ = ["MMLongBenchQuestion", "run_ingest"] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py deleted file mode 100644 index 70229dc158..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py +++ /dev/null @@ -1,57 +0,0 @@ -"""MMLongBench-Doc prompt template. - -Both arms get the same prompt — only the document delivery channel -differs (native PDF embedded in the OpenRouter request vs SurfSense -RAG retrieval). The format hint in the prompt mirrors what the -upstream paper uses so the grader's regex can reliably extract the -answer. -""" - -from __future__ import annotations - -# --------------------------------------------------------------------------- -# Per-format hint blocks -# --------------------------------------------------------------------------- - -_FORMAT_HINTS: dict[str, str] = { - "str": ( - "Respond with the answer as a short phrase, no full sentence. " - "Format your final line as `Answer: `." - ), - "int": ("Respond with a single integer only. Format your final line as `Answer: `."), - "float": ( - "Respond with a single decimal number only (no units). " - "Format your final line as `Answer: `." - ), - "list": ( - "Respond with a comma-separated list of items, no extra text. " - "Format your final line as `Answer: item1, item2, item3`." - ), - "none": ( - "If the answer cannot be determined from the document, say so explicitly. " - "Format your final line as `Answer: Not answerable`." - ), -} - - -_PROMPT = """\ -You are a document-understanding assistant. Use ONLY the provided -document to answer the question. The document may contain text, -tables, charts, figures, and images. If the answer is in a chart or -image, read it carefully. Do not use external knowledge. - -Question: {question} - -{format_hint} -""" - - -def build_prompt(question: str, *, answer_format: str) -> str: - """Assemble the full prompt for one MMLongBench question.""" - - fmt = (answer_format or "str").strip().lower() - hint = _FORMAT_HINTS.get(fmt, _FORMAT_HINTS["str"]) - return _PROMPT.format(question=question.strip(), format_hint=hint) - - -__all__ = ["build_prompt"] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py deleted file mode 100644 index 782ba5d9ac..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py +++ /dev/null @@ -1,742 +0,0 @@ -"""MMLongBench-Doc runner — head-to-head Native PDF (vision) vs SurfSense (vision RAG). - -Differences from a typical MCQ head-to-head: - -* Open-ended answers (Str / Int / Float / List / Not-answerable) — uses - ``extract_freeform_answer`` instead of ``extract_answer_letter``. -* Format-aware grader (see ``.grader``) returns both binary correctness - (for accuracy / McNemar) and continuous F1 (for nuanced reporting). -* Native arm requires a vision-capable model — we don't enforce this - in code (operator's choice via ``setup --provider-model``) but we - emit a warning if the pinned slug looks text-only. -""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import logging -import os -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from ....core.arms import ArmRequest, ArmResult, NativePdfArm, SurfSenseArm -from ....core.config import utc_iso_timestamp -from ....core.ingest_settings import ( - IngestSettings, - add_ingest_settings_args, - format_ingest_settings_md, - is_settings_header, -) -from ....core.metrics.comparison import ( - bootstrap_delta_ci, - mcnemar_test, - paired_aggregate, -) -from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci -from ....core.parse.freeform_answer import extract_freeform_answer -from ....core.providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine -from ....core.registry import ( - ReportSection, - RunArtifact, - RunContext, -) -from ....core.scenarios import format_scenario_md -from .grader import GradeResult, grade -from .prompt import build_prompt - -logger = logging.getLogger(__name__) - - -# --------------------------------------------------------------------------- -# Question + map row shapes -# --------------------------------------------------------------------------- - - -@dataclass -class MMLBQuestion: - qid: str # synthesised from doc_id + index - doc_id: str # filename inside the documents/ folder - doc_type: str - question: str - gold_answer: str - answer_format: str - evidence_pages: list[int] - evidence_sources: list[str] - pdf_path: Path - document_id: int | None # SurfSense doc id (None if upload skipped) - - -def _load_doc_map(map_path: Path) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]: - """Read the doc map JSONL. - - Returns ``(rows, settings)`` where ``settings`` is the - ``__settings__`` header blob (or ``{}`` for legacy maps). - """ - - rows: dict[str, dict[str, Any]] = {} - settings: dict[str, Any] = {} - with map_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - if is_settings_header(row): - settings = dict(row["__settings__"]) - continue - rows[str(row["doc_id"])] = row - return rows, settings - - -def _load_questions( - questions_jsonl: Path, - doc_map: dict[str, dict[str, Any]], - *, - doc_filter: list[str] | None, - format_filter: str | None, - sample_n: int | None, - skip_unanswerable: bool, -) -> list[MMLBQuestion]: - out: list[MMLBQuestion] = [] - per_doc_counter: dict[str, int] = {} - with questions_jsonl.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - doc_id = str(row.get("doc_id") or "").strip() - if not doc_id: - continue - if doc_filter and doc_id not in doc_filter: - continue - map_row = doc_map.get(doc_id) - if map_row is None: - logger.debug("No doc-map entry for %s; skipping", doc_id) - continue - answer_format = str(row.get("answer_format") or "").strip().lower() - if format_filter and format_filter != "all" and format_filter != answer_format: - continue - gold = str(row.get("answer") or "").strip() - if skip_unanswerable and answer_format == "none": - continue - idx = per_doc_counter.get(doc_id, 0) - per_doc_counter[doc_id] = idx + 1 - out.append( - MMLBQuestion( - qid=f"{doc_id}::Q{idx:03d}", - doc_id=doc_id, - doc_type=str(row.get("doc_type") or "").strip(), - question=str(row.get("question") or "").strip(), - gold_answer=gold, - answer_format=answer_format, - evidence_pages=list(row.get("evidence_pages") or []), - evidence_sources=list(row.get("evidence_sources") or []), - pdf_path=Path(map_row["pdf_path"]), - document_id=map_row.get("document_id"), - ) - ) - out.sort(key=lambda q: (q.doc_id, q.qid)) - if sample_n is not None and sample_n > 0: - out = out[:sample_n] - return out - - -# --------------------------------------------------------------------------- -# Bounded concurrency helper -# --------------------------------------------------------------------------- - - -async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]: - sem = asyncio.Semaphore(max(1, concurrency)) - - async def _wrap(coro): - async with sem: - return await coro - - return await asyncio.gather(*(_wrap(c) for c in coros)) - - -# --------------------------------------------------------------------------- -# Benchmark -# --------------------------------------------------------------------------- - - -_DESCRIPTION = ( - "MMLongBench-Doc (135 long PDFs, 1,091 multimodal questions) — " - "Native PDF (vision) vs SurfSense (vision RAG) head-to-head." -) - - -_TEXT_ONLY_HINTS = ("gpt-5.4-mini", "gpt-3.5", "text-only", "instruct-") - -# MMLongBench-Doc PDFs are long documents with figures, charts, and -# tables. Vision LLM at ingest is the whole point; flip --no-vision-llm -# to measure how much SurfSense degrades on real document images. -_DEFAULT_INGEST_SETTINGS = IngestSettings( - use_vision_llm=True, - processing_mode="basic", -) - - -class MMLongBenchDocBenchmark: - """Long-document multimodal RAG vs native vision.""" - - suite: str = "multimodal_doc" - name: str = "mmlongbench" - headline: bool = True - description: str = _DESCRIPTION - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: - parser.add_argument( - "--docs", - default=None, - help="Comma-separated doc_ids (filenames) to run (default: all).", - ) - parser.add_argument( - "--format", - default="all", - choices=["all", "str", "int", "float", "list", "none"], - help="Filter to one answer format. 'none' = unanswerable probes only.", - ) - parser.add_argument( - "--n", - dest="sample_n", - type=int, - default=None, - help="Run only the first N questions after filters apply.", - ) - parser.add_argument( - "--skip-unanswerable", - dest="skip_unanswerable", - action="store_true", - help="Drop ~22%% unanswerable questions (use to compare against baselines that don't include them).", - ) - parser.add_argument( - "--concurrency", - type=int, - default=4, - help="Parallel question workers per arm.", - ) - parser.add_argument( - "--no-mentions", - dest="no_mentions", - action="store_true", - help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).", - ) - parser.add_argument( - "--pdf-engine", - default="native", - choices=[e.value for e in PdfEngine], - help="OpenRouter file-parser engine for the native arm.", - ) - parser.add_argument( - "--max-output-tokens", - type=int, - default=512, - help="Cap on completion length for both arms.", - ) - # Ingest-only knobs (forwarded by the CLI to ingest.run_ingest). - parser.add_argument( - "--max-docs", - dest="max_docs", - type=int, - default=None, - help="(ingest only) cap on number of unique PDFs to download + upload.", - ) - parser.add_argument( - "--upload-batch-size", - dest="upload_batch_size", - type=int, - default=8, - help="(ingest only) PDFs per fileupload call.", - ) - parser.add_argument( - "--skip-upload", - dest="skip_upload", - action="store_true", - help="(ingest only) cache PDFs locally but don't push to SurfSense.", - ) - # Per-upload knobs forwarded to /documents/fileupload at ingest; - # ignored at run-time (runner reads the resolved settings out of - # the doc-map manifest header). - add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS) - - async def ingest(self, ctx: RunContext, **opts: Any) -> None: - from .ingest import run_ingest - - settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts) - await run_ingest( - ctx, - max_docs=opts.get("max_docs"), - upload_batch_size=int(opts.get("upload_batch_size") or 8), - skip_upload=bool(opts.get("skip_upload", False)), - settings=settings, - ) - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: - docs_raw: str | None = opts.get("docs") - doc_filter = [d.strip() for d in docs_raw.split(",")] if docs_raw else None - format_filter = opts.get("format") or "all" - sample_n = opts.get("sample_n") - skip_unanswerable = bool(opts.get("skip_unanswerable")) - concurrency = int(opts.get("concurrency") or 4) - no_mentions = bool(opts.get("no_mentions")) - pdf_engine_name = opts.get("pdf_engine") or "native" - max_output_tokens = int(opts.get("max_output_tokens") or 512) - - bench_dir = ctx.benchmark_data_dir() - questions_jsonl = bench_dir / "questions.jsonl" - map_path = ctx.maps_dir() / "mmlongbench_doc_map.jsonl" - if not questions_jsonl.exists() or not map_path.exists(): - raise RuntimeError( - "MMLongBench-Doc not ingested for this suite. Run " - "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first." - ) - - doc_map, ingest_settings = _load_doc_map(map_path) - questions = _load_questions( - questions_jsonl, - doc_map, - doc_filter=doc_filter, - format_filter=None if format_filter == "all" else format_filter, - sample_n=sample_n, - skip_unanswerable=skip_unanswerable, - ) - if not questions: - raise RuntimeError( - "No MMLongBench questions matched the filters; broaden --docs/--format/--n." - ) - logger.info("MMLongBench-Doc: scheduled %d questions", len(questions)) - - api_key = os.environ.get("OPENROUTER_API_KEY") - if not api_key: - raise RuntimeError("OPENROUTER_API_KEY env var is required for the native arm.") - - # Native arm slug differs from SurfSense slug only in cost-arbitrage - # scenario; otherwise both arms answer with provider_model. - native_arm_model = ctx.native_arm_model - if any(hint in native_arm_model.lower() for hint in _TEXT_ONLY_HINTS): - if ctx.scenario == "symmetric-cheap": - logger.info( - "symmetric-cheap: native arm pinned to text-only %r as " - "intended; expect it to lose on image-bearing pages " - "(SurfSense answers from vision-extracted chunks).", - native_arm_model, - ) - else: - logger.warning( - "Native arm slug %r looks text-only; image content in " - "PDFs will be ignored. Re-pin via " - "`setup --provider-model anthropic/claude-sonnet-4.5` " - "(or pass --native-arm-model and --scenario cost-arbitrage " - "to make this asymmetry explicit).", - native_arm_model, - ) - - provider = OpenRouterPdfProvider( - api_key=api_key, - base_url=ctx.config.openrouter_base_url, - model=native_arm_model, - engine=PdfEngine(pdf_engine_name), - ) - native_arm = NativePdfArm(provider=provider, max_output_tokens=max_output_tokens) - surf_arm = SurfSenseArm( - client=ctx.new_chat_client(), - search_space_id=ctx.search_space_id, - ephemeral_threads=True, - ) - - run_timestamp = utc_iso_timestamp() - run_dir = ctx.runs_dir(run_timestamp=run_timestamp) - raw_path = run_dir / "raw.jsonl" - - async def _native_one(q: MMLBQuestion) -> ArmResult: - return await native_arm.answer(_make_native_request(q, max_output_tokens)) - - async def _surf_one(q: MMLBQuestion) -> ArmResult: - return await surf_arm.answer(_make_surfsense_request(q, no_mentions=no_mentions)) - - native_results, surf_results = await asyncio.gather( - _gather_with_limit((_native_one(q) for q in questions), concurrency=concurrency), - _gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency), - ) - - native_grades = [_grade_one(q, r) for q, r in zip(questions, native_results, strict=False)] - surf_grades = [_grade_one(q, r) for q, r in zip(questions, surf_results, strict=False)] - - with raw_path.open("w", encoding="utf-8") as fh: - for q, n_res, s_res, n_g, s_g in zip( - questions, native_results, surf_results, native_grades, surf_grades, strict=False - ): - meta = { - "qid": q.qid, - "doc_id": q.doc_id, - "doc_type": q.doc_type, - "answer_format": q.answer_format, - "gold": q.gold_answer, - "evidence_pages": q.evidence_pages, - "evidence_sources": q.evidence_sources, - "document_id": q.document_id, - } - fh.write( - json.dumps( - { - **meta, - **n_res.to_jsonl(), - "graded": _grade_to_jsonl(n_g), - } - ) - + "\n" - ) - fh.write( - json.dumps( - { - **meta, - **s_res.to_jsonl(), - "graded": _grade_to_jsonl(s_g), - } - ) - + "\n" - ) - - metrics = _compute_metrics( - questions, native_results, surf_results, native_grades, surf_grades - ) - artifact = RunArtifact( - suite=self.suite, - benchmark=self.name, - run_timestamp=run_timestamp, - raw_path=raw_path, - metrics=metrics, - extra={ - "n_questions": len(questions), - "concurrency": concurrency, - "format_filter": format_filter, - "skip_unanswerable": skip_unanswerable, - "no_mentions": no_mentions, - "pdf_engine": pdf_engine_name, - "scenario": ctx.scenario, - "provider_model": ctx.provider_model, - "native_arm_model": native_arm_model, - "vision_provider_model": ctx.vision_provider_model, - "chat_model_id": ctx.chat_model_id, - "ingest_settings": ingest_settings, - }, - ) - - manifest_path = run_dir / "run_artifact.json" - manifest_path.write_text( - json.dumps( - { - "suite": self.suite, - "benchmark": self.name, - "raw_path": "raw.jsonl", - "metrics": metrics, - "extra": artifact.extra, - }, - indent=2, - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - return artifact - - def report_section(self, artifacts: list[RunArtifact]) -> ReportSection: - if not artifacts: - return ReportSection( - title="MMLongBench-Doc — Native PDF (vision) vs SurfSense (vision RAG)", - headline=True, - body_md="(no run artifacts found)", - body_json={}, - ) - latest = max(artifacts, key=lambda a: a.run_timestamp) - m = latest.metrics - native = m.get("native", {}) - surf = m.get("surfsense", {}) - delta = m.get("delta", {}) - per_format = m.get("per_format", {}) - extra = latest.extra - - body_lines: list[str] = [] - body_lines.append( - f"- Sample size: {extra.get('n_questions', '?')} questions " - f"(format filter: `{extra.get('format_filter', 'all')}`, " - f"skip-unanswerable: `{extra.get('skip_unanswerable', False)}`, " - f"engine: `{extra.get('pdf_engine', 'native')}`)." - ) - body_lines.append(format_scenario_md(extra)) - body_lines.append(format_ingest_settings_md(extra.get("ingest_settings"))) - body_lines.append( - "- Native arm (OpenRouter `chat/completions` + file plugin, " - f"`{extra.get('native_arm_model') or extra.get('provider_model', '?')}`):" - ) - body_lines.append(_arm_summary_lines(native, indent=" ")) - body_lines.append( - "- SurfSense arm (`POST /api/v1/new_chat`, vision RAG over chunks, " - f"`{extra.get('provider_model', '?')}`):" - ) - body_lines.append(_arm_summary_lines(surf, indent=" ")) - body_lines.append("- Delta (paired):") - body_lines.append( - f" - Accuracy: SurfSense {_pp(delta.get('accuracy_pp'))} pp " - f"(McNemar p={_fmt(delta.get('mcnemar_p_value'), 4)}, " - f"method={delta.get('mcnemar_method')})" - ) - body_lines.append(f" - F1 (mean): SurfSense {_pp(delta.get('f1_pp'))} pp") - body_lines.append( - f" - Bootstrap 95% CI on accuracy delta: " - f"[{_pp(delta.get('bootstrap_ci_low'))}pp, {_pp(delta.get('bootstrap_ci_high'))}pp]" - ) - body_lines.append( - f" - Cost / question: native ${_dollars(native.get('cost_micros_mean'))}, " - f"surfsense ${_dollars(surf.get('cost_micros_mean'))} " - f"(SurfSense delta {_pct_change(delta.get('cost_micros_pct'))})" - ) - body_lines.append( - f" - Latency p50: native {_ms_to_s(native.get('latency_ms_median'))}, " - f"surfsense {_ms_to_s(surf.get('latency_ms_median'))} " - f"(SurfSense delta {_pct_change(delta.get('latency_ms_pct'))})" - ) - if per_format: - body_lines.append("- Per-format split (accuracy delta in pp):") - for fmt, vals in sorted(per_format.items()): - body_lines.append( - f" - {fmt}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp " - f"(n={vals.get('n')}, native acc={vals.get('native_accuracy', 0) * 100:.1f}%, " - f"surf acc={vals.get('surfsense_accuracy', 0) * 100:.1f}%)" - ) - - return ReportSection( - title="MMLongBench-Doc — Native PDF (vision) vs SurfSense (vision RAG)", - headline=True, - body_md="\n".join(body_lines), - body_json=m, - ) - - -# --------------------------------------------------------------------------- -# Per-question helpers -# --------------------------------------------------------------------------- - - -def _make_native_request(q: MMLBQuestion, max_tokens: int) -> ArmRequest: - prompt = build_prompt(q.question, answer_format=q.answer_format) - return ArmRequest( - question_id=q.qid, - prompt=prompt, - pdf_paths=[q.pdf_path], - options={"max_tokens": max_tokens}, - ) - - -def _make_surfsense_request(q: MMLBQuestion, *, no_mentions: bool) -> ArmRequest: - prompt = build_prompt(q.question, answer_format=q.answer_format) - mentions: list[int] | None = None - if not no_mentions and q.document_id is not None: - mentions = [int(q.document_id)] - return ArmRequest( - question_id=q.qid, - prompt=prompt, - mentioned_document_ids=mentions, - ) - - -def _grade_one(q: MMLBQuestion, result: ArmResult) -> GradeResult: - pred_text = extract_freeform_answer(result.raw_text or "") - return grade(pred=pred_text, gold=q.gold_answer, answer_format=q.answer_format) - - -def _grade_to_jsonl(g: GradeResult) -> dict[str, Any]: - return { - "correct": g.correct, - "f1": g.f1, - "method": g.method, - "normalised_pred": g.normalised_pred, - "normalised_gold": g.normalised_gold, - } - - -# --------------------------------------------------------------------------- -# Metrics aggregation -# --------------------------------------------------------------------------- - - -def _compute_metrics( - questions: list[MMLBQuestion], - native_results: list[ArmResult], - surf_results: list[ArmResult], - native_grades: list[GradeResult], - surf_grades: list[GradeResult], -) -> dict[str, Any]: - native_correct = [g.correct for g in native_grades] - surf_correct = [g.correct for g in surf_grades] - native_f1 = [g.f1 for g in native_grades] - surf_f1 = [g.f1 for g in surf_grades] - - native_costs = [float(r.cost_micros) for r in native_results] - surf_costs = [float(r.cost_micros) for r in surf_results] - native_latencies = [float(r.latency_ms) for r in native_results] - surf_latencies = [float(r.latency_ms) for r in surf_results] - native_in_tokens = [float(r.input_tokens) for r in native_results] - native_out_tokens = [float(r.output_tokens) for r in native_results] - - native_acc = accuracy_with_wilson_ci(sum(native_correct), len(native_correct)) - surf_acc = accuracy_with_wilson_ci(sum(surf_correct), len(surf_correct)) - mc = mcnemar_test(native_correct, surf_correct) - boot = bootstrap_delta_ci(native_correct, surf_correct, n_resamples=2000) - - native_cost_agg = paired_aggregate(native_costs) - surf_cost_agg = paired_aggregate(surf_costs) - native_latency_agg = paired_aggregate(native_latencies) - surf_latency_agg = paired_aggregate(surf_latencies) - - cost_pct = _safe_pct(surf_cost_agg.mean, native_cost_agg.mean) - latency_pct = _safe_pct(surf_latency_agg.median, native_latency_agg.median) - - per_format_pairs: dict[str, list[tuple[bool, bool]]] = {} - for q, n_ok, s_ok in zip(questions, native_correct, surf_correct, strict=False): - per_format_pairs.setdefault(q.answer_format or "unknown", []).append((n_ok, s_ok)) - - per_format: dict[str, dict[str, Any]] = {} - for fmt, pairs in per_format_pairs.items(): - n_correct = [a for a, _ in pairs] - s_correct = [b for _, b in pairs] - per_format[fmt] = { - "n": len(pairs), - "native_accuracy": (sum(n_correct) / len(pairs)) if pairs else 0.0, - "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0, - "delta_accuracy_pp": ( - 100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs) if pairs else 0.0 - ), - } - - native_f1_mean = sum(native_f1) / len(native_f1) if native_f1 else 0.0 - surf_f1_mean = sum(surf_f1) / len(surf_f1) if surf_f1 else 0.0 - - return { - "native": { - **native_acc.to_dict(), - "f1_mean": native_f1_mean, - "cost_micros_mean": native_cost_agg.mean, - "cost_micros_median": native_cost_agg.median, - "latency_ms_mean": native_latency_agg.mean, - "latency_ms_median": native_latency_agg.median, - "latency_ms_p95": native_latency_agg.p95, - "input_tokens_mean": (sum(native_in_tokens) / len(native_in_tokens)) - if native_in_tokens - else 0.0, - "output_tokens_mean": (sum(native_out_tokens) / len(native_out_tokens)) - if native_out_tokens - else 0.0, - }, - "surfsense": { - **surf_acc.to_dict(), - "f1_mean": surf_f1_mean, - "cost_micros_mean": surf_cost_agg.mean, - "cost_micros_median": surf_cost_agg.median, - "latency_ms_mean": surf_latency_agg.mean, - "latency_ms_median": surf_latency_agg.median, - "latency_ms_p95": surf_latency_agg.p95, - }, - "delta": { - "accuracy_pp": 100.0 * (surf_acc.accuracy - native_acc.accuracy), - "f1_pp": 100.0 * (surf_f1_mean - native_f1_mean), - "mcnemar_p_value": mc.p_value, - "mcnemar_method": mc.method, - "mcnemar_b_native_only": mc.b, - "mcnemar_c_surfsense_only": mc.c, - "bootstrap_ci_low": 100.0 * boot.ci_low, - "bootstrap_ci_high": 100.0 * boot.ci_high, - "cost_micros_pct": cost_pct, - "latency_ms_pct": latency_pct, - }, - "per_format": per_format, - } - - -def _safe_pct(numerator: float, denominator: float) -> float | None: - if denominator == 0: - return None - return 100.0 * (numerator - denominator) / denominator - - -# --------------------------------------------------------------------------- -# Tiny formatting helpers used by report_section -# --------------------------------------------------------------------------- - - -def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str: - if not d: - return f"{indent}(no data)" - acc = d.get("accuracy", 0.0) - low = d.get("ci_low", 0.0) - high = d.get("ci_high", 0.0) - f1 = d.get("f1_mean", 0.0) - lines = [ - f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)", - f"{indent}- F1 (token-level mean): {f1 * 100:.1f}%", - f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), " - f"${_dollars(d.get('cost_micros_median'))} (median)", - f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, " - f"p95 {_ms_to_s(d.get('latency_ms_p95'))}", - ] - if "input_tokens_mean" in d: - lines.append( - f"{indent}- Mean tokens / question: in {d.get('input_tokens_mean', 0):.0f}, " - f"out {d.get('output_tokens_mean', 0):.0f}" - ) - return "\n".join(lines) - - -def _dollars(micros: Any) -> str: - if micros is None: - return "?" - try: - return f"{(float(micros) / 1_000_000):.4f}" - except (TypeError, ValueError): - return "?" - - -def _ms_to_s(ms: Any) -> str: - if ms is None: - return "?" - try: - return f"{float(ms) / 1000:.1f}s" - except (TypeError, ValueError): - return "?" - - -def _pp(value: Any) -> str: - if value is None: - return "?" - try: - return f"{float(value):+.1f}" - except (TypeError, ValueError): - return "?" - - -def _pct_change(value: Any) -> str: - if value is None: - return "?" - try: - return f"{float(value):+.0f}%" - except (TypeError, ValueError): - return "?" - - -def _fmt(value: Any, ndigits: int) -> str: - if value is None: - return "?" - try: - return f"{float(value):.{ndigits}f}" - except (TypeError, ValueError): - return "?" - - -__all__ = ["MMLBQuestion", "MMLongBenchDocBenchmark"] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py deleted file mode 100644 index f6985e93d1..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -"""parser_compare — six-way head-to-head on long multimodal PDFs. - -Same 5 mmlongbench PDFs that ``mmlongbench`` already ingested -(``search_space_id=55``), one question per PDF for the smoke run. - -The point of this benchmark is to disentangle TWO orthogonal -dimensions of "how good is our multimodal pipeline?": - -1. **Parser quality** — Azure DI prebuilt-read vs prebuilt-layout vs - LlamaParse parse_page_with_llm vs parse_page_with_agent. We run - each parser directly (bypassing ``/documents/fileupload`` because - the backend's parser routing is global, not per-call) and stuff the - resulting markdown into a long-context prompt. - -2. **Context-management strategy** — full-context stuffing (no chunk - selection, the model sees everything) vs SurfSense's agentic - retrieval over chunks of the same documents. - -Six arms, all answered by ``anthropic/claude-sonnet-4.5``: - -* ``native_pdf`` — PDF attached natively via OpenRouter - (gold-standard reference). -* ``azure_basic_lc`` — Azure DI ``prebuilt-read`` markdown stuffed - into the prompt. -* ``azure_premium_lc`` — Azure DI ``prebuilt-layout`` markdown stuffed. -* ``llamacloud_basic_lc`` — LlamaParse ``parse_page_with_llm`` markdown stuffed. -* ``llamacloud_premium_lc`` — LlamaParse ``parse_page_with_agent`` markdown stuffed. -* ``surfsense_agentic`` — SurfSense ``/api/v1/new_chat`` with - ``mentioned_document_ids`` scoped to the - one source PDF, retrieving chunks from - the existing search_space=55 ingestion - (vision_llm=on, processing_mode=premium, - ETL_SERVICE=LLAMACLOUD with Azure DI - fallback ⇒ effectively azure_premium). - -The report includes preprocessing cost ($1 / 1k pages basic, $10 / 1k -pages premium) on top of the OpenRouter LLM cost so each arm's true -total-cost-per-question is directly comparable. -""" - -from __future__ import annotations - -from ....core import registry as _registry -from .runner import ParserCompareBenchmark - -_registry.register(ParserCompareBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py deleted file mode 100644 index a7fce60d13..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py +++ /dev/null @@ -1,365 +0,0 @@ -"""parser_compare ingestion: pre-extract markdown 4 ways per PDF. - -For each PDF in scope, we run all four (parser × mode) combinations -in parallel and persist the resulting markdown alongside the PDF: - - data/multimodal_doc/parser_compare/extractions/ - .azure_basic.md - .azure_premium.md - .llamacloud_basic.md - .llamacloud_premium.md - -A manifest at ``maps/parser_compare_doc_map.jsonl`` records, per PDF: - -* ``doc_id`` — filename of the source PDF. -* ``pdf_path`` — local cached PDF path. -* ``document_id`` — SurfSense document id (carried over from - mmlongbench's existing ingestion so the - SurfSense agentic arm can scope retrieval). -* ``pages`` — page count via pypdf (drives preprocessing cost). -* ``extractions`` — map of ``arm_name -> {markdown_path, chars, - elapsed_s, status, error}``. - -The runner reads this manifest, loads the markdown for each long-context -arm, and uses ``document_id`` for the SurfSense arm. - -Source PDFs come from the existing mmlongbench ingestion — no new -download or upload happens here. The point of this benchmark is -parser quality on the same physical PDFs SurfSense already has, so -re-using mmlongbench's PDF cache is correct. -""" - -from __future__ import annotations - -import asyncio -import json -import logging -import time -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -from ....core.config import set_suite_state -from ....core.parsers import ( - count_pdf_pages, - parse_with_azure_di, - parse_with_llamacloud, -) -from ....core.registry import RunContext - -logger = logging.getLogger(__name__) - - -# Order matters for the manifest only (deterministic JSONL diffs); -# the runner doesn't rely on it. -PARSER_ARMS: tuple[tuple[str, str, str], ...] = ( - ("azure_basic_lc", "azure", "basic"), - ("azure_premium_lc", "azure", "premium"), - ("llamacloud_basic_lc", "llamacloud", "basic"), - ("llamacloud_premium_lc", "llamacloud", "premium"), -) - - -@dataclass -class ExtractionResult: - arm: str - parser: str - mode: str - markdown_path: Path | None = None - chars: int = 0 - elapsed_s: float = 0.0 - status: str = "ok" # "ok" | "failed" - error: str | None = None - - def to_jsonl(self) -> dict[str, Any]: - return { - "arm": self.arm, - "parser": self.parser, - "mode": self.mode, - "markdown_path": str(self.markdown_path) if self.markdown_path else None, - "chars": self.chars, - "elapsed_s": round(self.elapsed_s, 2), - "status": self.status, - "error": self.error, - } - - -@dataclass -class PdfManifestRow: - doc_id: str - pdf_path: Path - document_id: int | None - pages: int - extractions: dict[str, ExtractionResult] = field(default_factory=dict) - - def to_jsonl(self) -> dict[str, Any]: - return { - "doc_id": self.doc_id, - "pdf_path": str(self.pdf_path), - "document_id": self.document_id, - "pages": self.pages, - "extractions": {arm: ext.to_jsonl() for arm, ext in self.extractions.items()}, - } - - -# --------------------------------------------------------------------------- -# Single-PDF extraction -# --------------------------------------------------------------------------- - - -async def _run_one_extraction( - pdf_path: Path, - *, - parser: str, - mode: str, - out_path: Path, - estimated_pages: int, -) -> tuple[str, float]: - """Invoke the requested parser, persist markdown, return (markdown, elapsed_s).""" - - started = time.monotonic() - if parser == "azure": - markdown = await parse_with_azure_di(pdf_path, processing_mode=mode) - elif parser == "llamacloud": - markdown = await parse_with_llamacloud( - pdf_path, - processing_mode=mode, - estimated_pages=estimated_pages, - ) - else: - raise ValueError(f"Unknown parser {parser!r}") - out_path.parent.mkdir(parents=True, exist_ok=True) - await asyncio.to_thread(out_path.write_text, markdown, encoding="utf-8") - return markdown, time.monotonic() - started - - -async def _extract_one_pdf( - pdf_path: Path, - *, - extractions_dir: Path, - force_reextract: bool, -) -> dict[str, ExtractionResult]: - """Run all four parser combos for ``pdf_path``, returning per-arm results. - - Re-uses any cached ``.md`` already on disk unless ``force_reextract``. - The four parser invocations run concurrently — they're independent - HTTP-bound jobs and the providers don't share state. - """ - - estimated_pages = count_pdf_pages(pdf_path) or 50 - out: dict[str, ExtractionResult] = {} - coros = [] - arm_specs: list[tuple[str, str, str, Path]] = [] - - for arm_name, parser, mode in PARSER_ARMS: - out_path = extractions_dir / f"{pdf_path.stem}.{arm_name}.md" - arm_specs.append((arm_name, parser, mode, out_path)) - - if out_path.exists() and not force_reextract: - cached = out_path.read_text(encoding="utf-8") - out[arm_name] = ExtractionResult( - arm=arm_name, - parser=parser, - mode=mode, - markdown_path=out_path, - chars=len(cached), - elapsed_s=0.0, - status="ok", - error="(cached)", - ) - logger.info( - "Cached extraction reused: %s (%d chars)", - out_path.name, - len(cached), - ) - coros.append(_noop()) - else: - coros.append( - _run_one_extraction( - pdf_path, - parser=parser, - mode=mode, - out_path=out_path, - estimated_pages=estimated_pages, - ) - ) - - results = await asyncio.gather(*coros, return_exceptions=True) - for (arm_name, parser, mode, out_path), result in zip(arm_specs, results, strict=True): - if arm_name in out: - continue # cached — already populated above - if isinstance(result, Exception): - err = result - err_msg = f"{type(err).__name__}: {err}" - logger.warning( - "Extraction FAILED for %s [%s/%s]: %s", - pdf_path.name, - parser, - mode, - err_msg, - ) - out[arm_name] = ExtractionResult( - arm=arm_name, - parser=parser, - mode=mode, - status="failed", - error=err_msg, - ) - else: - markdown, elapsed = result - out[arm_name] = ExtractionResult( - arm=arm_name, - parser=parser, - mode=mode, - markdown_path=out_path, - chars=len(markdown), - elapsed_s=elapsed, - status="ok", - ) - return out - - -async def _noop() -> tuple[str, float]: - """Placeholder so cached entries align with parallel gather indexing.""" - - return ("", 0.0) - - -# --------------------------------------------------------------------------- -# Public entry point -# --------------------------------------------------------------------------- - - -def _read_existing_mmlongbench_map(map_path: Path) -> list[dict[str, Any]]: - """Read the mmlongbench doc map (skipping its ``__settings__`` header).""" - - if not map_path.exists(): - raise RuntimeError( - f"mmlongbench doc map not found at {map_path}. Run " - "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first." - ) - rows: list[dict[str, Any]] = [] - with map_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - if "__settings__" in row: - continue - rows.append(row) - return rows - - -async def run_ingest( - ctx: RunContext, - *, - docs_filter: list[str] | None = None, - max_docs: int | None = None, - force_reextract: bool = False, - pdf_concurrency: int = 2, -) -> None: - """Pre-extract all four parser markdowns for each PDF. - - Parameters - ---------- - docs_filter : list[str] | None - Specific filenames to extract (default: all PDFs from - mmlongbench's existing manifest). - max_docs : int | None - Cap on number of PDFs to process. Default: all. - force_reextract : bool - Re-call parsers even if a cached ``.md`` already exists. Off - by default — extractions are deterministic and parser calls - cost real money. - pdf_concurrency : int - How many PDFs to extract in parallel. Each PDF triggers four - parser HTTP calls, so total in-flight = 4 * pdf_concurrency. - Default 2 keeps us comfortably under both Azure DI and - LlamaCloud per-IP rate limits. - """ - - # Pull the source PDFs and document_ids from mmlongbench's existing - # ingestion. parser_compare doesn't re-upload; SurfSense's agentic - # arm queries the same search_space=55 chunks. - mmlb_map = ctx.suite_state.ingestion_maps.get("mmlongbench") - if not mmlb_map: - raise RuntimeError( - "Suite state has no mmlongbench ingestion map. Run " - "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first " - "so parser_compare can re-use those PDFs." - ) - src_rows = _read_existing_mmlongbench_map(Path(mmlb_map)) - - rows_in_scope = src_rows - if docs_filter: - wanted = set(docs_filter) - rows_in_scope = [r for r in rows_in_scope if r["doc_id"] in wanted] - if max_docs is not None and max_docs > 0: - rows_in_scope = rows_in_scope[:max_docs] - - if not rows_in_scope: - raise RuntimeError("No PDFs in scope for parser_compare. Check --docs / --max-docs.") - - bench_dir = ctx.benchmark_data_dir() - extractions_dir = bench_dir / "extractions" - extractions_dir.mkdir(parents=True, exist_ok=True) - - sem = asyncio.Semaphore(max(1, pdf_concurrency)) - manifest_rows: list[PdfManifestRow] = [] - - async def _process(row: dict[str, Any]) -> PdfManifestRow: - pdf_path = Path(row["pdf_path"]) - async with sem: - extractions = await _extract_one_pdf( - pdf_path, - extractions_dir=extractions_dir, - force_reextract=force_reextract, - ) - return PdfManifestRow( - doc_id=str(row["doc_id"]), - pdf_path=pdf_path, - document_id=row.get("document_id"), - pages=count_pdf_pages(pdf_path), - extractions=extractions, - ) - - logger.info( - "parser_compare: extracting %d PDFs x 4 parsers (concurrency=%d)", - len(rows_in_scope), - pdf_concurrency, - ) - manifest_rows = await asyncio.gather(*(_process(r) for r in rows_in_scope)) - - # Persist manifest - map_path = ctx.maps_dir() / "parser_compare_doc_map.jsonl" - with map_path.open("w", encoding="utf-8") as fh: - for mr in manifest_rows: - fh.write(json.dumps(mr.to_jsonl()) + "\n") - logger.info("parser_compare manifest -> %s", map_path) - - # Update suite state so the runner can find us via - # ctx.suite_state.ingestion_maps. - new_state = ctx.suite_state - new_state.ingestion_maps["parser_compare"] = str(map_path) - set_suite_state(ctx.config, ctx.suite, new_state) - - # Quick summary log - total_extractions = sum(len(mr.extractions) for mr in manifest_rows) - failures = sum( - 1 for mr in manifest_rows for ext in mr.extractions.values() if ext.status != "ok" - ) - logger.info( - "parser_compare ingest done: %d PDFs, %d extractions, %d failures", - len(manifest_rows), - total_extractions, - failures, - ) - - -__all__ = [ - "ExtractionResult", - "PARSER_ARMS", - "PdfManifestRow", - "run_ingest", -] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py deleted file mode 100644 index ccde69e71f..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py +++ /dev/null @@ -1,109 +0,0 @@ -"""Prompt templates for the three input modalities in parser_compare. - -We deliberately reuse the *same* core question framing as -``mmlongbench/prompt.py`` so byte-identical questions reach all six -arms; only the document delivery channel changes. - -Three templates: - -* ``build_native_pdf_prompt`` — bare question + format hint. - The PDF rides as a separate file - part (``NativePdfArm`` handles it). -* ``build_long_context_prompt`` — question + format hint + the - parser-extracted markdown wrapped - in fenced ```` tags so - the model can clearly delimit - "context" from "instruction". -* ``build_surfsense_prompt`` — bare question + format hint - (chunks come from RAG retrieval, - not from the prompt). - -The ```` tag is doc-aware: even though parser_compare runs -one PDF per question today, we keep the wrapper plural so this is -trivial to extend to multi-doc later. -""" - -from __future__ import annotations - -# --------------------------------------------------------------------------- -# Per-format hint blocks (same lookup as mmlongbench/prompt.py) -# --------------------------------------------------------------------------- - -_FORMAT_HINTS: dict[str, str] = { - "str": ( - "Respond with the answer as a short phrase, no full sentence. " - "Format your final line as `Answer: `." - ), - "int": ("Respond with a single integer only. Format your final line as `Answer: `."), - "float": ( - "Respond with a single decimal number only (no units). " - "Format your final line as `Answer: `." - ), - "list": ( - "Respond with a comma-separated list of items, no extra text. " - "Format your final line as `Answer: item1, item2, item3`." - ), - "none": ( - "If the answer cannot be determined from the document, say so explicitly. " - "Format your final line as `Answer: Not answerable`." - ), -} - - -def _format_hint(answer_format: str) -> str: - fmt = (answer_format or "str").strip().lower() - return _FORMAT_HINTS.get(fmt, _FORMAT_HINTS["str"]) - - -_BASE_INSTRUCTION = ( - "You are a document-understanding assistant. Use ONLY the provided " - "document to answer the question. The document may contain text, " - "tables, charts, figures, and images. If the answer is in a chart " - "or image, read it carefully. Do not use external knowledge." -) - - -def build_native_pdf_prompt(question: str, *, answer_format: str) -> str: - """Prompt for ``NativePdfArm`` — PDF attached separately as a file part.""" - - return f"{_BASE_INSTRUCTION}\n\nQuestion: {question.strip()}\n\n{_format_hint(answer_format)}\n" - - -def build_surfsense_prompt(question: str, *, answer_format: str) -> str: - """Prompt for ``SurfSenseArm`` — chunks retrieved by the agent.""" - - # SurfSense's agent already injects retrieved chunks via its tool - # loop; the prompt only carries the user-visible question + format - # hint, mirroring how a human asks the SurfSense UI. - return f"{_BASE_INSTRUCTION}\n\nQuestion: {question.strip()}\n\n{_format_hint(answer_format)}\n" - - -def build_long_context_prompt( - question: str, - *, - answer_format: str, - document_markdown: str, - document_label: str, -) -> str: - """Prompt for the four long-context arms — markdown stuffed inline. - - ``document_label`` is a short human-readable name (e.g. the PDF - filename) so the model can reason about source provenance even - though only one document is in scope. - """ - - return ( - f"{_BASE_INSTRUCTION}\n\n" - f'\n' - f"{document_markdown.strip()}\n" - f"\n\n" - f"Question: {question.strip()}\n\n" - f"{_format_hint(answer_format)}\n" - ) - - -__all__ = [ - "build_long_context_prompt", - "build_native_pdf_prompt", - "build_surfsense_prompt", -] diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py deleted file mode 100644 index 6c009995f9..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py +++ /dev/null @@ -1,835 +0,0 @@ -"""parser_compare runner — six-arm head-to-head on n shared questions. - -For each (PDF, question) pair we issue six LLM calls (all sonnet 4.5): - -* ``native_pdf`` — PDF attached natively. -* ``azure_basic_lc`` — Azure prebuilt-read markdown stuffed. -* ``azure_premium_lc`` — Azure prebuilt-layout markdown stuffed. -* ``llamacloud_basic_lc`` — LlamaParse parse_page_with_llm markdown stuffed. -* ``llamacloud_premium_lc`` — LlamaParse parse_page_with_agent markdown stuffed. -* ``surfsense_agentic`` — SurfSense /api/v1/new_chat retrieval over chunks. - -Cost reporting: - -* ``llm_cost_per_q`` — mean OpenRouter ``usage.cost`` reported by - the chat-completions API. Zero for the - SurfSense agentic arm because the SSE - stream doesn't surface per-call cost yet - (a known gap; we annotate it in the - report rather than estimating). -* ``preprocess_cost_total`` — pages * $/1k according to the user's - tariff: - * basic : $1 / 1k pages - * premium : $10 / 1k pages - * native_pdf : $0 (no preprocessing) - * surfsense_agentic : $10 / 1k pages - (existing mmlongbench ingest used - processing_mode=premium with Azure DI). -* ``preprocess_cost_per_q`` — preprocess_cost_total / n_questions. -* ``total_cost_per_q`` — llm_cost_per_q + preprocess_cost_per_q. - -The grader is reused from ``mmlongbench/grader.py`` (deterministic, -format-aware) so the metric is directly comparable to the existing -mmlongbench runs. -""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import logging -import os -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from ....core.arms import ( - ArmRequest, - ArmResult, - BareLlmArm, - NativePdfArm, - SurfSenseArm, -) -from ....core.config import utc_iso_timestamp -from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci -from ....core.parse.freeform_answer import extract_freeform_answer -from ....core.providers.openrouter_chat import OpenRouterChatProvider -from ....core.providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine -from ....core.registry import ReportSection, RunArtifact, RunContext -from ..mmlongbench.grader import GradeResult, grade -from .ingest import PARSER_ARMS -from .prompt import ( - build_long_context_prompt, - build_native_pdf_prompt, - build_surfsense_prompt, -) - -logger = logging.getLogger(__name__) - - -# Cost tariff (per the user's spec: $1 / 1k pages basic, $10 / 1k pages premium). -# Held as dollars-per-page so per-PDF math is a pure multiply. -PREPROCESS_USD_PER_PAGE = { - "basic": 1.0 / 1000.0, - "premium": 10.0 / 1000.0, -} - -ARM_NAMES = ( - "native_pdf", - "azure_basic_lc", - "azure_premium_lc", - "llamacloud_basic_lc", - "llamacloud_premium_lc", - "surfsense_agentic", -) - -# What ingest mode each LC arm corresponds to (drives preprocess cost). -_LC_ARM_MODE: dict[str, str] = { - "azure_basic_lc": "basic", - "azure_premium_lc": "premium", - "llamacloud_basic_lc": "basic", - "llamacloud_premium_lc": "premium", -} - -# The SurfSense agentic arm is fed by the existing mmlongbench -# ingestion. That ingestion was performed with vision_llm=on and -# processing_mode=premium, and the backend's ETL routes premium-mode -# PDFs through Azure DI prebuilt-layout when AZURE_DI_KEY is set. So -# the preprocessing cost is the premium tariff. -SURFSENSE_INGEST_MODE = "premium" - - -# --------------------------------------------------------------------------- -# Question + PDF row shapes -# --------------------------------------------------------------------------- - - -@dataclass -class PCQuestion: - qid: str - doc_id: str - question: str - gold_answer: str - answer_format: str - pdf_path: Path - document_id: int | None - pages: int - extractions: dict[str, Path] # arm_name -> markdown path (only successes) - - -def _read_doc_map(map_path: Path) -> dict[str, dict[str, Any]]: - out: dict[str, dict[str, Any]] = {} - with map_path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - out[str(row["doc_id"])] = row - return out - - -def _select_questions( - questions_jsonl: Path, - doc_map: dict[str, dict[str, Any]], - *, - docs_filter: list[str] | None, - sample_per_doc: int, - skip_unanswerable: bool, - skip_format: list[str] | None, -) -> list[PCQuestion]: - """Pick the first ``sample_per_doc`` questions per PDF in scope. - - Defaults to one per PDF (n=5 across 5 PDFs ⇒ 5 questions). Filters - out unanswerable probes by default since they're noise at small n. - """ - - out: list[PCQuestion] = [] - per_doc_taken: dict[str, int] = {} - per_doc_idx: dict[str, int] = {} - skip_format_set = {f.lower() for f in (skip_format or [])} - - with questions_jsonl.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - row = json.loads(line) - doc_id = str(row.get("doc_id") or "") - if not doc_id: - continue - if docs_filter and doc_id not in docs_filter: - continue - map_row = doc_map.get(doc_id) - if map_row is None: - continue - - answer_format = str(row.get("answer_format") or "").strip().lower() - idx = per_doc_idx.get(doc_id, 0) - per_doc_idx[doc_id] = idx + 1 - - if skip_unanswerable and answer_format == "none": - continue - if answer_format in skip_format_set: - continue - - if per_doc_taken.get(doc_id, 0) >= sample_per_doc: - continue - - extractions: dict[str, Path] = {} - for arm_name, ext_blob in (map_row.get("extractions") or {}).items(): - if ext_blob.get("status") == "ok" and ext_blob.get("markdown_path"): - extractions[arm_name] = Path(ext_blob["markdown_path"]) - - out.append( - PCQuestion( - qid=f"{doc_id}::Q{idx:03d}", - doc_id=doc_id, - question=str(row.get("question") or "").strip(), - gold_answer=str(row.get("answer") or "").strip(), - answer_format=answer_format, - pdf_path=Path(map_row["pdf_path"]), - document_id=map_row.get("document_id"), - pages=int(map_row.get("pages", 0)), - extractions=extractions, - ) - ) - per_doc_taken[doc_id] = per_doc_taken.get(doc_id, 0) + 1 - - out.sort(key=lambda q: (q.doc_id, q.qid)) - return out - - -# --------------------------------------------------------------------------- -# Bounded concurrency helper -# --------------------------------------------------------------------------- - - -async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]: - sem = asyncio.Semaphore(max(1, concurrency)) - - async def _wrap(coro): - async with sem: - return await coro - - return await asyncio.gather(*(_wrap(c) for c in coros)) - - -# --------------------------------------------------------------------------- -# Benchmark -# --------------------------------------------------------------------------- - - -_DESCRIPTION = ( - "parser_compare — 6-arm head-to-head on shared MMLongBench-Doc PDFs: " - "native PDF + (Azure DI / LlamaCloud) x (basic / premium) long-context " - "stuffing + SurfSense agentic retrieval. Reports preprocessing dollars " - "($1 / 1k pages basic, $10 / 1k pages premium) on top of LLM cost." -) - - -class ParserCompareBenchmark: - """6-arm parser + agentic-vs-non-agentic head-to-head.""" - - suite: str = "multimodal_doc" - name: str = "parser_compare" - headline: bool = True - description: str = _DESCRIPTION - - # ------------------------------------------------------------------ - # CLI flags - # ------------------------------------------------------------------ - - def add_run_args(self, parser: argparse.ArgumentParser) -> None: - parser.add_argument( - "--docs", - default=None, - help="Comma-separated doc_ids to include (default: all in manifest).", - ) - parser.add_argument( - "--sample-per-doc", - type=int, - default=1, - help="Take the first N answerable questions per PDF (default 1).", - ) - parser.add_argument( - "--skip-unanswerable", - dest="skip_unanswerable", - action="store_true", - default=True, - help="Drop 'None' format probes (default true; we want signal not " - "hallucination probes for n=5).", - ) - parser.add_argument( - "--include-unanswerable", - dest="skip_unanswerable", - action="store_false", - help="Override --skip-unanswerable; include unanswerable probes too.", - ) - parser.add_argument( - "--skip-format", - default=None, - help="Comma-separated answer_format values to skip (e.g. 'none,float').", - ) - parser.add_argument( - "--concurrency", - type=int, - default=2, - help="Parallel question workers per arm (default 2).", - ) - parser.add_argument( - "--no-mentions", - dest="no_mentions", - action="store_true", - help="SurfSense arm: skip mentioned_document_ids (full-corpus retrieval).", - ) - parser.add_argument( - "--pdf-engine", - default="native", - choices=[e.value for e in PdfEngine], - help="OpenRouter file-parser engine for native_pdf arm.", - ) - parser.add_argument( - "--max-output-tokens", - type=int, - default=512, - help="Cap on completion length for every arm.", - ) - parser.add_argument( - "--llm-model", - default="anthropic/claude-sonnet-4.5", - help="OpenRouter slug used by the 5 OpenRouter-driven arms. " - "SurfSense arm uses whatever provider_model is pinned on the suite.", - ) - parser.add_argument( - "--skip-arms", - default=None, - help="Comma-separated arm names to skip (e.g. 'llamacloud_premium_lc').", - ) - # Ingest-only flags (forwarded by the CLI to ingest.run_ingest). - parser.add_argument( - "--max-docs", - type=int, - default=None, - help="(ingest only) cap number of unique PDFs to process.", - ) - parser.add_argument( - "--force-reextract", - action="store_true", - help="(ingest only) re-call parsers even if cached .md exists.", - ) - parser.add_argument( - "--pdf-concurrency", - type=int, - default=2, - help="(ingest only) parallel PDFs (each fans out to 4 parsers).", - ) - - # ------------------------------------------------------------------ - # Lifecycle: ingest delegates to .ingest.run_ingest - # ------------------------------------------------------------------ - - async def ingest(self, ctx: RunContext, **opts: Any) -> None: - from .ingest import run_ingest - - docs_raw: str | None = opts.get("docs") - docs_filter = [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None - await run_ingest( - ctx, - docs_filter=docs_filter, - max_docs=opts.get("max_docs"), - force_reextract=bool(opts.get("force_reextract", False)), - pdf_concurrency=int(opts.get("pdf_concurrency") or 2), - ) - - # ------------------------------------------------------------------ - # Run - # ------------------------------------------------------------------ - - async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact: - docs_raw: str | None = opts.get("docs") - docs_filter = [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None - sample_per_doc = int(opts.get("sample_per_doc") or 1) - skip_unanswerable = bool(opts.get("skip_unanswerable", True)) - skip_format_raw: str | None = opts.get("skip_format") - skip_format = ( - [f.strip() for f in skip_format_raw.split(",") if f.strip()] - if skip_format_raw - else None - ) - concurrency = int(opts.get("concurrency") or 2) - no_mentions = bool(opts.get("no_mentions")) - pdf_engine_name = opts.get("pdf_engine") or "native" - max_output_tokens = int(opts.get("max_output_tokens") or 512) - llm_model = str(opts.get("llm_model") or "anthropic/claude-sonnet-4.5") - skip_arms_raw: str | None = opts.get("skip_arms") - skip_arms = ( - {a.strip() for a in skip_arms_raw.split(",") if a.strip()} if skip_arms_raw else set() - ) - - active_arms = [a for a in ARM_NAMES if a not in skip_arms] - if not active_arms: - raise RuntimeError("All arms skipped; nothing to run.") - - bench_dir = ctx.benchmark_data_dir() - # parser_compare reuses mmlongbench's questions.jsonl (already - # downloaded by `ingest multimodal_doc mmlongbench`). - questions_jsonl = bench_dir.parent / "mmlongbench" / "questions.jsonl" - map_path = ctx.maps_dir() / "parser_compare_doc_map.jsonl" - if not questions_jsonl.exists(): - raise RuntimeError( - "Missing mmlongbench questions at " - f"{questions_jsonl}. Run " - "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first." - ) - if not map_path.exists(): - raise RuntimeError( - "parser_compare doc map missing. Run " - "`python -m surfsense_evals ingest multimodal_doc parser_compare` first." - ) - - doc_map = _read_doc_map(map_path) - questions = _select_questions( - questions_jsonl, - doc_map, - docs_filter=docs_filter, - sample_per_doc=sample_per_doc, - skip_unanswerable=skip_unanswerable, - skip_format=skip_format, - ) - if not questions: - raise RuntimeError("No questions matched filters; broaden --docs / --skip-format.") - logger.info( - "parser_compare: scheduled %d questions across %d arms (%s)", - len(questions), - len(active_arms), - ",".join(active_arms), - ) - - api_key = os.environ.get("OPENROUTER_API_KEY") - if not api_key: - raise RuntimeError("OPENROUTER_API_KEY env var is required.") - - # Build arms - arms: dict[str, Any] = {} - if "native_pdf" in active_arms: - native_provider = OpenRouterPdfProvider( - api_key=api_key, - base_url=ctx.config.openrouter_base_url, - model=llm_model, - engine=PdfEngine(pdf_engine_name), - ) - arms["native_pdf"] = NativePdfArm( - provider=native_provider, - max_output_tokens=max_output_tokens, - ) - for arm_name, _, _ in PARSER_ARMS: - if arm_name in active_arms: - lc_provider = OpenRouterChatProvider( - api_key=api_key, - base_url=ctx.config.openrouter_base_url, - model=llm_model, - ) - arms[arm_name] = BareLlmArm( - provider=lc_provider, - max_output_tokens=max_output_tokens, - name=arm_name, - ) - if "surfsense_agentic" in active_arms: - surf = SurfSenseArm( - client=ctx.new_chat_client(), - search_space_id=ctx.search_space_id, - ephemeral_threads=True, - ) - # Override the default "surfsense" name so the metrics - # bucket lines up with the rest of parser_compare's arms. - surf.name = "surfsense_agentic" - arms["surfsense_agentic"] = surf - - run_timestamp = utc_iso_timestamp() - run_dir = ctx.runs_dir(run_timestamp=run_timestamp) - raw_path = run_dir / "raw.jsonl" - - # ---- per-arm answer coroutine helpers ---- - - def _native_req(q: PCQuestion) -> ArmRequest: - return ArmRequest( - question_id=q.qid, - prompt=build_native_pdf_prompt(q.question, answer_format=q.answer_format), - pdf_paths=[q.pdf_path], - options={"max_tokens": max_output_tokens}, - ) - - def _lc_req(q: PCQuestion, arm_name: str) -> ArmRequest: - md_path = q.extractions.get(arm_name) - if md_path is None or not md_path.exists(): - raise FileNotFoundError(f"Missing extraction for {arm_name} on {q.doc_id}") - markdown = md_path.read_text(encoding="utf-8") - return ArmRequest( - question_id=q.qid, - prompt=build_long_context_prompt( - q.question, - answer_format=q.answer_format, - document_markdown=markdown, - document_label=q.doc_id, - ), - ) - - def _surf_req(q: PCQuestion) -> ArmRequest: - mentions: list[int] | None = None - if not no_mentions and q.document_id is not None: - mentions = [int(q.document_id)] - return ArmRequest( - question_id=q.qid, - prompt=build_surfsense_prompt(q.question, answer_format=q.answer_format), - mentioned_document_ids=mentions, - ) - - async def _answer_one(arm_name: str, q: PCQuestion) -> ArmResult: - arm = arms[arm_name] - try: - if arm_name == "native_pdf": - return await arm.answer(_native_req(q)) - if arm_name == "surfsense_agentic": - return await arm.answer(_surf_req(q)) - return await arm.answer(_lc_req(q, arm_name)) - except FileNotFoundError as exc: - return ArmResult( - arm=arm_name, - question_id=q.qid, - raw_text="", - error=f"FileNotFoundError: {exc}", - ) - - # Run all arms in parallel (each arm bounded by `concurrency`). - per_arm_tasks: dict[str, list] = { - arm_name: [_answer_one(arm_name, q) for q in questions] for arm_name in active_arms - } - per_arm_results: dict[str, list[ArmResult]] = {} - gathered = await asyncio.gather( - *[ - _gather_with_limit(per_arm_tasks[arm_name], concurrency=concurrency) - for arm_name in active_arms - ] - ) - for arm_name, results in zip(active_arms, gathered, strict=True): - per_arm_results[arm_name] = results - - # Grade - per_arm_grades: dict[str, list[GradeResult]] = {} - for arm_name in active_arms: - per_arm_grades[arm_name] = [ - grade( - pred=extract_freeform_answer(r.raw_text or ""), - gold=q.gold_answer, - answer_format=q.answer_format, - ) - for q, r in zip(questions, per_arm_results[arm_name], strict=True) - ] - - # Persist raw.jsonl - with raw_path.open("w", encoding="utf-8") as fh: - for i, q in enumerate(questions): - base = { - "qid": q.qid, - "doc_id": q.doc_id, - "answer_format": q.answer_format, - "gold": q.gold_answer, - "pages": q.pages, - "document_id": q.document_id, - } - for arm_name in active_arms: - res = per_arm_results[arm_name][i] - g = per_arm_grades[arm_name][i] - fh.write( - json.dumps( - { - **base, - **res.to_jsonl(), - "graded": { - "correct": g.correct, - "f1": g.f1, - "method": g.method, - "normalised_pred": g.normalised_pred, - "normalised_gold": g.normalised_gold, - }, - } - ) - + "\n" - ) - - # Aggregate per-arm metrics + cost - metrics = _compute_metrics( - questions, - per_arm_results, - per_arm_grades, - active_arms, - ) - - artifact = RunArtifact( - suite=self.suite, - benchmark=self.name, - run_timestamp=run_timestamp, - raw_path=raw_path, - metrics=metrics, - extra={ - "n_questions": len(questions), - "n_pdfs": len({q.doc_id for q in questions}), - "active_arms": list(active_arms), - "concurrency": concurrency, - "no_mentions": no_mentions, - "pdf_engine": pdf_engine_name, - "llm_model": llm_model, - "scenario": ctx.scenario, - "provider_model": ctx.provider_model, - "vision_provider_model": ctx.vision_provider_model, - "chat_model_id": ctx.chat_model_id, - "preprocess_tariff": { - "basic_per_1k_pages": 1.0, - "premium_per_1k_pages": 10.0, - }, - }, - ) - - manifest_path = run_dir / "run_artifact.json" - manifest_path.write_text( - json.dumps( - { - "suite": self.suite, - "benchmark": self.name, - "raw_path": "raw.jsonl", - "metrics": metrics, - "extra": artifact.extra, - }, - indent=2, - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - return artifact - - # ------------------------------------------------------------------ - # Report - # ------------------------------------------------------------------ - - def report_section(self, artifacts: list[RunArtifact]) -> ReportSection: - if not artifacts: - return ReportSection( - title="Parser × agent-vs-stuffing comparison", - headline=True, - body_md="(no run artifacts found)", - body_json={}, - ) - latest = max(artifacts, key=lambda a: a.run_timestamp) - m = latest.metrics - extra = latest.extra - per_arm = m.get("per_arm", {}) - active_arms = list(extra.get("active_arms", per_arm.keys())) - - n_q = extra.get("n_questions", "?") - n_pdfs = extra.get("n_pdfs", "?") - - body: list[str] = [] - body.append( - f"- Sample size: **{n_q} questions across {n_pdfs} PDFs** " - f"(LLM: `{extra.get('llm_model', '?')}`, " - f"engine: `{extra.get('pdf_engine', 'native')}`)." - ) - body.append("- Preprocess tariff: basic = $1 / 1k pages, premium = $10 / 1k pages.") - body.append("") - body.append("### Per-arm summary") - body.append("") - body.append( - "| Arm | Accuracy | F1 mean | LLM $/Q | Preprocess $ total | Total $/Q | Latency p50 |" - ) - body.append("|---|---:|---:|---:|---:|---:|---:|") - for arm_name in active_arms: - row = per_arm.get(arm_name) - if not row: - body.append(f"| `{arm_name}` | (no data) | | | | | |") - continue - body.append( - f"| `{arm_name}` " - f"| {row['accuracy'] * 100:.1f}% " - f"({row['n_correct']}/{row['n']}) " - f"| {row['f1_mean'] * 100:.1f}% " - f"| ${row['llm_cost_per_q']:.4f} " - f"| ${row['preprocess_cost_total']:.4f} " - f"| ${row['total_cost_per_q']:.4f} " - f"| {row['latency_ms_median'] / 1000:.1f}s |" - ) - body.append("") - - # Notes / caveats - body.append("### Notes") - body.append("") - body.append( - "- `surfsense_agentic` LLM cost shows as $0.0000 because the " - "`/api/v1/new_chat` SSE stream does not surface per-call token " - "or cost yet (a known instrumentation gap). Preprocessing cost " - "is the premium tariff because the underlying mmlongbench " - "ingestion was performed with `processing_mode=premium` + " - "`vision_llm=on` + Azure DI." - ) - body.append( - "- Long-context arms include the **same PDF text** for every " - "question against that PDF, so the OpenRouter input cost is " - "dominated by markdown size; preprocessing cost is paid once " - "across all questions sharing a PDF." - ) - body.append( - "- Preprocessing $ total is computed as " - "`pages_processed_per_arm × tariff`, summed across the unique " - "PDFs in scope. With one question per PDF (n=5), preprocess $ " - "= preprocess $ / Q." - ) - if extra.get("scenario"): - body.append( - f"- Scenario: `{extra.get('scenario')}` " - f"(suite-pinned `provider_model`: " - f"`{extra.get('provider_model', '?')}`)." - ) - - # Per-PDF breakdown if useful - per_pdf = m.get("per_pdf", {}) - if per_pdf: - body.append("") - body.append("### Per-PDF correctness") - body.append("") - header = "| Doc | Pages | " + " | ".join(f"`{a}`" for a in active_arms) + " |" - sep = "|---|---:|" + "|".join(":---:" for _ in active_arms) + "|" - body.append(header) - body.append(sep) - for doc_id, info in sorted(per_pdf.items()): - row_cells = [] - for arm_name in active_arms: - g = info.get("arms", {}).get(arm_name, {}) - if not g: - row_cells.append("?") - else: - row_cells.append("✓" if g.get("correct") else "✗") - body.append( - f"| `{doc_id}` | {info.get('pages', '?')} | " + " | ".join(row_cells) + " |" - ) - - return ReportSection( - title="Parser × agent-vs-stuffing — long PDFs (sonnet 4.5)", - headline=True, - body_md="\n".join(body), - body_json=m, - ) - - -# --------------------------------------------------------------------------- -# Metrics -# --------------------------------------------------------------------------- - - -def _compute_metrics( - questions: list[PCQuestion], - per_arm_results: dict[str, list[ArmResult]], - per_arm_grades: dict[str, list[GradeResult]], - active_arms: Iterable[str], -) -> dict[str, Any]: - """Aggregate per-arm metrics + the user's preprocessing cost overlay.""" - - # Sum unique PDF pages — preprocessing pays per unique PDF, not per question. - pdf_pages: dict[str, int] = {} - for q in questions: - pdf_pages.setdefault(q.doc_id, q.pages) - - per_arm: dict[str, dict[str, Any]] = {} - for arm_name in active_arms: - results = per_arm_results[arm_name] - grades = per_arm_grades[arm_name] - n = len(grades) - n_correct = sum(1 for g in grades if g.correct) - f1_sum = sum(g.f1 for g in grades) - acc_with_ci = accuracy_with_wilson_ci(n_correct, n) - - # LLM cost: sum of per-call cost_micros across questions, then average. - cost_micros_total = sum(int(r.cost_micros or 0) for r in results) - llm_cost_per_q = (cost_micros_total / 1_000_000.0) / n if n else 0.0 - - # Preprocessing cost depends on which mode this arm corresponds to. - if arm_name == "native_pdf": - preprocess_per_page = 0.0 - preprocess_label = "n/a (PDF attached natively)" - elif arm_name in _LC_ARM_MODE: - mode = _LC_ARM_MODE[arm_name] - preprocess_per_page = PREPROCESS_USD_PER_PAGE[mode] - preprocess_label = f"{mode} tier ($/{mode}/page = ${preprocess_per_page:.4f})" - elif arm_name == "surfsense_agentic": - preprocess_per_page = PREPROCESS_USD_PER_PAGE[SURFSENSE_INGEST_MODE] - preprocess_label = ( - f"{SURFSENSE_INGEST_MODE} tier (ingested by SurfSense at " - f"processing_mode=premium + vision_llm=on)" - ) - else: - preprocess_per_page = 0.0 - preprocess_label = "unknown" - - preprocess_cost_total = sum(pages * preprocess_per_page for pages in pdf_pages.values()) - preprocess_cost_per_q = preprocess_cost_total / n if n else 0.0 - total_cost_per_q = llm_cost_per_q + preprocess_cost_per_q - - latencies = sorted(int(r.latency_ms or 0) for r in results) - latency_median = latencies[len(latencies) // 2] if latencies else 0 - latency_p95 = ( - latencies[int(len(latencies) * 0.95)] - if len(latencies) >= 20 - else (latencies[-1] if latencies else 0) - ) - - in_tokens = [int(r.input_tokens or 0) for r in results] - out_tokens = [int(r.output_tokens or 0) for r in results] - - per_arm[arm_name] = { - **acc_with_ci.to_dict(), - "n": n, - "n_correct": n_correct, - "f1_mean": f1_sum / n if n else 0.0, - "llm_cost_per_q": llm_cost_per_q, - "preprocess_per_page_usd": preprocess_per_page, - "preprocess_cost_total": preprocess_cost_total, - "preprocess_cost_per_q": preprocess_cost_per_q, - "total_cost_per_q": total_cost_per_q, - "preprocess_label": preprocess_label, - "latency_ms_median": latency_median, - "latency_ms_p95": latency_p95, - "input_tokens_mean": (sum(in_tokens) / len(in_tokens)) if in_tokens else 0.0, - "output_tokens_mean": (sum(out_tokens) / len(out_tokens)) if out_tokens else 0.0, - } - - # Per-PDF breakdown (correct / not for each arm) - per_pdf: dict[str, dict[str, Any]] = {} - for i, q in enumerate(questions): - slot = per_pdf.setdefault( - q.doc_id, - { - "pages": q.pages, - "arms": {}, - }, - ) - for arm_name in active_arms: - slot["arms"].setdefault( - arm_name, - { - "correct": per_arm_grades[arm_name][i].correct, - "f1": per_arm_grades[arm_name][i].f1, - }, - ) - - return { - "per_arm": per_arm, - "per_pdf": per_pdf, - "n_questions": len(questions), - "n_unique_pdfs": len(pdf_pages), - "total_pages_in_scope": sum(pdf_pages.values()), - } - - -__all__ = ["ParserCompareBenchmark", "PCQuestion"] diff --git a/surfsense_evals/src/surfsense_evals/suites/research/__init__.py b/surfsense_evals/src/surfsense_evals/suites/research/__init__.py deleted file mode 100644 index 03fe24c028..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/research/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Research / multi-document RAG benchmarks. - -Distinct from ``multimodal_doc`` (PDF-bound) and ``medical`` (one -question = one source PDF). Benchmarks here put *retrieval and -reasoning across many documents* in the critical path — the regime -where SurfSense's chunk-level RAG should shine versus "pour the -entire document into the LLM" or "ask the LLM cold". - -* ``frames`` (google/frames-benchmark) — 824 multi-hop Wikipedia - questions; tests bare-LLM vs SurfSense over a shared ~330-doc - corpus. -* ``crag`` (facebookresearch/CRAG, KDD Cup 2024) — 2,706 web QA - pairs with 5 pre-retrieved HTML pages each; tests bare-LLM vs - long-context-stuffed LLM vs SurfSense over the question's 5 - scoped pages — the closest comparison to a competing RAG product. -""" - -from __future__ import annotations diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py deleted file mode 100644 index b9658ef688..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -"""CRAG — Comprehensive RAG Benchmark (Yang et al., Meta, KDD Cup 2024). - -Source: https://github.com/facebookresearch/CRAG (Tasks 1 & 2) -Paper: https://arxiv.org/abs/2406.04744 - -CRAG ships ~2,706 factual QA pairs, each paired with **5 full HTML -pages** retrieved as the top-5 of a real web search at ``query_time``. -The benchmark spans 5 domains (finance, music, movie, sports, open) -and 8 question types (simple, comparison, aggregation, set, multi-hop, -post-processing, false_premise, simple_w_condition) — heads/torsos/ -tails of entity popularity — and an explicit static→real-time -freshness axis. - -Why CRAG demonstrates SurfSense more clearly than FRAMES --------------------------------------------------------- -FRAMES tested SurfSense vs. *no retrieval at all* — a fair "naive -prompting" baseline (the published 40.8% number) but not a competing -RAG product. CRAG enables a three-way comparison: - -* ``bare_llm`` — chat completion with the question only. CRAG - paper: ≤34% accuracy ("LLM cold"). -* ``long_context`` — stuff all 5 extracted page texts straight into - the prompt (the "naive RAG" / "straightforward RAG" arm in the - paper). Published baseline: ~44%. -* ``surfsense`` — POST ``/api/v1/new_chat`` with retrieval scoped - to the question's 5 ingested pages (``mentioned_document_ids``). - -So the headline becomes "SurfSense vs. context-stuffed long-context -LLM, both fed the same 5 pages" — a head-to-head against the simplest -realistic RAG strategy, not against an unarmed model. - -Scoring follows the CRAG paper: each prediction is graded as -**correct** (+1), **missing/I-don't-know** (0), or **incorrect** (-1), -and the headline metric is the *Truthfulness Score*: -``(#correct - #incorrect) / total`` — penalising hallucinations -relative to refusals. -""" - -from __future__ import annotations - -from ....core import registry as _registry -from .runner import CragBenchmark - -_registry.register(CragBenchmark()) diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py deleted file mode 100644 index 7154e6d145..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py +++ /dev/null @@ -1,340 +0,0 @@ -"""CRAG dataset loader — download ``crag_task_1_and_2_dev_v4.jsonl.bz2`` and parse. - -The CRAG repo (``facebookresearch/CRAG``) ships Tasks 1 & 2 as a -single bzip2-compressed JSONL on GitHub raw. Each row carries: - -* ``interaction_id`` — opaque per-question id (we keep verbatim) -* ``query_time`` — wall clock of the original web search -* ``domain`` — finance | music | movie | sports | open -* ``question_type`` — simple | comparison | aggregation | set | - multi-hop | post-processing | false_premise | - simple_w_condition -* ``static_or_dynamic`` — static | slow-changing | fast-changing | real-time -* ``query`` — the question -* ``answer`` — gold short answer -* ``alt_ans`` — list[str] of alternative valid answers - (paraphrases / synonyms / unit variants) -* ``split`` — 0 = validation, 1 = public test -* ``popularity`` — head | torso | tail (KG questions); empty for web -* ``search_results`` — list of up to 5 ``{page_name, page_url, - page_snippet, page_result, page_last_modified}``; - ``page_result`` is full HTML. - -We materialise this into ``CragQuestion`` objects keeping ``pages`` as -a list of ``CragPage`` so downstream ingest can save each as its own -file and SurfSense can dedupe on filename. -""" - -from __future__ import annotations - -import bz2 -import hashlib -import io -import json -import logging -import urllib.request -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -logger = logging.getLogger(__name__) - - -# Tasks 1 & 2 share the same JSONL on the public CRAG repo. -CRAG_TASK_1_2_URL = ( - "https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/" - "crag_task_1_and_2_dev_v4.jsonl.bz2" -) -CRAG_TASK_1_2_FILENAME = "crag_task_1_and_2_dev_v4.jsonl.bz2" - - -# --------------------------------------------------------------------------- -# Question / page dataclasses -# --------------------------------------------------------------------------- - - -@dataclass -class CragPage: - """One of the up-to-5 pre-retrieved web pages for a CRAG question.""" - - page_name: str - page_url: str - page_snippet: str - page_html: str - page_last_modified: str | None = None - - @property - def url_hash(self) -> str: - """Stable 12-hex digest of the page URL for filename keys. - - We can't use the raw URL as a filename (slashes, query strings, - unicode), and we *do* want collision-safety across the whole - ingest sample. ``sha1[:12]`` gives us 48 bits of namespace - which is overkill for a corpus capped at a few thousand pages. - """ - - return hashlib.sha1(self.page_url.encode("utf-8")).hexdigest()[:12] - - -@dataclass -class CragQuestion: - """One row of CRAG (Tasks 1 & 2).""" - - qid: str # synthesised "C00000".."C02705" - interaction_id: str - query_time: str - query: str - gold_answer: str - alt_answers: list[str] - domain: str - question_type: str - static_or_dynamic: str - popularity: str # may be "" for web-sourced questions - split: int # 0=validation, 1=public_test - raw_index: int # row index in the source JSONL - pages: list[CragPage] = field(default_factory=list) - - def to_dict(self) -> dict[str, Any]: - return { - "qid": self.qid, - "interaction_id": self.interaction_id, - "query_time": self.query_time, - "query": self.query, - "gold_answer": self.gold_answer, - "alt_answers": list(self.alt_answers), - "domain": self.domain, - "question_type": self.question_type, - "static_or_dynamic": self.static_or_dynamic, - "popularity": self.popularity, - "split": self.split, - "raw_index": self.raw_index, - "n_pages": len(self.pages), - "page_urls": [p.page_url for p in self.pages], - } - - -# --------------------------------------------------------------------------- -# Download + decompress -# --------------------------------------------------------------------------- - - -def download_task_1_2(cache_dir: Path) -> Path: - """Download the bz2 archive into ``cache_dir`` (skip if cached). - - Returns the path to the local ``.jsonl.bz2``. We use stdlib - ``urllib`` rather than ``httpx`` to keep the download synchronous - and trivially resumable (re-running the function is a no-op once - the file is on disk and non-empty). - """ - - cache_dir.mkdir(parents=True, exist_ok=True) - dest = cache_dir / CRAG_TASK_1_2_FILENAME - if dest.exists() and dest.stat().st_size > 0: - logger.debug("CRAG bz2 already cached at %s", dest) - return dest - - logger.info("Downloading CRAG (Tasks 1 & 2) from %s ...", CRAG_TASK_1_2_URL) - tmp = dest.with_suffix(dest.suffix + ".part") - req = urllib.request.Request( - CRAG_TASK_1_2_URL, - headers={"User-Agent": "SurfSense-Evals/0.1 (CRAG dataset fetch)"}, - ) - with urllib.request.urlopen(req, timeout=600) as response, tmp.open("wb") as fh: - chunk = response.read(1 << 20) - while chunk: - fh.write(chunk) - chunk = response.read(1 << 20) - tmp.replace(dest) - logger.info("CRAG bz2 downloaded: %s (%.1f MiB)", dest, dest.stat().st_size / 1024 / 1024) - return dest - - -# --------------------------------------------------------------------------- -# Parse -# --------------------------------------------------------------------------- - - -def _parse_pages(raw_search_results: Any) -> list[CragPage]: - if not isinstance(raw_search_results, list): - return [] - pages: list[CragPage] = [] - for entry in raw_search_results: - if not isinstance(entry, dict): - continue - url = str(entry.get("page_url") or "").strip() - html = str(entry.get("page_result") or "") - if not url or not html.strip(): - # No URL or empty HTML => useless for retrieval. - continue - pages.append( - CragPage( - page_name=str(entry.get("page_name") or "").strip(), - page_url=url, - page_snippet=str(entry.get("page_snippet") or "").strip(), - page_html=html, - page_last_modified=( - str(entry.get("page_last_modified")).strip() - if entry.get("page_last_modified") - else None - ), - ) - ) - return pages - - -def _parse_alt_answers(raw: Any) -> list[str]: - if isinstance(raw, list): - return [str(x).strip() for x in raw if str(x).strip()] - if isinstance(raw, str) and raw.strip(): - return [raw.strip()] - return [] - - -def iter_questions(jsonl_bz2_path: Path) -> list[CragQuestion]: - """Stream-decompress + parse the CRAG JSONL into ``CragQuestion`` objects. - - The bz2 expansion ratio is ~10x and the decompressed file is - multi-GB; we therefore decompress *line by line* via - ``bz2.open(..., "rt")``. Each row is a single (potentially very - large, due to embedded HTML) JSON object. We keep the entire row - in memory because we materialise the pages to disk immediately - after parsing in the ingest pipeline — the runner never holds - more than the current sample's worth of HTML. - """ - - out: list[CragQuestion] = [] - with bz2.open(jsonl_bz2_path, mode="rt", encoding="utf-8") as fh: - for raw_idx, line in enumerate(fh): - line = line.strip() - if not line: - continue - try: - row = json.loads(line) - except json.JSONDecodeError as exc: - logger.warning("Skipping malformed CRAG row %d: %s", raw_idx, exc) - continue - query = str(row.get("query") or "").strip() - answer = str(row.get("answer") or "").strip() - if not query or not answer: - logger.debug("Skipping CRAG row %d with missing query/answer", raw_idx) - continue - interaction_id = str(row.get("interaction_id") or "").strip() - pages = _parse_pages(row.get("search_results")) - out.append( - CragQuestion( - qid=f"C{raw_idx:05d}", - interaction_id=interaction_id, - query_time=str(row.get("query_time") or "").strip(), - query=query, - gold_answer=answer, - alt_answers=_parse_alt_answers(row.get("alt_ans")), - domain=str(row.get("domain") or "").strip().lower(), - question_type=str(row.get("question_type") or "").strip().lower(), - static_or_dynamic=str(row.get("static_or_dynamic") or "").strip().lower(), - popularity=str(row.get("popularity") or "").strip().lower(), - split=int(row.get("split") or 0), - raw_index=raw_idx, - pages=pages, - ) - ) - return out - - -def stratified_sample( - questions: list[CragQuestion], - *, - n: int, - seed: int = 17, -) -> list[CragQuestion]: - """Take ``n`` questions that roughly preserve the domain × question-type mix. - - CRAG is only ~2.7k rows so naive head-of-list sampling badly - over-weights ``finance`` (because the dataset isn't shuffled by - domain). We bucket on ``(domain, question_type)`` and round-robin - pick from each bucket until we hit ``n`` — this gives every - bucket a fair shot and keeps the sample composition stable across - re-runs (deterministic via the seeded shuffle inside each bucket). - """ - - if n <= 0 or n >= len(questions): - return list(questions) - import random - - rng = random.Random(seed) - buckets: dict[tuple[str, str], list[CragQuestion]] = {} - for q in questions: - buckets.setdefault((q.domain, q.question_type), []).append(q) - for items in buckets.values(): - rng.shuffle(items) - - keys = sorted(buckets.keys()) - chosen: list[CragQuestion] = [] - cursor = 0 - while len(chosen) < n and any(buckets[k] for k in keys): - key = keys[cursor % len(keys)] - cursor += 1 - if buckets[key]: - chosen.append(buckets[key].pop()) - chosen.sort(key=lambda q: q.raw_index) - return chosen - - -def write_questions_jsonl(questions: list[CragQuestion], dest: Path) -> None: - """Persist a parsed copy (without page HTML) under the benchmark data dir.""" - - dest.parent.mkdir(parents=True, exist_ok=True) - with dest.open("w", encoding="utf-8") as fh: - for q in questions: - fh.write(json.dumps(q.to_dict()) + "\n") - - -# --------------------------------------------------------------------------- -# Reading the lightweight questions.jsonl back -# --------------------------------------------------------------------------- - - -def load_questions_jsonl(path: Path) -> list[dict[str, Any]]: - """Re-load the lightweight (no-HTML) questions JSONL from disk.""" - - out: list[dict[str, Any]] = [] - if not path.exists(): - return out - with path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - try: - out.append(json.loads(line)) - except json.JSONDecodeError: - continue - return out - - -# --------------------------------------------------------------------------- -# Convenience: decompress a snippet to memory for tests -# --------------------------------------------------------------------------- - - -def decompress_to_memory(jsonl_bz2_path: Path) -> io.StringIO: - """For tests / one-off scripts: read the whole bz2 into a StringIO. - - Avoids leaking gigabytes; use ``iter_questions`` in production. - """ - - with bz2.open(jsonl_bz2_path, mode="rb") as fh: - return io.StringIO(fh.read().decode("utf-8")) - - -__all__ = [ - "CRAG_TASK_1_2_FILENAME", - "CRAG_TASK_1_2_URL", - "CragPage", - "CragQuestion", - "decompress_to_memory", - "download_task_1_2", - "iter_questions", - "load_questions_jsonl", - "stratified_sample", - "write_questions_jsonl", -] diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py deleted file mode 100644 index e49660a6f2..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py +++ /dev/null @@ -1,571 +0,0 @@ -"""CRAG 3-class grader: ``correct`` (+1) / ``missing`` (0) / ``incorrect`` (-1). - -The CRAG paper's headline metric is the **Truthfulness Score**: - - score = (#correct - #incorrect) / total - -which rewards calibrated abstention — refusing to answer is neutral -(0), guessing wrong is negative (-1). Grading is therefore a 3-class -problem rather than the 2-class accuracy used for FRAMES. - -Pipeline per (pred, gold, alt_ans, question_type): - -1. Detect refusal first (``Answer: I don't know`` / "I don't know" / - "no information") → ``missing`` (deterministic, never billed). -2. ``false_premise`` questions: gold is canonically "the question - contains a false premise" — reward any answer that flags the - false premise (substring "false premise" / "incorrect premise" / - "no such") as correct. -3. Run the FRAMES-style deterministic shortcut (exact / numeric / - substring) on ``pred`` against ``gold ∪ alt_ans``. Hit → correct. -4. Fall through to the LLM judge (if configured), which returns one - of ``{correct, missing, incorrect}`` — verbatim CRAG protocol. -5. No judge configured → record ``incorrect`` (pessimistic but at - least monotone with the deterministic grader). - -The judge is throttled by an asyncio.Semaphore so it doesn't outrun -the OpenRouter rate limit; the pre-judge deterministic pass keeps -the bill bounded (most easy "Beyoncé"-vs-"Beyoncé Knowles" cases -short-circuit before we burn judge tokens). -""" - -from __future__ import annotations - -import asyncio -import json -import logging -import re -import string -from collections.abc import Sequence -from dataclasses import dataclass -from typing import Any, Literal - -from ....core.providers.openrouter_chat import OpenRouterChatProvider - -logger = logging.getLogger(__name__) - - -GradeClass = Literal["correct", "missing", "incorrect"] - - -# --------------------------------------------------------------------------- -# Public type -# --------------------------------------------------------------------------- - - -@dataclass -class CragGradeResult: - """One graded (pred, gold) pair under CRAG's 3-class rubric.""" - - grade: GradeClass - score: int # +1 / 0 / -1 - method: str # exact, numeric, substring, refusal, - # false_premise_correct, false_premise_miss, - # llm_judge, lexical_miss, ... - normalised_pred: str = "" - normalised_gold: str = "" - judge_rationale: str = "" - - @property - def correct(self) -> bool: - return self.grade == "correct" - - @property - def missing(self) -> bool: - return self.grade == "missing" - - @property - def incorrect(self) -> bool: - return self.grade == "incorrect" - - def to_dict(self) -> dict[str, Any]: - return { - "grade": self.grade, - "score": self.score, - "method": self.method, - "normalised_pred": self.normalised_pred, - "normalised_gold": self.normalised_gold, - "judge_rationale": self.judge_rationale, - } - - -def _grade_to_score(grade: GradeClass) -> int: - return {"correct": 1, "missing": 0, "incorrect": -1}[grade] - - -# --------------------------------------------------------------------------- -# Normalisation -# --------------------------------------------------------------------------- - - -_PUNCT_TABLE = str.maketrans({c: " " for c in string.punctuation}) -_ARTICLES = re.compile(r"\b(a|an|the)\b", re.IGNORECASE) -_WS = re.compile(r"\s+") - - -def _normalise(s: str) -> str: - s = (s or "").lower() - s = s.translate(_PUNCT_TABLE) - s = _ARTICLES.sub(" ", s) - s = _WS.sub(" ", s).strip() - return s - - -_WORD_NUMBERS = { - "zero": 0, - "one": 1, - "two": 2, - "three": 3, - "four": 4, - "five": 5, - "six": 6, - "seven": 7, - "eight": 8, - "nine": 9, - "ten": 10, - "eleven": 11, - "twelve": 12, - "thirteen": 13, - "fourteen": 14, - "fifteen": 15, - "sixteen": 16, - "seventeen": 17, - "eighteen": 18, - "nineteen": 19, - "twenty": 20, -} - -_NUMERIC_RE = re.compile(r"-?\d+(?:[.,]\d+)?") - - -def _maybe_number(s: str) -> float | None: - """Extract a single numeric value from raw lowercased text.""" - - raw = (s or "").strip().lower() - if not raw: - return None - match = _NUMERIC_RE.search(raw) - if match: - try: - return float(match.group(0).replace(",", "")) - except ValueError: - pass - for tok in _normalise(s).split(): - if tok in _WORD_NUMBERS: - return float(_WORD_NUMBERS[tok]) - return None - - -def _whole_word_substring(haystack: str, needle: str) -> bool: - if not needle: - return False - return f" {needle} " in f" {haystack} " - - -# --------------------------------------------------------------------------- -# Refusal detection -# --------------------------------------------------------------------------- - - -_REFUSAL_PATTERNS = [ - re.compile(r"\bi\s+don'?t\s+know\b", re.IGNORECASE), - re.compile(r"\bi\s+do\s+not\s+know\b", re.IGNORECASE), - re.compile(r"\bnot\s+enough\s+information\b", re.IGNORECASE), - re.compile(r"\binsufficient\s+information\b", re.IGNORECASE), - re.compile(r"\bcannot\s+(?:be\s+)?(?:answered|determined)\b", re.IGNORECASE), - re.compile(r"\bunable\s+to\s+(?:answer|determine)\b", re.IGNORECASE), - re.compile(r"\bno\s+(?:information|data|evidence)\b", re.IGNORECASE), -] - - -def _is_refusal(pred: str) -> bool: - """Cheap deterministic check for "I don't know" -shaped responses.""" - - if not pred or not pred.strip(): - return True # empty answer is a de facto refusal - return any(p.search(pred) for p in _REFUSAL_PATTERNS) - - -# --------------------------------------------------------------------------- -# False-premise handling -# --------------------------------------------------------------------------- - - -_FALSE_PREMISE_PATTERNS = [ - re.compile(r"false\s+premise", re.IGNORECASE), - re.compile(r"incorrect\s+premise", re.IGNORECASE), - re.compile(r"premise\s+(?:is|of)\s+the\s+question", re.IGNORECASE), - re.compile(r"\bno\s+such\b", re.IGNORECASE), - re.compile(r"never\s+(?:happened|occurred|existed)", re.IGNORECASE), - re.compile(r"\bdid\s+not\s+(?:happen|occur|exist)\b", re.IGNORECASE), - re.compile(r"\bdoes\s+not\s+exist\b", re.IGNORECASE), - re.compile(r"is\s+not\s+(?:true|correct|accurate)", re.IGNORECASE), - re.compile(r"\bisn'?t\s+(?:true|correct|accurate)\b", re.IGNORECASE), - re.compile(r"\binvalid\s+(?:premise|question|assumption)\b", re.IGNORECASE), -] - - -def _flags_false_premise(pred: str) -> bool: - return any(p.search(pred) for p in _FALSE_PREMISE_PATTERNS) - - -# --------------------------------------------------------------------------- -# Deterministic grader -# --------------------------------------------------------------------------- - - -def grade_deterministic( - *, - pred: str, - gold: str, - alt_answers: Sequence[str] = (), - question_type: str = "", -) -> CragGradeResult: - """Try to grade without the LLM judge. Returns a final result. - - Always returns *some* result — the caller checks ``method`` to - decide whether the LLM judge should overturn it. ``lexical_miss`` - and ``false_premise_unclear`` are the two methods that trigger the - judge fallback. - """ - - qtype = (question_type or "").lower() - n_pred = _normalise(pred) - n_gold = _normalise(gold) - - if _is_refusal(pred): - # CRAG protocol: refusal is *missing* (0), even on false-premise - # questions where one might argue refusal == correct. We - # follow the paper's grading literally. - return CragGradeResult( - grade="missing", - score=0, - method="refusal", - normalised_pred=n_pred, - normalised_gold=n_gold, - ) - - # Empty-gold guard (shouldn't happen, but defensively): - if not n_gold: - return CragGradeResult( - grade="incorrect", - score=-1, - method="empty_gold", - normalised_pred=n_pred, - normalised_gold=n_gold, - ) - - # False-premise questions: gold is typically "the question contains - # a false premise" / "no such X" / similar. Any answer that - # explicitly flags the false premise is correct. - if qtype == "false_premise": - if _flags_false_premise(pred): - return CragGradeResult( - grade="correct", - score=1, - method="false_premise_flagged", - normalised_pred=n_pred, - normalised_gold=n_gold, - ) - # If the model commits to *any* concrete answer on a false- - # premise question without flagging the premise, it is wrong. - # But we don't classify ourselves — let the judge decide on - # the off chance the gold itself is e.g. "no" and the pred - # is "no" without explicit "false premise" wording. - return CragGradeResult( - grade="incorrect", - score=-1, - method="false_premise_unclear", - normalised_pred=n_pred, - normalised_gold=n_gold, - ) - - # All non-false-premise questions: try the standard chain against - # gold and each alt answer. First match wins. - candidates = [gold, *list(alt_answers)] - for candidate in candidates: - if not candidate or not str(candidate).strip(): - continue - cand_norm = _normalise(candidate) - if not cand_norm: - continue - if n_pred == cand_norm: - return CragGradeResult( - grade="correct", - score=1, - method="exact", - normalised_pred=n_pred, - normalised_gold=cand_norm, - ) - p_num = _maybe_number(pred) - c_num = _maybe_number(candidate) - if p_num is not None and c_num is not None: - # Pure 1% relative tolerance for CRAG (currency, counts, - # ratios). Unlike FRAMES (which uses a 0.5 absolute floor - # for year-shaped answers), CRAG's numeric questions are - # often small-value (stock prices, percentages) where a - # 0.5 floor would let "$2.05" match "$2.17". The judge is - # the safety net for borderline rounding cases. - tol = abs(c_num) * 0.01 - if abs(p_num - c_num) <= tol: - return CragGradeResult( - grade="correct", - score=1, - method="numeric", - normalised_pred=n_pred, - normalised_gold=cand_norm, - ) - # Numeric question with different numbers — keep looking - # at other candidates rather than declaring miss now; - # alt answers may include word forms that pass. - if _whole_word_substring(n_pred, cand_norm): - return CragGradeResult( - grade="correct", - score=1, - method="substring", - normalised_pred=n_pred, - normalised_gold=cand_norm, - ) - if _whole_word_substring(cand_norm, n_pred) and len(n_pred) >= 3: - return CragGradeResult( - grade="correct", - score=1, - method="substring_reverse", - normalised_pred=n_pred, - normalised_gold=cand_norm, - ) - - return CragGradeResult( - grade="incorrect", - score=-1, - method="lexical_miss", - normalised_pred=n_pred, - normalised_gold=n_gold, - ) - - -# --------------------------------------------------------------------------- -# LLM-as-judge (3-class) -# --------------------------------------------------------------------------- - - -_JUDGE_SYSTEM = ( - "You are an impartial grader for short-answer factual questions, " - "following the CRAG benchmark rubric. Given a question, the gold " - "answer (and any alternative valid answers), and a model's " - "prediction, classify the prediction into exactly one of three " - "categories:\n\n" - '* "correct" — the prediction expresses the same factual ' - "content as the gold answer (paraphrasing OK; numbers as words " - "OK; partial-but-correct names OK; non-contradictory extra " - "detail OK).\n" - '* "missing" — the prediction explicitly refuses, says "I ' - "don't know\", says there is insufficient information, or hedges " - "without committing.\n" - '* "incorrect" — the prediction commits to a fact that is ' - "different from the gold answer, or fails to flag a false " - "premise when the question contains one.\n\n" - "Special case: if the question contains a false premise and the " - "gold answer says so, then a prediction that flags the false " - 'premise is "correct".\n\n' - "Respond with ONLY a JSON object on a single line:\n" - '{"grade": "correct"|"missing"|"incorrect", "rationale": ""}' -) - - -_JUDGE_TEMPLATE = """\ -Question: {question} -Question type: {question_type} -Gold answer: {gold} -{alt_block}Model prediction: {pred} - -Decide whether the prediction is correct, missing, or incorrect. -""" - - -@dataclass -class CragJudgeConfig: - api_key: str - model: str = "anthropic/claude-sonnet-4.5" - base_url: str = "https://openrouter.ai/api/v1" - max_tokens: int = 200 - concurrency: int = 4 - - -class CragLlmJudge: - """Async LLM judge over OpenRouter chat completions, 3-class output.""" - - def __init__(self, *, config: CragJudgeConfig) -> None: - self._config = config - self._provider = OpenRouterChatProvider( - api_key=config.api_key, - base_url=config.base_url, - model=config.model, - ) - self._sem = asyncio.Semaphore(max(1, config.concurrency)) - - @property - def model(self) -> str: - return self._config.model - - async def judge( - self, - *, - question: str, - gold: str, - alt_answers: Sequence[str], - pred: str, - question_type: str = "", - ) -> tuple[GradeClass, str]: - """Return ``(grade, rationale)``. Errors return incorrect + reason.""" - - alt_block = "" - if alt_answers: - alt_lines = "\n".join(f" - {a}" for a in alt_answers if a) - if alt_lines: - alt_block = f"Alternative valid answers:\n{alt_lines}\n" - prompt = _JUDGE_TEMPLATE.format( - question=question, - question_type=question_type or "unknown", - gold=gold, - alt_block=alt_block, - pred=pred, - ) - try: - async with self._sem: - response = await self._provider.complete( - prompt=prompt, - system_prompt=_JUDGE_SYSTEM, - max_tokens=self._config.max_tokens, - ) - except Exception as exc: # noqa: BLE001 - return "incorrect", f"judge_error: {type(exc).__name__}: {exc}" - return _parse_judge_response(response.text) - - -def _parse_judge_response(text: str) -> tuple[GradeClass, str]: - """Parse the judge reply into a 3-class label + rationale.""" - - if not text or not text.strip(): - return "incorrect", "judge_returned_empty" - match = re.search(r"\{[^{}]*\}", text, flags=re.DOTALL) - candidate = match.group(0) if match else text - try: - data = json.loads(candidate) - except (json.JSONDecodeError, ValueError): - lowered = text.strip().lower() - if "correct" in lowered and "incorrect" not in lowered: - return "correct", "yes (parser_fallback)" - if "missing" in lowered or "i don" in lowered: - return "missing", "missing (parser_fallback)" - return "incorrect", f"unparseable_judge_response: {text[:200]}" - raw_grade = str(data.get("grade") or "").strip().lower() - rationale = str(data.get("rationale", "")).strip()[:280] - if raw_grade in {"correct", "missing", "incorrect"}: - return raw_grade, rationale # type: ignore[return-value] - return "incorrect", f"unknown_grade={raw_grade!r}; {rationale}" - - -# --------------------------------------------------------------------------- -# Combined grader -# --------------------------------------------------------------------------- - - -# Methods that should *not* trigger the LLM judge — the deterministic -# verdict is conclusive (refusal, exact match, numeric mismatch, etc.). -_TERMINAL_METHODS = frozenset( - { - "refusal", - "exact", - "numeric", - "substring", - "substring_reverse", - "false_premise_flagged", - "empty_gold", - } -) - - -async def grade_with_judge( - *, - pred: str, - gold: str, - alt_answers: Sequence[str], - question: str, - question_type: str, - judge: CragLlmJudge | None, -) -> CragGradeResult: - """One row → deterministic shortcut → optional LLM judge fallback.""" - - det = grade_deterministic( - pred=pred, - gold=gold, - alt_answers=alt_answers, - question_type=question_type, - ) - if det.method in _TERMINAL_METHODS: - return det - if judge is None: - return det # ``lexical_miss`` / ``false_premise_unclear`` → keep as-is - grade, rationale = await judge.judge( - question=question, - gold=gold, - alt_answers=alt_answers, - pred=pred, - question_type=question_type, - ) - return CragGradeResult( - grade=grade, - score=_grade_to_score(grade), - method="llm_judge", - normalised_pred=det.normalised_pred, - normalised_gold=det.normalised_gold, - judge_rationale=rationale, - ) - - -@dataclass -class CragGradeRow: - """One row to grade. Mirrors the FRAMES grader's tuple but typed.""" - - qid: str - question: str - gold: str - alt_answers: list[str] - pred: str - question_type: str = "" - - -async def grade_many( - *, - rows: Sequence[CragGradeRow], - judge: CragLlmJudge | None, -) -> list[CragGradeResult]: - """Grade every row concurrently. Judge enforces its own concurrency cap.""" - - if not rows: - return [] - coros = [ - grade_with_judge( - pred=r.pred, - gold=r.gold, - alt_answers=r.alt_answers, - question=r.question, - question_type=r.question_type, - judge=judge, - ) - for r in rows - ] - return list(await asyncio.gather(*coros)) - - -__all__ = [ - "CragGradeResult", - "CragGradeRow", - "CragJudgeConfig", - "CragLlmJudge", - "GradeClass", - "grade_deterministic", - "grade_many", - "grade_with_judge", -] diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py deleted file mode 100644 index 271d43d567..0000000000 --- a/surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py +++ /dev/null @@ -1,222 +0,0 @@ -"""HTML → markdown for CRAG pages, with boilerplate removal. - -Each CRAG page is a *full* HTML document (nav, ads, recommended-for- -you, footer, ...). Without removing that boilerplate, retrieval over -the chunks would surface menu items and "subscribe to our newsletter" -boxes instead of the actual page content. We use ``trafilatura``, -which is purpose-built for main-content extraction (the same library -Common Crawl downstream pipelines use). It outputs clean prose with -section headers, lists, and tables preserved. - -Extraction policy: -1. ``trafilatura.extract`` with ``output_format="markdown"`` — main - content only, headers preserved, tables kept. -2. If extraction fails or returns < 200 chars (paywalled / JS-only - page / extraction confused), fall back to a plain stdlib - ``HTMLParser`` that strips tags and collapses whitespace. Some - text is better than no text — SurfSense's chunker handles noisy - prose. - -We *intentionally* keep the page name and URL as visible H1 / link -metadata so the SurfSense chunker preserves doc identity at the top of -the first chunk (mirrors what we do for FRAMES Wikipedia pages). -""" - -from __future__ import annotations - -import html -import logging -import re -from dataclasses import dataclass -from html.parser import HTMLParser - -logger = logging.getLogger(__name__) - - -_MIN_TRAFILATURA_LENGTH = 200 -_MAX_OUTPUT_CHARS = 200_000 # cap to keep upload payloads sane - - -@dataclass -class ExtractionResult: - """Outcome of converting one HTML blob to plain markdown.""" - - text: str - method: str # "trafilatura" | "fallback_strip" | "empty" - n_chars: int - - @property - def ok(self) -> bool: - return self.n_chars > 0 - - -# --------------------------------------------------------------------------- -# Trafilatura wrapper (lazy import so tests / small scripts don't pay) -# --------------------------------------------------------------------------- - - -def _trafilatura_extract(html_text: str, *, url: str) -> str | None: - try: - import trafilatura - except ImportError: # pragma: no cover - dependency is required - logger.warning("trafilatura not installed; falling back to strip-tags only") - return None - try: - text = trafilatura.extract( - html_text, - url=url or None, - output_format="markdown", - include_links=False, - include_images=False, - include_tables=True, - favor_recall=True, - ) - except Exception as exc: # noqa: BLE001 - trafilatura raises a zoo - logger.debug("trafilatura.extract crashed for %s: %s", url, exc) - return None - if not text: - return None - return text.strip() - - -# --------------------------------------------------------------------------- -# Stdlib fallback: strip HTML tags -# --------------------------------------------------------------------------- - - -class _StripHTMLParser(HTMLParser): - """Collect text content, treating block tags as paragraph breaks. - - We deliberately drop `` - - - - -

Tech News Site

Subscribe to our newsletter

-
-
-

Apple posts $90B revenue in Q3 2024

-

Apple Inc. announced its Q3 2024 financial results today, reporting - $90 billion in revenue, beating analyst expectations of $87 billion.

-

The company saw growth across iPhone, services, and wearables. - CEO Tim Cook attributed the performance to strong demand in emerging - markets, particularly India.

-

Segment breakdown

-
    -
  • iPhone: $45B
    • -
  • Services: $24B
    • -
  • Mac: $7B
    • -
-
-
-

Copyright 2024 Tech News Site. All rights reserved.

- -""" - - -class TestExtractMainContent: - def test_extracts_main_article(self) -> None: - result = extract_main_content( - _RICH_HTML, - url="https://example.com/apple", - page_name="Apple Q3 Earnings", - ) - assert result.ok - assert "Apple" in result.text - assert "Q3 2024" in result.text - # Header line is prepended. - assert result.text.startswith("# Apple Q3 Earnings") - assert "Source: https://example.com/apple" in result.text - - def test_strips_boilerplate(self) -> None: - result = extract_main_content( - _RICH_HTML, - url="https://example.com/apple", - page_name="Apple Q3 Earnings", - ) - assert result.ok - # Boilerplate strings should NOT make it through. - assert "Subscribe to our newsletter" not in result.text - assert "Copyright 2024 Tech News Site" not in result.text - assert "const a=1" not in result.text # script content - - def test_includes_last_modified_when_provided(self) -> None: - result = extract_main_content( - _RICH_HTML, - url="https://example.com/apple", - page_name="Apple Q3 Earnings", - last_modified="2024-08-01", - ) - assert "Last modified: 2024-08-01" in result.text - - def test_empty_html_returns_empty_result(self) -> None: - result = extract_main_content("", url="https://x.test/") - assert not result.ok - assert result.method == "empty" - assert result.n_chars == 0 - - def test_whitespace_only_html_is_empty(self) -> None: - result = extract_main_content(" \n ", url="https://x.test/") - assert not result.ok - - def test_garbage_html_falls_back(self) -> None: - # Trafilatura should reject this, fallback strip should still yield text. - result = extract_main_content( - "<>not a tag>>>The brown fox< None: - html = """ - -

This is content one.

-

This is content two.

- - """ - result = extract_main_content( - html, - url="https://x.test/", - page_name="Title", - ) - assert result.ok - assert "content one" in result.text - assert "content two" in result.text - - def test_html_entities_decoded(self) -> None: - html = """ -
-

Tom & Jerry — classic cartoon © 1940.

-

It's a story about a cat <Tom> and a mouse <Jerry>.

-
- """ - result = extract_main_content(html, url="https://x.test/") - assert result.ok - # & should be decoded - assert "&" not in result.text - assert "Tom" in result.text and "Jerry" in result.text - - -class TestOutputCapping: - def test_long_output_is_truncated(self) -> None: - # Generate enough content to exceed 200k cap. - body = "

" + ("hello world " * 50_000) + "

" - html = f"
{body}
" - result = extract_main_content(html, url="https://x.test/", page_name="long") - assert result.ok - # The body text itself + the metadata header. Truncation marker - # appears either at the body limit or before EOF. - if "[...truncated...]" in result.text: - # The truncation kicked in. - assert len(result.text) <= 250_000 # header + 200k cap + slack diff --git a/surfsense_evals/tests/suites/test_frames_dataset.py b/surfsense_evals/tests/suites/test_frames_dataset.py deleted file mode 100644 index f76dc0b6ff..0000000000 --- a/surfsense_evals/tests/suites/test_frames_dataset.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Tests for the FRAMES dataset parser. - -Network-free: we round-trip a tiny fixture TSV through pandas and -``load_questions`` to confirm: - -* row indices become zero-padded ``Q###`` ids, -* ``wiki_links`` (Python list literal) is materialised correctly, -* ``reasoning_types`` is split on the pipe separator, -* missing Prompt/Answer rows are dropped, and -* the legacy ``wikipedia_link_*`` per-cell fallback works when - ``wiki_links`` is missing/empty. -""" - -from __future__ import annotations - -import textwrap -from pathlib import Path - -from surfsense_evals.suites.research.frames.dataset import ( - FramesQuestion, - _parse_reasoning_types, - _parse_wiki_links, - load_questions, -) - -# --------------------------------------------------------------------------- -# Pure-function tests -# --------------------------------------------------------------------------- - - -class TestParseWikiLinks: - def test_python_list_literal(self) -> None: - s = "['https://en.wikipedia.org/wiki/A', 'https://en.wikipedia.org/wiki/B']" - assert _parse_wiki_links(s) == [ - "https://en.wikipedia.org/wiki/A", - "https://en.wikipedia.org/wiki/B", - ] - - def test_none_or_empty(self) -> None: - assert _parse_wiki_links(None) == [] - assert _parse_wiki_links("") == [] - assert _parse_wiki_links("[]") == [] - - def test_unquoted_csv_fallback(self) -> None: - # Defensive: non-Python-list strings still split on commas. - s = "https://a, https://b" - assert _parse_wiki_links(s) == ["https://a", "https://b"] - - def test_already_a_list(self) -> None: - assert _parse_wiki_links(["x", "y"]) == ["x", "y"] - - -class TestParseReasoningTypes: - def test_pipe_separated(self) -> None: - assert _parse_reasoning_types("Numerical reasoning | Multiple constraints") == [ - "Numerical reasoning", - "Multiple constraints", - ] - - def test_single_tag(self) -> None: - assert _parse_reasoning_types("Tabular reasoning") == ["Tabular reasoning"] - - def test_empty(self) -> None: - assert _parse_reasoning_types(None) == [] - assert _parse_reasoning_types("") == [] - - -# --------------------------------------------------------------------------- -# Round-trip via pandas -# --------------------------------------------------------------------------- - - -def _write_tsv(path: Path, body: str) -> None: - """Helper that writes a tab-separated fixture exactly as the user typed it.""" - - path.write_text(textwrap.dedent(body), encoding="utf-8") - - -def test_load_questions_basic(tmp_path: Path) -> None: - tsv = tmp_path / "test.tsv" - rows = [ - # Header (first column is unnamed → pandas treats as index) - "\tPrompt\tAnswer\twikipedia_link_1\twikipedia_link_2\treasoning_types\twiki_links", - # Row 0 - "0\tWho was the 15th president?\tJames Buchanan\t" - "https://en.wikipedia.org/wiki/James_Buchanan\t\t" - "Multiple constraints\t" - "['https://en.wikipedia.org/wiki/James_Buchanan']", - # Row 1 - "1\tHow many years between A and B?\t87\t" - "https://en.wikipedia.org/wiki/A\thttps://en.wikipedia.org/wiki/B\t" - "Numerical reasoning | Temporal reasoning\t" - "['https://en.wikipedia.org/wiki/A', 'https://en.wikipedia.org/wiki/B']", - # Row 2 (intentionally missing Prompt — should be dropped) - "2\t\tunused\t\t\t\t", - ] - tsv.write_text("\n".join(rows) + "\n", encoding="utf-8") - - questions = load_questions(tsv) - assert len(questions) == 2 - - q0, q1 = questions - assert isinstance(q0, FramesQuestion) - assert q0.qid == "Q000" - assert q0.raw_index == 0 - assert q0.gold_answer == "James Buchanan" - assert q0.wiki_urls == ["https://en.wikipedia.org/wiki/James_Buchanan"] - assert q0.reasoning_types == ["Multiple constraints"] - - assert q1.qid == "Q001" - assert q1.gold_answer == "87" - assert q1.wiki_urls == [ - "https://en.wikipedia.org/wiki/A", - "https://en.wikipedia.org/wiki/B", - ] - assert q1.reasoning_types == ["Numerical reasoning", "Temporal reasoning"] - - -def test_load_questions_falls_back_to_per_cell_links(tmp_path: Path) -> None: - """When ``wiki_links`` is empty, the loader should glue the - ``wikipedia_link_*`` cells back together.""" - - tsv = tmp_path / "test.tsv" - rows = [ - "\tPrompt\tAnswer\twikipedia_link_1\twikipedia_link_2\treasoning_types\twiki_links", - "0\tQ?\tA\t" - "https://en.wikipedia.org/wiki/Cell1\thttps://en.wikipedia.org/wiki/Cell2\t" - "Numerical reasoning\t", - ] - tsv.write_text("\n".join(rows) + "\n", encoding="utf-8") - questions = load_questions(tsv) - assert len(questions) == 1 - assert questions[0].wiki_urls == [ - "https://en.wikipedia.org/wiki/Cell1", - "https://en.wikipedia.org/wiki/Cell2", - ] - - -def test_load_questions_to_dict_round_trip(tmp_path: Path) -> None: - tsv = tmp_path / "test.tsv" - rows = [ - "\tPrompt\tAnswer\treasoning_types\twiki_links", - "0\tQ?\tParis\tTemporal reasoning\t['https://en.wikipedia.org/wiki/Paris']", - ] - tsv.write_text("\n".join(rows) + "\n", encoding="utf-8") - - [q] = load_questions(tsv) - d = q.to_dict() - assert d["qid"] == "Q000" - assert d["wiki_urls"] == ["https://en.wikipedia.org/wiki/Paris"] - assert d["reasoning_types"] == ["Temporal reasoning"] diff --git a/surfsense_evals/tests/suites/test_frames_grader.py b/surfsense_evals/tests/suites/test_frames_grader.py deleted file mode 100644 index d4bbad79ab..0000000000 --- a/surfsense_evals/tests/suites/test_frames_grader.py +++ /dev/null @@ -1,157 +0,0 @@ -"""Tests for the FRAMES grader's deterministic shortcut. - -The LLM-judge fallback is excluded here (network call); we just -confirm the rule-based path picks up obvious correct/incorrect -cases and routes the ambiguous ones to ``lexical_miss`` so the -runner knows to consult the judge. -""" - -from __future__ import annotations - -from surfsense_evals.suites.research.frames.grader import ( - _maybe_number, - _normalise, - _whole_word_substring, - grade_deterministic, -) - - -class TestNormalisation: - def test_lowercase_and_punct_stripped(self) -> None: - assert _normalise("Jane Ballou.") == "jane ballou" - - def test_articles_removed(self) -> None: - assert _normalise("The Eiffel Tower") == "eiffel tower" - - def test_whitespace_squashed(self) -> None: - assert _normalise(" multi space\tinput ") == "multi space input" - - def test_empty_returns_empty(self) -> None: - assert _normalise("") == "" - assert _normalise(None) == "" # type: ignore[arg-type] - - -class TestNumericExtraction: - def test_simple_int(self) -> None: - assert _maybe_number("42") == 42.0 - - def test_int_with_commas(self) -> None: - assert _maybe_number("1,234") == 1234.0 - - def test_year_in_sentence(self) -> None: - assert _maybe_number("It was published in 1847.") == 1847.0 - - def test_word_number(self) -> None: - assert _maybe_number("five") == 5.0 - assert _maybe_number("Twenty") == 20.0 - - def test_no_number_returns_none(self) -> None: - assert _maybe_number("Jane Ballou") is None - assert _maybe_number("") is None - - -class TestWholeWordSubstring: - def test_phrase_match(self) -> None: - assert _whole_word_substring("president of the united states", "united states") - - def test_word_boundary_required(self) -> None: - # "states" should NOT match inside "statesman" - assert not _whole_word_substring("the renowned statesman", "states") - - def test_empty_needle(self) -> None: - assert not _whole_word_substring("anything", "") - - -class TestExactMatch: - def test_identical(self) -> None: - r = grade_deterministic(pred="Jane Ballou", gold="Jane Ballou") - assert r.correct is True - assert r.method == "exact" - - def test_case_insensitive(self) -> None: - r = grade_deterministic(pred="paris", gold="Paris") - assert r.correct is True - assert r.method == "exact" - - def test_punctuation_ignored(self) -> None: - r = grade_deterministic(pred="Jane Ballou.", gold="Jane Ballou") - assert r.correct is True - - -class TestNumericPath: - def test_int_match(self) -> None: - r = grade_deterministic(pred="The answer is 87", gold="87") - assert r.correct is True - assert r.method == "numeric" - - def test_word_number_matches_digit(self) -> None: - r = grade_deterministic(pred="five", gold="5") - assert r.correct is True - assert r.method == "numeric" - - def test_off_by_more_than_tolerance_fails(self) -> None: - r = grade_deterministic(pred="86", gold="87") - # 86 vs 87, abs diff = 1, tol = max(0.01*87, 0.5) = 0.87 → fails - assert r.correct is False - assert r.method == "numeric_miss" - - def test_within_one_percent_passes(self) -> None: - r = grade_deterministic(pred="100", gold="101") - # 1.0 abs diff, tol = max(0.01*101, 0.5) = 1.01 → passes - assert r.correct is True - - -class TestSubstringPath: - def test_pred_contains_gold(self) -> None: - r = grade_deterministic( - pred="The answer is Jane Ballou according to records", - gold="Jane Ballou", - ) - assert r.correct is True - assert r.method == "substring" - - def test_gold_contains_pred_with_minimum_length(self) -> None: - # Gold = "John F Kennedy", pred = "Kennedy" → reverse substring, - # ≥3 chars, but the FRAMES style usually accepts this. - r = grade_deterministic(pred="Kennedy", gold="John F. Kennedy") - assert r.correct is True - assert r.method == "substring_reverse" - - def test_too_short_pred_no_reverse_credit(self) -> None: - r = grade_deterministic(pred="of", gold="World of Warcraft") - # "of" passes length but is a stopword; the article-stripping - # normaliser removes it from gold, so substring fails. Either - # way, the grader should NOT credit this. - assert r.correct is False - - -class TestLexicalMiss: - def test_completely_different_pred_falls_through(self) -> None: - r = grade_deterministic(pred="London", gold="Paris") - assert r.correct is False - assert r.method == "lexical_miss" - - def test_empty_pred(self) -> None: - r = grade_deterministic(pred="", gold="Paris") - assert r.correct is False - assert r.method == "empty_pred" - - def test_empty_gold_defensive(self) -> None: - r = grade_deterministic(pred="something", gold="") - # Defensive guard — gold should never be empty in practice. - assert r.correct is False - assert r.method == "empty_gold" - - -class TestGradeResultShape: - def test_dict_has_all_expected_keys(self) -> None: - r = grade_deterministic(pred="Paris", gold="Paris") - d = r.to_dict() - assert set(d) >= { - "correct", - "f1", - "method", - "normalised_pred", - "normalised_gold", - "judge_rationale", - } diff --git a/surfsense_evals/tests/suites/test_frames_wiki_fetch.py b/surfsense_evals/tests/suites/test_frames_wiki_fetch.py deleted file mode 100644 index 483c7aa584..0000000000 --- a/surfsense_evals/tests/suites/test_frames_wiki_fetch.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Tests for the FRAMES Wikipedia fetcher. - -We mock the MW API with respx so tests are network-free. Coverage: - -* URL → title parsing (percent-encoded, underscores, redirects) -* Filename safety (slashes, special chars) -* Cache hit short-circuits the API call -* Missing pages return ``None`` (not an exception) -* Successful fetches write ``# Title`` markdown to disk -""" - -from __future__ import annotations - -from pathlib import Path - -import httpx -import pytest -import respx - -from surfsense_evals.suites.research.frames.wiki_fetch import ( - WIKI_API, - WikiFetcher, - cache_filename_for_title, - title_from_url, -) - - -class TestTitleFromUrl: - def test_basic(self) -> None: - assert title_from_url("https://en.wikipedia.org/wiki/James_Buchanan") == "James Buchanan" - - def test_percent_encoded(self) -> None: - assert ( - title_from_url("https://en.wikipedia.org/wiki/Charlotte_Bront%C3%AB") - == "Charlotte Brontë" - ) - - def test_query_string_dropped(self) -> None: - assert title_from_url("https://en.wikipedia.org/wiki/Foo?action=edit") == "Foo" - - def test_non_wiki_raises(self) -> None: - with pytest.raises(ValueError): - title_from_url("https://example.com/wiki/Foo") - - -class TestCacheFilename: - def test_simple(self) -> None: - assert cache_filename_for_title("James Buchanan") == "James_Buchanan.md" - - def test_unicode_replaced_with_underscore(self) -> None: - # Brontë's diaeresis is non-ASCII so the regex replaces it with `_`. - # The space → `_` happens after the unicode swap, so the final - # name has exactly one underscore for the diaeresis. Acceptable: - # filenames stay round-trippable as long as the rule is deterministic. - assert cache_filename_for_title("Charlotte Brontë") == "Charlotte_Bront_.md" - - def test_slashes_replaced(self) -> None: - # Wikipedia titles can contain ``/`` (e.g. "I/O"), which would - # break the filesystem layout if not sanitised. - assert cache_filename_for_title("I/O") == "I_O.md" - - -@pytest.mark.asyncio -@respx.mock -async def test_fetch_success_writes_markdown(tmp_path: Path) -> None: - respx.get(WIKI_API).mock( - return_value=httpx.Response( - 200, - json={ - "query": { - "pages": [ - { - "pageid": 1, - "title": "James Buchanan", - "extract": "James Buchanan was the 15th president of the United States.", - } - ] - } - }, - ) - ) - fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100) # disable throttle - article = await fetcher.fetch("https://en.wikipedia.org/wiki/James_Buchanan") - assert article is not None - assert article.title == "James Buchanan" - body = article.markdown_path.read_text(encoding="utf-8") - assert body.startswith("# James Buchanan") - assert "15th president" in body - - -@pytest.mark.asyncio -@respx.mock -async def test_fetch_missing_page_returns_none(tmp_path: Path) -> None: - respx.get(WIKI_API).mock( - return_value=httpx.Response( - 200, - json={ - "query": { - "pages": [ - { - "title": "DoesNotExist", - "missing": True, - } - ] - } - }, - ) - ) - fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100) - article = await fetcher.fetch("https://en.wikipedia.org/wiki/DoesNotExist") - assert article is None - assert not (tmp_path / "DoesNotExist.md").exists() - - -@pytest.mark.asyncio -@respx.mock -async def test_fetch_cache_hit_skips_api(tmp_path: Path) -> None: - # Pre-populate the cache. - cached = tmp_path / cache_filename_for_title("Cached Page") - cached.write_text("# Cached Page\n\nfrom disk\n", encoding="utf-8") - fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100) - - # No respx mock registered; if the fetcher hits the network, respx - # would error out (it intercepts everything inside the decorator). - article = await fetcher.fetch("https://en.wikipedia.org/wiki/Cached_Page") - assert article is not None - assert article.markdown_path == cached - assert article.markdown_path.read_text(encoding="utf-8").endswith("from disk\n") diff --git a/surfsense_evals/tests/suites/test_mmlongbench_grader.py b/surfsense_evals/tests/suites/test_mmlongbench_grader.py deleted file mode 100644 index 89005b3cd8..0000000000 --- a/surfsense_evals/tests/suites/test_mmlongbench_grader.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Tests for the MMLongBench-Doc format-aware grader. - -The grader is the critical correctness piece for the open-ended -benchmark (no MCQ shortcut), so we cover all five formats with -representative happy-path + edge-case rows. -""" - -from __future__ import annotations - -import pytest - -from surfsense_evals.suites.multimodal_doc.mmlongbench.grader import grade - - -class TestStrFormat: - def test_exact_match(self) -> None: - r = grade(pred="Apollo 11", gold="Apollo 11", answer_format="Str") - assert r.correct is True - assert r.f1 == 1.0 - assert r.method == "str_norm" - - def test_lowercase_normalised(self) -> None: - r = grade(pred="paris", gold="Paris", answer_format="Str") - assert r.correct is True - - def test_punctuation_difference_drops_to_substring(self) -> None: - # "N.A.S.A." normalises to "n a s a" (whitespace tokens) which - # doesn't equal "nasa" — but the F1 token overlap is still 0 - # because none of the single letters appear standalone in "nasa". - # We assert the grader fails closed rather than over-claiming. - r = grade(pred="N.A.S.A.", gold="NASA", answer_format="Str") - assert r.correct is False # explicit: this is a failure mode we accept - - def test_substring_credit(self) -> None: - r = grade(pred="The answer is Paris.", gold="Paris", answer_format="Str") - assert r.correct is True - - def test_completely_wrong(self) -> None: - r = grade(pred="London", gold="Paris", answer_format="Str") - assert r.correct is False - assert r.f1 < 0.5 - - def test_empty_pred(self) -> None: - r = grade(pred="", gold="Paris", answer_format="Str") - assert r.correct is False - assert r.f1 == 0.0 - - -class TestIntFormat: - def test_exact_int(self) -> None: - assert grade(pred="42", gold="42", answer_format="Int").correct is True - - def test_int_in_sentence(self) -> None: - assert grade(pred="The answer is 42 years.", gold="42", answer_format="Int").correct is True - - def test_int_with_commas(self) -> None: - assert grade(pred="1,500", gold="1500", answer_format="Int").correct is True - - def test_wrong_int(self) -> None: - assert grade(pred="41", gold="42", answer_format="Int").correct is False - - def test_no_int_in_pred(self) -> None: - assert grade(pred="not answerable", gold="42", answer_format="Int").correct is False - - -class TestFloatFormat: - def test_exact_float(self) -> None: - assert grade(pred="3.14", gold="3.14", answer_format="Float").correct is True - - def test_within_tolerance(self) -> None: - # 1% tolerance — 3.14 vs 3.13 is well within. - assert grade(pred="3.13", gold="3.14", answer_format="Float").correct is True - - def test_outside_tolerance(self) -> None: - assert grade(pred="3.5", gold="3.14", answer_format="Float").correct is False - - def test_european_decimal_comma(self) -> None: - # ``3,14`` should parse as 3.14 - assert grade(pred="3,14", gold="3.14", answer_format="Float").correct is True - - def test_zero_gold_with_small_abs_diff(self) -> None: - # Absolute tolerance of 0.01 should kick in for near-zero golds. - assert grade(pred="0.005", gold="0", answer_format="Float").correct is True - - -class TestListFormat: - def test_exact_set_match(self) -> None: - r = grade(pred="apple, banana, cherry", gold="apple, banana, cherry", answer_format="List") - assert r.correct is True - assert r.f1 == pytest.approx(1.0) - - def test_set_match_different_order(self) -> None: - r = grade(pred="cherry, apple, banana", gold="apple, banana, cherry", answer_format="List") - assert r.correct is True - - def test_partial_overlap_gives_f1(self) -> None: - r = grade(pred="apple, banana", gold="apple, banana, cherry", answer_format="List") - assert r.correct is False - assert 0.0 < r.f1 < 1.0 - - def test_extra_items_lower_precision(self) -> None: - r = grade( - pred="apple, banana, cherry, date", gold="apple, banana, cherry", answer_format="List" - ) - assert 0.0 < r.f1 < 1.0 - # Recall=1, precision=3/4 → F1 ~= 0.857 - assert r.f1 == pytest.approx(2 * (3 / 4) * 1 / (3 / 4 + 1), rel=1e-3) - - -class TestNoneFormat: - def test_unknown_phrase_credited(self) -> None: - for phrase in ("Not answerable", "I cannot answer this.", "No answer", "N/A"): - r = grade(pred=phrase, gold="Not answerable", answer_format="None") - assert r.correct is True, phrase - - def test_actual_answer_marked_wrong(self) -> None: - # The arm hallucinated an answer when it should have said "I don't know". - r = grade(pred="The answer is 42.", gold="Not answerable", answer_format="None") - assert r.correct is False - - -class TestUnknownFormatFallsBackToStr: - def test_blank_format_uses_str_grader(self) -> None: - r = grade(pred="Paris", gold="Paris", answer_format="") - assert r.correct is True - assert r.method == "str_norm" - - def test_garbage_format_uses_str_grader(self) -> None: - r = grade(pred="Paris", gold="Paris", answer_format="quux") - assert r.correct is True - assert r.method == "str_norm" diff --git a/surfsense_evals/tests/test_integration_smoke.py b/surfsense_evals/tests/test_integration_smoke.py deleted file mode 100644 index 1c89ae5ab1..0000000000 --- a/surfsense_evals/tests/test_integration_smoke.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Opt-in integration smoke against ``http://localhost:8000``. - -Run with ``pytest -m integration``. Skipped by default. Touches the -real backend — requires it to be reachable, OPENROUTER_API_KEY -unrelated, and one credential mode set. -""" - -from __future__ import annotations - -import os - -import httpx -import pytest - -from surfsense_evals.core.auth import acquire_token, client_with_auth -from surfsense_evals.core.config import load_config - -pytestmark = pytest.mark.integration - - -@pytest.mark.asyncio -async def test_smoke_against_localhost(): - if "SURFSENSE_API_BASE" not in os.environ: - pytest.skip("SURFSENSE_API_BASE not set; skipping integration smoke") - config = load_config() - if config.credential_mode() == "none": - pytest.skip("No credentials in environment; skipping integration smoke") - bundle = await acquire_token(config) - async with client_with_auth(config, bundle) as client: - response = await client.get(f"{config.surfsense_api_base}/api/v1/model-connections/global") - try: - response.raise_for_status() - except httpx.HTTPStatusError as exc: - pytest.fail(f"Backend rejected smoke call: {exc!s}") - assert isinstance(response.json(), list) diff --git a/surfsense_evals/uv.lock b/surfsense_evals/uv.lock deleted file mode 100644 index d2af421628..0000000000 --- a/surfsense_evals/uv.lock +++ /dev/null @@ -1,2684 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.12" -resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", -] - -[[package]] -name = "aiohappyeyeballs" -version = "2.6.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 }, -] - -[[package]] -name = "aiohttp" -version = "3.13.5" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "aiohappyeyeballs" }, - { name = "aiosignal" }, - { name = "attrs" }, - { name = "frozenlist" }, - { name = "multidict" }, - { name = "propcache" }, - { name = "yarl" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/be/6f/353954c29e7dcce7cf00280a02c75f30e133c00793c7a2ed3776d7b2f426/aiohttp-3.13.5-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:023ecba036ddd840b0b19bf195bfae970083fd7024ce1ac22e9bba90464620e9", size = 748876 }, - { url = "https://files.pythonhosted.org/packages/f5/1b/428a7c64687b3b2e9cd293186695affc0e1e54a445d0361743b231f11066/aiohttp-3.13.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15c933ad7920b7d9a20de151efcd05a6e38302cbf0e10c9b2acb9a42210a2416", size = 499557 }, - { url = "https://files.pythonhosted.org/packages/29/47/7be41556bfbb6917069d6a6634bb7dd5e163ba445b783a90d40f5ac7e3a7/aiohttp-3.13.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ab2899f9fa2f9f741896ebb6fa07c4c883bfa5c7f2ddd8cf2aafa86fa981b2d2", size = 500258 }, - { url = "https://files.pythonhosted.org/packages/67/84/c9ecc5828cb0b3695856c07c0a6817a99d51e2473400f705275a2b3d9239/aiohttp-3.13.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a60eaa2d440cd4707696b52e40ed3e2b0f73f65be07fd0ef23b6b539c9c0b0b4", size = 1749199 }, - { url = "https://files.pythonhosted.org/packages/f0/d3/3c6d610e66b495657622edb6ae7c7fd31b2e9086b4ec50b47897ad6042a9/aiohttp-3.13.5-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:55b3bdd3292283295774ab585160c4004f4f2f203946997f49aac032c84649e9", size = 1721013 }, - { url = "https://files.pythonhosted.org/packages/49/a0/24409c12217456df0bae7babe3b014e460b0b38a8e60753d6cb339f6556d/aiohttp-3.13.5-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c2b2355dc094e5f7d45a7bb262fe7207aa0460b37a0d87027dcf21b5d890e7d5", size = 1781501 }, - { url = "https://files.pythonhosted.org/packages/98/9d/b65ec649adc5bccc008b0957a9a9c691070aeac4e41cea18559fef49958b/aiohttp-3.13.5-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b38765950832f7d728297689ad78f5f2cf79ff82487131c4d26fe6ceecdc5f8e", size = 1878981 }, - { url = "https://files.pythonhosted.org/packages/57/d8/8d44036d7eb7b6a8ec4c5494ea0c8c8b94fbc0ed3991c1a7adf230df03bf/aiohttp-3.13.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b18f31b80d5a33661e08c89e202edabf1986e9b49c42b4504371daeaa11b47c1", size = 1767934 }, - { url = "https://files.pythonhosted.org/packages/31/04/d3f8211f273356f158e3464e9e45484d3fb8c4ce5eb2f6fe9405c3273983/aiohttp-3.13.5-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:33add2463dde55c4f2d9635c6ab33ce154e5ecf322bd26d09af95c5f81cfa286", size = 1566671 }, - { url = "https://files.pythonhosted.org/packages/41/db/073e4ebe00b78e2dfcacff734291651729a62953b48933d765dc513bf798/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:327cc432fdf1356fb4fbc6fe833ad4e9f6aacb71a8acaa5f1855e4b25910e4a9", size = 1705219 }, - { url = "https://files.pythonhosted.org/packages/48/45/7dfba71a2f9fd97b15c95c06819de7eb38113d2cdb6319669195a7d64270/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:7c35b0bf0b48a70b4cb4fc5d7bed9b932532728e124874355de1a0af8ec4bc88", size = 1743049 }, - { url = "https://files.pythonhosted.org/packages/18/71/901db0061e0f717d226386a7f471bb59b19566f2cae5f0d93874b017271f/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:df23d57718f24badef8656c49743e11a89fd6f5358fa8a7b96e728fda2abf7d3", size = 1749557 }, - { url = "https://files.pythonhosted.org/packages/08/d5/41eebd16066e59cd43728fe74bce953d7402f2b4ddfdfef2c0e9f17ca274/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:02e048037a6501a5ec1f6fc9736135aec6eb8a004ce48838cb951c515f32c80b", size = 1558931 }, - { url = "https://files.pythonhosted.org/packages/30/e6/4a799798bf05740e66c3a1161079bda7a3dd8e22ca392481d7a7f9af82a6/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:31cebae8b26f8a615d2b546fee45d5ffb76852ae6450e2a03f42c9102260d6fe", size = 1774125 }, - { url = "https://files.pythonhosted.org/packages/84/63/7749337c90f92bc2cb18f9560d67aa6258c7060d1397d21529b8004fcf6f/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:888e78eb5ca55a615d285c3c09a7a91b42e9dd6fc699b166ebd5dee87c9ccf14", size = 1732427 }, - { url = "https://files.pythonhosted.org/packages/98/de/cf2f44ff98d307e72fb97d5f5bbae3bfcb442f0ea9790c0bf5c5c2331404/aiohttp-3.13.5-cp312-cp312-win32.whl", hash = "sha256:8bd3ec6376e68a41f9f95f5ed170e2fcf22d4eb27a1f8cb361d0508f6e0557f3", size = 433534 }, - { url = "https://files.pythonhosted.org/packages/aa/ca/eadf6f9c8fa5e31d40993e3db153fb5ed0b11008ad5d9de98a95045bed84/aiohttp-3.13.5-cp312-cp312-win_amd64.whl", hash = "sha256:110e448e02c729bcebb18c60b9214a87ba33bac4a9fa5e9a5f139938b56c6cb1", size = 460446 }, - { url = "https://files.pythonhosted.org/packages/78/e9/d76bf503005709e390122d34e15256b88f7008e246c4bdbe915cd4f1adce/aiohttp-3.13.5-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5029cc80718bbd545123cd8fe5d15025eccaaaace5d0eeec6bd556ad6163d61", size = 742930 }, - { url = "https://files.pythonhosted.org/packages/57/00/4b7b70223deaebd9bb85984d01a764b0d7bd6526fcdc73cca83bcbe7243e/aiohttp-3.13.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4bb6bf5811620003614076bdc807ef3b5e38244f9d25ca5fe888eaccea2a9832", size = 496927 }, - { url = "https://files.pythonhosted.org/packages/9c/f5/0fb20fb49f8efdcdce6cd8127604ad2c503e754a8f139f5e02b01626523f/aiohttp-3.13.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a84792f8631bf5a94e52d9cc881c0b824ab42717165a5579c760b830d9392ac9", size = 497141 }, - { url = "https://files.pythonhosted.org/packages/3b/86/b7c870053e36a94e8951b803cb5b909bfbc9b90ca941527f5fcafbf6b0fa/aiohttp-3.13.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:57653eac22c6a4c13eb22ecf4d673d64a12f266e72785ab1c8b8e5940d0e8090", size = 1732476 }, - { url = "https://files.pythonhosted.org/packages/b5/e5/4e161f84f98d80c03a238671b4136e6530453d65262867d989bbe78244d0/aiohttp-3.13.5-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e5e5f7debc7a57af53fdf5c5009f9391d9f4c12867049d509bf7bb164a6e295b", size = 1706507 }, - { url = "https://files.pythonhosted.org/packages/d4/56/ea11a9f01518bd5a2a2fcee869d248c4b8a0cfa0bb13401574fa31adf4d4/aiohttp-3.13.5-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c719f65bebcdf6716f10e9eff80d27567f7892d8988c06de12bbbd39307c6e3a", size = 1773465 }, - { url = "https://files.pythonhosted.org/packages/eb/40/333ca27fb74b0383f17c90570c748f7582501507307350a79d9f9f3c6eb1/aiohttp-3.13.5-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d97f93fdae594d886c5a866636397e2bcab146fd7a132fd6bb9ce182224452f8", size = 1873523 }, - { url = "https://files.pythonhosted.org/packages/f0/d2/e2f77eef1acb7111405433c707dc735e63f67a56e176e72e9e7a2cd3f493/aiohttp-3.13.5-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3df334e39d4c2f899a914f1dba283c1aadc311790733f705182998c6f7cae665", size = 1754113 }, - { url = "https://files.pythonhosted.org/packages/fb/56/3f653d7f53c89669301ec9e42c95233e2a0c0a6dd051269e6e678db4fdb0/aiohttp-3.13.5-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fe6970addfea9e5e081401bcbadf865d2b6da045472f58af08427e108d618540", size = 1562351 }, - { url = "https://files.pythonhosted.org/packages/ec/a6/9b3e91eb8ae791cce4ee736da02211c85c6f835f1bdfac0594a8a3b7018c/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7becdf835feff2f4f335d7477f121af787e3504b48b449ff737afb35869ba7bb", size = 1693205 }, - { url = "https://files.pythonhosted.org/packages/98/fc/bfb437a99a2fcebd6b6eaec609571954de2ed424f01c352f4b5504371dd3/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:676e5651705ad5d8a70aeb8eb6936c436d8ebbd56e63436cb7dd9bb36d2a9a46", size = 1730618 }, - { url = "https://files.pythonhosted.org/packages/e4/b6/c8534862126191a034f68153194c389addc285a0f1347d85096d349bbc15/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:9b16c653d38eb1a611cc898c41e76859ca27f119d25b53c12875fd0474ae31a8", size = 1745185 }, - { url = "https://files.pythonhosted.org/packages/0b/93/4ca8ee2ef5236e2707e0fd5fecb10ce214aee1ff4ab307af9c558bda3b37/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:999802d5fa0389f58decd24b537c54aa63c01c3219ce17d1214cbda3c2b22d2d", size = 1557311 }, - { url = "https://files.pythonhosted.org/packages/57/ae/76177b15f18c5f5d094f19901d284025db28eccc5ae374d1d254181d33f4/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ec707059ee75732b1ba130ed5f9580fe10ff75180c812bc267ded039db5128c6", size = 1773147 }, - { url = "https://files.pythonhosted.org/packages/01/a4/62f05a0a98d88af59d93b7fcac564e5f18f513cb7471696ac286db970d6a/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2d6d44a5b48132053c2f6cd5c8cb14bc67e99a63594e336b0f2af81e94d5530c", size = 1730356 }, - { url = "https://files.pythonhosted.org/packages/e4/85/fc8601f59dfa8c9523808281f2da571f8b4699685f9809a228adcc90838d/aiohttp-3.13.5-cp313-cp313-win32.whl", hash = "sha256:329f292ed14d38a6c4c435e465f48bebb47479fd676a0411936cc371643225cc", size = 432637 }, - { url = "https://files.pythonhosted.org/packages/c0/1b/ac685a8882896acf0f6b31d689e3792199cfe7aba37969fa91da63a7fa27/aiohttp-3.13.5-cp313-cp313-win_amd64.whl", hash = "sha256:69f571de7500e0557801c0b51f4780482c0ec5fe2ac851af5a92cfce1af1cb83", size = 458896 }, - { url = "https://files.pythonhosted.org/packages/5d/ce/46572759afc859e867a5bc8ec3487315869013f59281ce61764f76d879de/aiohttp-3.13.5-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:eb4639f32fd4a9904ab8fb45bf3383ba71137f3d9d4ba25b3b3f3109977c5b8c", size = 745721 }, - { url = "https://files.pythonhosted.org/packages/13/fe/8a2efd7626dbe6049b2ef8ace18ffda8a4dfcbe1bcff3ac30c0c7575c20b/aiohttp-3.13.5-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:7e5dc4311bd5ac493886c63cbf76ab579dbe4641268e7c74e48e774c74b6f2be", size = 497663 }, - { url = "https://files.pythonhosted.org/packages/9b/91/cc8cc78a111826c54743d88651e1687008133c37e5ee615fee9b57990fac/aiohttp-3.13.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:756c3c304d394977519824449600adaf2be0ccee76d206ee339c5e76b70ded25", size = 499094 }, - { url = "https://files.pythonhosted.org/packages/0a/33/a8362cb15cf16a3af7e86ed11962d5cd7d59b449202dc576cdc731310bde/aiohttp-3.13.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecc26751323224cf8186efcf7fbcbc30f4e1d8c7970659daf25ad995e4032a56", size = 1726701 }, - { url = "https://files.pythonhosted.org/packages/45/0c/c091ac5c3a17114bd76cbf85d674650969ddf93387876cf67f754204bd77/aiohttp-3.13.5-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:10a75acfcf794edf9d8db50e5a7ec5fc818b2a8d3f591ce93bc7b1210df016d2", size = 1683360 }, - { url = "https://files.pythonhosted.org/packages/23/73/bcee1c2b79bc275e964d1446c55c54441a461938e70267c86afaae6fba27/aiohttp-3.13.5-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:0f7a18f258d124cd678c5fe072fe4432a4d5232b0657fca7c1847f599233c83a", size = 1773023 }, - { url = "https://files.pythonhosted.org/packages/c7/ef/720e639df03004fee2d869f771799d8c23046dec47d5b81e396c7cda583a/aiohttp-3.13.5-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:df6104c009713d3a89621096f3e3e88cc323fd269dbd7c20afe18535094320be", size = 1853795 }, - { url = "https://files.pythonhosted.org/packages/bd/c9/989f4034fb46841208de7aeeac2c6d8300745ab4f28c42f629ba77c2d916/aiohttp-3.13.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:241a94f7de7c0c3b616627aaad530fe2cb620084a8b144d3be7b6ecfe95bae3b", size = 1730405 }, - { url = "https://files.pythonhosted.org/packages/ce/75/ee1fd286ca7dc599d824b5651dad7b3be7ff8d9a7e7b3fe9820d9180f7db/aiohttp-3.13.5-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c974fb66180e58709b6fc402846f13791240d180b74de81d23913abe48e96d94", size = 1558082 }, - { url = "https://files.pythonhosted.org/packages/c3/20/1e9e6650dfc436340116b7aa89ff8cb2bbdf0abc11dfaceaad8f74273a10/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:6e27ea05d184afac78aabbac667450c75e54e35f62238d44463131bd3f96753d", size = 1692346 }, - { url = "https://files.pythonhosted.org/packages/d8/40/8ebc6658d48ea630ac7903912fe0dd4e262f0e16825aa4c833c56c9f1f56/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a79a6d399cef33a11b6f004c67bb07741d91f2be01b8d712d52c75711b1e07c7", size = 1698891 }, - { url = "https://files.pythonhosted.org/packages/d8/78/ea0ae5ec8ba7a5c10bdd6e318f1ba5e76fcde17db8275188772afc7917a4/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:c632ce9c0b534fbe25b52c974515ed674937c5b99f549a92127c85f771a78772", size = 1742113 }, - { url = "https://files.pythonhosted.org/packages/8a/66/9d308ed71e3f2491be1acb8769d96c6f0c47d92099f3bc9119cada27b357/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:fceedde51fbd67ee2bcc8c0b33d0126cc8b51ef3bbde2f86662bd6d5a6f10ec5", size = 1553088 }, - { url = "https://files.pythonhosted.org/packages/da/a6/6cc25ed8dfc6e00c90f5c6d126a98e2cf28957ad06fa1036bd34b6f24a2c/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f92995dfec9420bb69ae629abf422e516923ba79ba4403bc750d94fb4a6c68c1", size = 1757976 }, - { url = "https://files.pythonhosted.org/packages/c1/2b/cce5b0ffe0de99c83e5e36d8f828e4161e415660a9f3e58339d07cce3006/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:20ae0ff08b1f2c8788d6fb85afcb798654ae6ba0b747575f8562de738078457b", size = 1712444 }, - { url = "https://files.pythonhosted.org/packages/6c/cf/9e1795b4160c58d29421eafd1a69c6ce351e2f7c8d3c6b7e4ca44aea1a5b/aiohttp-3.13.5-cp314-cp314-win32.whl", hash = "sha256:b20df693de16f42b2472a9c485e1c948ee55524786a0a34345511afdd22246f3", size = 438128 }, - { url = "https://files.pythonhosted.org/packages/22/4d/eaedff67fc805aeba4ba746aec891b4b24cebb1a7d078084b6300f79d063/aiohttp-3.13.5-cp314-cp314-win_amd64.whl", hash = "sha256:f85c6f327bf0b8c29da7d93b1cabb6363fb5e4e160a32fa241ed2dce21b73162", size = 464029 }, - { url = "https://files.pythonhosted.org/packages/79/11/c27d9332ee20d68dd164dc12a6ecdef2e2e35ecc97ed6cf0d2442844624b/aiohttp-3.13.5-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:1efb06900858bb618ff5cee184ae2de5828896c448403d51fb633f09e109be0a", size = 778758 }, - { url = "https://files.pythonhosted.org/packages/04/fb/377aead2e0a3ba5f09b7624f702a964bdf4f08b5b6728a9799830c80041e/aiohttp-3.13.5-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:fee86b7c4bd29bdaf0d53d14739b08a106fdda809ca5fe032a15f52fae5fe254", size = 512883 }, - { url = "https://files.pythonhosted.org/packages/bb/a6/aa109a33671f7a5d3bd78b46da9d852797c5e665bfda7d6b373f56bff2ec/aiohttp-3.13.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:20058e23909b9e65f9da62b396b77dfa95965cbe840f8def6e572538b1d32e36", size = 516668 }, - { url = "https://files.pythonhosted.org/packages/79/b3/ca078f9f2fa9563c36fb8ef89053ea2bb146d6f792c5104574d49d8acb63/aiohttp-3.13.5-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cf20a8d6868cb15a73cab329ffc07291ba8c22b1b88176026106ae39aa6df0f", size = 1883461 }, - { url = "https://files.pythonhosted.org/packages/b7/e3/a7ad633ca1ca497b852233a3cce6906a56c3225fb6d9217b5e5e60b7419d/aiohttp-3.13.5-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:330f5da04c987f1d5bdb8ae189137c77139f36bd1cb23779ca1a354a4b027800", size = 1747661 }, - { url = "https://files.pythonhosted.org/packages/33/b9/cd6fe579bed34a906d3d783fe60f2fa297ef55b27bb4538438ee49d4dc41/aiohttp-3.13.5-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6f1cbf0c7926d315c3c26c2da41fd2b5d2fe01ac0e157b78caefc51a782196cf", size = 1863800 }, - { url = "https://files.pythonhosted.org/packages/c0/3f/2c1e2f5144cefa889c8afd5cf431994c32f3b29da9961698ff4e3811b79a/aiohttp-3.13.5-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:53fc049ed6390d05423ba33103ded7281fe897cf97878f369a527070bd95795b", size = 1958382 }, - { url = "https://files.pythonhosted.org/packages/66/1d/f31ec3f1013723b3babe3609e7f119c2c2fb6ef33da90061a705ef3e1bc8/aiohttp-3.13.5-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:898703aa2667e3c5ca4c54ca36cd73f58b7a38ef87a5606414799ebce4d3fd3a", size = 1803724 }, - { url = "https://files.pythonhosted.org/packages/0e/b4/57712dfc6f1542f067daa81eb61da282fab3e6f1966fca25db06c4fc62d5/aiohttp-3.13.5-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0494a01ca9584eea1e5fbd6d748e61ecff218c51b576ee1999c23db7066417d8", size = 1640027 }, - { url = "https://files.pythonhosted.org/packages/25/3c/734c878fb43ec083d8e31bf029daae1beafeae582d1b35da234739e82ee7/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:6cf81fe010b8c17b09495cbd15c1d35afbc8fb405c0c9cf4738e5ae3af1d65be", size = 1806644 }, - { url = "https://files.pythonhosted.org/packages/20/a5/f671e5cbec1c21d044ff3078223f949748f3a7f86b14e34a365d74a5d21f/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:c564dd5f09ddc9d8f2c2d0a301cd30a79a2cc1b46dd1a73bef8f0038863d016b", size = 1791630 }, - { url = "https://files.pythonhosted.org/packages/0b/63/fb8d0ad63a0b8a99be97deac8c04dacf0785721c158bdf23d679a87aa99e/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:2994be9f6e51046c4f864598fd9abeb4fba6e88f0b2152422c9666dcd4aea9c6", size = 1809403 }, - { url = "https://files.pythonhosted.org/packages/59/0c/bfed7f30662fcf12206481c2aac57dedee43fe1c49275e85b3a1e1742294/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:157826e2fa245d2ef46c83ea8a5faf77ca19355d278d425c29fda0beb3318037", size = 1634924 }, - { url = "https://files.pythonhosted.org/packages/17/d6/fd518d668a09fd5a3319ae5e984d4d80b9a4b3df4e21c52f02251ef5a32e/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:a8aca50daa9493e9e13c0f566201a9006f080e7c50e5e90d0b06f53146a54500", size = 1836119 }, - { url = "https://files.pythonhosted.org/packages/78/b7/15fb7a9d52e112a25b621c67b69c167805cb1f2ab8f1708a5c490d1b52fe/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3b13560160d07e047a93f23aaa30718606493036253d5430887514715b67c9d9", size = 1772072 }, - { url = "https://files.pythonhosted.org/packages/7e/df/57ba7f0c4a553fc2bd8b6321df236870ec6fd64a2a473a8a13d4f733214e/aiohttp-3.13.5-cp314-cp314t-win32.whl", hash = "sha256:9a0f4474b6ea6818b41f82172d799e4b3d29e22c2c520ce4357856fced9af2f8", size = 471819 }, - { url = "https://files.pythonhosted.org/packages/62/29/2f8418269e46454a26171bfdd6a055d74febf32234e474930f2f60a17145/aiohttp-3.13.5-cp314-cp314t-win_amd64.whl", hash = "sha256:18a2f6c1182c51baa1d28d68fea51513cb2a76612f038853c0ad3c145423d3d9", size = 505441 }, -] - -[[package]] -name = "aiosignal" -version = "1.4.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "frozenlist" }, - { name = "typing-extensions", marker = "python_full_version < '3.13'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490 }, -] - -[[package]] -name = "aiosqlite" -version = "0.22.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/4e/8a/64761f4005f17809769d23e518d915db74e6310474e733e3593cfc854ef1/aiosqlite-0.22.1.tar.gz", hash = "sha256:043e0bd78d32888c0a9ca90fc788b38796843360c855a7262a532813133a0650", size = 14821 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/00/b7/e3bf5133d697a08128598c8d0abc5e16377b51465a33756de24fa7dee953/aiosqlite-0.22.1-py3-none-any.whl", hash = "sha256:21c002eb13823fad740196c5a2e9d8e62f6243bd9e7e4a1f87fb5e44ecb4fceb", size = 17405 }, -] - -[[package]] -name = "annotated-doc" -version = "0.0.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303 }, -] - -[[package]] -name = "annotated-types" -version = "0.7.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 }, -] - -[[package]] -name = "anyio" -version = "4.13.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "idna" }, - { name = "typing-extensions", marker = "python_full_version < '3.13'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353 }, -] - -[[package]] -name = "attrs" -version = "26.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548 }, -] - -[[package]] -name = "azure-ai-documentintelligence" -version = "1.0.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "azure-core" }, - { name = "isodate" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/44/7b/8115cd713e2caa5e44def85f2b7ebd02a74ae74d7113ba20bdd41fd6dd80/azure_ai_documentintelligence-1.0.2.tar.gz", hash = "sha256:4d75a2513f2839365ebabc0e0e1772f5601b3a8c9a71e75da12440da13b63484", size = 170940 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d9/75/c9ec040f23082f54ffb1977ff8f364c2d21c79a640a13d1c1809e7fd6b1a/azure_ai_documentintelligence-1.0.2-py3-none-any.whl", hash = "sha256:e1fb446abbdeccc9759d897898a0fe13141ed29f9ad11fc705f951925822ed59", size = 106005 }, -] - -[[package]] -name = "azure-core" -version = "1.41.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "requests" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/a6/f3/b416179e408990df5db0d516283022dde0f5d0111d98c1a848e41853e81c/azure_core-1.41.0.tar.gz", hash = "sha256:f46ff5dfcd230f25cf1c19e8a34b8dc08a337b2503e268bb600a16c00db8ad5a", size = 381042 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/5b/db/325c6d7312d2200251c52323878281045aaffcb5586612296484e4280eaa/azure_core-1.41.0-py3-none-any.whl", hash = "sha256:522b4011e8180b1a3dcd2024396a4e7fe9ac37fb8597db47163d230b5efe892d", size = 220920 }, -] - -[[package]] -name = "babel" -version = "2.18.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845 }, -] - -[[package]] -name = "banks" -version = "2.4.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "deprecated" }, - { name = "filetype" }, - { name = "griffe" }, - { name = "jinja2" }, - { name = "platformdirs" }, - { name = "pydantic" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/bd/51/08fb68d23f4b0f6256fe85dc86e9576941550f890b079352fba719e07b39/banks-2.4.2.tar.gz", hash = "sha256:cda6013bd377ea7b701933578bfb9370fc21ad70bc13cedfc3f5cb2c034ca3dc", size = 188633 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/00/b6/8dc5477681b782e2f99de703e7a99828883364b9e03a60d3e2c47053d56a/banks-2.4.2-py3-none-any.whl", hash = "sha256:5fe407cc48c101f3e13d1cf732b83b8246003337612f13c0705d2e81f6faffb7", size = 35050 }, -] - -[[package]] -name = "certifi" -version = "2026.4.22" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/25/ee/6caf7a40c36a1220410afe15a1cc64993a1f864871f698c0f93acb72842a/certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580", size = 137077 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/22/30/7cd8fdcdfbc5b869528b079bfb76dcdf6056b1a2097a662e5e8c04f42965/certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a", size = 135707 }, -] - -[[package]] -name = "charset-normalizer" -version = "3.4.7" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e7/a1/67fe25fac3c7642725500a3f6cfe5821ad557c3abb11c9d20d12c7008d3e/charset_normalizer-3.4.7.tar.gz", hash = "sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5", size = 144271 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0c/eb/4fc8d0a7110eb5fc9cc161723a34a8a6c200ce3b4fbf681bc86feee22308/charset_normalizer-3.4.7-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46", size = 311328 }, - { url = "https://files.pythonhosted.org/packages/f8/e3/0fadc706008ac9d7b9b5be6dc767c05f9d3e5df51744ce4cc9605de7b9f4/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6178f72c5508bfc5fd446a5905e698c6212932f25bcdd4b47a757a50605a90e2", size = 208061 }, - { url = "https://files.pythonhosted.org/packages/42/f0/3dd1045c47f4a4604df85ec18ad093912ae1344ac706993aff91d38773a2/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e1421b502d83040e6d7fb2fb18dff63957f720da3d77b2fbd3187ceb63755d7b", size = 229031 }, - { url = "https://files.pythonhosted.org/packages/dc/67/675a46eb016118a2fbde5a277a5d15f4f69d5f3f5f338e5ee2f8948fcf43/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:edac0f1ab77644605be2cbba52e6b7f630731fc42b34cb0f634be1a6eface56a", size = 225239 }, - { url = "https://files.pythonhosted.org/packages/4b/f8/d0118a2f5f23b02cd166fa385c60f9b0d4f9194f574e2b31cef350ad7223/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5649fd1c7bade02f320a462fdefd0b4bd3ce036065836d4f42e0de958038e116", size = 216589 }, - { url = "https://files.pythonhosted.org/packages/b1/f1/6d2b0b261b6c4ceef0fcb0d17a01cc5bc53586c2d4796fa04b5c540bc13d/charset_normalizer-3.4.7-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:203104ed3e428044fd943bc4bf45fa73c0730391f9621e37fe39ecf477b128cb", size = 202733 }, - { url = "https://files.pythonhosted.org/packages/6f/c0/7b1f943f7e87cc3db9626ba17807d042c38645f0a1d4415c7a14afb5591f/charset_normalizer-3.4.7-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:298930cec56029e05497a76988377cbd7457ba864beeea92ad7e844fe74cd1f1", size = 212652 }, - { url = "https://files.pythonhosted.org/packages/38/dd/5a9ab159fe45c6e72079398f277b7d2b523e7f716acc489726115a910097/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:708838739abf24b2ceb208d0e22403dd018faeef86ddac04319a62ae884c4f15", size = 211229 }, - { url = "https://files.pythonhosted.org/packages/d5/ff/531a1cad5ca855d1c1a8b69cb71abfd6d85c0291580146fda7c82857caa1/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:0f7eb884681e3938906ed0434f20c63046eacd0111c4ba96f27b76084cd679f5", size = 203552 }, - { url = "https://files.pythonhosted.org/packages/c1/4c/a5fb52d528a8ca41f7598cb619409ece30a169fbdf9cdce592e53b46c3a6/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:4dc1e73c36828f982bfe79fadf5919923f8a6f4df2860804db9a98c48824ce8d", size = 230806 }, - { url = "https://files.pythonhosted.org/packages/59/7a/071feed8124111a32b316b33ae4de83d36923039ef8cf48120266844285b/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:aed52fea0513bac0ccde438c188c8a471c4e0f457c2dd20cdbf6ea7a450046c7", size = 212316 }, - { url = "https://files.pythonhosted.org/packages/fd/35/f7dba3994312d7ba508e041eaac39a36b120f32d4c8662b8814dab876431/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:fea24543955a6a729c45a73fe90e08c743f0b3334bbf3201e6c4bc1b0c7fa464", size = 227274 }, - { url = "https://files.pythonhosted.org/packages/8a/2d/a572df5c9204ab7688ec1edc895a73ebded3b023bb07364710b05dd1c9be/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:bb6d88045545b26da47aa879dd4a89a71d1dce0f0e549b1abcb31dfe4a8eac49", size = 218468 }, - { url = "https://files.pythonhosted.org/packages/86/eb/890922a8b03a568ca2f336c36585a4713c55d4d67bf0f0c78924be6315ca/charset_normalizer-3.4.7-cp312-cp312-win32.whl", hash = "sha256:2257141f39fe65a3fdf38aeccae4b953e5f3b3324f4ff0daf9f15b8518666a2c", size = 148460 }, - { url = "https://files.pythonhosted.org/packages/35/d9/0e7dffa06c5ab081f75b1b786f0aefc88365825dfcd0ac544bdb7b2b6853/charset_normalizer-3.4.7-cp312-cp312-win_amd64.whl", hash = "sha256:5ed6ab538499c8644b8a3e18debabcd7ce684f3fa91cf867521a7a0279cab2d6", size = 159330 }, - { url = "https://files.pythonhosted.org/packages/9e/5d/481bcc2a7c88ea6b0878c299547843b2521ccbc40980cb406267088bc701/charset_normalizer-3.4.7-cp312-cp312-win_arm64.whl", hash = "sha256:56be790f86bfb2c98fb742ce566dfb4816e5a83384616ab59c49e0604d49c51d", size = 147828 }, - { url = "https://files.pythonhosted.org/packages/c1/3b/66777e39d3ae1ddc77ee606be4ec6d8cbd4c801f65e5a1b6f2b11b8346dd/charset_normalizer-3.4.7-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:f496c9c3cc02230093d8330875c4c3cdfc3b73612a5fd921c65d39cbcef08063", size = 309627 }, - { url = "https://files.pythonhosted.org/packages/2e/4e/b7f84e617b4854ade48a1b7915c8ccfadeba444d2a18c291f696e37f0d3b/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ea948db76d31190bf08bd371623927ee1339d5f2a0b4b1b4a4439a65298703c", size = 207008 }, - { url = "https://files.pythonhosted.org/packages/c4/bb/ec73c0257c9e11b268f018f068f5d00aa0ef8c8b09f7753ebd5f2880e248/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a277ab8928b9f299723bc1a2dabb1265911b1a76341f90a510368ca44ad9ab66", size = 228303 }, - { url = "https://files.pythonhosted.org/packages/85/fb/32d1f5033484494619f701e719429c69b766bfc4dbc61aa9e9c8c166528b/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3bec022aec2c514d9cf199522a802bd007cd588ab17ab2525f20f9c34d067c18", size = 224282 }, - { url = "https://files.pythonhosted.org/packages/fa/07/330e3a0dda4c404d6da83b327270906e9654a24f6c546dc886a0eb0ffb23/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e044c39e41b92c845bc815e5ae4230804e8e7bc29e399b0437d64222d92809dd", size = 215595 }, - { url = "https://files.pythonhosted.org/packages/e3/7c/fc890655786e423f02556e0216d4b8c6bcb6bdfa890160dc66bf52dee468/charset_normalizer-3.4.7-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:f495a1652cf3fbab2eb0639776dad966c2fb874d79d87ca07f9d5f059b8bd215", size = 201986 }, - { url = "https://files.pythonhosted.org/packages/d8/97/bfb18b3db2aed3b90cf54dc292ad79fdd5ad65c4eae454099475cbeadd0d/charset_normalizer-3.4.7-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e712b419df8ba5e42b226c510472b37bd57b38e897d3eca5e8cfd410a29fa859", size = 211711 }, - { url = "https://files.pythonhosted.org/packages/6f/a5/a581c13798546a7fd557c82614a5c65a13df2157e9ad6373166d2a3e645d/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7804338df6fcc08105c7745f1502ba68d900f45fd770d5bdd5288ddccb8a42d8", size = 210036 }, - { url = "https://files.pythonhosted.org/packages/8c/bf/b3ab5bcb478e4193d517644b0fb2bf5497fbceeaa7a1bc0f4d5b50953861/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:481551899c856c704d58119b5025793fa6730adda3571971af568f66d2424bb5", size = 202998 }, - { url = "https://files.pythonhosted.org/packages/e7/4e/23efd79b65d314fa320ec6017b4b5834d5c12a58ba4610aa353af2e2f577/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f59099f9b66f0d7145115e6f80dd8b1d847176df89b234a5a6b3f00437aa0832", size = 230056 }, - { url = "https://files.pythonhosted.org/packages/b9/9f/1e1941bc3f0e01df116e68dc37a55c4d249df5e6fa77f008841aef68264f/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:f59ad4c0e8f6bba240a9bb85504faa1ab438237199d4cce5f622761507b8f6a6", size = 211537 }, - { url = "https://files.pythonhosted.org/packages/80/0f/088cbb3020d44428964a6c97fe1edfb1b9550396bf6d278330281e8b709c/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:3dedcc22d73ec993f42055eff4fcfed9318d1eeb9a6606c55892a26964964e48", size = 226176 }, - { url = "https://files.pythonhosted.org/packages/6a/9f/130394f9bbe06f4f63e22641d32fc9b202b7e251c9aef4db044324dac493/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:64f02c6841d7d83f832cd97ccf8eb8a906d06eb95d5276069175c696b024b60a", size = 217723 }, - { url = "https://files.pythonhosted.org/packages/73/55/c469897448a06e49f8fa03f6caae97074fde823f432a98f979cc42b90e69/charset_normalizer-3.4.7-cp313-cp313-win32.whl", hash = "sha256:4042d5c8f957e15221d423ba781e85d553722fc4113f523f2feb7b188cc34c5e", size = 148085 }, - { url = "https://files.pythonhosted.org/packages/5d/78/1b74c5bbb3f99b77a1715c91b3e0b5bdb6fe302d95ace4f5b1bec37b0167/charset_normalizer-3.4.7-cp313-cp313-win_amd64.whl", hash = "sha256:3946fa46a0cf3e4c8cb1cc52f56bb536310d34f25f01ca9b6c16afa767dab110", size = 158819 }, - { url = "https://files.pythonhosted.org/packages/68/86/46bd42279d323deb8687c4a5a811fd548cb7d1de10cf6535d099877a9a9f/charset_normalizer-3.4.7-cp313-cp313-win_arm64.whl", hash = "sha256:80d04837f55fc81da168b98de4f4b797ef007fc8a79ab71c6ec9bc4dd662b15b", size = 147915 }, - { url = "https://files.pythonhosted.org/packages/97/c8/c67cb8c70e19ef1960b97b22ed2a1567711de46c4ddf19799923adc836c2/charset_normalizer-3.4.7-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:c36c333c39be2dbca264d7803333c896ab8fa7d4d6f0ab7edb7dfd7aea6e98c0", size = 309234 }, - { url = "https://files.pythonhosted.org/packages/99/85/c091fdee33f20de70d6c8b522743b6f831a2f1cd3ff86de4c6a827c48a76/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1c2aed2e5e41f24ea8ef1590b8e848a79b56f3a5564a65ceec43c9d692dc7d8a", size = 208042 }, - { url = "https://files.pythonhosted.org/packages/87/1c/ab2ce611b984d2fd5d86a5a8a19c1ae26acac6bad967da4967562c75114d/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54523e136b8948060c0fa0bc7b1b50c32c186f2fceee897a495406bb6e311d2b", size = 228706 }, - { url = "https://files.pythonhosted.org/packages/a8/29/2b1d2cb00bf085f59d29eb773ce58ec2d325430f8c216804a0a5cd83cbca/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:715479b9a2802ecac752a3b0efa2b0b60285cf962ee38414211abdfccc233b41", size = 224727 }, - { url = "https://files.pythonhosted.org/packages/47/5c/032c2d5a07fe4d4855fea851209cca2b6f03ebeb6d4e3afdb3358386a684/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bd6c2a1c7573c64738d716488d2cdd3c00e340e4835707d8fdb8dc1a66ef164e", size = 215882 }, - { url = "https://files.pythonhosted.org/packages/2c/c2/356065d5a8b78ed04499cae5f339f091946a6a74f91e03476c33f0ab7100/charset_normalizer-3.4.7-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:c45e9440fb78f8ddabcf714b68f936737a121355bf59f3907f4e17721b9d1aae", size = 200860 }, - { url = "https://files.pythonhosted.org/packages/0c/cd/a32a84217ced5039f53b29f460962abb2d4420def55afabe45b1c3c7483d/charset_normalizer-3.4.7-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3534e7dcbdcf757da6b85a0bbf5b6868786d5982dd959b065e65481644817a18", size = 211564 }, - { url = "https://files.pythonhosted.org/packages/44/86/58e6f13ce26cc3b8f4a36b94a0f22ae2f00a72534520f4ae6857c4b81f89/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e8ac484bf18ce6975760921bb6148041faa8fef0547200386ea0b52b5d27bf7b", size = 211276 }, - { url = "https://files.pythonhosted.org/packages/8f/fe/d17c32dc72e17e155e06883efa84514ca375f8a528ba2546bee73fc4df81/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a5fe03b42827c13cdccd08e6c0247b6a6d4b5e3cdc53fd1749f5896adcdc2356", size = 201238 }, - { url = "https://files.pythonhosted.org/packages/6a/29/f33daa50b06525a237451cdb6c69da366c381a3dadcd833fa5676bc468b3/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:2d6eb928e13016cea4f1f21d1e10c1cebd5a421bc57ddf5b1142ae3f86824fab", size = 230189 }, - { url = "https://files.pythonhosted.org/packages/b6/6e/52c84015394a6a0bdcd435210a7e944c5f94ea1055f5cc5d56c5fe368e7b/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e74327fb75de8986940def6e8dee4f127cc9752bee7355bb323cc5b2659b6d46", size = 211352 }, - { url = "https://files.pythonhosted.org/packages/8c/d7/4353be581b373033fb9198bf1da3cf8f09c1082561e8e922aa7b39bf9fe8/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d6038d37043bced98a66e68d3aa2b6a35505dc01328cd65217cefe82f25def44", size = 227024 }, - { url = "https://files.pythonhosted.org/packages/30/45/99d18aa925bd1740098ccd3060e238e21115fffbfdcb8f3ece837d0ace6c/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7579e913a5339fb8fa133f6bbcfd8e6749696206cf05acdbdca71a1b436d8e72", size = 217869 }, - { url = "https://files.pythonhosted.org/packages/5c/05/5ee478aa53f4bb7996482153d4bfe1b89e0f087f0ab6b294fcf92d595873/charset_normalizer-3.4.7-cp314-cp314-win32.whl", hash = "sha256:5b77459df20e08151cd6f8b9ef8ef1f961ef73d85c21a555c7eed5b79410ec10", size = 148541 }, - { url = "https://files.pythonhosted.org/packages/48/77/72dcb0921b2ce86420b2d79d454c7022bf5be40202a2a07906b9f2a35c97/charset_normalizer-3.4.7-cp314-cp314-win_amd64.whl", hash = "sha256:92a0a01ead5e668468e952e4238cccd7c537364eb7d851ab144ab6627dbbe12f", size = 159634 }, - { url = "https://files.pythonhosted.org/packages/c6/a3/c2369911cd72f02386e4e340770f6e158c7980267da16af8f668217abaa0/charset_normalizer-3.4.7-cp314-cp314-win_arm64.whl", hash = "sha256:67f6279d125ca0046a7fd386d01b311c6363844deac3e5b069b514ba3e63c246", size = 148384 }, - { url = "https://files.pythonhosted.org/packages/94/09/7e8a7f73d24dba1f0035fbbf014d2c36828fc1bf9c88f84093e57d315935/charset_normalizer-3.4.7-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:effc3f449787117233702311a1b7d8f59cba9ced946ba727bdc329ec69028e24", size = 330133 }, - { url = "https://files.pythonhosted.org/packages/8d/da/96975ddb11f8e977f706f45cddd8540fd8242f71ecdb5d18a80723dcf62c/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fbccdc05410c9ee21bbf16a35f4c1d16123dcdeb8a1d38f33654fa21d0234f79", size = 216257 }, - { url = "https://files.pythonhosted.org/packages/e5/e8/1d63bf8ef2d388e95c64b2098f45f84758f6d102a087552da1485912637b/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:733784b6d6def852c814bce5f318d25da2ee65dd4839a0718641c696e09a2960", size = 234851 }, - { url = "https://files.pythonhosted.org/packages/9b/40/e5ff04233e70da2681fa43969ad6f66ca5611d7e669be0246c4c7aaf6dc8/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a89c23ef8d2c6b27fd200a42aa4ac72786e7c60d40efdc76e6011260b6e949c4", size = 233393 }, - { url = "https://files.pythonhosted.org/packages/be/c1/06c6c49d5a5450f76899992f1ee40b41d076aee9279b49cf9974d2f313d5/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c114670c45346afedc0d947faf3c7f701051d2518b943679c8ff88befe14f8e", size = 223251 }, - { url = "https://files.pythonhosted.org/packages/2b/9f/f2ff16fb050946169e3e1f82134d107e5d4ae72647ec8a1b1446c148480f/charset_normalizer-3.4.7-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:a180c5e59792af262bf263b21a3c49353f25945d8d9f70628e73de370d55e1e1", size = 206609 }, - { url = "https://files.pythonhosted.org/packages/69/d5/a527c0cd8d64d2eab7459784fb4169a0ac76e5a6fc5237337982fd61347e/charset_normalizer-3.4.7-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3c9a494bc5ec77d43cea229c4f6db1e4d8fe7e1bbffa8b6f0f0032430ff8ab44", size = 220014 }, - { url = "https://files.pythonhosted.org/packages/7e/80/8a7b8104a3e203074dc9aa2c613d4b726c0e136bad1cc734594b02867972/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8d828b6667a32a728a1ad1d93957cdf37489c57b97ae6c4de2860fa749b8fc1e", size = 218979 }, - { url = "https://files.pythonhosted.org/packages/02/9a/b759b503d507f375b2b5c153e4d2ee0a75aa215b7f2489cf314f4541f2c0/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:cf1493cd8607bec4d8a7b9b004e699fcf8f9103a9284cc94962cb73d20f9d4a3", size = 209238 }, - { url = "https://files.pythonhosted.org/packages/c2/4e/0f3f5d47b86bdb79256e7290b26ac847a2832d9a4033f7eb2cd4bcf4bb5b/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0c96c3b819b5c3e9e165495db84d41914d6894d55181d2d108cc1a69bfc9cce0", size = 236110 }, - { url = "https://files.pythonhosted.org/packages/96/23/bce28734eb3ed2c91dcf93abeb8a5cf393a7b2749725030bb630e554fdd8/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:752a45dc4a6934060b3b0dab47e04edc3326575f82be64bc4fc293914566503e", size = 219824 }, - { url = "https://files.pythonhosted.org/packages/2c/6f/6e897c6984cc4d41af319b077f2f600fc8214eb2fe2d6bcb79141b882400/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:8778f0c7a52e56f75d12dae53ae320fae900a8b9b4164b981b9c5ce059cd1fcb", size = 233103 }, - { url = "https://files.pythonhosted.org/packages/76/22/ef7bd0fe480a0ae9b656189ec00744b60933f68b4f42a7bb06589f6f576a/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ce3412fbe1e31eb81ea42f4169ed94861c56e643189e1e75f0041f3fe7020abe", size = 225194 }, - { url = "https://files.pythonhosted.org/packages/c5/a7/0e0ab3e0b5bc1219bd80a6a0d4d72ca74d9250cb2382b7c699c147e06017/charset_normalizer-3.4.7-cp314-cp314t-win32.whl", hash = "sha256:c03a41a8784091e67a39648f70c5f97b5b6a37f216896d44d2cdcb82615339a0", size = 159827 }, - { url = "https://files.pythonhosted.org/packages/7a/1d/29d32e0fb40864b1f878c7f5a0b343ae676c6e2b271a2d55cc3a152391da/charset_normalizer-3.4.7-cp314-cp314t-win_amd64.whl", hash = "sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c", size = 174168 }, - { url = "https://files.pythonhosted.org/packages/de/32/d92444ad05c7a6e41fb2036749777c163baf7a0301a040cb672d6b2b1ae9/charset_normalizer-3.4.7-cp314-cp314t-win_arm64.whl", hash = "sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d", size = 153018 }, - { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958 }, -] - -[[package]] -name = "click" -version = "8.3.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/bb/63/f9e1ea081ce35720d8b92acde70daaedace594dc93b693c869e0d5910718/click-8.3.3.tar.gz", hash = "sha256:398329ad4837b2ff7cbe1dd166a4c0f8900c3ca3a218de04466f38f6497f18a2", size = 328061 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ae/44/c1221527f6a71a01ec6fbad7fa78f1d50dfa02217385cf0fa3eec7087d59/click-8.3.3-py3-none-any.whl", hash = "sha256:a2bf429bb3033c89fa4936ffb35d5cb471e3719e1f3c8a7c3fff0b8314305613", size = 110502 }, -] - -[[package]] -name = "colorama" -version = "0.4.6" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 }, -] - -[[package]] -name = "courlan" -version = "1.3.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "babel" }, - { name = "tld" }, - { name = "urllib3" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/6f/54/6d6ceeff4bed42e7a10d6064d35ee43a810e7b3e8beb4abeae8cff4713ae/courlan-1.3.2.tar.gz", hash = "sha256:0b66f4db3a9c39a6e22dd247c72cfaa57d68ea660e94bb2c84ec7db8712af190", size = 206382 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8e/ca/6a667ccbe649856dcd3458bab80b016681b274399d6211187c6ab969fc50/courlan-1.3.2-py3-none-any.whl", hash = "sha256:d0dab52cf5b5b1000ee2839fbc2837e93b2514d3cb5bb61ae158a55b7a04c6be", size = 33848 }, -] - -[[package]] -name = "dataclasses-json" -version = "0.6.7" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "marshmallow" }, - { name = "typing-inspect" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/64/a4/f71d9cf3a5ac257c993b5ca3f93df5f7fb395c725e7f1e6479d2514173c3/dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0", size = 32227 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c3/be/d0d44e092656fe7a06b55e6103cbce807cdbdee17884a5367c68c9860853/dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a", size = 28686 }, -] - -[[package]] -name = "datasets" -version = "4.8.5" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "dill" }, - { name = "filelock" }, - { name = "fsspec", extra = ["http"] }, - { name = "httpx" }, - { name = "huggingface-hub" }, - { name = "multiprocess" }, - { name = "numpy" }, - { name = "packaging" }, - { name = "pandas" }, - { name = "pyarrow" }, - { name = "pyyaml" }, - { name = "requests" }, - { name = "tqdm" }, - { name = "xxhash" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/66/34/14cd8e76f907f7d4dca2334cfeec9f81d30fd15c25a015f99aaea694eaed/datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772", size = 605649 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/65/99/00f3196036501b53032c4b1ab8337a0b978dee832ed276dae3815df4e8b5/datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff", size = 528973 }, -] - -[[package]] -name = "dateparser" -version = "1.4.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "python-dateutil" }, - { name = "pytz" }, - { name = "regex" }, - { name = "tzlocal" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/46/2d/a0ccdb78788064fa0dc901b8524e50615c42be1d78b78d646d0b28d09180/dateparser-1.4.0.tar.gz", hash = "sha256:97a21840d5ecdf7630c584f673338a5afac5dfe84f647baf4d7e8df98f9354a4", size = 321512 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b4/0b/3c3bb7cbe757279e693a0be6049048012f794d01f81099609ecd53b899f0/dateparser-1.4.0-py3-none-any.whl", hash = "sha256:7902b8e85d603494bf70a5a0b1decdddb2270b9c6e6b2bc8a57b93476c0df378", size = 300379 }, -] - -[[package]] -name = "deprecated" -version = "1.3.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "wrapt" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/49/85/12f0a49a7c4ffb70572b6c2ef13c90c88fd190debda93b23f026b25f9634/deprecated-1.3.1.tar.gz", hash = "sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223", size = 2932523 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/84/d0/205d54408c08b13550c733c4b85429e7ead111c7f0014309637425520a9a/deprecated-1.3.1-py2.py3-none-any.whl", hash = "sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f", size = 11298 }, -] - -[[package]] -name = "dill" -version = "0.4.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019 }, -] - -[[package]] -name = "dirtyjson" -version = "1.0.8" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/db/04/d24f6e645ad82ba0ef092fa17d9ef7a21953781663648a01c9371d9e8e98/dirtyjson-1.0.8.tar.gz", hash = "sha256:90ca4a18f3ff30ce849d100dcf4a003953c79d3a2348ef056f1d9c22231a25fd", size = 30782 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/68/69/1bcf70f81de1b4a9f21b3a62ec0c83bdff991c88d6cc2267d02408457e88/dirtyjson-1.0.8-py3-none-any.whl", hash = "sha256:125e27248435a58acace26d5c2c4c11a1c0de0a9c5124c5a94ba78e517d74f53", size = 25197 }, -] - -[[package]] -name = "filelock" -version = "3.29.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812 }, -] - -[[package]] -name = "filetype" -version = "1.2.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/bb/29/745f7d30d47fe0f251d3ad3dc2978a23141917661998763bebb6da007eb1/filetype-1.2.0.tar.gz", hash = "sha256:66b56cd6474bf41d8c54660347d37afcc3f7d1970648de365c102ef77548aadb", size = 998020 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/18/79/1b8fa1bb3568781e84c9200f951c735f3f157429f44be0495da55894d620/filetype-1.2.0-py2.py3-none-any.whl", hash = "sha256:7ce71b6880181241cf7ac8697a2f1eb6a8bd9b429f7ad6d27b8db9ba5f1c2d25", size = 19970 }, -] - -[[package]] -name = "frozenlist" -version = "1.8.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2d/f5/c831fac6cc817d26fd54c7eaccd04ef7e0288806943f7cc5bbf69f3ac1f0/frozenlist-1.8.0.tar.gz", hash = "sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad", size = 45875 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/69/29/948b9aa87e75820a38650af445d2ef2b6b8a6fab1a23b6bb9e4ef0be2d59/frozenlist-1.8.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:78f7b9e5d6f2fdb88cdde9440dc147259b62b9d3b019924def9f6478be254ac1", size = 87782 }, - { url = "https://files.pythonhosted.org/packages/64/80/4f6e318ee2a7c0750ed724fa33a4bdf1eacdc5a39a7a24e818a773cd91af/frozenlist-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:229bf37d2e4acdaf808fd3f06e854a4a7a3661e871b10dc1f8f1896a3b05f18b", size = 50594 }, - { url = "https://files.pythonhosted.org/packages/2b/94/5c8a2b50a496b11dd519f4a24cb5496cf125681dd99e94c604ccdea9419a/frozenlist-1.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f833670942247a14eafbb675458b4e61c82e002a148f49e68257b79296e865c4", size = 50448 }, - { url = "https://files.pythonhosted.org/packages/6a/bd/d91c5e39f490a49df14320f4e8c80161cfcce09f1e2cde1edd16a551abb3/frozenlist-1.8.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:494a5952b1c597ba44e0e78113a7266e656b9794eec897b19ead706bd7074383", size = 242411 }, - { url = "https://files.pythonhosted.org/packages/8f/83/f61505a05109ef3293dfb1ff594d13d64a2324ac3482be2cedc2be818256/frozenlist-1.8.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96f423a119f4777a4a056b66ce11527366a8bb92f54e541ade21f2374433f6d4", size = 243014 }, - { url = "https://files.pythonhosted.org/packages/d8/cb/cb6c7b0f7d4023ddda30cf56b8b17494eb3a79e3fda666bf735f63118b35/frozenlist-1.8.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3462dd9475af2025c31cc61be6652dfa25cbfb56cbbf52f4ccfe029f38decaf8", size = 234909 }, - { url = "https://files.pythonhosted.org/packages/31/c5/cd7a1f3b8b34af009fb17d4123c5a778b44ae2804e3ad6b86204255f9ec5/frozenlist-1.8.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4c800524c9cd9bac5166cd6f55285957fcfc907db323e193f2afcd4d9abd69b", size = 250049 }, - { url = "https://files.pythonhosted.org/packages/c0/01/2f95d3b416c584a1e7f0e1d6d31998c4a795f7544069ee2e0962a4b60740/frozenlist-1.8.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d6a5df73acd3399d893dafc71663ad22534b5aa4f94e8a2fabfe856c3c1b6a52", size = 256485 }, - { url = "https://files.pythonhosted.org/packages/ce/03/024bf7720b3abaebcff6d0793d73c154237b85bdf67b7ed55e5e9596dc9a/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:405e8fe955c2280ce66428b3ca55e12b3c4e9c336fb2103a4937e891c69a4a29", size = 237619 }, - { url = "https://files.pythonhosted.org/packages/69/fa/f8abdfe7d76b731f5d8bd217827cf6764d4f1d9763407e42717b4bed50a0/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:908bd3f6439f2fef9e85031b59fd4f1297af54415fb60e4254a95f75b3cab3f3", size = 250320 }, - { url = "https://files.pythonhosted.org/packages/f5/3c/b051329f718b463b22613e269ad72138cc256c540f78a6de89452803a47d/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:294e487f9ec720bd8ffcebc99d575f7eff3568a08a253d1ee1a0378754b74143", size = 246820 }, - { url = "https://files.pythonhosted.org/packages/0f/ae/58282e8f98e444b3f4dd42448ff36fa38bef29e40d40f330b22e7108f565/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:74c51543498289c0c43656701be6b077f4b265868fa7f8a8859c197006efb608", size = 250518 }, - { url = "https://files.pythonhosted.org/packages/8f/96/007e5944694d66123183845a106547a15944fbbb7154788cbf7272789536/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:776f352e8329135506a1d6bf16ac3f87bc25b28e765949282dcc627af36123aa", size = 239096 }, - { url = "https://files.pythonhosted.org/packages/66/bb/852b9d6db2fa40be96f29c0d1205c306288f0684df8fd26ca1951d461a56/frozenlist-1.8.0-cp312-cp312-win32.whl", hash = "sha256:433403ae80709741ce34038da08511d4a77062aa924baf411ef73d1146e74faf", size = 39985 }, - { url = "https://files.pythonhosted.org/packages/b8/af/38e51a553dd66eb064cdf193841f16f077585d4d28394c2fa6235cb41765/frozenlist-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:34187385b08f866104f0c0617404c8eb08165ab1272e884abc89c112e9c00746", size = 44591 }, - { url = "https://files.pythonhosted.org/packages/a7/06/1dc65480ab147339fecc70797e9c2f69d9cea9cf38934ce08df070fdb9cb/frozenlist-1.8.0-cp312-cp312-win_arm64.whl", hash = "sha256:fe3c58d2f5db5fbd18c2987cba06d51b0529f52bc3a6cdc33d3f4eab725104bd", size = 40102 }, - { url = "https://files.pythonhosted.org/packages/2d/40/0832c31a37d60f60ed79e9dfb5a92e1e2af4f40a16a29abcc7992af9edff/frozenlist-1.8.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8d92f1a84bb12d9e56f818b3a746f3efba93c1b63c8387a73dde655e1e42282a", size = 85717 }, - { url = "https://files.pythonhosted.org/packages/30/ba/b0b3de23f40bc55a7057bd38434e25c34fa48e17f20ee273bbde5e0650f3/frozenlist-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:96153e77a591c8adc2ee805756c61f59fef4cf4073a9275ee86fe8cba41241f7", size = 49651 }, - { url = "https://files.pythonhosted.org/packages/0c/ab/6e5080ee374f875296c4243c381bbdef97a9ac39c6e3ce1d5f7d42cb78d6/frozenlist-1.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f21f00a91358803399890ab167098c131ec2ddd5f8f5fd5fe9c9f2c6fcd91e40", size = 49417 }, - { url = "https://files.pythonhosted.org/packages/d5/4e/e4691508f9477ce67da2015d8c00acd751e6287739123113a9fca6f1604e/frozenlist-1.8.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:fb30f9626572a76dfe4293c7194a09fb1fe93ba94c7d4f720dfae3b646b45027", size = 234391 }, - { url = "https://files.pythonhosted.org/packages/40/76/c202df58e3acdf12969a7895fd6f3bc016c642e6726aa63bd3025e0fc71c/frozenlist-1.8.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eaa352d7047a31d87dafcacbabe89df0aa506abb5b1b85a2fb91bc3faa02d822", size = 233048 }, - { url = "https://files.pythonhosted.org/packages/f9/c0/8746afb90f17b73ca5979c7a3958116e105ff796e718575175319b5bb4ce/frozenlist-1.8.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:03ae967b4e297f58f8c774c7eabcce57fe3c2434817d4385c50661845a058121", size = 226549 }, - { url = "https://files.pythonhosted.org/packages/7e/eb/4c7eefc718ff72f9b6c4893291abaae5fbc0c82226a32dcd8ef4f7a5dbef/frozenlist-1.8.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f6292f1de555ffcc675941d65fffffb0a5bcd992905015f85d0592201793e0e5", size = 239833 }, - { url = "https://files.pythonhosted.org/packages/c2/4e/e5c02187cf704224f8b21bee886f3d713ca379535f16893233b9d672ea71/frozenlist-1.8.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:29548f9b5b5e3460ce7378144c3010363d8035cea44bc0bf02d57f5a685e084e", size = 245363 }, - { url = "https://files.pythonhosted.org/packages/1f/96/cb85ec608464472e82ad37a17f844889c36100eed57bea094518bf270692/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ec3cc8c5d4084591b4237c0a272cc4f50a5b03396a47d9caaf76f5d7b38a4f11", size = 229314 }, - { url = "https://files.pythonhosted.org/packages/5d/6f/4ae69c550e4cee66b57887daeebe006fe985917c01d0fff9caab9883f6d0/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:517279f58009d0b1f2e7c1b130b377a349405da3f7621ed6bfae50b10adf20c1", size = 243365 }, - { url = "https://files.pythonhosted.org/packages/7a/58/afd56de246cf11780a40a2c28dc7cbabbf06337cc8ddb1c780a2d97e88d8/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:db1e72ede2d0d7ccb213f218df6a078a9c09a7de257c2fe8fcef16d5925230b1", size = 237763 }, - { url = "https://files.pythonhosted.org/packages/cb/36/cdfaf6ed42e2644740d4a10452d8e97fa1c062e2a8006e4b09f1b5fd7d63/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:b4dec9482a65c54a5044486847b8a66bf10c9cb4926d42927ec4e8fd5db7fed8", size = 240110 }, - { url = "https://files.pythonhosted.org/packages/03/a8/9ea226fbefad669f11b52e864c55f0bd57d3c8d7eb07e9f2e9a0b39502e1/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:21900c48ae04d13d416f0e1e0c4d81f7931f73a9dfa0b7a8746fb2fe7dd970ed", size = 233717 }, - { url = "https://files.pythonhosted.org/packages/1e/0b/1b5531611e83ba7d13ccc9988967ea1b51186af64c42b7a7af465dcc9568/frozenlist-1.8.0-cp313-cp313-win32.whl", hash = "sha256:8b7b94a067d1c504ee0b16def57ad5738701e4ba10cec90529f13fa03c833496", size = 39628 }, - { url = "https://files.pythonhosted.org/packages/d8/cf/174c91dbc9cc49bc7b7aab74d8b734e974d1faa8f191c74af9b7e80848e6/frozenlist-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:878be833caa6a3821caf85eb39c5ba92d28e85df26d57afb06b35b2efd937231", size = 43882 }, - { url = "https://files.pythonhosted.org/packages/c1/17/502cd212cbfa96eb1388614fe39a3fc9ab87dbbe042b66f97acb57474834/frozenlist-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:44389d135b3ff43ba8cc89ff7f51f5a0bb6b63d829c8300f79a2fe4fe61bcc62", size = 39676 }, - { url = "https://files.pythonhosted.org/packages/d2/5c/3bbfaa920dfab09e76946a5d2833a7cbdf7b9b4a91c714666ac4855b88b4/frozenlist-1.8.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:e25ac20a2ef37e91c1b39938b591457666a0fa835c7783c3a8f33ea42870db94", size = 89235 }, - { url = "https://files.pythonhosted.org/packages/d2/d6/f03961ef72166cec1687e84e8925838442b615bd0b8854b54923ce5b7b8a/frozenlist-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:07cdca25a91a4386d2e76ad992916a85038a9b97561bf7a3fd12d5d9ce31870c", size = 50742 }, - { url = "https://files.pythonhosted.org/packages/1e/bb/a6d12b7ba4c3337667d0e421f7181c82dda448ce4e7ad7ecd249a16fa806/frozenlist-1.8.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4e0c11f2cc6717e0a741f84a527c52616140741cd812a50422f83dc31749fb52", size = 51725 }, - { url = "https://files.pythonhosted.org/packages/bc/71/d1fed0ffe2c2ccd70b43714c6cab0f4188f09f8a67a7914a6b46ee30f274/frozenlist-1.8.0-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b3210649ee28062ea6099cfda39e147fa1bc039583c8ee4481cb7811e2448c51", size = 284533 }, - { url = "https://files.pythonhosted.org/packages/c9/1f/fb1685a7b009d89f9bf78a42d94461bc06581f6e718c39344754a5d9bada/frozenlist-1.8.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:581ef5194c48035a7de2aefc72ac6539823bb71508189e5de01d60c9dcd5fa65", size = 292506 }, - { url = "https://files.pythonhosted.org/packages/e6/3b/b991fe1612703f7e0d05c0cf734c1b77aaf7c7d321df4572e8d36e7048c8/frozenlist-1.8.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3ef2d026f16a2b1866e1d86fc4e1291e1ed8a387b2c333809419a2f8b3a77b82", size = 274161 }, - { url = "https://files.pythonhosted.org/packages/ca/ec/c5c618767bcdf66e88945ec0157d7f6c4a1322f1473392319b7a2501ded7/frozenlist-1.8.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5500ef82073f599ac84d888e3a8c1f77ac831183244bfd7f11eaa0289fb30714", size = 294676 }, - { url = "https://files.pythonhosted.org/packages/7c/ce/3934758637d8f8a88d11f0585d6495ef54b2044ed6ec84492a91fa3b27aa/frozenlist-1.8.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:50066c3997d0091c411a66e710f4e11752251e6d2d73d70d8d5d4c76442a199d", size = 300638 }, - { url = "https://files.pythonhosted.org/packages/fc/4f/a7e4d0d467298f42de4b41cbc7ddaf19d3cfeabaf9ff97c20c6c7ee409f9/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5c1c8e78426e59b3f8005e9b19f6ff46e5845895adbde20ece9218319eca6506", size = 283067 }, - { url = "https://files.pythonhosted.org/packages/dc/48/c7b163063d55a83772b268e6d1affb960771b0e203b632cfe09522d67ea5/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:eefdba20de0d938cec6a89bd4d70f346a03108a19b9df4248d3cf0d88f1b0f51", size = 292101 }, - { url = "https://files.pythonhosted.org/packages/9f/d0/2366d3c4ecdc2fd391e0afa6e11500bfba0ea772764d631bbf82f0136c9d/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cf253e0e1c3ceb4aaff6df637ce033ff6535fb8c70a764a8f46aafd3d6ab798e", size = 289901 }, - { url = "https://files.pythonhosted.org/packages/b8/94/daff920e82c1b70e3618a2ac39fbc01ae3e2ff6124e80739ce5d71c9b920/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:032efa2674356903cd0261c4317a561a6850f3ac864a63fc1583147fb05a79b0", size = 289395 }, - { url = "https://files.pythonhosted.org/packages/e3/20/bba307ab4235a09fdcd3cc5508dbabd17c4634a1af4b96e0f69bfe551ebd/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6da155091429aeba16851ecb10a9104a108bcd32f6c1642867eadaee401c1c41", size = 283659 }, - { url = "https://files.pythonhosted.org/packages/fd/00/04ca1c3a7a124b6de4f8a9a17cc2fcad138b4608e7a3fc5877804b8715d7/frozenlist-1.8.0-cp313-cp313t-win32.whl", hash = "sha256:0f96534f8bfebc1a394209427d0f8a63d343c9779cda6fc25e8e121b5fd8555b", size = 43492 }, - { url = "https://files.pythonhosted.org/packages/59/5e/c69f733a86a94ab10f68e496dc6b7e8bc078ebb415281d5698313e3af3a1/frozenlist-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5d63a068f978fc69421fb0e6eb91a9603187527c86b7cd3f534a5b77a592b888", size = 48034 }, - { url = "https://files.pythonhosted.org/packages/16/6c/be9d79775d8abe79b05fa6d23da99ad6e7763a1d080fbae7290b286093fd/frozenlist-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf0a7e10b077bf5fb9380ad3ae8ce20ef919a6ad93b4552896419ac7e1d8e042", size = 41749 }, - { url = "https://files.pythonhosted.org/packages/f1/c8/85da824b7e7b9b6e7f7705b2ecaf9591ba6f79c1177f324c2735e41d36a2/frozenlist-1.8.0-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:cee686f1f4cadeb2136007ddedd0aaf928ab95216e7691c63e50a8ec066336d0", size = 86127 }, - { url = "https://files.pythonhosted.org/packages/8e/e8/a1185e236ec66c20afd72399522f142c3724c785789255202d27ae992818/frozenlist-1.8.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:119fb2a1bd47307e899c2fac7f28e85b9a543864df47aa7ec9d3c1b4545f096f", size = 49698 }, - { url = "https://files.pythonhosted.org/packages/a1/93/72b1736d68f03fda5fdf0f2180fb6caaae3894f1b854d006ac61ecc727ee/frozenlist-1.8.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4970ece02dbc8c3a92fcc5228e36a3e933a01a999f7094ff7c23fbd2beeaa67c", size = 49749 }, - { url = "https://files.pythonhosted.org/packages/a7/b2/fabede9fafd976b991e9f1b9c8c873ed86f202889b864756f240ce6dd855/frozenlist-1.8.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:cba69cb73723c3f329622e34bdbf5ce1f80c21c290ff04256cff1cd3c2036ed2", size = 231298 }, - { url = "https://files.pythonhosted.org/packages/3a/3b/d9b1e0b0eed36e70477ffb8360c49c85c8ca8ef9700a4e6711f39a6e8b45/frozenlist-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:778a11b15673f6f1df23d9586f83c4846c471a8af693a22e066508b77d201ec8", size = 232015 }, - { url = "https://files.pythonhosted.org/packages/dc/94/be719d2766c1138148564a3960fc2c06eb688da592bdc25adcf856101be7/frozenlist-1.8.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0325024fe97f94c41c08872db482cf8ac4800d80e79222c6b0b7b162d5b13686", size = 225038 }, - { url = "https://files.pythonhosted.org/packages/e4/09/6712b6c5465f083f52f50cf74167b92d4ea2f50e46a9eea0523d658454ae/frozenlist-1.8.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:97260ff46b207a82a7567b581ab4190bd4dfa09f4db8a8b49d1a958f6aa4940e", size = 240130 }, - { url = "https://files.pythonhosted.org/packages/f8/d4/cd065cdcf21550b54f3ce6a22e143ac9e4836ca42a0de1022da8498eac89/frozenlist-1.8.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:54b2077180eb7f83dd52c40b2750d0a9f175e06a42e3213ce047219de902717a", size = 242845 }, - { url = "https://files.pythonhosted.org/packages/62/c3/f57a5c8c70cd1ead3d5d5f776f89d33110b1addae0ab010ad774d9a44fb9/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2f05983daecab868a31e1da44462873306d3cbfd76d1f0b5b69c473d21dbb128", size = 229131 }, - { url = "https://files.pythonhosted.org/packages/6c/52/232476fe9cb64f0742f3fde2b7d26c1dac18b6d62071c74d4ded55e0ef94/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:33f48f51a446114bc5d251fb2954ab0164d5be02ad3382abcbfe07e2531d650f", size = 240542 }, - { url = "https://files.pythonhosted.org/packages/5f/85/07bf3f5d0fb5414aee5f47d33c6f5c77bfe49aac680bfece33d4fdf6a246/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:154e55ec0655291b5dd1b8731c637ecdb50975a2ae70c606d100750a540082f7", size = 237308 }, - { url = "https://files.pythonhosted.org/packages/11/99/ae3a33d5befd41ac0ca2cc7fd3aa707c9c324de2e89db0e0f45db9a64c26/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:4314debad13beb564b708b4a496020e5306c7333fa9a3ab90374169a20ffab30", size = 238210 }, - { url = "https://files.pythonhosted.org/packages/b2/60/b1d2da22f4970e7a155f0adde9b1435712ece01b3cd45ba63702aea33938/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:073f8bf8becba60aa931eb3bc420b217bb7d5b8f4750e6f8b3be7f3da85d38b7", size = 231972 }, - { url = "https://files.pythonhosted.org/packages/3f/ab/945b2f32de889993b9c9133216c068b7fcf257d8595a0ac420ac8677cab0/frozenlist-1.8.0-cp314-cp314-win32.whl", hash = "sha256:bac9c42ba2ac65ddc115d930c78d24ab8d4f465fd3fc473cdedfccadb9429806", size = 40536 }, - { url = "https://files.pythonhosted.org/packages/59/ad/9caa9b9c836d9ad6f067157a531ac48b7d36499f5036d4141ce78c230b1b/frozenlist-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:3e0761f4d1a44f1d1a47996511752cf3dcec5bbdd9cc2b4fe595caf97754b7a0", size = 44330 }, - { url = "https://files.pythonhosted.org/packages/82/13/e6950121764f2676f43534c555249f57030150260aee9dcf7d64efda11dd/frozenlist-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:d1eaff1d00c7751b7c6662e9c5ba6eb2c17a2306ba5e2a37f24ddf3cc953402b", size = 40627 }, - { url = "https://files.pythonhosted.org/packages/c0/c7/43200656ecc4e02d3f8bc248df68256cd9572b3f0017f0a0c4e93440ae23/frozenlist-1.8.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d3bb933317c52d7ea5004a1c442eef86f426886fba134ef8cf4226ea6ee1821d", size = 89238 }, - { url = "https://files.pythonhosted.org/packages/d1/29/55c5f0689b9c0fb765055629f472c0de484dcaf0acee2f7707266ae3583c/frozenlist-1.8.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8009897cdef112072f93a0efdce29cd819e717fd2f649ee3016efd3cd885a7ed", size = 50738 }, - { url = "https://files.pythonhosted.org/packages/ba/7d/b7282a445956506fa11da8c2db7d276adcbf2b17d8bb8407a47685263f90/frozenlist-1.8.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2c5dcbbc55383e5883246d11fd179782a9d07a986c40f49abe89ddf865913930", size = 51739 }, - { url = "https://files.pythonhosted.org/packages/62/1c/3d8622e60d0b767a5510d1d3cf21065b9db874696a51ea6d7a43180a259c/frozenlist-1.8.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:39ecbc32f1390387d2aa4f5a995e465e9e2f79ba3adcac92d68e3e0afae6657c", size = 284186 }, - { url = "https://files.pythonhosted.org/packages/2d/14/aa36d5f85a89679a85a1d44cd7a6657e0b1c75f61e7cad987b203d2daca8/frozenlist-1.8.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92db2bf818d5cc8d9c1f1fc56b897662e24ea5adb36ad1f1d82875bd64e03c24", size = 292196 }, - { url = "https://files.pythonhosted.org/packages/05/23/6bde59eb55abd407d34f77d39a5126fb7b4f109a3f611d3929f14b700c66/frozenlist-1.8.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:2dc43a022e555de94c3b68a4ef0b11c4f747d12c024a520c7101709a2144fb37", size = 273830 }, - { url = "https://files.pythonhosted.org/packages/d2/3f/22cff331bfad7a8afa616289000ba793347fcd7bc275f3b28ecea2a27909/frozenlist-1.8.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb89a7f2de3602cfed448095bab3f178399646ab7c61454315089787df07733a", size = 294289 }, - { url = "https://files.pythonhosted.org/packages/a4/89/5b057c799de4838b6c69aa82b79705f2027615e01be996d2486a69ca99c4/frozenlist-1.8.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:33139dc858c580ea50e7e60a1b0ea003efa1fd42e6ec7fdbad78fff65fad2fd2", size = 300318 }, - { url = "https://files.pythonhosted.org/packages/30/de/2c22ab3eb2a8af6d69dc799e48455813bab3690c760de58e1bf43b36da3e/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:168c0969a329b416119507ba30b9ea13688fafffac1b7822802537569a1cb0ef", size = 282814 }, - { url = "https://files.pythonhosted.org/packages/59/f7/970141a6a8dbd7f556d94977858cfb36fa9b66e0892c6dd780d2219d8cd8/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:28bd570e8e189d7f7b001966435f9dac6718324b5be2990ac496cf1ea9ddb7fe", size = 291762 }, - { url = "https://files.pythonhosted.org/packages/c1/15/ca1adae83a719f82df9116d66f5bb28bb95557b3951903d39135620ef157/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b2a095d45c5d46e5e79ba1e5b9cb787f541a8dee0433836cea4b96a2c439dcd8", size = 289470 }, - { url = "https://files.pythonhosted.org/packages/ac/83/dca6dc53bf657d371fbc88ddeb21b79891e747189c5de990b9dfff2ccba1/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:eab8145831a0d56ec9c4139b6c3e594c7a83c2c8be25d5bcf2d86136a532287a", size = 289042 }, - { url = "https://files.pythonhosted.org/packages/96/52/abddd34ca99be142f354398700536c5bd315880ed0a213812bc491cff5e4/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:974b28cf63cc99dfb2188d8d222bc6843656188164848c4f679e63dae4b0708e", size = 283148 }, - { url = "https://files.pythonhosted.org/packages/af/d3/76bd4ed4317e7119c2b7f57c3f6934aba26d277acc6309f873341640e21f/frozenlist-1.8.0-cp314-cp314t-win32.whl", hash = "sha256:342c97bf697ac5480c0a7ec73cd700ecfa5a8a40ac923bd035484616efecc2df", size = 44676 }, - { url = "https://files.pythonhosted.org/packages/89/76/c615883b7b521ead2944bb3480398cbb07e12b7b4e4d073d3752eb721558/frozenlist-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:06be8f67f39c8b1dc671f5d83aaefd3358ae5cdcf8314552c57e7ed3e6475bdd", size = 49451 }, - { url = "https://files.pythonhosted.org/packages/e0/a3/5982da14e113d07b325230f95060e2169f5311b1017ea8af2a29b374c289/frozenlist-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:102e6314ca4da683dca92e3b1355490fed5f313b768500084fbe6371fddfdb79", size = 42507 }, - { url = "https://files.pythonhosted.org/packages/9a/9a/e35b4a917281c0b8419d4207f4334c8e8c5dbf4f3f5f9ada73958d937dcc/frozenlist-1.8.0-py3-none-any.whl", hash = "sha256:0c18a16eab41e82c295618a77502e17b195883241c563b00f0aa5106fc4eaa0d", size = 13409 }, -] - -[[package]] -name = "fsspec" -version = "2026.2.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/51/7c/f60c259dcbf4f0c47cc4ddb8f7720d2dcdc8888c8e5ad84c73ea4531cc5b/fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff", size = 313441 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e6/ab/fb21f4c939bb440104cc2b396d3be1d9b7a9fd3c6c2a53d98c45b3d7c954/fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437", size = 202505 }, -] - -[package.optional-dependencies] -http = [ - { name = "aiohttp" }, -] - -[[package]] -name = "greenlet" -version = "3.5.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/3c/3f/dbf99fb14bfeb88c28f16729215478c0e265cacd6dc22270c8f31bb6892f/greenlet-3.5.0.tar.gz", hash = "sha256:d419647372241bc68e957bf38d5c1f98852155e4146bd1e4121adea81f4f01e4", size = 196995 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ef/32/f2ce6d4cac3e55bc6173f92dbe627e782e1850f89d986c3606feb63aafa7/greenlet-3.5.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:db2910d3c809444e0a20147361f343fe2798e106af8d9d8506f5305302655a9f", size = 286228 }, - { url = "https://files.pythonhosted.org/packages/b7/aa/caed9e5adf742315fc7be2a84196373aab4816e540e38ba0d76cb7584d68/greenlet-3.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3ec9ea74e7268ace7f9aab1b1a4e730193fc661b39a993cd91c606c32d4a3628", size = 601775 }, - { url = "https://files.pythonhosted.org/packages/c7/af/90ae08497400a941595d12774447f752d3dfe0fbb012e35b76bc5c0ff37e/greenlet-3.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54d243512da35485fc7a6bf3c178fdda6327a9d6506fcdd62b1abd1e41b2927b", size = 614436 }, - { url = "https://files.pythonhosted.org/packages/3f/e9/4eeadf8cb3403ac274245ba75f07844abc7fa5f6787583fc9156ba741e0f/greenlet-3.5.0-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:41353ec2ecedf7aa8f682753a41919f8718031a6edac46b8d3dc7ed9e1ceb136", size = 620610 }, - { url = "https://files.pythonhosted.org/packages/2b/e0/2e13df68f367e2f9960616927d60857dd7e56aaadd59a47c644216b2f920/greenlet-3.5.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d280a7f5c331622c69f97eb167f33577ff2d1df282c41cd15907fc0a3ca198c", size = 611388 }, - { url = "https://files.pythonhosted.org/packages/ee/ef/f913b3c0eb7d26d86a2401c5e1546c9d46b657efee724b06f6f4ac5d8824/greenlet-3.5.0-cp312-cp312-manylinux_2_39_riscv64.whl", hash = "sha256:58c1c374fe2b3d852f9b6b11a7dff4c85404e51b9a596fd9e89cf904eb09866d", size = 422775 }, - { url = "https://files.pythonhosted.org/packages/82/f7/393c64055132ac0d488ef6be549253b7e6274194863967ddc0bc8f5b87b8/greenlet-3.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1eb67d5adefb5bd2e182d42678a328979a209e4e82eb93575708185d31d1f588", size = 1570768 }, - { url = "https://files.pythonhosted.org/packages/b8/4b/eaf7735253522cf56d1b74d672a58f54fc114702ceaf05def59aae72f6e1/greenlet-3.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2628d6c86f6cb0cb45e0c3c54058bbec559f57eaae699447748cb3928150577e", size = 1635983 }, - { url = "https://files.pythonhosted.org/packages/4c/fe/4fb3a0805bd5165da5ebf858da7cc01cce8061674106d2cf5bdab32cbfde/greenlet-3.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:d4d9f0624c775f2dfc56ba54d515a8c771044346852a918b405914f6b19d7fd8", size = 238840 }, - { url = "https://files.pythonhosted.org/packages/cb/cb/baa584cb00532126ffe12d9787db0a60c5a4f55c27bfe2666df5d4c30a32/greenlet-3.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:83ed9f27f1680b50e89f40f6df348a290ea234b249a4003d366663a12eab94f2", size = 235615 }, - { url = "https://files.pythonhosted.org/packages/0c/58/fc576f99037ce19c5aa16628e4c3226b6d1419f72a62c79f5f40576e6eb3/greenlet-3.5.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:5a5ed18de6a0f6cc7087f1563f6bd93fc7df1c19165ca01e9bde5a5dc281d106", size = 285066 }, - { url = "https://files.pythonhosted.org/packages/4a/ba/b28ddbe6bfad6a8ac196ef0e8cff37bc65b79735995b9e410923fffeeb70/greenlet-3.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a717fbc46d8a354fa675f7c1e813485b6ba3885f9bef0cd56e5ba27d758ff5b", size = 604414 }, - { url = "https://files.pythonhosted.org/packages/09/06/4b69f8f0b67603a8be2790e55107a190b376f2627fe0eaf5695d85ffb3cd/greenlet-3.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ddc090c5c1792b10246a78e8c2163ebbe04cf877f9d785c230a7b27b39ad038e", size = 617349 }, - { url = "https://files.pythonhosted.org/packages/6a/15/a643b4ecd09969e30b8a150d5919960caae0abe4f5af75ab040b1ab85e78/greenlet-3.5.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:4964101b8585c144cbda5532b1aa644255126c08a265dae90c16e7a0e63aaa9d", size = 623234 }, - { url = "https://files.pythonhosted.org/packages/8a/17/a3918541fd0ddefe024a69de6d16aa7b46d36ac19562adaa63c7fa180eff/greenlet-3.5.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2094acd54b272cb6eae8c03dd87b3fa1820a4cef18d6889c378d503500a1dc13", size = 613927 }, - { url = "https://files.pythonhosted.org/packages/77/18/3b13d5ef1275b0ffaf933b05efa21408ac4ca95823c7411d79682e4fdcff/greenlet-3.5.0-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:7022615368890680e67b9965d33f5773aade330d5343bbe25560135aaa849eae", size = 425243 }, - { url = "https://files.pythonhosted.org/packages/ee/e1/bd0af6213c7dd33175d8a462d4c1fe1175124ebed4855bc1475a5b5242c2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5e05ba267789ea87b5a155cf0e810b1ab88bf18e9e8740813945ceb8ee4350ba", size = 1570893 }, - { url = "https://files.pythonhosted.org/packages/9b/2a/0789702f864f5382cb476b93d7a9c823c10472658102ccd65f415747d2e2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0ecec963079cd58cbd14723582384f11f166fd58883c15dcbfb342e0bc9b5846", size = 1636060 }, - { url = "https://files.pythonhosted.org/packages/b2/8f/22bf9df92bbff0eb07842b60f7e63bf7675a9742df628437a9f02d09137f/greenlet-3.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:728d9667d8f2f586644b748dbd9bb67e50d6a9381767d1357714ea6825bb3bf5", size = 238740 }, - { url = "https://files.pythonhosted.org/packages/b6/b7/9c5c3d653bd4ff614277c049ac676422e2c557db47b4fe43e6313fc005dc/greenlet-3.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:47422135b1d308c14b2c6e758beedb1acd33bb91679f5670edf77bf46244722b", size = 235525 }, - { url = "https://files.pythonhosted.org/packages/94/5e/a70f31e3e8d961c4ce589c15b28e4225d63704e431a23932a3808cbcc867/greenlet-3.5.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:f35807464c4c58c55f0d31dfa83c541a5615d825c2fe3d2b95360cf7c4e3c0a8", size = 285564 }, - { url = "https://files.pythonhosted.org/packages/af/a6/046c0a28e21833e4086918218cfb3d8bed51c075a1b700f20b9d7861c0f4/greenlet-3.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55fa7ea52771be44af0de27d8b80c02cd18c2c3cddde6c847ecebdf72418b6a1", size = 651166 }, - { url = "https://files.pythonhosted.org/packages/47/f8/4af27f71c5ff32a7fbc516adb46370d9c4ae2bc7bd3dc7d066ac542b4b15/greenlet-3.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a97e4821aa710603f94de0da25f25096454d78ffdace5dc77f3a006bc01abba3", size = 663792 }, - { url = "https://files.pythonhosted.org/packages/fb/89/2dadb89793c37ee8b4c237857188293e9060dc085f19845c292e00f8e091/greenlet-3.5.0-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bf2d8a80bec89ab46221ae45c5373d5ba0bd36c19aa8508e85c6cd7e5106cd37", size = 668086 }, - { url = "https://files.pythonhosted.org/packages/a3/59/1bd6d7428d6ed9106efbb8c52310c60fd04f6672490f452aeaa3829aa436/greenlet-3.5.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f52a464e4ed91780bdfbbdd2b97197f3accaa629b98c200f4dffada759f3ae7", size = 660933 }, - { url = "https://files.pythonhosted.org/packages/82/35/75722be7e26a2af4cbd2dc35b0ed382dacf9394b7e75551f76ed1abe87f2/greenlet-3.5.0-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:1bae92a1dd94c5f9d9493c3a212dd874c202442047cf96446412c862feca83a2", size = 470799 }, - { url = "https://files.pythonhosted.org/packages/83/e4/b903e5a5fae1e8a28cdd32a0cfbfd560b668c25b692f67768822ddc5f40f/greenlet-3.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:762612baf1161ccb8437c0161c668a688223cba28e1bf038f4eb47b13e39ccdf", size = 1618401 }, - { url = "https://files.pythonhosted.org/packages/0e/e3/5ec408a329acb854fb607a122e1ee5fb3ff649f9a97952948a90803c0d8e/greenlet-3.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:57a43c6079a89713522bc4bcb9f75070ecf5d3dbad7792bfe42239362cbf2a16", size = 1682038 }, - { url = "https://files.pythonhosted.org/packages/91/20/6b165108058767ee643c55c5c4904d591a830ee2b3c7dbd359828fbc829f/greenlet-3.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:3bc59be3945ae9750b9e7d45067d01ae3fe90ea5f9ade99239dabdd6e28a5033", size = 239835 }, - { url = "https://files.pythonhosted.org/packages/4e/62/1c498375cee177b55d980c1db319f26470e5309e54698c8f8fc06c0fd539/greenlet-3.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:a96fcee45e03fe30a62669fd16ab5c9d3c172660d3085605cb1e2d1280d3c988", size = 236862 }, - { url = "https://files.pythonhosted.org/packages/78/a8/4522939255bb5409af4e87132f915446bf3622c2c292d14d3c38d128ae82/greenlet-3.5.0-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:a10a732421ab4fec934783ce3e54763470d0181db6e3468f9103a275c3ed1853", size = 293614 }, - { url = "https://files.pythonhosted.org/packages/15/5e/8744c52e2c027b5a8772a01561934c8835f869733e101f62075c60430340/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7fc391b1566f2907d17aaebe78f8855dc45675159a775fcf9e61f8ee0078e87f", size = 650723 }, - { url = "https://files.pythonhosted.org/packages/00/ef/7b4c39c03cf46ceca512c5d3f914afd85aa30b2cc9a93015b0dd73e4be6c/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:680bd0e7ad5e8daa8a4aa89f68fd6adc834b8a8036dc256533f7e08f4a4b01f7", size = 656529 }, - { url = "https://files.pythonhosted.org/packages/5f/5c/0602239503b124b70e39355cbdb39361ecfe65b87a5f2f63752c32f5286f/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:1aa4ce8debcd4ea7fb2e150f3036588c41493d1d52c43538924ae1819003f4ce", size = 657015 }, - { url = "https://files.pythonhosted.org/packages/0b/b5/c7768f352f5c010f92064d0063f987e7dc0cd290a6d92a34109015ce4aa1/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ddb36c7d6c9c0a65f18c7258634e0c416c6ab59caac8c987b96f80c2ebda0112", size = 654364 }, - { url = "https://files.pythonhosted.org/packages/38/51/8699f865f125dc952384cb432b0f7138aa4d8f2969a7d12d0df5b94d054d/greenlet-3.5.0-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:728a73687e39ae9ca34e4694cbf2f049d3fbc7174639468d0f67200a97d8f9e2", size = 488275 }, - { url = "https://files.pythonhosted.org/packages/ef/d0/079ebe12e4b1fc758857ce5be1a5e73f06870f2101e52611d1e71925ce54/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e5ddf316ced87539144621453c3aef229575825fe60c604e62bedc4003f372b2", size = 1614204 }, - { url = "https://files.pythonhosted.org/packages/6d/89/6c2fb63df3596552d20e58fb4d96669243388cf680cff222758812c7bfaa/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4a448128607be0de65342dc9b31be7f948ef4cc0bc8832069350abefd310a8f2", size = 1675480 }, - { url = "https://files.pythonhosted.org/packages/15/32/77ee8a6c1564fc345a491a4e85b3bf360e4cf26eac98c4532d2fdb96e01f/greenlet-3.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d60097128cb0a1cab9ea541186ea13cd7b847b8449a7787c2e2350da0cb82d86", size = 245324 }, -] - -[[package]] -name = "griffe" -version = "2.0.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "griffecli" }, - { name = "griffelib" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/4a/49/eb6d2935e27883af92c930ed40cc4c69bcd32c402be43b8ca4ab20510f67/griffe-2.0.2.tar.gz", hash = "sha256:c5d56326d159f274492e9bf93a9895cec101155d944caa66d0fc4e0c13751b92", size = 293757 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/94/c0/2bb018eecf9a83c68db9cd9fffd9dab25f102ad30ed869451046e46d1187/griffe-2.0.2-py3-none-any.whl", hash = "sha256:2b31816460aee1996af26050a1fc6927a2e5936486856707f55508e4c9b5960b", size = 5141 }, -] - -[[package]] -name = "griffecli" -version = "2.0.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama" }, - { name = "griffelib" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/79/e0/6a7d661d71bb043656a109b91d84a42b5342752542074ec83b16a6eb97f0/griffecli-2.0.2.tar.gz", hash = "sha256:40a1ad4181fc39685d025e119ae2c5b669acdc1f19b705fb9bf971f4e6f6dffb", size = 56281 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2e/e8/90d93356c88ac34c20cb5edffca68138df55ca9bbd1a06eccfbcec8fdbe5/griffecli-2.0.2-py3-none-any.whl", hash = "sha256:0d44d39e59afa81e288a3e1c3bf352cc4fa537483326ac06b8bb6a51fd8303a0", size = 9500 }, -] - -[[package]] -name = "griffelib" -version = "2.0.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9d/82/74f4a3310cdabfbb10da554c3a672847f1ed33c6f61dd472681ce7f1fe67/griffelib-2.0.2.tar.gz", hash = "sha256:3cf20b3bc470e83763ffbf236e0076b1211bac1bc67de13daf494640f2de707e", size = 166461 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/11/8c/c9138d881c79aa0ea9ed83cbd58d5ca75624378b38cee225dcf5c42cc91f/griffelib-2.0.2-py3-none-any.whl", hash = "sha256:925c857658fb1ba40c0772c37acbc2ab650bd794d9c1b9726922e36ea4117ea1", size = 142357 }, -] - -[[package]] -name = "h11" -version = "0.16.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515 }, -] - -[[package]] -name = "hf-xet" -version = "1.5.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/74/d8/5c06fc76461418326a7decf8367480c35be11a41fd938633929c60a9ec6b/hf_xet-1.5.0.tar.gz", hash = "sha256:e0fb0a34d9f406eed88233e829a67ec016bec5af19e480eac65a233ea289a948", size = 837196 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/68/9b/6912c99070915a4f28119e3c5b52a9abd1eec0ad5cb293b8c967a0c6f5a2/hf_xet-1.5.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:7d70fe2ce97b9db73b9c9b9c81fe3693640aec83416a966c446afea54acfae3c", size = 4023383 }, - { url = "https://files.pythonhosted.org/packages/0f/6d/9563cfde59b5d8128a9c7ec972a087f4c782e4f7bac5a85234edfd5d5e49/hf_xet-1.5.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:73a0dae8c71de3b0633a45c73f4a4a5ed09e94b43441d82981a781d4f12baa42", size = 3792751 }, - { url = "https://files.pythonhosted.org/packages/07/a5/ed5a0cf35b49a0571af5a8f53416dad1877a718c021c9937c3a53cb45781/hf_xet-1.5.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a60290ec57e9b71767fba7c3645ddafdd0759974b540441510c629c6db6db24a", size = 4456058 }, - { url = "https://files.pythonhosted.org/packages/60/fb/3ae8bf2a7a37a4197d0195d7247fd25b3952e15cb8a599e285dfaa6f52b3/hf_xet-1.5.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:e5de0f6deada0dada870bb376a11bcd1f08abf3a968a6d118f33e72d1b1eb480", size = 4250783 }, - { url = "https://files.pythonhosted.org/packages/a2/9b/8bae40d4d91525085137196e84eb0ed49cf65b5e96e5c3ecdadd8bd0fac2/hf_xet-1.5.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c799d49f1a5544a0ef7591c0ee75e0d6b93d6f56dc7a4979f59f7518d2872216", size = 4445594 }, - { url = "https://files.pythonhosted.org/packages/13/59/c74efbbd4e8728172b2cc72a2bc014d2947a4b7bdced932fbd3f5da1a4e5/hf_xet-1.5.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2baea1b0b989e5c152fe81425f7745ddc8901280ba3d97c98d8cdece7b706c60", size = 4663995 }, - { url = "https://files.pythonhosted.org/packages/73/32/8e1e0410af64cda9b139d1dcebdc993a8ff9c8c7c0e2696ae356d75ccc0d/hf_xet-1.5.0-cp313-cp313t-win_amd64.whl", hash = "sha256:526345b3ed45f374f6317349df489167606736c876241ba984105afe7fd4839d", size = 3966608 }, - { url = "https://files.pythonhosted.org/packages/fc/34/a8febc8f4edbea8b3e21b02ebc8b628679b84ba7e45cde624a7736b51500/hf_xet-1.5.0-cp313-cp313t-win_arm64.whl", hash = "sha256:786d28e2eb8315d5035544b9d137b4a842d600c434bb91bf7d0d953cce906ad4", size = 3796946 }, - { url = "https://files.pythonhosted.org/packages/2a/20/8fc8996afe5815fa1a6be8e9e5c02f24500f409d599e905800d498a4e14d/hf_xet-1.5.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:872d5601e6deea30d15865ede55d29eac6daf5a534ab417b99b6ef6b076dd96c", size = 4023495 }, - { url = "https://files.pythonhosted.org/packages/32/6a/93d84463c00cecb561a7508aa6303e35ee2894294eac14245526924415fe/hf_xet-1.5.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9929561f5abf4581c8ea79587881dfef6b8abb2a0d8a51915936fc2a614f4e73", size = 3792731 }, - { url = "https://files.pythonhosted.org/packages/9d/5a/8ec8e0c863b382d00b3c2e2af6ded6b06371be617144a625903a6d562f4b/hf_xet-1.5.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f7b7bbae318e583a86fb21e5a4a175d6721d628a2874f4bd022d0e660c32a682", size = 4456738 }, - { url = "https://files.pythonhosted.org/packages/c5/ca/f7effa1a67717da2bcc6b6c28f71c6ca648c77acaec4e2c32f40cbe16d85/hf_xet-1.5.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:cf7b2dc6f31a4ea754bb50f74cde482dcf5d366d184076d8530b9872787f3761", size = 4251622 }, - { url = "https://files.pythonhosted.org/packages/65/f2/19247dba3e231cf77dec59ddfb878f00057635ff773d099c9b59d37812c3/hf_xet-1.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8dbcbab554c9ef158ef2c991545c3e970ddd8cc7acdcd0a78c5a41095dab4ded", size = 4445667 }, - { url = "https://files.pythonhosted.org/packages/7f/64/6f116801a3bcfb6f59f5c251f48cadc47ea54026441c4a385079286a94fa/hf_xet-1.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5906bf7718d3636dc13402914736abe723492cb730f744834f5f5b67d3a12702", size = 4664619 }, - { url = "https://files.pythonhosted.org/packages/5c/e8/069542d37946ed08669b127e1496fa99e78196d71de8d41eda5e9f1b7a58/hf_xet-1.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:5f3dc2248fc01cc0a00cd392ab497f1ca373fcbc7e3f2da1f452480b384e839e", size = 3966802 }, - { url = "https://files.pythonhosted.org/packages/f9/91/fc6fdec27b14d04e88c386ac0a0129732b53fa23f7c4a78f4b83a039c567/hf_xet-1.5.0-cp314-cp314t-win_arm64.whl", hash = "sha256:b285cea1b5bab46b758772716ba8d6854a1a0310fed1c249d678a8b38601e5a0", size = 3797168 }, - { url = "https://files.pythonhosted.org/packages/3d/fb/69ff198a82cae7eb1a69fb84d93b3a3e4816564d76817fe541ddc96874eb/hf_xet-1.5.0-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:dad0dc84e941b8ba3c860659fe1fdc35c049d47cce293f003287757e971a8f56", size = 4030814 }, - { url = "https://files.pythonhosted.org/packages/9b/ff/edcc2b40162bef3ff78e14ab637e5f3b89243d6aee72f5949d3bb6a5af83/hf_xet-1.5.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:fd6e5a9b0fdac4ed03ed45ef79254a655b1aaab514a02202617fbf643f5fdf7a", size = 3798444 }, - { url = "https://files.pythonhosted.org/packages/49/4d/103f76b04310e5e57656696cc184690d20c466af0bca3ca88f8c8ea5d4f3/hf_xet-1.5.0-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3531b1823a0e6d77d80f9ed15ca0e00f0d115094f8ac033d5cae88f4564cc949", size = 4465986 }, - { url = "https://files.pythonhosted.org/packages/c4/a2/546f47f464737b3edbab6f8ddb57f2599b93d2cbb66f06abb475ccb48651/hf_xet-1.5.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:9a0ee58cd18d5ea799f7ed11290bbccbe56bdd8b1d97ca74b9cc49a3945d7a3b", size = 4259865 }, - { url = "https://files.pythonhosted.org/packages/95/7f/1be593c1f28613be2e196473481cd81bfc5910795e30a34e8f744f6cac4f/hf_xet-1.5.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1e60df5a42e9bed8628b6416af2cba4cba57ae9f02de226a06b020d98e1aab18", size = 4459835 }, - { url = "https://files.pythonhosted.org/packages/aa/b2/703569fc881f3284487e68cda7b42179978480da3c438042a6bbbb4a671c/hf_xet-1.5.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:4b35549ce62601b84da4ff9b24d970032ace3d4430f52d91bcbb26c901d6c690", size = 4672414 }, - { url = "https://files.pythonhosted.org/packages/af/37/1b6def445c567286b50aa3b33828158e135b1be44938dde59f11382a500c/hf_xet-1.5.0-cp37-abi3-win_amd64.whl", hash = "sha256:2806c7c17b4d23f8d88f7c4814f838c3b6150773fe339c20af23e1cfaf2797e4", size = 3977238 }, - { url = "https://files.pythonhosted.org/packages/62/94/3b66b148778ee100dcfd69c2ca22b57b41b44d3063ceec934f209e9184ce/hf_xet-1.5.0-cp37-abi3-win_arm64.whl", hash = "sha256:b6c9df403040248c76d808d3e047d64db2d923bae593eb244c41e425cf6cd7be", size = 3806916 }, -] - -[[package]] -name = "htmldate" -version = "1.9.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "charset-normalizer" }, - { name = "dateparser" }, - { name = "lxml" }, - { name = "python-dateutil" }, - { name = "urllib3" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/9d/10/ead9dabc999f353c3aa5d0dc0835b1e355215a5ecb489a7f4ef2ddad5e33/htmldate-1.9.4.tar.gz", hash = "sha256:1129063e02dd0354b74264de71e950c0c3fcee191178321418ccad2074cc8ed0", size = 44690 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a1/bd/adfcdaaad5805c0c5156aeefd64c1e868c05e9c1cd6fd21751f168cd88c7/htmldate-1.9.4-py3-none-any.whl", hash = "sha256:1b94bcc4e08232a5b692159903acf95548b6a7492dddca5bb123d89d6325921c", size = 31558 }, -] - -[[package]] -name = "httpcore" -version = "1.0.9" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "certifi" }, - { name = "h11" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784 }, -] - -[[package]] -name = "httpx" -version = "0.28.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "anyio" }, - { name = "certifi" }, - { name = "httpcore" }, - { name = "idna" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 }, -] - -[[package]] -name = "httpx-sse" -version = "0.4.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0f/4c/751061ffa58615a32c31b2d82e8482be8dd4a89154f003147acee90f2be9/httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d", size = 15943 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/fd/6668e5aec43ab844de6fc74927e155a3b37bf40d7c3790e49fc0406b6578/httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc", size = 8960 }, -] - -[[package]] -name = "huggingface-hub" -version = "1.14.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "filelock" }, - { name = "fsspec" }, - { name = "hf-xet", marker = "platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" }, - { name = "httpx" }, - { name = "packaging" }, - { name = "pyyaml" }, - { name = "tqdm" }, - { name = "typer" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/39/40/43109e943fd718b0ccd0cd61eb4f1c347df22bf81f5874c6f22adf44bcff/huggingface_hub-1.14.0.tar.gz", hash = "sha256:d6d2c9cd6be1d02ae9ec6672d5587d10a427f377db688e82528f426a041622c2", size = 782365 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/89/a5/33b49ba7bea7c41bb37f74ec0f8beea0831e052330196633fe2c77516ea6/huggingface_hub-1.14.0-py3-none-any.whl", hash = "sha256:efe075535c62e130b30e836b138e13785f6f043d1f0539e0a39aa411a99e90b8", size = 661479 }, -] - -[[package]] -name = "idna" -version = "3.15" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/82/77/7b3966d0b9d1d31a36ddf1746926a11dface89a83409bf1483f0237aa758/idna-3.15.tar.gz", hash = "sha256:ca962446ea538f7092a95e057da437618e886f4d349216d2b1e294abfdb65fdc", size = 199245 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/23/408243171aa9aaba178d3e2559159c24c1171a641aa83b67bdd3394ead8e/idna-3.15-py3-none-any.whl", hash = "sha256:048adeaf8c2d788c40fee287673ccaa74c24ffd8dcf09ffa555a2fbb59f10ac8", size = 72340 }, -] - -[[package]] -name = "iniconfig" -version = "2.3.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 }, -] - -[[package]] -name = "isodate" -version = "0.7.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/54/4d/e940025e2ce31a8ce1202635910747e5a87cc3a6a6bb2d00973375014749/isodate-0.7.2.tar.gz", hash = "sha256:4cd1aa0f43ca76f4a6c6c0292a85f40b35ec2e43e315b59f06e6d32171a953e6", size = 29705 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/15/aa/0aca39a37d3c7eb941ba736ede56d689e7be91cab5d9ca846bde3999eba6/isodate-0.7.2-py3-none-any.whl", hash = "sha256:28009937d8031054830160fce6d409ed342816b543597cece116d966c6d99e15", size = 22320 }, -] - -[[package]] -name = "jinja2" -version = "3.1.6" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "markupsafe" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899 }, -] - -[[package]] -name = "joblib" -version = "1.5.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071 }, -] - -[[package]] -name = "justext" -version = "3.0.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "lxml", extra = ["html-clean"] }, -] -sdist = { url = "https://files.pythonhosted.org/packages/49/f3/45890c1b314f0d04e19c1c83d534e611513150939a7cf039664d9ab1e649/justext-3.0.2.tar.gz", hash = "sha256:13496a450c44c4cd5b5a75a5efcd9996066d2a189794ea99a49949685a0beb05", size = 828521 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f2/ac/52f4e86d1924a7fc05af3aeb34488570eccc39b4af90530dd6acecdf16b5/justext-3.0.2-py2.py3-none-any.whl", hash = "sha256:62b1c562b15c3c6265e121cc070874243a443bfd53060e869393f09d6b6cc9a7", size = 837940 }, -] - -[[package]] -name = "llama-cloud" -version = "0.1.46" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "certifi" }, - { name = "httpx" }, - { name = "pydantic" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/40/f3/f4d6520f8d546e6c5a02f6ebeed5c09774a074b8d2c24ad559ace97a56a6/llama_cloud-0.1.46.tar.gz", hash = "sha256:e86f8791c053590d70cc59e0fc13ce72f9b681a8e658bc61df86d0285288d8ee", size = 127752 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c4/3a/6caaea28c8c804add33c91d356ed7d5a5412d6c9598e1450af95a15e0bcd/llama_cloud-0.1.46-py3-none-any.whl", hash = "sha256:6c6546c09c04a038c86d84d42f00eae8fd3bff49991ad3aab844bd866ecdf352", size = 361989 }, -] - -[[package]] -name = "llama-cloud-services" -version = "0.6.94" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "click" }, - { name = "llama-cloud" }, - { name = "llama-index-core" }, - { name = "packaging" }, - { name = "platformdirs" }, - { name = "pydantic" }, - { name = "python-dotenv" }, - { name = "tenacity" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/d3/91/c3c94a58c44d0a12e0df2d5038b188fc283877f56cf2f6c41c60f43258e6/llama_cloud_services-0.6.94.tar.gz", hash = "sha256:127b8440d3d3a964d0c4b3f5fe7fcac3ead482f7645971cc8ae30768dcf63306", size = 64114 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/14/ab/876486e4f1c137cfeca8f876abd18eeec35a66a0fd8adb15afba7b28aa8c/llama_cloud_services-0.6.94-py3-none-any.whl", hash = "sha256:ac89785f3689d71298511f751bcf4ca16952a616bd75ff06e0ff164f04b0775b", size = 77098 }, -] - -[[package]] -name = "llama-index-core" -version = "0.14.21" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "aiohttp" }, - { name = "aiosqlite" }, - { name = "banks" }, - { name = "dataclasses-json" }, - { name = "deprecated" }, - { name = "dirtyjson" }, - { name = "filetype" }, - { name = "fsspec" }, - { name = "httpx" }, - { name = "llama-index-workflows" }, - { name = "nest-asyncio" }, - { name = "networkx" }, - { name = "nltk" }, - { name = "numpy" }, - { name = "pillow" }, - { name = "platformdirs" }, - { name = "pydantic" }, - { name = "pyyaml" }, - { name = "requests" }, - { name = "setuptools" }, - { name = "sqlalchemy", extra = ["asyncio"] }, - { name = "tenacity" }, - { name = "tiktoken" }, - { name = "tinytag" }, - { name = "tqdm" }, - { name = "typing-extensions" }, - { name = "typing-inspect" }, - { name = "wrapt" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/7c/43/d6d2a368865e68c25d3400c017fb772daab71427f08c4e36c591f729dbc3/llama_index_core-0.14.21.tar.gz", hash = "sha256:29706defbe2f429d28330a4eea010f9d92d42db92539382f8c800e19590cae45", size = 11581087 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/88/23/55ec5f35a5c7f35b60d3928bcd2e867076440036a280cf4d07481719c249/llama_index_core-0.14.21-py3-none-any.whl", hash = "sha256:4a807d31e54d066068e076eb4d066efbf95e2d2a00dcbe0eba3d9340a04cad42", size = 11916624 }, -] - -[[package]] -name = "llama-index-instrumentation" -version = "0.5.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "deprecated" }, - { name = "pydantic" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/4e/d0/671b23ccff255c9bce132a84ffd5a6f4541ceefdeab9c1786b08c9722f2e/llama_index_instrumentation-0.5.0.tar.gz", hash = "sha256:eeb724648b25d149de882a5ac9e21c5acb1ce780da214bda2b075341af29ad8e", size = 43831 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c3/45/6dcaccef44e541ffa138e4b45e33e0d40ab2a7d845338483954fcf77bc75/llama_index_instrumentation-0.5.0-py3-none-any.whl", hash = "sha256:aaab83cddd9dd434278891012d8995f47a3bc7ed1736a371db90965348c56a21", size = 16444 }, -] - -[[package]] -name = "llama-index-workflows" -version = "2.20.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "llama-index-instrumentation" }, - { name = "pydantic" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/c4/ec/05f3db99a2e6e252e3939e7751cad2fb1322dc6d32f4cf5c795cf7ddcad3/llama_index_workflows-2.20.0.tar.gz", hash = "sha256:df2760fea9e100c97a4e919d255461e344413acac4382d17d8217337806e4772", size = 97410 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/71/5f/385231406d777cb4b608fd8ebe3577dbd90962770717181e6b91b44fb1b8/llama_index_workflows-2.20.0-py3-none-any.whl", hash = "sha256:36f6b6ace77f837d9907078aea7e830251afe96a58daecff5ed090c88c55095d", size = 121238 }, -] - -[[package]] -name = "lxml" -version = "6.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/28/30/9abc9e34c657c33834eaf6cd02124c61bdf5944d802aa48e69be8da3585d/lxml-6.1.0.tar.gz", hash = "sha256:bfd57d8008c4965709a919c3e9a98f76c2c7cb319086b3d26858250620023b13", size = 4197006 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/d4/9326838b59dc36dfae42eec9656b97520f9997eee1de47b8316aaeed169c/lxml-6.1.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d2f17a16cd8751e8eb233a7e41aecdf8e511712e00088bf9be455f604cd0d28d", size = 8570663 }, - { url = "https://files.pythonhosted.org/packages/d8/a4/053745ce1f8303ccbb788b86c0db3a91b973675cefc42566a188637b7c40/lxml-6.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f0cea5b1d3e6e77d71bd2b9972eb2446221a69dc52bb0b9c3c6f6e5700592d93", size = 4624024 }, - { url = "https://files.pythonhosted.org/packages/90/97/a517944b20f8fd0932ad2109482bee4e29fe721416387a363306667941f6/lxml-6.1.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc46da94826188ed45cb53bd8e3fc076ae22675aea2087843d4735627f867c6d", size = 4930895 }, - { url = "https://files.pythonhosted.org/packages/94/7c/e08a970727d556caa040a44773c7b7e3ad0f0d73dedc863543e9a8b931f2/lxml-6.1.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9147d8e386ec3b82c3b15d88927f734f565b0aaadef7def562b853adca45784a", size = 5093820 }, - { url = "https://files.pythonhosted.org/packages/88/ee/2a5c2aa2c32016a226ca25d3e1056a8102ea6e1fe308bf50213586635400/lxml-6.1.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5715e0e28736a070f3f34a7ccc09e2fdcba0e3060abbcf61a1a5718ff6d6b105", size = 5005790 }, - { url = "https://files.pythonhosted.org/packages/e3/38/a0db9be8f38ad6043ab9429487c128dd1d30f07956ef43040402f8da49e8/lxml-6.1.0-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4937460dc5df0cdd2f06a86c285c28afda06aefa3af949f9477d3e8df430c485", size = 5630827 }, - { url = "https://files.pythonhosted.org/packages/31/ba/3c13d3fc24b7cacf675f808a3a1baabf43a30d0cd24c98f94548e9aa58eb/lxml-6.1.0-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bc783ee3147e60a25aa0445ea82b3e8aabb83b240f2b95d32cb75587ff781814", size = 5240445 }, - { url = "https://files.pythonhosted.org/packages/55/ba/eeef4ccba09b2212fe239f46c1692a98db1878e0872ae320756488878a94/lxml-6.1.0-cp312-cp312-manylinux_2_28_i686.whl", hash = "sha256:40d9189f80075f2e1f88db21ef815a2b17b28adf8e50aaf5c789bfe737027f32", size = 5350121 }, - { url = "https://files.pythonhosted.org/packages/7e/01/1da87c7b587c38d0cbe77a01aae3b9c1c49ed47d76918ef3db8fc151b1ca/lxml-6.1.0-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:05b9b8787e35bec69e68daf4952b2e6dfcfb0db7ecf1a06f8cdfbbac4eb71aad", size = 4694949 }, - { url = "https://files.pythonhosted.org/packages/a1/88/7db0fe66d5aaf128443ee1623dec3db1576f3e4c17751ec0ef5866468590/lxml-6.1.0-cp312-cp312-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0f0f08beb0182e3e9a86fae124b3c47a7b41b7b69b225e1377db983802404e54", size = 5243901 }, - { url = "https://files.pythonhosted.org/packages/00/a8/1346726af7d1f6fca1f11223ba34001462b0a3660416986d37641708d57c/lxml-6.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:73becf6d8c81d4c76b1014dbd3584cb26d904492dcf73ca85dc8bff08dcd6d2d", size = 5048054 }, - { url = "https://files.pythonhosted.org/packages/2e/b7/85057012f035d1a0c87e02f8c723ca3c3e6e0728bcf4cb62080b21b1c1e3/lxml-6.1.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:1ae225f66e5938f4fa29d37e009a3bb3b13032ac57eb4eb42afa44f6e4054e69", size = 4777324 }, - { url = "https://files.pythonhosted.org/packages/75/6c/ad2f94a91073ef570f33718040e8e160d5fb93331cf1ab3ca1323f939e2d/lxml-6.1.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:690022c7fae793b0489aa68a658822cea83e0d5933781811cabbf5ea3bcfe73d", size = 5645702 }, - { url = "https://files.pythonhosted.org/packages/3b/89/0bb6c0bd549c19004c60eea9dc554dd78fd647b72314ef25d460e0d208c6/lxml-6.1.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:63aeafc26aac0be8aff14af7871249e87ea1319be92090bfd632ec68e03b16a5", size = 5232901 }, - { url = "https://files.pythonhosted.org/packages/a1/d9/d609a11fb567da9399f525193e2b49847b5a409cdebe737f06a8b7126bdc/lxml-6.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:264c605ab9c0e4aa1a679636f4582c4d3313700009fac3ec9c3412ed0d8f3e1d", size = 5261333 }, - { url = "https://files.pythonhosted.org/packages/a6/3a/ac3f99ec8ac93089e7dd556f279e0d14c24de0a74a507e143a2e4b496e7c/lxml-6.1.0-cp312-cp312-win32.whl", hash = "sha256:56971379bc5ee8037c5a0f09fa88f66cdb7d37c3e38af3e45cf539f41131ac1f", size = 3596289 }, - { url = "https://files.pythonhosted.org/packages/f2/a7/0a915557538593cb1bbeedcd40e13c7a261822c26fecbbdb71dad0c2f540/lxml-6.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:bba078de0031c219e5dd06cf3e6bf8fb8e6e64a77819b358f53bb132e3e03366", size = 3997059 }, - { url = "https://files.pythonhosted.org/packages/92/96/a5dc078cf0126fbfbc35611d77ecd5da80054b5893e28fb213a5613b9e1d/lxml-6.1.0-cp312-cp312-win_arm64.whl", hash = "sha256:c3592631e652afa34999a088f98ba7dfc7d6aff0d535c410bea77a71743f3819", size = 3659552 }, - { url = "https://files.pythonhosted.org/packages/08/03/69347590f1cf4a6d5a4944bb6099e6d37f334784f16062234e1f892fdb1d/lxml-6.1.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a0092f2b107b69601adf562a57c956fbb596e05e3e6651cabd3054113b007e45", size = 8559689 }, - { url = "https://files.pythonhosted.org/packages/3f/58/25e00bb40b185c974cfe156c110474d9a8a8390d5f7c92a4e328189bb60e/lxml-6.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:fc7140d7a7386e6b545d41b7358f4d02b656d4053f5fa6859f92f4b9c2572c4d", size = 4617892 }, - { url = "https://files.pythonhosted.org/packages/f5/54/92ad98a94ac318dc4f97aaac22ff8d1b94212b2ae8af5b6e9b354bf825f7/lxml-6.1.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:419c58fc92cc3a2c3fa5f78c63dbf5da70c1fa9c1b25f25727ecee89a96c7de2", size = 4923489 }, - { url = "https://files.pythonhosted.org/packages/15/3b/a20aecfab42bdf4f9b390590d345857ad3ffd7c51988d1c89c53a0c73faf/lxml-6.1.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:37fabd1452852636cf38ecdcc9dd5ca4bba7a35d6c53fa09725deeb894a87491", size = 5082162 }, - { url = "https://files.pythonhosted.org/packages/45/26/2cdb3d281ac1bd175603e290cbe4bad6eff127c0f8de90bafd6f8548f0fd/lxml-6.1.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a2853c8b2170cc6cd54a6b4d50d2c1a8a7aeca201f23804b4898525c7a152cfc", size = 4993247 }, - { url = "https://files.pythonhosted.org/packages/f6/05/d735aef963740022a08185c84821f689fc903acb3d50326e6b1e9886cc22/lxml-6.1.0-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8e369cbd690e788c8d15e56222d91a09c6a417f49cbc543040cba0fe2e25a79e", size = 5613042 }, - { url = "https://files.pythonhosted.org/packages/ee/b8/ead7c10efff731738c72e59ed6eb5791854879fbed7ae98781a12006263a/lxml-6.1.0-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e69aa6805905807186eb00e66c6d97a935c928275182eb02ee40ba00da9623b2", size = 5228304 }, - { url = "https://files.pythonhosted.org/packages/6b/10/e9842d2ec322ea65f0a7270aa0315a53abed06058b88ef1b027f620e7a5f/lxml-6.1.0-cp313-cp313-manylinux_2_28_i686.whl", hash = "sha256:4bd1bdb8a9e0e2dd229de19b5f8aebac80e916921b4b2c6ef8a52bc131d0c1f9", size = 5341578 }, - { url = "https://files.pythonhosted.org/packages/89/54/40d9403d7c2775fa7301d3ddd3464689bfe9ba71acc17dfff777071b4fdc/lxml-6.1.0-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:cbd7b79cdcb4986ad78a2662625882747f09db5e4cd7b2ae178a88c9c51b3dfe", size = 4700209 }, - { url = "https://files.pythonhosted.org/packages/85/b2/bbdcc2cf45dfc7dfffef4fd97e5c47b15919b6a365247d95d6f684ef5e82/lxml-6.1.0-cp313-cp313-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:43e4d297f11080ec9d64a4b1ad7ac02b4484c9f0e2179d9c4ef78e886e747b88", size = 5232365 }, - { url = "https://files.pythonhosted.org/packages/48/5a/b06875665e53aaba7127611a7bed3b7b9658e20b22bc2dd217a0b7ab0091/lxml-6.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cc16682cc987a3da00aa56a3aa3075b08edb10d9b1e476938cfdbee8f3b67181", size = 5043654 }, - { url = "https://files.pythonhosted.org/packages/e9/9c/e71a069d09641c1a7abeb30e693f828c7c90a41cbe3d650b2d734d876f85/lxml-6.1.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:d6d8efe71429635f0559579092bb5e60560d7b9115ee38c4adbea35632e7fa24", size = 4769326 }, - { url = "https://files.pythonhosted.org/packages/cc/06/7a9cd84b3d4ed79adf35f874750abb697dec0b4a81a836037b36e47c091a/lxml-6.1.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e39ab3a28af7784e206d8606ec0e4bcad0190f63a492bca95e94e5a4aef7f6e", size = 5635879 }, - { url = "https://files.pythonhosted.org/packages/cc/f0/9d57916befc1e54c451712c7ee48e9e74e80ae4d03bdce49914e0aee42cd/lxml-6.1.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:9eb667bf50856c4a58145f8ca2d5e5be160191e79eb9e30855a476191b3c3495", size = 5224048 }, - { url = "https://files.pythonhosted.org/packages/99/75/90c4eefda0c08c92221fe0753db2d6699a4c628f76ff4465ec20dea84cc1/lxml-6.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7f4a77d6f7edf9230cee3e1f7f6764722a41604ee5681844f18db9a81ea0ec33", size = 5250241 }, - { url = "https://files.pythonhosted.org/packages/5e/73/16596f7e4e38fa33084b9ccbccc22a15f82a290a055126f2c1541236d2ff/lxml-6.1.0-cp313-cp313-win32.whl", hash = "sha256:28902146ffbe5222df411c5d19e5352490122e14447e98cd118907ee3fd6ee62", size = 3596938 }, - { url = "https://files.pythonhosted.org/packages/8e/63/981401c5680c1eb30893f00a19641ac80db5d1e7086c62cb4b13ed813038/lxml-6.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:4a1503c56e4e2b38dc76f2f2da7bae69670c0f1933e27cfa34b2fa5876410b16", size = 3995728 }, - { url = "https://files.pythonhosted.org/packages/e7/e8/c358a38ac3e541d16a1b527e4e9cb78c0419b0506a070ace11777e5e8404/lxml-6.1.0-cp313-cp313-win_arm64.whl", hash = "sha256:e0af85773850417d994d019741239b901b22c6680206f46a34766926e466141d", size = 3658372 }, - { url = "https://files.pythonhosted.org/packages/eb/45/cee4cf203ef0bab5c52afc118da61d6b460c928f2893d40023cfa27e0b80/lxml-6.1.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:ab863fd37458fed6456525f297d21239d987800c46e67da5ef04fc6b3dd93ac8", size = 8576713 }, - { url = "https://files.pythonhosted.org/packages/8a/a7/eda05babeb7e046839204eaf254cd4d7c9130ce2bbf0d9e90ea41af5654d/lxml-6.1.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:6fd8b1df8254ff4fd93fd31da1fc15770bde23ac045be9bb1f87425702f61cc9", size = 4623874 }, - { url = "https://files.pythonhosted.org/packages/e7/e9/db5846de9b436b91890a62f29d80cd849ea17948a49bf532d5278ee69a9e/lxml-6.1.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:47024feaae386a92a146af0d2aeed65229bf6fff738e6a11dda6b0015fb8fd03", size = 4949535 }, - { url = "https://files.pythonhosted.org/packages/5a/ba/0d3593373dcae1d68f40dc3c41a5a92f2544e68115eb2f62319a4c2a6500/lxml-6.1.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3f00972f84450204cd5d93a5395965e348956aaceaadec693a22ec743f8ae3eb", size = 5086881 }, - { url = "https://files.pythonhosted.org/packages/43/76/759a7484539ad1af0d125a9afe9c3fb5f82a8779fd1f5f56319d9e4ea2fd/lxml-6.1.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97faa0860e13b05b15a51fb4986421ef7a30f0b3334061c416e0981e9450ca4c", size = 5031305 }, - { url = "https://files.pythonhosted.org/packages/dc/b9/c1f0daf981a11e47636126901fd4ab82429e18c57aeb0fc3ad2940b42d8b/lxml-6.1.0-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:972a6451204798675407beaad97b868d0c733d9a74dafefc63120b81b8c2de28", size = 5647522 }, - { url = "https://files.pythonhosted.org/packages/31/e6/1f533dcd205275363d9ba3511bcec52fa2df86abf8abe6a5f2c599f0dc31/lxml-6.1.0-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fe022f20bc4569ec66b63b3fb275a3d628d9d32da6326b2982584104db6d3086", size = 5239310 }, - { url = "https://files.pythonhosted.org/packages/c3/8c/4175fb709c78a6e315ed814ed33be3defd8b8721067e70419a6cf6f971da/lxml-6.1.0-cp314-cp314-manylinux_2_28_i686.whl", hash = "sha256:75c4c7c619a744f972f4451bf5adf6d0fb00992a1ffc9fd78e13b0bc817cc99f", size = 5350799 }, - { url = "https://files.pythonhosted.org/packages/fd/77/6ffdebc5994975f0dde4acb59761902bd9d9bb84422b9a0bd239a7da9ca8/lxml-6.1.0-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:3648f20d25102a22b6061c688beb3a805099ea4beb0a01ce62975d926944d292", size = 4697693 }, - { url = "https://files.pythonhosted.org/packages/f8/f1/565f36bd5c73294602d48e04d23f81ff4c8736be6ba5e1d1ec670ac9be80/lxml-6.1.0-cp314-cp314-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:77b9f99b17cbf14026d1e618035077060fc7195dd940d025149f3e2e830fbfcb", size = 5250708 }, - { url = "https://files.pythonhosted.org/packages/5a/11/a68ab9dd18c5c499404deb4005f4bc4e0e88e5b72cd755ad96efec81d18d/lxml-6.1.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:32662519149fd7a9db354175aa5e417d83485a8039b8aaa62f873ceee7ea4cad", size = 5084737 }, - { url = "https://files.pythonhosted.org/packages/ab/78/e8f41e2c74f4af564e6a0348aea69fb6daaefa64bc071ef469823d22cc18/lxml-6.1.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:73d658216fc173cf2c939e90e07b941c5e12736b0bf6a99e7af95459cfe8eabb", size = 4737817 }, - { url = "https://files.pythonhosted.org/packages/06/2d/aa4e117aa2ce2f3b35d9ff246be74a2f8e853baba5d2a92c64744474603a/lxml-6.1.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ac4db068889f8772a4a698c5980ec302771bb545e10c4b095d4c8be26749616f", size = 5670753 }, - { url = "https://files.pythonhosted.org/packages/08/f5/dd745d50c0409031dbfcc4881740542a01e54d6f0110bd420fa7782110b8/lxml-6.1.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:45e9dfbd1b661eb64ba0d4dbe762bd210c42d86dd1e5bd2bdf89d634231beb43", size = 5238071 }, - { url = "https://files.pythonhosted.org/packages/3e/74/ad424f36d0340a904665867dab310a3f1f4c96ff4039698de83b77f44c1f/lxml-6.1.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:89e8d73d09ac696a5ba42ec69787913d53284f12092f651506779314f10ba585", size = 5264319 }, - { url = "https://files.pythonhosted.org/packages/53/36/a15d8b3514ec889bfd6aa3609107fcb6c9189f8dc347f1c0b81eded8d87c/lxml-6.1.0-cp314-cp314-win32.whl", hash = "sha256:ebe33f4ec1b2de38ceb225a1749a2965855bffeef435ba93cd2d5d540783bf2f", size = 3657139 }, - { url = "https://files.pythonhosted.org/packages/1a/a4/263ebb0710851a3c6c937180a9a86df1206fdfe53cc43005aa2237fd7736/lxml-6.1.0-cp314-cp314-win_amd64.whl", hash = "sha256:398443df51c538bd578529aa7e5f7afc6c292644174b47961f3bf87fe5741120", size = 4064195 }, - { url = "https://files.pythonhosted.org/packages/80/68/2000f29d323b6c286de077ad20b429fc52272e44eae6d295467043e56012/lxml-6.1.0-cp314-cp314-win_arm64.whl", hash = "sha256:8c8984e1d8c4b3949e419158fda14d921ff703a9ed8a47236c6eb7a2b6cb4946", size = 3741870 }, - { url = "https://files.pythonhosted.org/packages/30/e9/21383c7c8d43799f0da90224c0d7c921870d476ec9b3e01e1b2c0b8237c5/lxml-6.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:1081dd10bc6fa437db2500e13993abf7cc30716d0a2f40e65abb935f02ec559c", size = 8827548 }, - { url = "https://files.pythonhosted.org/packages/a5/01/c6bc11cd587030dd4f719f65c5657960649fe3e19196c844c75bf32cd0d6/lxml-6.1.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:dabecc48db5f42ba348d1f5d5afdc54c6c4cc758e676926c7cd327045749517d", size = 4735866 }, - { url = "https://files.pythonhosted.org/packages/f3/01/757132fff5f4acf25463b5298f1a46099f3a94480b806547b29ce5e385de/lxml-6.1.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e3dd5fe19c9e0ac818a9c7f132a5e43c1339ec1cbbfecb1a938bd3a47875b7c9", size = 4969476 }, - { url = "https://files.pythonhosted.org/packages/fd/fb/1bc8b9d27ed64be7c8903db6c89e74dc8c2cd9ec630a7462e4654316dc5b/lxml-6.1.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9e7b0a4ca6dcc007a4cef00a761bba2dea959de4bd2df98f926b33c92ca5dfb9", size = 5103719 }, - { url = "https://files.pythonhosted.org/packages/d5/e7/5bf82fa28133536a54601aae633b14988e89ed61d4c1eb6b899b023233aa/lxml-6.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d27bbe326c6b539c64b42638b18bc6003a8d88f76213a97ac9ed4f885efeab7", size = 5027890 }, - { url = "https://files.pythonhosted.org/packages/2d/20/e048db5d4b4ea0366648aa595f26bb764b2670903fc585b87436d0a5032c/lxml-6.1.0-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4e425db0c5445ef0ad56b0eec54f89b88b2d884656e536a90b2f52aecb4ca86", size = 5596008 }, - { url = "https://files.pythonhosted.org/packages/9a/c2/d10807bc8da4824b39e5bd01b5d05c077b6fd01bd91584167edf6b269d22/lxml-6.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4b89b098105b8599dc57adac95d1813409ac476d3c948a498775d3d0c6124bfb", size = 5224451 }, - { url = "https://files.pythonhosted.org/packages/3c/15/2ebea45bea427e7f0057e9ce7b2d62c5aba20c6b001cca89ed0aadb3ad41/lxml-6.1.0-cp314-cp314t-manylinux_2_28_i686.whl", hash = "sha256:c4a699432846df86cc3de502ee85f445ebad748a1c6021d445f3e514d2cd4b1c", size = 5312135 }, - { url = "https://files.pythonhosted.org/packages/31/e2/87eeae151b0be2a308d49a7ec444ff3eb192b14251e62addb29d0bf3778f/lxml-6.1.0-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:30e7b2ed63b6c8e97cca8af048589a788ab5c9c905f36d9cf1c2bb549f450d2f", size = 4639126 }, - { url = "https://files.pythonhosted.org/packages/a3/51/8a3f6a20902ad604dd746ec7b4000311b240d389dac5e9d95adefd349e0c/lxml-6.1.0-cp314-cp314t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:022981127642fe19866d2907d76241bb07ed21749601f727d5d5dd1ce5d1b773", size = 5232579 }, - { url = "https://files.pythonhosted.org/packages/6d/d2/650d619bdbe048d2c3f2c31edb00e35670a5e2d65b4fe3b61bce37b19121/lxml-6.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:23cad0cc86046d4222f7f418910e46b89971c5a45d3c8abfad0f64b7b05e4a9b", size = 5084206 }, - { url = "https://files.pythonhosted.org/packages/dd/8a/672ca1a3cbeabd1f511ca275a916c0514b747f4b85bdaae103b8fa92f307/lxml-6.1.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:21c3302068f50d1e8728c67c87ba92aa87043abee517aa2576cca1855326b405", size = 4758906 }, - { url = "https://files.pythonhosted.org/packages/be/f1/ef4b691da85c916cb2feb1eec7414f678162798ac85e042fa164419ac05c/lxml-6.1.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:be10838781cb3be19251e276910cd508fe127e27c3242e50521521a0f3781690", size = 5620553 }, - { url = "https://files.pythonhosted.org/packages/59/17/94e81def74107809755ac2782fdad4404420f1c92ca83433d117a6d5acf0/lxml-6.1.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:2173a7bffe97667bbf0767f8a99e587740a8c56fdf3befac4b09cb29a80276fd", size = 5229458 }, - { url = "https://files.pythonhosted.org/packages/21/55/c4be91b0f830a871fc1b0d730943d56013b683d4671d5198260e2eae722b/lxml-6.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c6854e9cf99c84beb004eecd7d3a3868ef1109bf2b1df92d7bc11e96a36c2180", size = 5247861 }, - { url = "https://files.pythonhosted.org/packages/c2/ca/77123e4d77df3cb1e968ade7b1f808f5d3a5c1c96b18a33895397de292c1/lxml-6.1.0-cp314-cp314t-win32.whl", hash = "sha256:00750d63ef0031a05331b9223463b1c7c02b9004cef2346a5b2877f0f9494dd2", size = 3897377 }, - { url = "https://files.pythonhosted.org/packages/64/ce/3554833989d074267c063209bae8b09815e5656456a2d332b947806b05ff/lxml-6.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:80410c3a7e3c617af04de17caa9f9f20adaa817093293d69eae7d7d0522836f5", size = 4392701 }, - { url = "https://files.pythonhosted.org/packages/2b/a0/9b916c68c0e57752c07f8f64b30138d9d4059dbeb27b90274dedbea128ff/lxml-6.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:26dd9f57ee3bd41e7d35b4c98a2ffd89ed11591649f421f0ec19f67d50ec67ac", size = 3817120 }, -] - -[package.optional-dependencies] -html-clean = [ - { name = "lxml-html-clean" }, -] - -[[package]] -name = "lxml-html-clean" -version = "0.4.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "lxml" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/9a/a4/5c62acfacd69ff4f5db395100f5cfb9b54e7ac8c69a235e4e939fd13f021/lxml_html_clean-0.4.4.tar.gz", hash = "sha256:58f39a9d632711202ed1d6d0b9b47a904e306c85de5761543b90e3e3f736acfb", size = 23899 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d9/76/7ffc1d3005cf7749123bc47cb3ea343cd97b0ac2211bab40f57283577d0e/lxml_html_clean-0.4.4-py3-none-any.whl", hash = "sha256:ce2ef506614ecb85ee1c5fe0a2aa45b06a19514ec7949e9c8f34f06925cfabcb", size = 14565 }, -] - -[[package]] -name = "markdown-it-py" -version = "4.2.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "mdurl" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/06/ff/7841249c247aa650a76b9ee4bbaeae59370dc8bfd2f6c01f3630c35eb134/markdown_it_py-4.2.0.tar.gz", hash = "sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49", size = 82454 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b3/81/4da04ced5a082363ecfa159c010d200ecbd959ae410c10c0264a38cac0f5/markdown_it_py-4.2.0-py3-none-any.whl", hash = "sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a", size = 91687 }, -] - -[[package]] -name = "markupsafe" -version = "3.0.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615 }, - { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020 }, - { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332 }, - { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947 }, - { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962 }, - { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760 }, - { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529 }, - { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015 }, - { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540 }, - { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105 }, - { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906 }, - { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622 }, - { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029 }, - { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374 }, - { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980 }, - { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990 }, - { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784 }, - { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588 }, - { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041 }, - { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543 }, - { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113 }, - { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911 }, - { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658 }, - { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066 }, - { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639 }, - { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569 }, - { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284 }, - { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801 }, - { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769 }, - { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642 }, - { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612 }, - { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200 }, - { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973 }, - { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619 }, - { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029 }, - { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408 }, - { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005 }, - { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048 }, - { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821 }, - { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606 }, - { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043 }, - { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747 }, - { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341 }, - { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073 }, - { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661 }, - { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069 }, - { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670 }, - { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598 }, - { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261 }, - { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835 }, - { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733 }, - { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672 }, - { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819 }, - { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426 }, - { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146 }, -] - -[[package]] -name = "marshmallow" -version = "3.26.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "packaging" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/55/79/de6c16cc902f4fc372236926b0ce2ab7845268dcc30fb2fbb7f71b418631/marshmallow-3.26.2.tar.gz", hash = "sha256:bbe2adb5a03e6e3571b573f42527c6fe926e17467833660bebd11593ab8dfd57", size = 222095 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/be/2f/5108cb3ee4ba6501748c4908b908e55f42a5b66245b4cfe0c99326e1ef6e/marshmallow-3.26.2-py3-none-any.whl", hash = "sha256:013fa8a3c4c276c24d26d84ce934dc964e2aa794345a0f8c7e5a7191482c8a73", size = 50964 }, -] - -[[package]] -name = "mdurl" -version = "0.1.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979 }, -] - -[[package]] -name = "multidict" -version = "6.7.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/1a/c2/c2d94cbe6ac1753f3fc980da97b3d930efe1da3af3c9f5125354436c073d/multidict-6.7.1.tar.gz", hash = "sha256:ec6652a1bee61c53a3e5776b6049172c53b6aaba34f18c9ad04f82712bac623d", size = 102010 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8d/9c/f20e0e2cf80e4b2e4b1c365bf5fe104ee633c751a724246262db8f1a0b13/multidict-6.7.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:a90f75c956e32891a4eda3639ce6dd86e87105271f43d43442a3aedf3cddf172", size = 76893 }, - { url = "https://files.pythonhosted.org/packages/fe/cf/18ef143a81610136d3da8193da9d80bfe1cb548a1e2d1c775f26b23d024a/multidict-6.7.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:3fccb473e87eaa1382689053e4a4618e7ba7b9b9b8d6adf2027ee474597128cd", size = 45456 }, - { url = "https://files.pythonhosted.org/packages/a9/65/1caac9d4cd32e8433908683446eebc953e82d22b03d10d41a5f0fefe991b/multidict-6.7.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b0fa96985700739c4c7853a43c0b3e169360d6855780021bfc6d0f1ce7c123e7", size = 43872 }, - { url = "https://files.pythonhosted.org/packages/cf/3b/d6bd75dc4f3ff7c73766e04e705b00ed6dbbaccf670d9e05a12b006f5a21/multidict-6.7.1-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:cb2a55f408c3043e42b40cc8eecd575afa27b7e0b956dfb190de0f8499a57a53", size = 251018 }, - { url = "https://files.pythonhosted.org/packages/fd/80/c959c5933adedb9ac15152e4067c702a808ea183a8b64cf8f31af8ad3155/multidict-6.7.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eb0ce7b2a32d09892b3dd6cc44877a0d02a33241fafca5f25c8b6b62374f8b75", size = 258883 }, - { url = "https://files.pythonhosted.org/packages/86/85/7ed40adafea3d4f1c8b916e3b5cc3a8e07dfcdcb9cd72800f4ed3ca1b387/multidict-6.7.1-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c3a32d23520ee37bf327d1e1a656fec76a2edd5c038bf43eddfa0572ec49c60b", size = 242413 }, - { url = "https://files.pythonhosted.org/packages/d2/57/b8565ff533e48595503c785f8361ff9a4fde4d67de25c207cd0ba3befd03/multidict-6.7.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9c90fed18bffc0189ba814749fdcc102b536e83a9f738a9003e569acd540a733", size = 268404 }, - { url = "https://files.pythonhosted.org/packages/e0/50/9810c5c29350f7258180dfdcb2e52783a0632862eb334c4896ac717cebcb/multidict-6.7.1-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:da62917e6076f512daccfbbde27f46fed1c98fee202f0559adec8ee0de67f71a", size = 269456 }, - { url = "https://files.pythonhosted.org/packages/f3/8d/5e5be3ced1d12966fefb5c4ea3b2a5b480afcea36406559442c6e31d4a48/multidict-6.7.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bfde23ef6ed9db7eaee6c37dcec08524cb43903c60b285b172b6c094711b3961", size = 256322 }, - { url = "https://files.pythonhosted.org/packages/31/6e/d8a26d81ac166a5592782d208dd90dfdc0a7a218adaa52b45a672b46c122/multidict-6.7.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3758692429e4e32f1ba0df23219cd0b4fc0a52f476726fff9337d1a57676a582", size = 253955 }, - { url = "https://files.pythonhosted.org/packages/59/4c/7c672c8aad41534ba619bcd4ade7a0dc87ed6b8b5c06149b85d3dd03f0cd/multidict-6.7.1-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:398c1478926eca669f2fd6a5856b6de9c0acf23a2cb59a14c0ba5844fa38077e", size = 251254 }, - { url = "https://files.pythonhosted.org/packages/7b/bd/84c24de512cbafbdbc39439f74e967f19570ce7924e3007174a29c348916/multidict-6.7.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:c102791b1c4f3ab36ce4101154549105a53dc828f016356b3e3bcae2e3a039d3", size = 252059 }, - { url = "https://files.pythonhosted.org/packages/fa/ba/f5449385510825b73d01c2d4087bf6d2fccc20a2d42ac34df93191d3dd03/multidict-6.7.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:a088b62bd733e2ad12c50dad01b7d0166c30287c166e137433d3b410add807a6", size = 263588 }, - { url = "https://files.pythonhosted.org/packages/d7/11/afc7c677f68f75c84a69fe37184f0f82fce13ce4b92f49f3db280b7e92b3/multidict-6.7.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:3d51ff4785d58d3f6c91bdbffcb5e1f7ddfda557727043aa20d20ec4f65e324a", size = 259642 }, - { url = "https://files.pythonhosted.org/packages/2b/17/ebb9644da78c4ab36403739e0e6e0e30ebb135b9caf3440825001a0bddcb/multidict-6.7.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fc5907494fccf3e7d3f94f95c91d6336b092b5fc83811720fae5e2765890dfba", size = 251377 }, - { url = "https://files.pythonhosted.org/packages/ca/a4/840f5b97339e27846c46307f2530a2805d9d537d8b8bd416af031cad7fa0/multidict-6.7.1-cp312-cp312-win32.whl", hash = "sha256:28ca5ce2fd9716631133d0e9a9b9a745ad7f60bac2bccafb56aa380fc0b6c511", size = 41887 }, - { url = "https://files.pythonhosted.org/packages/80/31/0b2517913687895f5904325c2069d6a3b78f66cc641a86a2baf75a05dcbb/multidict-6.7.1-cp312-cp312-win_amd64.whl", hash = "sha256:fcee94dfbd638784645b066074b338bc9cc155d4b4bffa4adce1615c5a426c19", size = 46053 }, - { url = "https://files.pythonhosted.org/packages/0c/5b/aba28e4ee4006ae4c7df8d327d31025d760ffa992ea23812a601d226e682/multidict-6.7.1-cp312-cp312-win_arm64.whl", hash = "sha256:ba0a9fb644d0c1a2194cf7ffb043bd852cea63a57f66fbd33959f7dae18517bf", size = 43307 }, - { url = "https://files.pythonhosted.org/packages/f2/22/929c141d6c0dba87d3e1d38fbdf1ba8baba86b7776469f2bc2d3227a1e67/multidict-6.7.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2b41f5fed0ed563624f1c17630cb9941cf2309d4df00e494b551b5f3e3d67a23", size = 76174 }, - { url = "https://files.pythonhosted.org/packages/c7/75/bc704ae15fee974f8fccd871305e254754167dce5f9e42d88a2def741a1d/multidict-6.7.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:84e61e3af5463c19b67ced91f6c634effb89ef8bfc5ca0267f954451ed4bb6a2", size = 45116 }, - { url = "https://files.pythonhosted.org/packages/79/76/55cd7186f498ed080a18440c9013011eb548f77ae1b297206d030eb1180a/multidict-6.7.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:935434b9853c7c112eee7ac891bc4cb86455aa631269ae35442cb316790c1445", size = 43524 }, - { url = "https://files.pythonhosted.org/packages/e9/3c/414842ef8d5a1628d68edee29ba0e5bcf235dbfb3ccd3ea303a7fe8c72ff/multidict-6.7.1-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:432feb25a1cb67fe82a9680b4d65fb542e4635cb3166cd9c01560651ad60f177", size = 249368 }, - { url = "https://files.pythonhosted.org/packages/f6/32/befed7f74c458b4a525e60519fe8d87eef72bb1e99924fa2b0f9d97a221e/multidict-6.7.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e82d14e3c948952a1a85503817e038cba5905a3352de76b9a465075d072fba23", size = 256952 }, - { url = "https://files.pythonhosted.org/packages/03/d6/c878a44ba877f366630c860fdf74bfb203c33778f12b6ac274936853c451/multidict-6.7.1-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4cfb48c6ea66c83bcaaf7e4dfa7ec1b6bbcf751b7db85a328902796dfde4c060", size = 240317 }, - { url = "https://files.pythonhosted.org/packages/68/49/57421b4d7ad2e9e60e25922b08ceb37e077b90444bde6ead629095327a6f/multidict-6.7.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1d540e51b7e8e170174555edecddbd5538105443754539193e3e1061864d444d", size = 267132 }, - { url = "https://files.pythonhosted.org/packages/b7/fe/ec0edd52ddbcea2a2e89e174f0206444a61440b40f39704e64dc807a70bd/multidict-6.7.1-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:273d23f4b40f3dce4d6c8a821c741a86dec62cded82e1175ba3d99be128147ed", size = 268140 }, - { url = "https://files.pythonhosted.org/packages/b0/73/6e1b01cbeb458807aa0831742232dbdd1fa92bfa33f52a3f176b4ff3dc11/multidict-6.7.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d624335fd4fa1c08a53f8b4be7676ebde19cd092b3895c421045ca87895b429", size = 254277 }, - { url = "https://files.pythonhosted.org/packages/6a/b2/5fb8c124d7561a4974c342bc8c778b471ebbeb3cc17df696f034a7e9afe7/multidict-6.7.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:12fad252f8b267cc75b66e8fc51b3079604e8d43a75428ffe193cd9e2195dfd6", size = 252291 }, - { url = "https://files.pythonhosted.org/packages/5a/96/51d4e4e06bcce92577fcd488e22600bd38e4fd59c20cb49434d054903bd2/multidict-6.7.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:03ede2a6ffbe8ef936b92cb4529f27f42be7f56afcdab5ab739cd5f27fb1cbf9", size = 250156 }, - { url = "https://files.pythonhosted.org/packages/db/6b/420e173eec5fba721a50e2a9f89eda89d9c98fded1124f8d5c675f7a0c0f/multidict-6.7.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:90efbcf47dbe33dcf643a1e400d67d59abeac5db07dc3f27d6bdeae497a2198c", size = 249742 }, - { url = "https://files.pythonhosted.org/packages/44/a3/ec5b5bd98f306bc2aa297b8c6f11a46714a56b1e6ef5ebda50a4f5d7c5fb/multidict-6.7.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:5c4b9bfc148f5a91be9244d6264c53035c8a0dcd2f51f1c3c6e30e30ebaa1c84", size = 262221 }, - { url = "https://files.pythonhosted.org/packages/cd/f7/e8c0d0da0cd1e28d10e624604e1a36bcc3353aaebdfdc3a43c72bc683a12/multidict-6.7.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:401c5a650f3add2472d1d288c26deebc540f99e2fb83e9525007a74cd2116f1d", size = 258664 }, - { url = "https://files.pythonhosted.org/packages/52/da/151a44e8016dd33feed44f730bd856a66257c1ee7aed4f44b649fb7edeb3/multidict-6.7.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:97891f3b1b3ffbded884e2916cacf3c6fc87b66bb0dde46f7357404750559f33", size = 249490 }, - { url = "https://files.pythonhosted.org/packages/87/af/a3b86bf9630b732897f6fc3f4c4714b90aa4361983ccbdcd6c0339b21b0c/multidict-6.7.1-cp313-cp313-win32.whl", hash = "sha256:e1c5988359516095535c4301af38d8a8838534158f649c05dd1050222321bcb3", size = 41695 }, - { url = "https://files.pythonhosted.org/packages/b2/35/e994121b0e90e46134673422dd564623f93304614f5d11886b1b3e06f503/multidict-6.7.1-cp313-cp313-win_amd64.whl", hash = "sha256:960c83bf01a95b12b08fd54324a4eb1d5b52c88932b5cba5d6e712bb3ed12eb5", size = 45884 }, - { url = "https://files.pythonhosted.org/packages/ca/61/42d3e5dbf661242a69c97ea363f2d7b46c567da8eadef8890022be6e2ab0/multidict-6.7.1-cp313-cp313-win_arm64.whl", hash = "sha256:563fe25c678aaba333d5399408f5ec3c383ca5b663e7f774dd179a520b8144df", size = 43122 }, - { url = "https://files.pythonhosted.org/packages/6d/b3/e6b21c6c4f314bb956016b0b3ef2162590a529b84cb831c257519e7fde44/multidict-6.7.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:c76c4bec1538375dad9d452d246ca5368ad6e1c9039dadcf007ae59c70619ea1", size = 83175 }, - { url = "https://files.pythonhosted.org/packages/fb/76/23ecd2abfe0957b234f6c960f4ade497f55f2c16aeb684d4ecdbf1c95791/multidict-6.7.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:57b46b24b5d5ebcc978da4ec23a819a9402b4228b8a90d9c656422b4bdd8a963", size = 48460 }, - { url = "https://files.pythonhosted.org/packages/c4/57/a0ed92b23f3a042c36bc4227b72b97eca803f5f1801c1ab77c8a212d455e/multidict-6.7.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e954b24433c768ce78ab7929e84ccf3422e46deb45a4dc9f93438f8217fa2d34", size = 46930 }, - { url = "https://files.pythonhosted.org/packages/b5/66/02ec7ace29162e447f6382c495dc95826bf931d3818799bbef11e8f7df1a/multidict-6.7.1-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3bd231490fa7217cc832528e1cd8752a96f0125ddd2b5749390f7c3ec8721b65", size = 242582 }, - { url = "https://files.pythonhosted.org/packages/58/18/64f5a795e7677670e872673aca234162514696274597b3708b2c0d276cce/multidict-6.7.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:253282d70d67885a15c8a7716f3a73edf2d635793ceda8173b9ecc21f2fb8292", size = 250031 }, - { url = "https://files.pythonhosted.org/packages/c8/ed/e192291dbbe51a8290c5686f482084d31bcd9d09af24f63358c3d42fd284/multidict-6.7.1-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0b4c48648d7649c9335cf1927a8b87fa692de3dcb15faa676c6a6f1f1aabda43", size = 228596 }, - { url = "https://files.pythonhosted.org/packages/1e/7e/3562a15a60cf747397e7f2180b0a11dc0c38d9175a650e75fa1b4d325e15/multidict-6.7.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:98bc624954ec4d2c7cb074b8eefc2b5d0ce7d482e410df446414355d158fe4ca", size = 257492 }, - { url = "https://files.pythonhosted.org/packages/24/02/7d0f9eae92b5249bb50ac1595b295f10e263dd0078ebb55115c31e0eaccd/multidict-6.7.1-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:1b99af4d9eec0b49927b4402bcbb58dea89d3e0db8806a4086117019939ad3dd", size = 255899 }, - { url = "https://files.pythonhosted.org/packages/00/e3/9b60ed9e23e64c73a5cde95269ef1330678e9c6e34dd4eb6b431b85b5a10/multidict-6.7.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6aac4f16b472d5b7dc6f66a0d49dd57b0e0902090be16594dc9ebfd3d17c47e7", size = 247970 }, - { url = "https://files.pythonhosted.org/packages/3e/06/538e58a63ed5cfb0bd4517e346b91da32fde409d839720f664e9a4ae4f9d/multidict-6.7.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:21f830fe223215dffd51f538e78c172ed7c7f60c9b96a2bf05c4848ad49921c3", size = 245060 }, - { url = "https://files.pythonhosted.org/packages/b2/2f/d743a3045a97c895d401e9bd29aaa09b94f5cbdf1bd561609e5a6c431c70/multidict-6.7.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:f5dd81c45b05518b9aa4da4aa74e1c93d715efa234fd3e8a179df611cc85e5f4", size = 235888 }, - { url = "https://files.pythonhosted.org/packages/38/83/5a325cac191ab28b63c52f14f1131f3b0a55ba3b9aa65a6d0bf2a9b921a0/multidict-6.7.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:eb304767bca2bb92fb9c5bd33cedc95baee5bb5f6c88e63706533a1c06ad08c8", size = 243554 }, - { url = "https://files.pythonhosted.org/packages/20/1f/9d2327086bd15da2725ef6aae624208e2ef828ed99892b17f60c344e57ed/multidict-6.7.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c9035dde0f916702850ef66460bc4239d89d08df4d02023a5926e7446724212c", size = 252341 }, - { url = "https://files.pythonhosted.org/packages/e8/2c/2a1aa0280cf579d0f6eed8ee5211c4f1730bd7e06c636ba2ee6aafda302e/multidict-6.7.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:af959b9beeb66c822380f222f0e0a1889331597e81f1ded7f374f3ecb0fd6c52", size = 246391 }, - { url = "https://files.pythonhosted.org/packages/e5/03/7ca022ffc36c5a3f6e03b179a5ceb829be9da5783e6fe395f347c0794680/multidict-6.7.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:41f2952231456154ee479651491e94118229844dd7226541788be783be2b5108", size = 243422 }, - { url = "https://files.pythonhosted.org/packages/dc/1d/b31650eab6c5778aceed46ba735bd97f7c7d2f54b319fa916c0f96e7805b/multidict-6.7.1-cp313-cp313t-win32.whl", hash = "sha256:df9f19c28adcb40b6aae30bbaa1478c389efd50c28d541d76760199fc1037c32", size = 47770 }, - { url = "https://files.pythonhosted.org/packages/ac/5b/2d2d1d522e51285bd61b1e20df8f47ae1a9d80839db0b24ea783b3832832/multidict-6.7.1-cp313-cp313t-win_amd64.whl", hash = "sha256:d54ecf9f301853f2c5e802da559604b3e95bb7a3b01a9c295c6ee591b9882de8", size = 53109 }, - { url = "https://files.pythonhosted.org/packages/3d/a3/cc409ba012c83ca024a308516703cf339bdc4b696195644a7215a5164a24/multidict-6.7.1-cp313-cp313t-win_arm64.whl", hash = "sha256:5a37ca18e360377cfda1d62f5f382ff41f2b8c4ccb329ed974cc2e1643440118", size = 45573 }, - { url = "https://files.pythonhosted.org/packages/91/cc/db74228a8be41884a567e88a62fd589a913708fcf180d029898c17a9a371/multidict-6.7.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8f333ec9c5eb1b7105e3b84b53141e66ca05a19a605368c55450b6ba208cb9ee", size = 75190 }, - { url = "https://files.pythonhosted.org/packages/d5/22/492f2246bb5b534abd44804292e81eeaf835388901f0c574bac4eeec73c5/multidict-6.7.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:a407f13c188f804c759fc6a9f88286a565c242a76b27626594c133b82883b5c2", size = 44486 }, - { url = "https://files.pythonhosted.org/packages/f1/4f/733c48f270565d78b4544f2baddc2fb2a245e5a8640254b12c36ac7ac68e/multidict-6.7.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0e161ddf326db5577c3a4cc2d8648f81456e8a20d40415541587a71620d7a7d1", size = 43219 }, - { url = "https://files.pythonhosted.org/packages/24/bb/2c0c2287963f4259c85e8bcbba9182ced8d7fca65c780c38e99e61629d11/multidict-6.7.1-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:1e3a8bb24342a8201d178c3b4984c26ba81a577c80d4d525727427460a50c22d", size = 245132 }, - { url = "https://files.pythonhosted.org/packages/a7/f9/44d4b3064c65079d2467888794dea218d1601898ac50222ab8a9a8094460/multidict-6.7.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97231140a50f5d447d3164f994b86a0bed7cd016e2682f8650d6a9158e14fd31", size = 252420 }, - { url = "https://files.pythonhosted.org/packages/8b/13/78f7275e73fa17b24c9a51b0bd9d73ba64bb32d0ed51b02a746eb876abe7/multidict-6.7.1-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:6b10359683bd8806a200fd2909e7c8ca3a7b24ec1d8132e483d58e791d881048", size = 233510 }, - { url = "https://files.pythonhosted.org/packages/4b/25/8167187f62ae3cbd52da7893f58cb036b47ea3fb67138787c76800158982/multidict-6.7.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:283ddac99f7ac25a4acadbf004cb5ae34480bbeb063520f70ce397b281859362", size = 264094 }, - { url = "https://files.pythonhosted.org/packages/a1/e7/69a3a83b7b030cf283fb06ce074a05a02322359783424d7edf0f15fe5022/multidict-6.7.1-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:538cec1e18c067d0e6103aa9a74f9e832904c957adc260e61cd9d8cf0c3b3d37", size = 260786 }, - { url = "https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7eee46ccb30ff48a1e35bb818cc90846c6be2b68240e42a78599166722cea709", size = 248483 }, - { url = "https://files.pythonhosted.org/packages/48/5a/d5a99e3acbca0e29c5d9cba8f92ceb15dce78bab963b308ae692981e3a5d/multidict-6.7.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa263a02f4f2dd2d11a7b1bb4362aa7cb1049f84a9235d31adf63f30143469a0", size = 248403 }, - { url = "https://files.pythonhosted.org/packages/35/48/e58cd31f6c7d5102f2a4bf89f96b9cf7e00b6c6f3d04ecc44417c00a5a3c/multidict-6.7.1-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:2e1425e2f99ec5bd36c15a01b690a1a2456209c5deed58f95469ffb46039ccbb", size = 240315 }, - { url = "https://files.pythonhosted.org/packages/94/33/1cd210229559cb90b6786c30676bb0c58249ff42f942765f88793b41fdce/multidict-6.7.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:497394b3239fc6f0e13a78a3e1b61296e72bf1c5f94b4c4eb80b265c37a131cd", size = 245528 }, - { url = "https://files.pythonhosted.org/packages/64/f2/6e1107d226278c876c783056b7db43d800bb64c6131cec9c8dfb6903698e/multidict-6.7.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:233b398c29d3f1b9676b4b6f75c518a06fcb2ea0b925119fb2c1bc35c05e1601", size = 258784 }, - { url = "https://files.pythonhosted.org/packages/4d/c1/11f664f14d525e4a1b5327a82d4de61a1db604ab34c6603bb3c2cc63ad34/multidict-6.7.1-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:93b1818e4a6e0930454f0f2af7dfce69307ca03cdcfb3739bf4d91241967b6c1", size = 251980 }, - { url = "https://files.pythonhosted.org/packages/e1/9f/75a9ac888121d0c5bbd4ecf4eead45668b1766f6baabfb3b7f66a410e231/multidict-6.7.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f33dc2a3abe9249ea5d8360f969ec7f4142e7ac45ee7014d8f8d5acddf178b7b", size = 243602 }, - { url = "https://files.pythonhosted.org/packages/9a/e7/50bf7b004cc8525d80dbbbedfdc7aed3e4c323810890be4413e589074032/multidict-6.7.1-cp314-cp314-win32.whl", hash = "sha256:3ab8b9d8b75aef9df299595d5388b14530839f6422333357af1339443cff777d", size = 40930 }, - { url = "https://files.pythonhosted.org/packages/e0/bf/52f25716bbe93745595800f36fb17b73711f14da59ed0bb2eba141bc9f0f/multidict-6.7.1-cp314-cp314-win_amd64.whl", hash = "sha256:5e01429a929600e7dab7b166062d9bb54a5eed752384c7384c968c2afab8f50f", size = 45074 }, - { url = "https://files.pythonhosted.org/packages/97/ab/22803b03285fa3a525f48217963da3a65ae40f6a1b6f6cf2768879e208f9/multidict-6.7.1-cp314-cp314-win_arm64.whl", hash = "sha256:4885cb0e817aef5d00a2e8451d4665c1808378dc27c2705f1bf4ef8505c0d2e5", size = 42471 }, - { url = "https://files.pythonhosted.org/packages/e0/6d/f9293baa6146ba9507e360ea0292b6422b016907c393e2f63fc40ab7b7b5/multidict-6.7.1-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:0458c978acd8e6ea53c81eefaddbbee9c6c5e591f41b3f5e8e194780fe026581", size = 82401 }, - { url = "https://files.pythonhosted.org/packages/7a/68/53b5494738d83558d87c3c71a486504d8373421c3e0dbb6d0db48ad42ee0/multidict-6.7.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:c0abd12629b0af3cf590982c0b413b1e7395cd4ec026f30986818ab95bfaa94a", size = 48143 }, - { url = "https://files.pythonhosted.org/packages/37/e8/5284c53310dcdc99ce5d66563f6e5773531a9b9fe9ec7a615e9bc306b05f/multidict-6.7.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:14525a5f61d7d0c94b368a42cff4c9a4e7ba2d52e2672a7b23d84dc86fb02b0c", size = 46507 }, - { url = "https://files.pythonhosted.org/packages/e4/fc/6800d0e5b3875568b4083ecf5f310dcf91d86d52573160834fb4bfcf5e4f/multidict-6.7.1-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:17307b22c217b4cf05033dabefe68255a534d637c6c9b0cc8382718f87be4262", size = 239358 }, - { url = "https://files.pythonhosted.org/packages/41/75/4ad0973179361cdf3a113905e6e088173198349131be2b390f9fa4da5fc6/multidict-6.7.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a7e590ff876a3eaf1c02a4dfe0724b6e69a9e9de6d8f556816f29c496046e59", size = 246884 }, - { url = "https://files.pythonhosted.org/packages/c3/9c/095bb28b5da139bd41fb9a5d5caff412584f377914bd8787c2aa98717130/multidict-6.7.1-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:5fa6a95dfee63893d80a34758cd0e0c118a30b8dcb46372bf75106c591b77889", size = 225878 }, - { url = "https://files.pythonhosted.org/packages/07/d0/c0a72000243756e8f5a277b6b514fa005f2c73d481b7d9e47cd4568aa2e4/multidict-6.7.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a0543217a6a017692aa6ae5cc39adb75e587af0f3a82288b1492eb73dd6cc2a4", size = 253542 }, - { url = "https://files.pythonhosted.org/packages/c0/6b/f69da15289e384ecf2a68837ec8b5ad8c33e973aa18b266f50fe55f24b8c/multidict-6.7.1-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f99fe611c312b3c1c0ace793f92464d8cd263cc3b26b5721950d977b006b6c4d", size = 252403 }, - { url = "https://files.pythonhosted.org/packages/a2/76/b9669547afa5a1a25cd93eaca91c0da1c095b06b6d2d8ec25b713588d3a1/multidict-6.7.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9004d8386d133b7e6135679424c91b0b854d2d164af6ea3f289f8f2761064609", size = 244889 }, - { url = "https://files.pythonhosted.org/packages/7e/a9/a50d2669e506dad33cfc45b5d574a205587b7b8a5f426f2fbb2e90882588/multidict-6.7.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e628ef0e6859ffd8273c69412a2465c4be4a9517d07261b33334b5ec6f3c7489", size = 241982 }, - { url = "https://files.pythonhosted.org/packages/c5/bb/1609558ad8b456b4827d3c5a5b775c93b87878fd3117ed3db3423dfbce1b/multidict-6.7.1-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:841189848ba629c3552035a6a7f5bf3b02eb304e9fea7492ca220a8eda6b0e5c", size = 232415 }, - { url = "https://files.pythonhosted.org/packages/d8/59/6f61039d2aa9261871e03ab9dc058a550d240f25859b05b67fd70f80d4b3/multidict-6.7.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:ce1bbd7d780bb5a0da032e095c951f7014d6b0a205f8318308140f1a6aba159e", size = 240337 }, - { url = "https://files.pythonhosted.org/packages/a1/29/fdc6a43c203890dc2ae9249971ecd0c41deaedfe00d25cb6564b2edd99eb/multidict-6.7.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b26684587228afed0d50cf804cc71062cc9c1cdf55051c4c6345d372947b268c", size = 248788 }, - { url = "https://files.pythonhosted.org/packages/a9/14/a153a06101323e4cf086ecee3faadba52ff71633d471f9685c42e3736163/multidict-6.7.1-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:9f9af11306994335398293f9958071019e3ab95e9a707dc1383a35613f6abcb9", size = 242842 }, - { url = "https://files.pythonhosted.org/packages/41/5f/604ae839e64a4a6efc80db94465348d3b328ee955e37acb24badbcd24d83/multidict-6.7.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b4938326284c4f1224178a560987b6cf8b4d38458b113d9b8c1db1a836e640a2", size = 240237 }, - { url = "https://files.pythonhosted.org/packages/5f/60/c3a5187bf66f6fb546ff4ab8fb5a077cbdd832d7b1908d4365c7f74a1917/multidict-6.7.1-cp314-cp314t-win32.whl", hash = "sha256:98655c737850c064a65e006a3df7c997cd3b220be4ec8fe26215760b9697d4d7", size = 48008 }, - { url = "https://files.pythonhosted.org/packages/0c/f7/addf1087b860ac60e6f382240f64fb99f8bfb532bb06f7c542b83c29ca61/multidict-6.7.1-cp314-cp314t-win_amd64.whl", hash = "sha256:497bde6223c212ba11d462853cfa4f0ae6ef97465033e7dc9940cdb3ab5b48e5", size = 53542 }, - { url = "https://files.pythonhosted.org/packages/4c/81/4629d0aa32302ef7b2ec65c75a728cc5ff4fa410c50096174c1632e70b3e/multidict-6.7.1-cp314-cp314t-win_arm64.whl", hash = "sha256:2bbd113e0d4af5db41d5ebfe9ccaff89de2120578164f86a5d17d5a576d1e5b2", size = 44719 }, - { url = "https://files.pythonhosted.org/packages/81/08/7036c080d7117f28a4af526d794aab6a84463126db031b007717c1a6676e/multidict-6.7.1-py3-none-any.whl", hash = "sha256:55d97cc6dae627efa6a6e548885712d4864b81110ac76fa4e534c03819fa4a56", size = 12319 }, -] - -[[package]] -name = "multiprocess" -version = "0.70.19" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "dill" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/a2/f2/e783ac7f2aeeed14e9e12801f22529cc7e6b7ab80928d6dcce4e9f00922d/multiprocess-0.70.19.tar.gz", hash = "sha256:952021e0e6c55a4a9fe4cd787895b86e239a40e76802a789d6305398d3975897", size = 2079989 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e3/45/8004d1e6b9185c1a444d6b55ac5682acf9d98035e54386d967366035a03a/multiprocess-0.70.19-py310-none-any.whl", hash = "sha256:97404393419dcb2a8385910864eedf47a3cadf82c66345b44f036420eb0b5d87", size = 134948 }, - { url = "https://files.pythonhosted.org/packages/86/c2/dec9722dc3474c164a0b6bcd9a7ed7da542c98af8cabce05374abab35edd/multiprocess-0.70.19-py311-none-any.whl", hash = "sha256:928851ae7973aea4ce0eaf330bbdafb2e01398a91518d5c8818802845564f45c", size = 144457 }, - { url = "https://files.pythonhosted.org/packages/71/70/38998b950a97ea279e6bd657575d22d1a2047256caf707d9a10fbce4f065/multiprocess-0.70.19-py312-none-any.whl", hash = "sha256:3a56c0e85dd5025161bac5ce138dcac1e49174c7d8e74596537e729fd5c53c28", size = 150281 }, - { url = "https://files.pythonhosted.org/packages/7f/74/d2c27e03cb84251dfe7249b8e82923643c6d48fa4883b9476b025e7dc7eb/multiprocess-0.70.19-py313-none-any.whl", hash = "sha256:8d5eb4ec5017ba2fab4e34a747c6d2c2b6fecfe9e7236e77988db91580ada952", size = 156414 }, - { url = "https://files.pythonhosted.org/packages/a0/61/af9115673a5870fd885247e2f1b68c4f1197737da315b520a91c757a861a/multiprocess-0.70.19-py314-none-any.whl", hash = "sha256:e8cc7fbdff15c0613f0a1f1f8744bef961b0a164c0ca29bdff53e9d2d93c5e5f", size = 160318 }, - { url = "https://files.pythonhosted.org/packages/7e/82/69e539c4c2027f1e1697e09aaa2449243085a0edf81ae2c6341e84d769b6/multiprocess-0.70.19-py39-none-any.whl", hash = "sha256:0d4b4397ed669d371c81dcd1ef33fd384a44d6c3de1bd0ca7ac06d837720d3c5", size = 133477 }, -] - -[[package]] -name = "mypy-extensions" -version = "1.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a2/6e/371856a3fb9d31ca8dac321cda606860fa4548858c0cc45d9d1d4ca2628b/mypy_extensions-1.1.0.tar.gz", hash = "sha256:52e68efc3284861e772bbcd66823fde5ae21fd2fdb51c62a211403730b916558", size = 6343 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/79/7b/2c79738432f5c924bef5071f933bcc9efd0473bac3b4aa584a6f7c1c8df8/mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505", size = 4963 }, -] - -[[package]] -name = "nest-asyncio" -version = "1.6.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 }, -] - -[[package]] -name = "networkx" -version = "3.6.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504 }, -] - -[[package]] -name = "nltk" -version = "3.9.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "click" }, - { name = "joblib" }, - { name = "regex" }, - { name = "tqdm" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/74/a1/b3b4adf15585a5bc4c357adde150c01ebeeb642173ded4d871e89468767c/nltk-3.9.4.tar.gz", hash = "sha256:ed03bc098a40481310320808b2db712d95d13ca65b27372f8a403949c8b523d0", size = 2946864 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9d/91/04e965f8e717ba0ab4bdca5c112deeab11c9e750d94c4d4602f050295d39/nltk-3.9.4-py3-none-any.whl", hash = "sha256:f2fa301c3a12718ce4a0e9305c5675299da5ad9e26068218b69d692fda84828f", size = 1552087 }, -] - -[[package]] -name = "numpy" -version = "2.4.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d7/9f/b8cef5bffa569759033adda9481211426f12f53299629b410340795c2514/numpy-2.4.4.tar.gz", hash = "sha256:2d390634c5182175533585cc89f3608a4682ccb173cc9bb940b2881c8d6f8fa0", size = 20731587 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/28/05/32396bec30fb2263770ee910142f49c1476d08e8ad41abf8403806b520ce/numpy-2.4.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15716cfef24d3a9762e3acdf87e27f58dc823d1348f765bbea6bef8c639bfa1b", size = 16689272 }, - { url = "https://files.pythonhosted.org/packages/c5/f3/a983d28637bfcd763a9c7aafdb6d5c0ebf3d487d1e1459ffdb57e2f01117/numpy-2.4.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:23cbfd4c17357c81021f21540da84ee282b9c8fba38a03b7b9d09ba6b951421e", size = 14699573 }, - { url = "https://files.pythonhosted.org/packages/9b/fd/e5ecca1e78c05106d98028114f5c00d3eddb41207686b2b7de3e477b0e22/numpy-2.4.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:8b3b60bb7cba2c8c81837661c488637eee696f59a877788a396d33150c35d842", size = 5204782 }, - { url = "https://files.pythonhosted.org/packages/de/2f/702a4594413c1a8632092beae8aba00f1d67947389369b3777aed783fdca/numpy-2.4.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:e4a010c27ff6f210ff4c6ef34394cd61470d01014439b192ec22552ee867f2a8", size = 6552038 }, - { url = "https://files.pythonhosted.org/packages/7f/37/eed308a8f56cba4d1fdf467a4fc67ef4ff4bf1c888f5fc980481890104b1/numpy-2.4.4-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f9e75681b59ddaa5e659898085ae0eaea229d054f2ac0c7e563a62205a700121", size = 15670666 }, - { url = "https://files.pythonhosted.org/packages/0a/0d/0e3ecece05b7a7e87ab9fb587855548da437a061326fff64a223b6dcb78a/numpy-2.4.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:81f4a14bee47aec54f883e0cad2d73986640c1590eb9bfaaba7ad17394481e6e", size = 16645480 }, - { url = "https://files.pythonhosted.org/packages/34/49/f2312c154b82a286758ee2f1743336d50651f8b5195db18cdb63675ff649/numpy-2.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:62d6b0f03b694173f9fcb1fb317f7222fd0b0b103e784c6549f5e53a27718c44", size = 17020036 }, - { url = "https://files.pythonhosted.org/packages/7b/e9/736d17bd77f1b0ec4f9901aaec129c00d59f5d84d5e79bba540ef12c2330/numpy-2.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fbc356aae7adf9e6336d336b9c8111d390a05df88f1805573ebb0807bd06fd1d", size = 18368643 }, - { url = "https://files.pythonhosted.org/packages/63/f6/d417977c5f519b17c8a5c3bc9e8304b0908b0e21136fe43bf628a1343914/numpy-2.4.4-cp312-cp312-win32.whl", hash = "sha256:0d35aea54ad1d420c812bfa0385c71cd7cc5bcf7c65fed95fc2cd02fe8c79827", size = 5961117 }, - { url = "https://files.pythonhosted.org/packages/2d/5b/e1deebf88ff431b01b7406ca3583ab2bbb90972bbe1c568732e49c844f7e/numpy-2.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:b5f0362dc928a6ecd9db58868fca5e48485205e3855957bdedea308f8672ea4a", size = 12320584 }, - { url = "https://files.pythonhosted.org/packages/58/89/e4e856ac82a68c3ed64486a544977d0e7bdd18b8da75b78a577ca31c4395/numpy-2.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:846300f379b5b12cc769334464656bc882e0735d27d9726568bc932fdc49d5ec", size = 10221450 }, - { url = "https://files.pythonhosted.org/packages/14/1d/d0a583ce4fefcc3308806a749a536c201ed6b5ad6e1322e227ee4848979d/numpy-2.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:08f2e31ed5e6f04b118e49821397f12767934cfdd12a1ce86a058f91e004ee50", size = 16684933 }, - { url = "https://files.pythonhosted.org/packages/c1/62/2b7a48fbb745d344742c0277f01286dead15f3f68e4f359fbfcf7b48f70f/numpy-2.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e823b8b6edc81e747526f70f71a9c0a07ac4e7ad13020aa736bb7c9d67196115", size = 14694532 }, - { url = "https://files.pythonhosted.org/packages/e5/87/499737bfba066b4a3bebff24a8f1c5b2dee410b209bc6668c9be692580f0/numpy-2.4.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:4a19d9dba1a76618dd86b164d608566f393f8ec6ac7c44f0cc879011c45e65af", size = 5199661 }, - { url = "https://files.pythonhosted.org/packages/cd/da/464d551604320d1491bc345efed99b4b7034143a85787aab78d5691d5a0e/numpy-2.4.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d2a8490669bfe99a233298348acc2d824d496dee0e66e31b66a6022c2ad74a5c", size = 6547539 }, - { url = "https://files.pythonhosted.org/packages/7d/90/8d23e3b0dafd024bf31bdec225b3bb5c2dbfa6912f8a53b8659f21216cbf/numpy-2.4.4-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45dbed2ab436a9e826e302fcdcbe9133f9b0006e5af7168afb8963a6520da103", size = 15668806 }, - { url = "https://files.pythonhosted.org/packages/d1/73/a9d864e42a01896bb5974475438f16086be9ba1f0d19d0bb7a07427c4a8b/numpy-2.4.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c901b15172510173f5cb310eae652908340f8dede90fff9e3bf6c0d8dfd92f83", size = 16632682 }, - { url = "https://files.pythonhosted.org/packages/34/fb/14570d65c3bde4e202a031210475ae9cde9b7686a2e7dc97ee67d2833b35/numpy-2.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:99d838547ace2c4aace6c4f76e879ddfe02bb58a80c1549928477862b7a6d6ed", size = 17019810 }, - { url = "https://files.pythonhosted.org/packages/8a/77/2ba9d87081fd41f6d640c83f26fb7351e536b7ce6dd9061b6af5904e8e46/numpy-2.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0aec54fd785890ecca25a6003fd9a5aed47ad607bbac5cd64f836ad8666f4959", size = 18357394 }, - { url = "https://files.pythonhosted.org/packages/a2/23/52666c9a41708b0853fa3b1a12c90da38c507a3074883823126d4e9d5b30/numpy-2.4.4-cp313-cp313-win32.whl", hash = "sha256:07077278157d02f65c43b1b26a3886bce886f95d20aabd11f87932750dfb14ed", size = 5959556 }, - { url = "https://files.pythonhosted.org/packages/57/fb/48649b4971cde70d817cf97a2a2fdc0b4d8308569f1dd2f2611959d2e0cf/numpy-2.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:5c70f1cc1c4efbe316a572e2d8b9b9cc44e89b95f79ca3331553fbb63716e2bf", size = 12317311 }, - { url = "https://files.pythonhosted.org/packages/ba/d8/11490cddd564eb4de97b4579ef6bfe6a736cc07e94c1598590ae25415e01/numpy-2.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:ef4059d6e5152fa1a39f888e344c73fdc926e1b2dd58c771d67b0acfbf2aa67d", size = 10222060 }, - { url = "https://files.pythonhosted.org/packages/99/5d/dab4339177a905aad3e2221c915b35202f1ec30d750dd2e5e9d9a72b804b/numpy-2.4.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4bbc7f303d125971f60ec0aaad5e12c62d0d2c925f0ab1273debd0e4ba37aba5", size = 14822302 }, - { url = "https://files.pythonhosted.org/packages/eb/e4/0564a65e7d3d97562ed6f9b0fd0fb0a6f559ee444092f105938b50043876/numpy-2.4.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:4d6d57903571f86180eb98f8f0c839fa9ebbfb031356d87f1361be91e433f5b7", size = 5327407 }, - { url = "https://files.pythonhosted.org/packages/29/8d/35a3a6ce5ad371afa58b4700f1c820f8f279948cca32524e0a695b0ded83/numpy-2.4.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:4636de7fd195197b7535f231b5de9e4b36d2c440b6e566d2e4e4746e6af0ca93", size = 6647631 }, - { url = "https://files.pythonhosted.org/packages/f4/da/477731acbd5a58a946c736edfdabb2ac5b34c3d08d1ba1a7b437fa0884df/numpy-2.4.4-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ad2e2ef14e0b04e544ea2fa0a36463f847f113d314aa02e5b402fdf910ef309e", size = 15727691 }, - { url = "https://files.pythonhosted.org/packages/e6/db/338535d9b152beabeb511579598418ba0212ce77cf9718edd70262cc4370/numpy-2.4.4-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a285b3b96f951841799528cd1f4f01cd70e7e0204b4abebac9463eecfcf2a40", size = 16681241 }, - { url = "https://files.pythonhosted.org/packages/e2/a9/ad248e8f58beb7a0219b413c9c7d8151c5d285f7f946c3e26695bdbbe2df/numpy-2.4.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:f8474c4241bc18b750be2abea9d7a9ec84f46ef861dbacf86a4f6e043401f79e", size = 17085767 }, - { url = "https://files.pythonhosted.org/packages/b5/1a/3b88ccd3694681356f70da841630e4725a7264d6a885c8d442a697e1146b/numpy-2.4.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4e874c976154687c1f71715b034739b45c7711bec81db01914770373d125e392", size = 18403169 }, - { url = "https://files.pythonhosted.org/packages/c2/c9/fcfd5d0639222c6eac7f304829b04892ef51c96a75d479214d77e3ce6e33/numpy-2.4.4-cp313-cp313t-win32.whl", hash = "sha256:9c585a1790d5436a5374bac930dad6ed244c046ed91b2b2a3634eb2971d21008", size = 6083477 }, - { url = "https://files.pythonhosted.org/packages/d5/e3/3938a61d1c538aaec8ed6fd6323f57b0c2d2d2219512434c5c878db76553/numpy-2.4.4-cp313-cp313t-win_amd64.whl", hash = "sha256:93e15038125dc1e5345d9b5b68aa7f996ec33b98118d18c6ca0d0b7d6198b7e8", size = 12457487 }, - { url = "https://files.pythonhosted.org/packages/97/6a/7e345032cc60501721ef94e0e30b60f6b0bd601f9174ebd36389a2b86d40/numpy-2.4.4-cp313-cp313t-win_arm64.whl", hash = "sha256:0dfd3f9d3adbe2920b68b5cd3d51444e13a10792ec7154cd0a2f6e74d4ab3233", size = 10292002 }, - { url = "https://files.pythonhosted.org/packages/6e/06/c54062f85f673dd5c04cbe2f14c3acb8c8b95e3384869bb8cc9bff8cb9df/numpy-2.4.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:f169b9a863d34f5d11b8698ead99febeaa17a13ca044961aa8e2662a6c7766a0", size = 16684353 }, - { url = "https://files.pythonhosted.org/packages/4c/39/8a320264a84404c74cc7e79715de85d6130fa07a0898f67fb5cd5bd79908/numpy-2.4.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2483e4584a1cb3092da4470b38866634bafb223cbcd551ee047633fd2584599a", size = 14704914 }, - { url = "https://files.pythonhosted.org/packages/91/fb/287076b2614e1d1044235f50f03748f31fa287e3dbe6abeb35cdfa351eca/numpy-2.4.4-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:2d19e6e2095506d1736b7d80595e0f252d76b89f5e715c35e06e937679ea7d7a", size = 5210005 }, - { url = "https://files.pythonhosted.org/packages/63/eb/fcc338595309910de6ecabfcef2419a9ce24399680bfb149421fa2df1280/numpy-2.4.4-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:6a246d5914aa1c820c9443ddcee9c02bec3e203b0c080349533fae17727dfd1b", size = 6544974 }, - { url = "https://files.pythonhosted.org/packages/44/5d/e7e9044032a716cdfaa3fba27a8e874bf1c5f1912a1ddd4ed071bf8a14a6/numpy-2.4.4-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:989824e9faf85f96ec9c7761cd8d29c531ad857bfa1daa930cba85baaecf1a9a", size = 15684591 }, - { url = "https://files.pythonhosted.org/packages/98/7c/21252050676612625449b4807d6b695b9ce8a7c9e1c197ee6216c8a65c7c/numpy-2.4.4-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27a8d92cd10f1382a67d7cf4db7ce18341b66438bdd9f691d7b0e48d104c2a9d", size = 16637700 }, - { url = "https://files.pythonhosted.org/packages/b1/29/56d2bbef9465db24ef25393383d761a1af4f446a1df9b8cded4fe3a5a5d7/numpy-2.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e44319a2953c738205bf3354537979eaa3998ed673395b964c1176083dd46252", size = 17035781 }, - { url = "https://files.pythonhosted.org/packages/e3/2b/a35a6d7589d21f44cea7d0a98de5ddcbb3d421b2622a5c96b1edf18707c3/numpy-2.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e892aff75639bbef0d2a2cfd55535510df26ff92f63c92cd84ef8d4ba5a5557f", size = 18362959 }, - { url = "https://files.pythonhosted.org/packages/64/c9/d52ec581f2390e0f5f85cbfd80fb83d965fc15e9f0e1aec2195faa142cde/numpy-2.4.4-cp314-cp314-win32.whl", hash = "sha256:1378871da56ca8943c2ba674530924bb8ca40cd228358a3b5f302ad60cf875fc", size = 6008768 }, - { url = "https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74", size = 12449181 }, - { url = "https://files.pythonhosted.org/packages/70/2e/14cda6f4d8e396c612d1bf97f22958e92148801d7e4f110cabebdc0eef4b/numpy-2.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:2c194dd721e54ecad9ad387c1d35e63dce5c4450c6dc7dd5611283dda239aabb", size = 10496035 }, - { url = "https://files.pythonhosted.org/packages/b1/e8/8fed8c8d848d7ecea092dc3469643f9d10bc3a134a815a3b033da1d2039b/numpy-2.4.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2aa0613a5177c264ff5921051a5719d20095ea586ca88cc802c5c218d1c67d3e", size = 14824958 }, - { url = "https://files.pythonhosted.org/packages/05/1a/d8007a5138c179c2bf33ef44503e83d70434d2642877ee8fbb230e7c0548/numpy-2.4.4-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:42c16925aa5a02362f986765f9ebabf20de75cdefdca827d14315c568dcab113", size = 5330020 }, - { url = "https://files.pythonhosted.org/packages/99/64/ffb99ac6ae93faf117bcbd5c7ba48a7f45364a33e8e458545d3633615dda/numpy-2.4.4-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:874f200b2a981c647340f841730fc3a2b54c9d940566a3c4149099591e2c4c3d", size = 6650758 }, - { url = "https://files.pythonhosted.org/packages/6e/6e/795cc078b78a384052e73b2f6281ff7a700e9bf53bcce2ee579d4f6dd879/numpy-2.4.4-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9b39d38a9bd2ae1becd7eac1303d031c5c110ad31f2b319c6e7d98b135c934d", size = 15729948 }, - { url = "https://files.pythonhosted.org/packages/5f/86/2acbda8cc2af5f3d7bfc791192863b9e3e19674da7b5e533fded124d1299/numpy-2.4.4-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b268594bccac7d7cf5844c7732e3f20c50921d94e36d7ec9b79e9857694b1b2f", size = 16679325 }, - { url = "https://files.pythonhosted.org/packages/bc/59/cafd83018f4aa55e0ac6fa92aa066c0a1877b77a615ceff1711c260ffae8/numpy-2.4.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ac6b31e35612a26483e20750126d30d0941f949426974cace8e6b5c58a3657b0", size = 17084883 }, - { url = "https://files.pythonhosted.org/packages/f0/85/a42548db84e65ece46ab2caea3d3f78b416a47af387fcbb47ec28e660dc2/numpy-2.4.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8e3ed142f2728df44263aaf5fb1f5b0b99f4070c553a0d7f033be65338329150", size = 18403474 }, - { url = "https://files.pythonhosted.org/packages/ed/ad/483d9e262f4b831000062e5d8a45e342166ec8aaa1195264982bca267e62/numpy-2.4.4-cp314-cp314t-win32.whl", hash = "sha256:dddbbd259598d7240b18c9d87c56a9d2fb3b02fe266f49a7c101532e78c1d871", size = 6155500 }, - { url = "https://files.pythonhosted.org/packages/c7/03/2fc4e14c7bd4ff2964b74ba90ecb8552540b6315f201df70f137faa5c589/numpy-2.4.4-cp314-cp314t-win_amd64.whl", hash = "sha256:a7164afb23be6e37ad90b2f10426149fd75aee07ca55653d2aa41e66c4ef697e", size = 12637755 }, - { url = "https://files.pythonhosted.org/packages/58/78/548fb8e07b1a341746bfbecb32f2c268470f45fa028aacdbd10d9bc73aab/numpy-2.4.4-cp314-cp314t-win_arm64.whl", hash = "sha256:ba203255017337d39f89bdd58417f03c4426f12beed0440cfd933cb15f8669c7", size = 10566643 }, -] - -[[package]] -name = "packaging" -version = "26.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d7/f1/e7a6dd94a8d4a5626c03e4e99c87f241ba9e350cd9e6d75123f992427270/packaging-26.2.tar.gz", hash = "sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661", size = 228134 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/df/b2/87e62e8c3e2f4b32e5fe99e0b86d576da1312593b39f47d8ceef365e95ed/packaging-26.2-py3-none-any.whl", hash = "sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e", size = 100195 }, -] - -[[package]] -name = "pandas" -version = "3.0.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, - { name = "python-dateutil" }, - { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/f8/87/4341c6252d1c47b08768c3d25ac487362bf403f0313ddae4a2a26c9b1b4c/pandas-3.0.3.tar.gz", hash = "sha256:696a4a00a2a2a35d4e5deb3fc946641b96c944f02230e4f76137fe35d806c4fc", size = 4651414 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/24/f1/392f8c5bfc16f66a0d2d41561c01627c228fe7ed2a0d056ef11315042570/pandas-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fed2ff7fd9779120e388e285fc029bd5cf9490cdd2e4166a9ee22c0e49a9ab09", size = 10357846 }, - { url = "https://files.pythonhosted.org/packages/cf/3d/b16412745651e855f357e5e66930248688378853a6e2698a214e331fba1f/pandas-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b168fc218fd80a6cbdbdbc1a97ddc7889ed057d7eb45f50d866ceab5f39904c4", size = 9899550 }, - { url = "https://files.pythonhosted.org/packages/31/a8/fa2535168fffcedf67f4f6de28d2dd903a747ca7c8ea6989451aaeb3a92f/pandas-3.0.3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c", size = 10412965 }, - { url = "https://files.pythonhosted.org/packages/65/b6/09b01cdbc15224e2850365192d17b7bdebb8bdbd8780ed221fcdf0d9a515/pandas-3.0.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6dc0b3fd2169c9157deed50b4d519553a3655c8c6a96027136d654592be973a9", size = 10894600 }, - { url = "https://files.pythonhosted.org/packages/c9/a4/2eb28f2fccb4ced4a2c79ab2a5dee9ade1ebf44922ebad6fea158c9f95d4/pandas-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7e65d5407dc0b394f509699650e4a2ec01c0514f21850f453fa60f3be79a5dbf", size = 11422824 }, - { url = "https://files.pythonhosted.org/packages/f8/45/830bb57f533a4604b355e07edcb8ea18cf88b5f94e5fca92f27052d7c597/pandas-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f8894dc474d648fe7b6ff0ca9b0bd73950d19952bc1a6534540762c5d79d305c", size = 11950889 }, - { url = "https://files.pythonhosted.org/packages/b9/c5/fc1b368f303087d20e8c9bf3d6ceb186263cfac0ade735cd938538bea839/pandas-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:c7be265b62cef88e253a941e4698604973736dcfe242fdb5198f0f7bc473cdcc", size = 9755463 }, - { url = "https://files.pythonhosted.org/packages/86/bd/fda8f9705b1b09c6ebe14bfc0fa0e4ec8584d54ea673628f157ff55131af/pandas-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:557409bc4178e70ee8d9ddb494798e51ebf6ea59330f6be22c51bab2a7db6c49", size = 9066158 }, - { url = "https://files.pythonhosted.org/packages/c5/90/62d8302883c44308c477e222c3daf7c813a34c8e96985882fbd53d964352/pandas-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:67b3b64c11910cfa29f4e94a14d3bff9ee693b6fc76055e7cad549cee0aec5fa", size = 10331071 }, - { url = "https://files.pythonhosted.org/packages/7f/ae/6a6493c783a101f165e4356953ba3c74d6f77f0042fa7d753da9dfbb640c/pandas-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:39436b377d56d2a2e52d0395bdbee171f01068e99af5250509aceeb929f765c7", size = 9875690 }, - { url = "https://files.pythonhosted.org/packages/62/7c/5df8e9f56c69a2769fbe9382a5ef8f2658c007e376434e1e2cbb57ad895f/pandas-3.0.3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d4be06d68f9ddcfc645b87534911da79a8fbffc7573c80e0edcf42a5020624d8", size = 10381634 }, - { url = "https://files.pythonhosted.org/packages/99/68/1237369725aa617bb358263d535803e3053fdbc593513ec5ed9c9896b5b6/pandas-3.0.3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a4eeb6830daf35a71cc09649bd823e2b542dac246cdee9614c6e4bd65028cd6a", size = 10891243 }, - { url = "https://files.pythonhosted.org/packages/25/93/77d108e8af7222b4a503ebde0e30215b1c2e4f8e53a526431890f22d5586/pandas-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1928e07221f82db493cd4af1e23c1bfca524a19a4699887975bff68f49a72bfb", size = 11388659 }, - { url = "https://files.pythonhosted.org/packages/d0/bd/eff5b4399f332ac386c853f6cd2bd3fa2ca0061b9f36ecd9c4d7c4265649/pandas-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:51b1fe551acb77dac643c6fda86084d8d446c10fe64b06a9cc29c4cc8540e7f2", size = 11942880 }, - { url = "https://files.pythonhosted.org/packages/2c/20/559ace4200982c3887d0b86bfd0d856a2143ef8ddab63cc07934951a964c/pandas-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:a82d532a3351d435432cd913edbccaf8b8e01d4dd0e5ced5a8d2e8ecd94c7e44", size = 9757091 }, - { url = "https://files.pythonhosted.org/packages/3a/66/69055a09fe200f29f922a3eeec4804611900b95f52d932ece3393c3c0c19/pandas-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:275c14e0fce14a2ec20eee474aecd305478ea3c1e6f6a9d8fe219a165542717e", size = 9057282 }, - { url = "https://files.pythonhosted.org/packages/57/0e/efe801b0e6811e8e650cd21b7f2608e30f08a7067e2bf6e8752b0d56ee3c/pandas-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:46997386d528eb40376ecd6b033cf4a8a1e5282580f68f43de875b78cba2199d", size = 10767016 }, - { url = "https://files.pythonhosted.org/packages/ea/dc/eb55135a1d5f0f0519f28da1f609a206d2cad1f9c35c32d51e38dd7261ae/pandas-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:261e308dfb22448384b7580cf719d2f998fe2966c92893c3e77d14008af1f066", size = 10420210 }, - { url = "https://files.pythonhosted.org/packages/c6/3e/b1d5d955ce33ffecb407465a60bc32769d74fcf68224b7ae67ae11d4dea4/pandas-3.0.3-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dd1a5d1def6a46002e964510bdc67c368aa0951df5d1d9f8365336f5a1f490cd", size = 10336126 }, - { url = "https://files.pythonhosted.org/packages/f5/76/a01261711ab60a22d71b862f0de20e4c504bf80457270ad8cb42110f6abc/pandas-3.0.3-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d72828c20c6d6e83e1e22a6a3b47b326b71664112fa9705dcbccfd7a39b62085", size = 10728051 }, - { url = "https://files.pythonhosted.org/packages/e9/21/ea191195e587b18cf682e97f433f81b2d0fbe341380e80a3e0d6e4403c8e/pandas-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d26cbe1fcfc12e8fd900e2454163e466b2d3af84f7c75481df7683ffc073d870", size = 11350796 }, - { url = "https://files.pythonhosted.org/packages/64/69/f0eaaf54939f0e8c6768fd06be9af2cef9b36048b96dfb9e1b2c685a807e/pandas-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:3e91cec1879ada0624fc3dc9953c5cbd60208e59c0db28f540c5d6d47502422f", size = 11799741 }, - { url = "https://files.pythonhosted.org/packages/45/a4/865e0e510cae5fc2194de4db28be638952de942571ba9125934fd9c01d47/pandas-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:08d789b41f87e0905880e293cedf6197ce71fe67cc081358b1e148a491b9bd13", size = 10499958 }, - { url = "https://files.pythonhosted.org/packages/86/54/effdcc3c0ff7a08037889200e148ebe94c16c4f653be078c7b3675955df1/pandas-3.0.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:3650109c0f22879df8bd6179ab9ee3d7f1d1d4e7e0094a3f0032d9f51e2e64ac", size = 10336065 }, - { url = "https://files.pythonhosted.org/packages/68/10/bf2d6738d72748b961a3751ab89522d58c54efc36a8e1a12161216cd45cf/pandas-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:bab900348131a7db1f69a7309ef141fd5680f1487094193bcbbb61791573bf8f", size = 9926101 }, - { url = "https://files.pythonhosted.org/packages/ae/e9/e35cf11c8a136e757b956f5f0efdcaa50aecde85ea055f1898dfc68262f3/pandas-3.0.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ba7e08b9ac1d54569cd1e256e3668975ed624d6826f7b68df0342b012007bddb", size = 10457553 }, - { url = "https://files.pythonhosted.org/packages/58/3b/1cdec6772bdbaf7b25dab360c59f03cadf05492dd724c6540af905389b07/pandas-3.0.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d71c63ae4ebdbf70209742096f1fc46a83a0613c99d4b23766cced9ff8cd62a", size = 10914065 }, - { url = "https://files.pythonhosted.org/packages/c4/c2/1ef644445fcd72e3627bceec77e3560636f87ddce4ed841afe76b83b5bf9/pandas-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e3a2ec42c98ffa2565a67e08e218d06d72576d758d90facb7c00805194d8f360", size = 11459188 }, - { url = "https://files.pythonhosted.org/packages/7e/49/4d8d4f42cbc9c4adc7a1870f269c02cbd6cd40d059622c06fb298addcbad/pandas-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:335f62418ed562cfc3c49e9e196375c28b729dcef8543abf4f9438e381bf3c76", size = 11982966 }, - { url = "https://files.pythonhosted.org/packages/38/55/792619469bab9882d8bbd5865d45a72f6478762d04a9af4bf0d08c503e95/pandas-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:3c20a521bbb85902f79f7270c80a59e1b5452d96d170c034f207181870f97ac5", size = 9876755 }, - { url = "https://files.pythonhosted.org/packages/2a/af/33c469653b0ba03b50c3a98192d4c07f0c75c66b263ceb097fce0ee97d31/pandas-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:a2d2dff8a04f3917b55ab3910c32990f8ddf7eceba114947838cefa976a68977", size = 9198658 }, - { url = "https://files.pythonhosted.org/packages/a2/fa/b8c257bd76b8bd060c3a9151c1fca05e9b9c5e3af5d0f549c0356f6d143d/pandas-3.0.3-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:0d589105b3c14645af1738ff279b2995102d8f7a03b0a66dc8d95550eb513e04", size = 10787242 }, - { url = "https://files.pythonhosted.org/packages/54/eb/f19206ffb0bf1919002969aa448b4702c6594845156a6f8050674855aac3/pandas-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:13fc1e853d9e04743d11ba75a985ccbc2a317fe07d8af61e445a6fd24dacd6a6", size = 10436369 }, - { url = "https://files.pythonhosted.org/packages/fd/24/c7c39fb4fe22b71a0c2d78bf0c585c600092d85f94f086d2b3b2f6ca27e2/pandas-3.0.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:819959dab7bbd0049c15623fbac4e29a191b9528160a61fb1032242d8ced2d9c", size = 10358306 }, - { url = "https://files.pythonhosted.org/packages/16/ec/dd2a9eb7fa1204df88c0864164e35b228ac581062ac612ba0a67fd812e4c/pandas-3.0.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:60ae316d3fd75d1858d450d0db0103ea2be3e7d4a95ec2f064f7e2ae63f7b028", size = 10758394 }, - { url = "https://files.pythonhosted.org/packages/95/6e/00c61ea8e85b4f6d8d35e11852a1a4998fc7fafc91c6a602d1cc9c972d64/pandas-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bd3a518890b400d32f9023722dc9a9a5c969f00b415419a3c06c043f09bb5d7d", size = 11375717 }, - { url = "https://files.pythonhosted.org/packages/31/89/8fc1c268969fac43688d65fd92e67df24bd128d53cb4d2eee534cd307399/pandas-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9c39be2d709d01fa972a0cabc522389fceca4f3969332ba25a7d6c5802cf976a", size = 11828897 }, - { url = "https://files.pythonhosted.org/packages/56/3b/e7d20dea247a3e6dc0bd8a6953854afbedc03951def4e7371e05e7263e25/pandas-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4db8c527972a821cf5286b40ccc57642a39bc62e62022b42f99f8a67fca8c3a1", size = 10900855 }, - { url = "https://files.pythonhosted.org/packages/0f/54/68a0978d1ef8502b8492099beaa6e7a0c1b32e3b5d4f677f5810cb08711c/pandas-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b2c95f8bfc1ee412bf482605d7bfd30c12d1d26bd59fdd91efeef1d4718decb1", size = 9466464 }, -] - -[[package]] -name = "pillow" -version = "12.2.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/8c/21/c2bcdd5906101a30244eaffc1b6e6ce71a31bd0742a01eb89e660ebfac2d/pillow-12.2.0.tar.gz", hash = "sha256:a830b1a40919539d07806aa58e1b114df53ddd43213d9c8b75847eee6c0182b5", size = 46987819 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/58/be/7482c8a5ebebbc6470b3eb791812fff7d5e0216c2be3827b30b8bb6603ed/pillow-12.2.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:2d192a155bbcec180f8564f693e6fd9bccff5a7af9b32e2e4bf8c9c69dbad6b5", size = 5308279 }, - { url = "https://files.pythonhosted.org/packages/d8/95/0a351b9289c2b5cbde0bacd4a83ebc44023e835490a727b2a3bd60ddc0f4/pillow-12.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f3f40b3c5a968281fd507d519e444c35f0ff171237f4fdde090dd60699458421", size = 4695490 }, - { url = "https://files.pythonhosted.org/packages/de/af/4e8e6869cbed569d43c416fad3dc4ecb944cb5d9492defaed89ddd6fe871/pillow-12.2.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:03e7e372d5240cc23e9f07deca4d775c0817bffc641b01e9c3af208dbd300987", size = 6284462 }, - { url = "https://files.pythonhosted.org/packages/e9/9e/c05e19657fd57841e476be1ab46c4d501bffbadbafdc31a6d665f8b737b6/pillow-12.2.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b86024e52a1b269467a802258c25521e6d742349d760728092e1bc2d135b4d76", size = 8094744 }, - { url = "https://files.pythonhosted.org/packages/2b/54/1789c455ed10176066b6e7e6da1b01e50e36f94ba584dc68d9eebfe9156d/pillow-12.2.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7371b48c4fa448d20d2714c9a1f775a81155050d383333e0a6c15b1123dda005", size = 6398371 }, - { url = "https://files.pythonhosted.org/packages/43/e3/fdc657359e919462369869f1c9f0e973f353f9a9ee295a39b1fea8ee1a77/pillow-12.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:62f5409336adb0663b7caa0da5c7d9e7bdbaae9ce761d34669420c2a801b2780", size = 7087215 }, - { url = "https://files.pythonhosted.org/packages/8b/f8/2f6825e441d5b1959d2ca5adec984210f1ec086435b0ed5f52c19b3b8a6e/pillow-12.2.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:01afa7cf67f74f09523699b4e88c73fb55c13346d212a59a2db1f86b0a63e8c5", size = 6509783 }, - { url = "https://files.pythonhosted.org/packages/67/f9/029a27095ad20f854f9dba026b3ea6428548316e057e6fc3545409e86651/pillow-12.2.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fc3d34d4a8fbec3e88a79b92e5465e0f9b842b628675850d860b8bd300b159f5", size = 7212112 }, - { url = "https://files.pythonhosted.org/packages/be/42/025cfe05d1be22dbfdb4f264fe9de1ccda83f66e4fc3aac94748e784af04/pillow-12.2.0-cp312-cp312-win32.whl", hash = "sha256:58f62cc0f00fd29e64b29f4fd923ffdb3859c9f9e6105bfc37ba1d08994e8940", size = 6378489 }, - { url = "https://files.pythonhosted.org/packages/5d/7b/25a221d2c761c6a8ae21bfa3874988ff2583e19cf8a27bf2fee358df7942/pillow-12.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:7f84204dee22a783350679a0333981df803dac21a0190d706a50475e361c93f5", size = 7084129 }, - { url = "https://files.pythonhosted.org/packages/10/e1/542a474affab20fd4a0f1836cb234e8493519da6b76899e30bcc5d990b8b/pillow-12.2.0-cp312-cp312-win_arm64.whl", hash = "sha256:af73337013e0b3b46f175e79492d96845b16126ddf79c438d7ea7ff27783a414", size = 2463612 }, - { url = "https://files.pythonhosted.org/packages/4a/01/53d10cf0dbad820a8db274d259a37ba50b88b24768ddccec07355382d5ad/pillow-12.2.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:8297651f5b5679c19968abefd6bb84d95fe30ef712eb1b2d9b2d31ca61267f4c", size = 4100837 }, - { url = "https://files.pythonhosted.org/packages/0f/98/f3a6657ecb698c937f6c76ee564882945f29b79bad496abcba0e84659ec5/pillow-12.2.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:50d8520da2a6ce0af445fa6d648c4273c3eeefbc32d7ce049f22e8b5c3daecc2", size = 4176528 }, - { url = "https://files.pythonhosted.org/packages/69/bc/8986948f05e3ea490b8442ea1c1d4d990b24a7e43d8a51b2c7d8b1dced36/pillow-12.2.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:766cef22385fa1091258ad7e6216792b156dc16d8d3fa607e7545b2b72061f1c", size = 3640401 }, - { url = "https://files.pythonhosted.org/packages/34/46/6c717baadcd62bc8ed51d238d521ab651eaa74838291bda1f86fe1f864c9/pillow-12.2.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5d2fd0fa6b5d9d1de415060363433f28da8b1526c1c129020435e186794b3795", size = 5308094 }, - { url = "https://files.pythonhosted.org/packages/71/43/905a14a8b17fdb1ccb58d282454490662d2cb89a6bfec26af6d3520da5ec/pillow-12.2.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:56b25336f502b6ed02e889f4ece894a72612fe885889a6e8c4c80239ff6e5f5f", size = 4695402 }, - { url = "https://files.pythonhosted.org/packages/73/dd/42107efcb777b16fa0393317eac58f5b5cf30e8392e266e76e51cff28c3d/pillow-12.2.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f1c943e96e85df3d3478f7b691f229887e143f81fedab9b20205349ab04d73ed", size = 6280005 }, - { url = "https://files.pythonhosted.org/packages/a8/68/b93e09e5e8549019e61acf49f65b1a8530765a7f812c77a7461bca7e4494/pillow-12.2.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:03f6fab9219220f041c74aeaa2939ff0062bd5c364ba9ce037197f4c6d498cd9", size = 8090669 }, - { url = "https://files.pythonhosted.org/packages/4b/6e/3ccb54ce8ec4ddd1accd2d89004308b7b0b21c4ac3d20fa70af4760a4330/pillow-12.2.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5cdfebd752ec52bf5bb4e35d9c64b40826bc5b40a13df7c3cda20a2c03a0f5ed", size = 6395194 }, - { url = "https://files.pythonhosted.org/packages/67/ee/21d4e8536afd1a328f01b359b4d3997b291ffd35a237c877b331c1c3b71c/pillow-12.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eedf4b74eda2b5a4b2b2fb4c006d6295df3bf29e459e198c90ea48e130dc75c3", size = 7082423 }, - { url = "https://files.pythonhosted.org/packages/78/5f/e9f86ab0146464e8c133fe85df987ed9e77e08b29d8d35f9f9f4d6f917ba/pillow-12.2.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:00a2865911330191c0b818c59103b58a5e697cae67042366970a6b6f1b20b7f9", size = 6505667 }, - { url = "https://files.pythonhosted.org/packages/ed/1e/409007f56a2fdce61584fd3acbc2bbc259857d555196cedcadc68c015c82/pillow-12.2.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1e1757442ed87f4912397c6d35a0db6a7b52592156014706f17658ff58bbf795", size = 7208580 }, - { url = "https://files.pythonhosted.org/packages/23/c4/7349421080b12fb35414607b8871e9534546c128a11965fd4a7002ccfbee/pillow-12.2.0-cp313-cp313-win32.whl", hash = "sha256:144748b3af2d1b358d41286056d0003f47cb339b8c43a9ea42f5fea4d8c66b6e", size = 6375896 }, - { url = "https://files.pythonhosted.org/packages/3f/82/8a3739a5e470b3c6cbb1d21d315800d8e16bff503d1f16b03a4ec3212786/pillow-12.2.0-cp313-cp313-win_amd64.whl", hash = "sha256:390ede346628ccc626e5730107cde16c42d3836b89662a115a921f28440e6a3b", size = 7081266 }, - { url = "https://files.pythonhosted.org/packages/c3/25/f968f618a062574294592f668218f8af564830ccebdd1fa6200f598e65c5/pillow-12.2.0-cp313-cp313-win_arm64.whl", hash = "sha256:8023abc91fba39036dbce14a7d6535632f99c0b857807cbbbf21ecc9f4717f06", size = 2463508 }, - { url = "https://files.pythonhosted.org/packages/4d/a4/b342930964e3cb4dce5038ae34b0eab4653334995336cd486c5a8c25a00c/pillow-12.2.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:042db20a421b9bafecc4b84a8b6e444686bd9d836c7fd24542db3e7df7baad9b", size = 5309927 }, - { url = "https://files.pythonhosted.org/packages/9f/de/23198e0a65a9cf06123f5435a5d95cea62a635697f8f03d134d3f3a96151/pillow-12.2.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:dd025009355c926a84a612fecf58bb315a3f6814b17ead51a8e48d3823d9087f", size = 4698624 }, - { url = "https://files.pythonhosted.org/packages/01/a6/1265e977f17d93ea37aa28aa81bad4fa597933879fac2520d24e021c8da3/pillow-12.2.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:88ddbc66737e277852913bd1e07c150cc7bb124539f94c4e2df5344494e0a612", size = 6321252 }, - { url = "https://files.pythonhosted.org/packages/3c/83/5982eb4a285967baa70340320be9f88e57665a387e3a53a7f0db8231a0cd/pillow-12.2.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d362d1878f00c142b7e1a16e6e5e780f02be8195123f164edf7eddd911eefe7c", size = 8126550 }, - { url = "https://files.pythonhosted.org/packages/4e/48/6ffc514adce69f6050d0753b1a18fd920fce8cac87620d5a31231b04bfc5/pillow-12.2.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2c727a6d53cb0018aadd8018c2b938376af27914a68a492f59dfcaca650d5eea", size = 6433114 }, - { url = "https://files.pythonhosted.org/packages/36/a3/f9a77144231fb8d40ee27107b4463e205fa4677e2ca2548e14da5cf18dce/pillow-12.2.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd8c21c98c5cc60653bcb311bef2ce0401642b7ce9d09e03a7da87c878289d4", size = 7115667 }, - { url = "https://files.pythonhosted.org/packages/c1/fc/ac4ee3041e7d5a565e1c4fd72a113f03b6394cc72ab7089d27608f8aaccb/pillow-12.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9f08483a632889536b8139663db60f6724bfcb443c96f1b18855860d7d5c0fd4", size = 6538966 }, - { url = "https://files.pythonhosted.org/packages/c0/a8/27fb307055087f3668f6d0a8ccb636e7431d56ed0750e07a60547b1e083e/pillow-12.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dac8d77255a37e81a2efcbd1fc05f1c15ee82200e6c240d7e127e25e365c39ea", size = 7238241 }, - { url = "https://files.pythonhosted.org/packages/ad/4b/926ab182c07fccae9fcb120043464e1ff1564775ec8864f21a0ebce6ac25/pillow-12.2.0-cp313-cp313t-win32.whl", hash = "sha256:ee3120ae9dff32f121610bb08e4313be87e03efeadfc6c0d18f89127e24d0c24", size = 6379592 }, - { url = "https://files.pythonhosted.org/packages/c2/c4/f9e476451a098181b30050cc4c9a3556b64c02cf6497ea421ac047e89e4b/pillow-12.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:325ca0528c6788d2a6c3d40e3568639398137346c3d6e66bb61db96b96511c98", size = 7085542 }, - { url = "https://files.pythonhosted.org/packages/00/a4/285f12aeacbe2d6dc36c407dfbbe9e96d4a80b0fb710a337f6d2ad978c75/pillow-12.2.0-cp313-cp313t-win_arm64.whl", hash = "sha256:2e5a76d03a6c6dcef67edabda7a52494afa4035021a79c8558e14af25313d453", size = 2465765 }, - { url = "https://files.pythonhosted.org/packages/bf/98/4595daa2365416a86cb0d495248a393dfc84e96d62ad080c8546256cb9c0/pillow-12.2.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:3adc9215e8be0448ed6e814966ecf3d9952f0ea40eb14e89a102b87f450660d8", size = 4100848 }, - { url = "https://files.pythonhosted.org/packages/0b/79/40184d464cf89f6663e18dfcf7ca21aae2491fff1a16127681bf1fa9b8cf/pillow-12.2.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:6a9adfc6d24b10f89588096364cc726174118c62130c817c2837c60cf08a392b", size = 4176515 }, - { url = "https://files.pythonhosted.org/packages/b0/63/703f86fd4c422a9cf722833670f4f71418fb116b2853ff7da722ea43f184/pillow-12.2.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:6a6e67ea2e6feda684ed370f9a1c52e7a243631c025ba42149a2cc5934dec295", size = 3640159 }, - { url = "https://files.pythonhosted.org/packages/71/e0/fb22f797187d0be2270f83500aab851536101b254bfa1eae10795709d283/pillow-12.2.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:2bb4a8d594eacdfc59d9e5ad972aa8afdd48d584ffd5f13a937a664c3e7db0ed", size = 5312185 }, - { url = "https://files.pythonhosted.org/packages/ba/8c/1a9e46228571de18f8e28f16fabdfc20212a5d019f3e3303452b3f0a580d/pillow-12.2.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:80b2da48193b2f33ed0c32c38140f9d3186583ce7d516526d462645fd98660ae", size = 4695386 }, - { url = "https://files.pythonhosted.org/packages/70/62/98f6b7f0c88b9addd0e87c217ded307b36be024d4ff8869a812b241d1345/pillow-12.2.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22db17c68434de69d8ecfc2fe821569195c0c373b25cccb9cbdacf2c6e53c601", size = 6280384 }, - { url = "https://files.pythonhosted.org/packages/5e/03/688747d2e91cfbe0e64f316cd2e8005698f76ada3130d0194664174fa5de/pillow-12.2.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7b14cc0106cd9aecda615dd6903840a058b4700fcb817687d0ee4fc8b6e389be", size = 8091599 }, - { url = "https://files.pythonhosted.org/packages/f6/35/577e22b936fcdd66537329b33af0b4ccfefaeabd8aec04b266528cddb33c/pillow-12.2.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cbeb542b2ebc6fcdacabf8aca8c1a97c9b3ad3927d46b8723f9d4f033288a0f", size = 6396021 }, - { url = "https://files.pythonhosted.org/packages/11/8d/d2532ad2a603ca2b93ad9f5135732124e57811d0168155852f37fbce2458/pillow-12.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4bfd07bc812fbd20395212969e41931001fd59eb55a60658b0e5710872e95286", size = 7083360 }, - { url = "https://files.pythonhosted.org/packages/5e/26/d325f9f56c7e039034897e7380e9cc202b1e368bfd04d4cbe6a441f02885/pillow-12.2.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9aba9a17b623ef750a4d11b742cbafffeb48a869821252b30ee21b5e91392c50", size = 6507628 }, - { url = "https://files.pythonhosted.org/packages/5f/f7/769d5632ffb0988f1c5e7660b3e731e30f7f8ec4318e94d0a5d674eb65a4/pillow-12.2.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:deede7c263feb25dba4e82ea23058a235dcc2fe1f6021025dc71f2b618e26104", size = 7209321 }, - { url = "https://files.pythonhosted.org/packages/6a/7a/c253e3c645cd47f1aceea6a8bacdba9991bf45bb7dfe927f7c893e89c93c/pillow-12.2.0-cp314-cp314-win32.whl", hash = "sha256:632ff19b2778e43162304d50da0181ce24ac5bb8180122cbe1bf4673428328c7", size = 6479723 }, - { url = "https://files.pythonhosted.org/packages/cd/8b/601e6566b957ca50e28725cb6c355c59c2c8609751efbecd980db44e0349/pillow-12.2.0-cp314-cp314-win_amd64.whl", hash = "sha256:4e6c62e9d237e9b65fac06857d511e90d8461a32adcc1b9065ea0c0fa3a28150", size = 7217400 }, - { url = "https://files.pythonhosted.org/packages/d6/94/220e46c73065c3e2951bb91c11a1fb636c8c9ad427ac3ce7d7f3359b9b2f/pillow-12.2.0-cp314-cp314-win_arm64.whl", hash = "sha256:b1c1fbd8a5a1af3412a0810d060a78b5136ec0836c8a4ef9aa11807f2a22f4e1", size = 2554835 }, - { url = "https://files.pythonhosted.org/packages/b6/ab/1b426a3974cb0e7da5c29ccff4807871d48110933a57207b5a676cccc155/pillow-12.2.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:57850958fe9c751670e49b2cecf6294acc99e562531f4bd317fa5ddee2068463", size = 5314225 }, - { url = "https://files.pythonhosted.org/packages/19/1e/dce46f371be2438eecfee2a1960ee2a243bbe5e961890146d2dee1ff0f12/pillow-12.2.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d5d38f1411c0ed9f97bcb49b7bd59b6b7c314e0e27420e34d99d844b9ce3b6f3", size = 4698541 }, - { url = "https://files.pythonhosted.org/packages/55/c3/7fbecf70adb3a0c33b77a300dc52e424dc22ad8cdc06557a2e49523b703d/pillow-12.2.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5c0a9f29ca8e79f09de89293f82fc9b0270bb4af1d58bc98f540cc4aedf03166", size = 6322251 }, - { url = "https://files.pythonhosted.org/packages/1c/3c/7fbc17cfb7e4fe0ef1642e0abc17fc6c94c9f7a16be41498e12e2ba60408/pillow-12.2.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1610dd6c61621ae1cf811bef44d77e149ce3f7b95afe66a4512f8c59f25d9ebe", size = 8127807 }, - { url = "https://files.pythonhosted.org/packages/ff/c3/a8ae14d6defd2e448493ff512fae903b1e9bd40b72efb6ec55ce0048c8ce/pillow-12.2.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0a34329707af4f73cf1782a36cd2289c0368880654a2c11f027bcee9052d35dd", size = 6433935 }, - { url = "https://files.pythonhosted.org/packages/6e/32/2880fb3a074847ac159d8f902cb43278a61e85f681661e7419e6596803ed/pillow-12.2.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8e9c4f5b3c546fa3458a29ab22646c1c6c787ea8f5ef51300e5a60300736905e", size = 7116720 }, - { url = "https://files.pythonhosted.org/packages/46/87/495cc9c30e0129501643f24d320076f4cc54f718341df18cc70ec94c44e1/pillow-12.2.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:fb043ee2f06b41473269765c2feae53fc2e2fbf96e5e22ca94fb5ad677856f06", size = 6540498 }, - { url = "https://files.pythonhosted.org/packages/18/53/773f5edca692009d883a72211b60fdaf8871cbef075eaa9d577f0a2f989e/pillow-12.2.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f278f034eb75b4e8a13a54a876cc4a5ab39173d2cdd93a638e1b467fc545ac43", size = 7239413 }, - { url = "https://files.pythonhosted.org/packages/c9/e4/4b64a97d71b2a83158134abbb2f5bd3f8a2ea691361282f010998f339ec7/pillow-12.2.0-cp314-cp314t-win32.whl", hash = "sha256:6bb77b2dcb06b20f9f4b4a8454caa581cd4dd0643a08bacf821216a16d9c8354", size = 6482084 }, - { url = "https://files.pythonhosted.org/packages/ba/13/306d275efd3a3453f72114b7431c877d10b1154014c1ebbedd067770d629/pillow-12.2.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6562ace0d3fb5f20ed7290f1f929cae41b25ae29528f2af1722966a0a02e2aa1", size = 7225152 }, - { url = "https://files.pythonhosted.org/packages/ff/6e/cf826fae916b8658848d7b9f38d88da6396895c676e8086fc0988073aaf8/pillow-12.2.0-cp314-cp314t-win_arm64.whl", hash = "sha256:aa88ccfe4e32d362816319ed727a004423aab09c5cea43c01a4b435643fa34eb", size = 2556579 }, -] - -[[package]] -name = "platformdirs" -version = "4.9.6" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9f/4a/0883b8e3802965322523f0b200ecf33d31f10991d0401162f4b23c698b42/platformdirs-4.9.6.tar.gz", hash = "sha256:3bfa75b0ad0db84096ae777218481852c0ebc6c727b3168c1b9e0118e458cf0a", size = 29400 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/75/a6/a0a304dc33b49145b21f4808d763822111e67d1c3a32b524a1baf947b6e1/platformdirs-4.9.6-py3-none-any.whl", hash = "sha256:e61adb1d5e5cb3441b4b7710bea7e4c12250ca49439228cc1021c00dcfac0917", size = 21348 }, -] - -[[package]] -name = "pluggy" -version = "1.6.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538 }, -] - -[[package]] -name = "propcache" -version = "0.5.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ec/44/c87281c333769159c50594f22610f77398a47ccbfbbf23074e744e86f87c/propcache-0.5.2.tar.gz", hash = "sha256:01c4fc7480cd0598bb4b57022df55b9ca296da7fc5a8760bd8451a7e63a7d427", size = 50208 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4a/cb/e27bc2b2737a0bb49962b275efa051e8f1c35a936df7d5139b6b658b7dc9/propcache-0.5.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:806719138ecd720339a12410fb9614ac9b2b2d3a5fdf8235d56981c36f4039ba", size = 95887 }, - { url = "https://files.pythonhosted.org/packages/e6/13/b8ae04c59392f8d11c6cd9fb4011d1dc7c86b81225c770280300e259ffe1/propcache-0.5.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:db2b80ea58eab4f86b2beec3cc8b39e8ff9276ac20e96b7cce43c8ae84cd6b5a", size = 54654 }, - { url = "https://files.pythonhosted.org/packages/2c/7d/49777a3e20b55863d4794384a38acd460c04157b0a00f8602b0d508b8431/propcache-0.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e5cbfac9f61484f7e9f3597775500cd3ebe8274e9b050c38f9525c77c97520bf", size = 55190 }, - { url = "https://files.pythonhosted.org/packages/44/c7/085d0cd63062e84044e3f05797749c3f8e3938ff3aeb0eb2f69d43fafc91/propcache-0.5.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5dbc581d2814337da56222fab8dc5f161cd798a434e49bac27930aaef798e144", size = 59995 }, - { url = "https://files.pythonhosted.org/packages/9c/42/32cf8e3009e92b2645cf1e944f701e8ea4e924dffde1ee26db860bcbf7e4/propcache-0.5.2-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:857187f381f88c8e2fa2fe56ab94879d011b883d5a2ee5a1b60a8cd2a06846d9", size = 63422 }, - { url = "https://files.pythonhosted.org/packages/9e/1b/f112433f99fc979431b87a39ef169e3f8df070d99a72792c56d6937ac48b/propcache-0.5.2-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:178b4a2cdaac1818e2bf1c5a99b94383fa73ea5382e032a48dec07dc5668dc42", size = 64342 }, - { url = "https://files.pythonhosted.org/packages/14/15/5574111ae50dd6e879456888c0eadd4c5a869959775854e18e18a6b345f3/propcache-0.5.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f328175a2cde1f0ff2c4ed8ce968b9dcfb55f3a7153f39e2957ed994da13476", size = 61639 }, - { url = "https://files.pythonhosted.org/packages/cc/da/4d775080b1490c0ae604acda868bd71aabe3a89ed16f2aa4339eb8a283e7/propcache-0.5.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:5671d09a36b06d0fd4a3da0fccbcae360e9b1570924171a15e9e0997f0249fba", size = 61588 }, - { url = "https://files.pythonhosted.org/packages/04/ac/f076982cbe2195ee9cf32de5a1e46951d9fb399fc207f390562dd0fd8fb2/propcache-0.5.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:80168e2ebe4d3ec6599d10ad8f520304ae1cad9b6c5a95372aef1b66b7bfb53a", size = 60029 }, - { url = "https://files.pythonhosted.org/packages/70/60/189be62e0dd898dce3b331e1b8c7a543cd3a405ac0c81fe8ee8a9d5d77e1/propcache-0.5.2-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:45f11346f884bc47444f6e6647131055844134c3175b629f84952e2b5cd62b64", size = 56774 }, - { url = "https://files.pythonhosted.org/packages/ea/9e/93377b9c7939c1ffae98f878dee955efadfd638078bc86dbc21f9d52f651/propcache-0.5.2-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:8e778ebd44ef4f66ed60a0416b06b489687db264a9c0b3620362f26489492913", size = 63532 }, - { url = "https://files.pythonhosted.org/packages/14/f9/590ef6cfb9b8028d516d287812ece32bb0bc5f11fbb9c8bf6b2e6313fec8/propcache-0.5.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:c0cb9ed24c8964e172768d455a38254c2dd8a552905729ce006cad3d3dda59b1", size = 61592 }, - { url = "https://files.pythonhosted.org/packages/b4/5e/70958b3034c297a630bba2f17ca7abc2d5f39a803ad7e370ab79d1ecd022/propcache-0.5.2-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:1d1ad32d9d4355e2be65574fd0bfd3677e7066b009cd5b9b2dee8aa6a6393b33", size = 64788 }, - { url = "https://files.pythonhosted.org/packages/12/fd/77fe5936d8c3086ca9048f7f415f122ed82e53884a9ec193646b42deef06/propcache-0.5.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c80f4ba3e8f00189165999a742ee526ebeccedf6c3f7beb0c7df821e9772435a", size = 62514 }, - { url = "https://files.pythonhosted.org/packages/cf/74/66bd798b5b3be70aa1b391f5cc9d6a0a5532d7fd3b19ec0b213e72e6ad9d/propcache-0.5.2-cp312-cp312-win32.whl", hash = "sha256:8c7972d8f193740d9175f0998ab38717e6cd322d5935c5b0fef8c0d323fd9031", size = 39018 }, - { url = "https://files.pythonhosted.org/packages/61/7c/5c0d34aa3024694d6dcb9271cdbdd08c4e47c1c0ad95ec7e7bc74cdea145/propcache-0.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:d9ee8826a7d47863a08ac44e1a5f611a462eefc3a194b492da242128bec75b42", size = 42322 }, - { url = "https://files.pythonhosted.org/packages/4d/91/875812f1a3feb20ceba818ef39fbe4d92f1081e04ac815c822496d0d038b/propcache-0.5.2-cp312-cp312-win_arm64.whl", hash = "sha256:2800a4a8ead6b28cccd1ec54b59346f0def7922ee1c7598e8499c733cfbb7c84", size = 38172 }, - { url = "https://files.pythonhosted.org/packages/c5/09/f049e45385503fe67db75a6b6186a7b9f0c3930366dc960522c312a825b1/propcache-0.5.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:099aaf4b4d1a02265b92a977edf00b5c4f63b3b17ac6de39b0d637c9cac0188a", size = 94457 }, - { url = "https://files.pythonhosted.org/packages/6b/65/83d1d05655baf63113731bd5a1008435e14f8d1e5a06cbe4ec5b23ad7a31/propcache-0.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:68ce1c44c7a813a7f71ea04315a8c7b330b63db99d059a797a4651bb6f69f117", size = 53835 }, - { url = "https://files.pythonhosted.org/packages/a9/12/a6ba6482bb5ea3260c000c9b20881c95fa11c6b30173715668259f844ed7/propcache-0.5.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:fc299c129490f55f254cd90be0deca4764e36e9a7c08b4aa588479a3bbed3098", size = 54545 }, - { url = "https://files.pythonhosted.org/packages/a9/19/7fa086f5764c59ec8a8e157cd93aa8497acc00aba9dcdec56bfffb32602d/propcache-0.5.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a6ae2198be502c10f09b2516e7b5d019816924bc3183a43ce792a7bd6625e6f4", size = 59886 }, - { url = "https://files.pythonhosted.org/packages/a1/e4/5d7663dc8235956c8f5281698a3af1d351d8820341ddd890f59d9a9127f2/propcache-0.5.2-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6041d31504dc1779d700e1edcfb08eea334b357620b06681a4eabb57a74e574e", size = 63261 }, - { url = "https://files.pythonhosted.org/packages/4a/4a/15a03adee24d6350da4292caeac44c34c033d2afe5e87eb370f38854560f/propcache-0.5.2-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f7eabc04151c78a9f4d5bbb5f1faf571e4defeb4b585e0fe95b60ff2dbe4d3d7", size = 64184 }, - { url = "https://files.pythonhosted.org/packages/8b/c6/979176efdaa3d239e36d503d5af63a0a773b36662ed8f52e5b6a6d9fd40e/propcache-0.5.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4db0ba63d693afd40d249bd93f842b5f144f8fcbb83de05660373bcf30517b1d", size = 61534 }, - { url = "https://files.pythonhosted.org/packages/c8/22/63e8cd1bae4c2d2be6493b6b7d10566ddafad88137cfbc99964a1119853c/propcache-0.5.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1dbcf7675229b35d31abb6547d8ebc8c27a830ac3f9a794edff6254873ec7c0a", size = 61500 }, - { url = "https://files.pythonhosted.org/packages/60/5a/28e5d9acbac1cc9ccb67045e8c1b943aa8d79fdf39c93bd73cacd68008ea/propcache-0.5.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d310c013aad2c72f1c3f2f8dd3279d460a858c551f97aeb8c63e4693cca7b4d2", size = 59994 }, - { url = "https://files.pythonhosted.org/packages/f3/40/db650677f554a95b9c01a7c9d93d629e93a15562f5deb4573c9ee136fed2/propcache-0.5.2-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:06187263ddad280d05b4d8a8b3bb7d164cbebd469236544a42e6d9b28ac6a4fa", size = 56884 }, - { url = "https://files.pythonhosted.org/packages/80/45/70b39b89516ff8b96bf732fa6fded8cef20f293cb1508690101c3c07ec51/propcache-0.5.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3115559b8effafd63b142ea5ed53d63a16ea6469cbc63dce4ee194b42db5d853", size = 63464 }, - { url = "https://files.pythonhosted.org/packages/f9/e2/fa59d3a89eac5534293124af4f1d0d0ada091ce4a0ab4610ce03fd2bdd8d/propcache-0.5.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c60462af8e6dc30c35407c7237ea908d777b22862bbee27bc4699c0d8bcdc45a", size = 61588 }, - { url = "https://files.pythonhosted.org/packages/0b/97/efb547a55c4bc7381cfb202d6a2239ac621045277bc1ea5dfd3a7f0516c0/propcache-0.5.2-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:40314bca9ac559716fe374094fc81c11dcc34b64fd6c585360f5775690505704", size = 64667 }, - { url = "https://files.pythonhosted.org/packages/92/56/f5c7d9b4b7595d5127da38974d791b2153f3d1eae6c674af3583ace92ad3/propcache-0.5.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:cfa21e036ce1e1db2be04ba3b85d2df1bb1702fa01932d984c5464c665228ff4", size = 62463 }, - { url = "https://files.pythonhosted.org/packages/bd/3b/484a3a65fc9f9f60c41dcd17b428bace5389544e2c680994534a20755066/propcache-0.5.2-cp313-cp313-win32.whl", hash = "sha256:f156a3529f38063b6dbaf356e15602a7f95f8055b1295a438433a6386f10463d", size = 38621 }, - { url = "https://files.pythonhosted.org/packages/1c/fd/3f0f10dba4dabad3bf53102be007abf55481067952bde0fdddff439e7c61/propcache-0.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:dfed59d0a5aeb01e242e66ff0300bc4a265a7c05f612d30016f0b60b1017d757", size = 41649 }, - { url = "https://files.pythonhosted.org/packages/90/ec/6ce619cc32bb500a482f811f9cd509368b4e58e638d13f2c68f370d6b475/propcache-0.5.2-cp313-cp313-win_arm64.whl", hash = "sha256:ba338430e87ceb9c8f0cf754de38a9860560261e56c00376debd628698a7364f", size = 37636 }, - { url = "https://files.pythonhosted.org/packages/1b/82/c1d268bbbf2ef981c5bf0fbbe746db617c66e3bcefe431a1aa8943fbe23a/propcache-0.5.2-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:a592f5f3da71c8691c788c13cb6734b6d17663d2e1cb8caddf0673d01ef8847d", size = 98872 }, - { url = "https://files.pythonhosted.org/packages/f4/d4/52c871e73e864e6b34c0e2d58ac1ec5ccd149497ddc7ad2137ae98323a35/propcache-0.5.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:6a997d0489e9668a384fcfd5061b857aa5361de73191cac204d04b889cfbbafa", size = 56257 }, - { url = "https://files.pythonhosted.org/packages/67/f0/9b90ca2a210b3d09bcfcd96ecd0f55545c091535abce2a45de2775cfd357/propcache-0.5.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:10734b5484ea113152ee25a91dccedf81631791805d2c9ccb054958e51842c94", size = 56696 }, - { url = "https://files.pythonhosted.org/packages/9d/0e/6e9d4ba07c8e56e21ddec1e75f12148142b21ca83a51871babce095334f4/propcache-0.5.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cafca7e56c12bb02ae16d283742bef25a61122e9dab2b5b3f2ccbe589ce32164", size = 62378 }, - { url = "https://files.pythonhosted.org/packages/65/19/c10badaa463dde8a27ce884f8ee2ec37e6035b7c9f5ff0c8f74f06f08dac/propcache-0.5.2-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f064f8d2b59177878b7615df1735cd8fe3462ed6be8c7b217d17a276489c2b7f", size = 65283 }, - { url = "https://files.pythonhosted.org/packages/b0/b6/93bea99ca80e19cef6512a8580e5b7857bbe09422d9daa7fd4ef5723306c/propcache-0.5.2-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f78abfa8dfc32376fd1aacf597b2f2fbbe0ea751419aee718af5d4f82537ef8c", size = 66616 }, - { url = "https://files.pythonhosted.org/packages/83/e4/5c7462e50625f051f37fb38b8224f7639f667184bbd34424ec83819bb1b7/propcache-0.5.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f7467da8a9822bf1a55336f877340c5bcbd3c482afc43a99771169f74a26dedc", size = 63773 }, - { url = "https://files.pythonhosted.org/packages/ca/b6/99238894047b13c823be25027e736626cd414a52a5e30d2c3347c2733529/propcache-0.5.2-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a6ddc6ac9e25de626c1f129c1b467d7ecd33ce2237d3fd0c4e429feef0a7ee1f", size = 63664 }, - { url = "https://files.pythonhosted.org/packages/85/1e/a3a1a63116a2b8edb415a8bb9a6f0c34bd03830b1e18e8ce2904e1dc1cf4/propcache-0.5.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:2f22cbbac9e26a8e864c0985ff1268d5d939d53d9d9411a9824279097e03a2cb", size = 62643 }, - { url = "https://files.pythonhosted.org/packages/e4/03/893cf147de2fc6543c5eaa07ad833170e7e2a2385725bbebe8c0503723bb/propcache-0.5.2-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:fc76378c62a0f04d0cd82fbb1a2cd2d7e28fcb40d5873f28a6c44e388aaa2751", size = 59595 }, - { url = "https://files.pythonhosted.org/packages/86/3b/04c1a2e12c57766568ba75ba72b3bf2042818d4c1425fab6fc07155c7cff/propcache-0.5.2-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:acd2c8edba48e31e58a363b8cf4e5c7db3b04b3f9e371f601df30d9b0d244836", size = 65711 }, - { url = "https://files.pythonhosted.org/packages/1c/34/80f8d0099f8d6bacc4de1624c85672681c8cd1149ca2da0e38fd120b817f/propcache-0.5.2-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:452b5065457eb9991ec5eb38ff41d6cd4c991c9ac7c531c4d5849ae473a9a13f", size = 64247 }, - { url = "https://files.pythonhosted.org/packages/f3/1a/8b08f3a5f1037e9e370c55883ceeeee0f6dd0416fb2d2d67b8bfc91f2a79/propcache-0.5.2-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:3430bb2bfe1331885c427745a751e774ee679fd4344f80b97bf879815fe8fa55", size = 67102 }, - { url = "https://files.pythonhosted.org/packages/34/68/8bdb7bb7756d76e005490649d10e4a8369e610c74d619f71e1aedf889e9c/propcache-0.5.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:cef6cea3922890dd6c9654971001fa797b526c16ab5e1e46c05fd6f877be7568", size = 64964 }, - { url = "https://files.pythonhosted.org/packages/0a/aa/50fb0b5d3968b61a510926ff8b8465f1d6e976b3ab74496d7a4b9fc42515/propcache-0.5.2-cp313-cp313t-win32.whl", hash = "sha256:72d61e16dd78228b58c5d47be830ff3da7e5f139abdf0aef9d86cde1c5cf2191", size = 42546 }, - { url = "https://files.pythonhosted.org/packages/ae/4c/0ddbae64321bd4a95bcbfc19307238016b5b1fee645c84626c8d539e5b74/propcache-0.5.2-cp313-cp313t-win_amd64.whl", hash = "sha256:0958834041a0166d343b8d2cedcd8bcbaeb4fdbe0cf08320c5379f143c3be6e7", size = 46330 }, - { url = "https://files.pythonhosted.org/packages/00/d9/9cddc8efb78d8af264c5ec9f6d10b62f57c515feda8d321595f56010fb23/propcache-0.5.2-cp313-cp313t-win_arm64.whl", hash = "sha256:6de8bd93ddde9b992cf2b2e0d796d501a19026b5b9fd87356d7d0779531a8d96", size = 40521 }, - { url = "https://files.pythonhosted.org/packages/e2/ea/23ee535d90ce8bcc465a3028eb3cc0ce3bd1005f4bb27710b30587de798d/propcache-0.5.2-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:46088abff4cba581dea21ae0467a480526cb25aa5f3c269e909f800328bc3999", size = 94662 }, - { url = "https://files.pythonhosted.org/packages/b5/06/c5a52f419b5d8972f8d46a7577476090d8e3263ff589ce40b5ca4968d5be/propcache-0.5.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:fc88b26f08d634f7bc819a7852e5214f5802641ab8d9fd5326892292eee1993e", size = 53928 }, - { url = "https://files.pythonhosted.org/packages/63/b1/4260d67d6bd85e58a66b72d54ce15d5de789b6f3870cc6bedf8ff9667401/propcache-0.5.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:97797ebb098e670a2f92dd66f32897e30d7615b14e7f59711de23e30a9072539", size = 54650 }, - { url = "https://files.pythonhosted.org/packages/70/06/2f46c318e3307cd7a6a7481def374ce838c0fe20084b39dd54b0879d0e99/propcache-0.5.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ba57fffe4ac99c5d30076161b5866336d97600769bad35cc68f7774b15298a4e", size = 59912 }, - { url = "https://files.pythonhosted.org/packages/4c/29/fe1aebec2ce57ab985a9c382bded1124431f85078113aa222c5d278430d4/propcache-0.5.2-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:583c19759d9eec1e5b69e2fbef36a7d9c326041be9746cb822d335c8cedc2979", size = 63300 }, - { url = "https://files.pythonhosted.org/packages/b4/18/2334b26768b6c82be8c69e83671b767d5ef426aa09b0cba6c2ea47816774/propcache-0.5.2-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d0326e2e5e1f3163fa306c834e48e8d490e5fae607a097a40c0648109b47ba80", size = 64208 }, - { url = "https://files.pythonhosted.org/packages/2b/76/7f1bfd6afff4c5e38e36a3c6d68eb5f4b7311ea80baf693db78d95b603c4/propcache-0.5.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e00820e192c8dbebcafb383ebbf99030895f09905e7a0eb2e0340a0bcc2bc825", size = 61633 }, - { url = "https://files.pythonhosted.org/packages/c4/46/b3ff8aba2b4953a3e50de2cf72f1b5748b8eca93b15f3dc2c84339084c09/propcache-0.5.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c66afea89b1e43725731d2004732a046fe6fe955d51f952c3e95a7314a284a39", size = 61724 }, - { url = "https://files.pythonhosted.org/packages/c5/01/814cfcafbcff954f94c01cf30e097ddc88a076b5440fbcf4570753437d40/propcache-0.5.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d4dc37dec6c6cdad0b57881a5658fd14fbf53e333b1a86cf86559f190e1d9ec4", size = 60069 }, - { url = "https://files.pythonhosted.org/packages/da/68/5c6f7622d510cc666a300687e06fd060c1a43361c0c9b20d284f06d8096a/propcache-0.5.2-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:5570dbcc97571c15f68068e529c92715a12f8d54030e272d264b377e22bd17a5", size = 57099 }, - { url = "https://files.pythonhosted.org/packages/55/27/9cb0b4c679124085327957d42521c99dba04c88c90c3e55a6f0b633ebccc/propcache-0.5.2-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:f814362777a9f841adddb200ecdf8f5cb1e5a3c4b7a86378edbd6ccb26edd702", size = 63391 }, - { url = "https://files.pythonhosted.org/packages/f0/9d/7258aaa5bdf60fc6f27591eef6fe52768cb0beda7140be477c8b12c9794a/propcache-0.5.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:196913dea116aeb5a2ba95af4ddcb7ea85559ae07d8eee8751688310d09168c3", size = 61626 }, - { url = "https://files.pythonhosted.org/packages/8e/0d/41c602003e8a9b16fe1e7eadf62c7bfba9d5474370b24200bf48b315f45f/propcache-0.5.2-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:6e7b8719005dd1175be4ab1cd25e9b98659a5e0347331506ec6760d2773a7fb5", size = 64781 }, - { url = "https://files.pythonhosted.org/packages/8b/f3/38e66b1856e9bd079deea015bc4a55f7767c0e4db2f7dcf69e7e680ba4ce/propcache-0.5.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:51f96d685ab16e88cab128cd37a52c5da540809c8b879fa047731bfcb4ad35a4", size = 62570 }, - { url = "https://files.pythonhosted.org/packages/95/ca/bbfe9b910ce57dde8bb4876b4520fc02a4e89497c10de26be936758a3aaa/propcache-0.5.2-cp314-cp314-win32.whl", hash = "sha256:cc6fc3cc62e8501d3ed62894425040d2728ecddb1ed072737a5c70bd537aa9f0", size = 39436 }, - { url = "https://files.pythonhosted.org/packages/61/d2/45c9defbaa1ea297035d9d4cce9e8f80daafbf19319c6007f157c6256ea9/propcache-0.5.2-cp314-cp314-win_amd64.whl", hash = "sha256:81e3a30b0bb60caa22033dd0f8a3618d1d67356212514f62c57db75cb0ef410c", size = 42373 }, - { url = "https://files.pythonhosted.org/packages/44/68/9ea5103f41d5217d7d6ec24db90018e23aebec070c3f9a6e54d12b841fd8/propcache-0.5.2-cp314-cp314-win_arm64.whl", hash = "sha256:0d2c9bf8528f135dbb805ce027567e09164f7efa51a2be07458a2c0420f292d0", size = 38554 }, - { url = "https://files.pythonhosted.org/packages/8a/81/fadf555f42d3b762eea8a53950b0489fdc0aa9da5f8ed9e10ce0a4e01b48/propcache-0.5.2-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:4bc8ff1feffc6a61c7002ffe84634c41b822e104990ae009f44a0834430070bb", size = 99395 }, - { url = "https://files.pythonhosted.org/packages/f5/c9/c61e134a686949cf7971af3a390148b1156f7be81c73bc0cd12c873e2d48/propcache-0.5.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:79aa3ff0a9b566633b642fa9caf7e21ed1c13d6feca718187873f199e1514078", size = 56653 }, - { url = "https://files.pythonhosted.org/packages/cb/73/daf935ea7048ddd7ec8eec5345b4a40b619d2d178b3c0a0900796bc3c794/propcache-0.5.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1b31822f4474c4036bae62de9402710051d431a606d6a0f907fec79935a071aa", size = 56914 }, - { url = "https://files.pythonhosted.org/packages/79/9f/aba959b435ea18617edd7cf0a7ad0b9c574b8fc7e3d2cd55fb59cb255d33/propcache-0.5.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:13fef48778b5a2a756523fdb781326b028ca75e32858b04f2cdd19f394564917", size = 62567 }, - { url = "https://files.pythonhosted.org/packages/6c/a1/859942de9a791ff42f6141736f5b37749b8f53e65edfa49638c67dd67e6a/propcache-0.5.2-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8b73ab70f1a3351fbc71f663b3e645af6dd0329100c353081cf69c37433fc6fe", size = 65542 }, - { url = "https://files.pythonhosted.org/packages/b5/61/315bc0fd6c0fc7f80a528b8afd209e5fc4a875ea79571b91b8f50f442907/propcache-0.5.2-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5538d2c13d93e4698af7e092b57bc7298fd35d1d58e656ae18f23ee0d0378e03", size = 66845 }, - { url = "https://files.pythonhosted.org/packages/47/f7/9f8122e3132e8e354ac41975ef8f1099be7d5a16bc7ae562734e993665c0/propcache-0.5.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cd645f03898405cabe694fb8bc35241e3a9c332ec85627584fe3de201452b335", size = 63985 }, - { url = "https://files.pythonhosted.org/packages/c8/54/c317819ec157cbf6f35df9df9657a6f82daf34d5faf15948b2f639c2192e/propcache-0.5.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a473b3440261e0c60706e732b2ed2f517857344fc21bf48fdfe211e2d98eb285", size = 63999 }, - { url = "https://files.pythonhosted.org/packages/5a/56/387e3f7dfce0a9233df41fb888aa1c30222cb4bbbf09537c02dd9bd85fe2/propcache-0.5.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7afa37062e6650640e932e4cc9297d81f9f42d9944029cc386b8247dea4da837", size = 62779 }, - { url = "https://files.pythonhosted.org/packages/a1/9c/596784cb5824ed61ee960d3f8655a3f0993e107c6e98ab6c818b7fb92ccb/propcache-0.5.2-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:8a90efd5777e996e42d568db9ac740b944d691e565cbfd31b2f7832f9184b2b8", size = 59796 }, - { url = "https://files.pythonhosted.org/packages/c2/3d/1a6cfa1726a48542c1e8784a0761421476a5b68e09b7f36bf95eb954aaba/propcache-0.5.2-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:f19bb891234d72535764d703bfed1153cc34f4214d5bd7150aee1eec9e8f4366", size = 66023 }, - { url = "https://files.pythonhosted.org/packages/e4/0e/05fd6990369477076e4e280bcb970de760fddf0161a46e988bc95f7940ec/propcache-0.5.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:32775082acd2d807ee3db715c7770d38767b817870acfa08c29e057f3c4d5b56", size = 64448 }, - { url = "https://files.pythonhosted.org/packages/cd/86/5f8da315a4309c62c10c0b2516b17492d5d3bbe1bb862b96604db67e2a37/propcache-0.5.2-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:9282fb1a3bccd038da9f768b927b24a0c753e466c086b7c4f3c6982851eefb2d", size = 67329 }, - { url = "https://files.pythonhosted.org/packages/da/d3/3368efe79ab21f0cdf86ef49895811c9cc933131d4cde1f28a624e22e712/propcache-0.5.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cc49723e2f60d6b32a0f0b08a3fd6d13203c07f1cd9566cfce0f12a917c967a2", size = 65172 }, - { url = "https://files.pythonhosted.org/packages/d5/07/127e8b0bacfb325396196f9d976a22453049b89b9b2b08477cc3145faa44/propcache-0.5.2-cp314-cp314t-win32.whl", hash = "sha256:2d7aa89ebca5acc98cba9d1472d976e394782f587bad6661003602a619fd1821", size = 43813 }, - { url = "https://files.pythonhosted.org/packages/88/fb/46dad6c0ae49ed230ab1b16c890c2b6314e2403e6c412976f4a72d64a527/propcache-0.5.2-cp314-cp314t-win_amd64.whl", hash = "sha256:d447bb0b3054be5818458fbb171208b1d9ff11eba14e18ca18b90cbb45767370", size = 47764 }, - { url = "https://files.pythonhosted.org/packages/e7/c4/a47d0a63aa309d10d59ede6e9d4cff03a344a79d1f0f4cd0cd74997b53e0/propcache-0.5.2-cp314-cp314t-win_arm64.whl", hash = "sha256:fe67a3d11cd9b4efabfa45c3d00ffba2b26811442a73a581a94b67c2b5faccf6", size = 41140 }, - { url = "https://files.pythonhosted.org/packages/3a/ed/1cdcab6ba3d6ab7feca11fc14f0eeea80755bb53ef4e892079f31b10a25f/propcache-0.5.2-py3-none-any.whl", hash = "sha256:be1ddfcbb376e3de5d2e2db1d58d6d67463e6b4f9f040c000de8e300295465fe", size = 14036 }, -] - -[[package]] -name = "pyarrow" -version = "24.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/91/13/13e1069b351bdc3881266e11147ffccf687505dbb0ea74036237f5d454a5/pyarrow-24.0.0.tar.gz", hash = "sha256:85fe721a14dd823aca09127acbb06c3ca723efbd436c004f16bca601b04dcc83", size = 1180261 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b4/a9/9686d9f07837f91f775e8932659192e02c74f9d8920524b480b85212cc68/pyarrow-24.0.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:6233c9ed9ab9d1db47de57d9753256d9dcffbf42db341576099f0fd9f6bf4810", size = 34981559 }, - { url = "https://files.pythonhosted.org/packages/80/b6/0ddf0e9b6ead3474ab087ae598c76b031fc45532bf6a63f3a553440fb258/pyarrow-24.0.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:f7616236ec1bc2b15bfdec22a71ab38851c86f8f05ff64f379e1278cf20c634a", size = 36663654 }, - { url = "https://files.pythonhosted.org/packages/7c/3b/926382efe8ce27ba729071d3566ade6dfb86bdf112f366000196b2f5780a/pyarrow-24.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:1617043b99bd33e5318ae18eb2919af09c71322ef1ca46566cdafc6e6712fb66", size = 45679394 }, - { url = "https://files.pythonhosted.org/packages/b3/7a/829f7d9dfd37c207206081d6dad474d81dde29952401f07f2ba507814818/pyarrow-24.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6165461f55ef6314f026de6638d661188e3455d3ec49834556a0ebbdbace18bb", size = 48863122 }, - { url = "https://files.pythonhosted.org/packages/5f/e8/f88ce625fe8babaae64e8db2d417c7653adb3019b08aae85c5ed787dc816/pyarrow-24.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3b13dedfe76a0ad2d1d859b0811b53827a4e9d93a0bcb05cf59333ab4980cc7e", size = 49376032 }, - { url = "https://files.pythonhosted.org/packages/36/7a/82c363caa145fff88fb475da50d3bf52bb024f61917be5424c3392eaf878/pyarrow-24.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:25ea65d868eb04015cd18e6df2fbe98f07e5bda2abefabcb88fce39a947716f6", size = 51929490 }, - { url = "https://files.pythonhosted.org/packages/66/1c/e3e72c8014ad2743ca64a701652c733cc5cbcee15c0463a32a8c55518d9e/pyarrow-24.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:295f0a7f2e242dabd513737cf076007dc5b2d59237e3eca37b05c0c6446f3826", size = 27355660 }, - { url = "https://files.pythonhosted.org/packages/6f/d3/a1abf004482026ddc17f4503db227787fa3cfe41ec5091ff20e4fea55e57/pyarrow-24.0.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:02b001b3ed4723caa44f6cd1af2d5c86aa2cf9971dacc2ffa55b21237713dfba", size = 34976759 }, - { url = "https://files.pythonhosted.org/packages/4f/4a/34f0a36d28a2dd32225301b79daad44e243dc1a2bb77d43b60749be255c4/pyarrow-24.0.0-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:04920d6a71aabd08a0417709efce97d45ea8e6fb733d9ca9ecffb13c67839f68", size = 36658471 }, - { url = "https://files.pythonhosted.org/packages/1f/78/543b94712ae8bb1a6023bcc1acf1a740fbff8286747c289cd9468fced2a5/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a964266397740257f16f7bb2e4f08a0c81454004beab8ff59dd531b73610e9f2", size = 45675981 }, - { url = "https://files.pythonhosted.org/packages/84/9f/8fb7c222b100d314137fa40ec050de56cd8c6d957d1cfff685ce72f15b17/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6f066b179d68c413374294bc1735f68475457c933258df594443bb9d88ddc2a0", size = 48859172 }, - { url = "https://files.pythonhosted.org/packages/a7/d3/1ea72538e6c8b3b475ed78d1049a2c518e655761ea50fe1171fc855fcab7/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1183baeb14c5f587b1ec52831e665718ce632caab84b7cd6b85fd44f96114495", size = 49385733 }, - { url = "https://files.pythonhosted.org/packages/c3/be/c3d8b06a1ba35f2260f8e1f771abbee7d5e345c0937aab90675706b1690a/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:806f24b4085453c197a5078218d1ee08783ebbba271badd153d1ae22a3ee804f", size = 51934335 }, - { url = "https://files.pythonhosted.org/packages/9c/62/89e07a1e7329d2cde3e3c6994ba0839a24977a2beda8be6005ea3d860b99/pyarrow-24.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:e4505fc6583f7b05ab854934896bcac8253b04ac1171a77dfb73efef92076d91", size = 27271748 }, - { url = "https://files.pythonhosted.org/packages/17/1a/cff3a59f80b5b1658549d46611b67163f65e0664431c076ad728bf9d5af4/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:1a4e45017efbf115032e4475ee876d525e0e36c742214fbe405332480ecd6275", size = 35238554 }, - { url = "https://files.pythonhosted.org/packages/a8/99/cce0f42a327bfef2c420fb6078a3eb834826e5d6697bf3009fe11d2ad051/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:7986f1fa71cee060ad00758bcc79d3a93bab8559bf978fab9e53472a2e25a17b", size = 36782301 }, - { url = "https://files.pythonhosted.org/packages/2a/66/8e560d5ff6793ca29aca213c53eec0dd482dd46cb93b2819e5aab52e4252/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:d3e0b61e8efb24ed38898e5cdc5fffa9124be480008d401a1f8071500494ae42", size = 45721929 }, - { url = "https://files.pythonhosted.org/packages/27/0c/a26e25505d030716e078d9f16eb74973cbf0b33b672884e9f9da1c83b871/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:55a3bc1e3df3b5567b7d27ef551b2283f0c68a5e86f1cd56abc569da4f31335b", size = 48825365 }, - { url = "https://files.pythonhosted.org/packages/5f/eb/771f9ecb0c65e73fe9dccdd1717901b9594f08c4515d000c7c62df573811/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:641f795b361874ac9da5294f8f443dfdbee355cf2bd9e3b8d97aaac2306b9b37", size = 49451819 }, - { url = "https://files.pythonhosted.org/packages/48/da/61ae89a88732f5a785646f3ec6125dbb640fa98a540eb2b9889caa561403/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8adc8e6ce5fccf5dc707046ae4914fd537def529709cc0d285d37a7f9cd442ca", size = 51909252 }, - { url = "https://files.pythonhosted.org/packages/cb/1a/8dd5cafab7b66573fa91c03d06d213356ad4edd71813aa75e08ce2b3a844/pyarrow-24.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:9b18371ad2f44044b81a8d23bc2d8a9b6a6226dca775e8e16cfee640473d6c5d", size = 27388127 }, - { url = "https://files.pythonhosted.org/packages/ad/80/d022a34ff05d2cbedd8ccf841fc1f532ecfa9eb5ed1711b56d0e0ea71fc9/pyarrow-24.0.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:1cc9057f0319e26333b357e17f3c2c022f1a83739b48a88b25bfd5fa2dc18838", size = 35007997 }, - { url = "https://files.pythonhosted.org/packages/1a/ff/f01485fda6f4e5d441afb8dd5e7681e4db18826c1e271852f5d3957d6a80/pyarrow-24.0.0-cp314-cp314-macosx_12_0_x86_64.whl", hash = "sha256:e6f1278ee4785b6db21229374a1c9e54ec7c549de5d1efc9630b6207de7e170b", size = 36678720 }, - { url = "https://files.pythonhosted.org/packages/9e/c2/2d2d5fea814237923f71b36495211f20b43a1576f9a4d6da7e751a64ec6f/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:adbbedc55506cbdabb830890444fb856bfb0060c46c6f8026c6c2f2cf86ae795", size = 45741852 }, - { url = "https://files.pythonhosted.org/packages/8e/3a/28ba9c1c1ebdbb5f1b94dfebb46f207e52e6a554b7fe4132540fde29a3a0/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:ae8a1145af31d903fa9bb166824d7abe9b4681a000b0159c9fb99c11bc11ad26", size = 48889852 }, - { url = "https://files.pythonhosted.org/packages/df/51/4a389acfd31dca009f8fb82d7f510bb4130f2b3a8e18cf00194d0687d8ac/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d7027eba1df3b2069e2e8d80f644fa0918b68c46432af3d088ddd390d063ecde", size = 49445207 }, - { url = "https://files.pythonhosted.org/packages/19/4b/0bab2b23d2ae901b1b9a03c0efd4b2d070256f8ce3fc43f6e58c167b2081/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e56a1ffe9bf7b727432b89104cc0849c21582949dd7bdcb34f17b2001a351a76", size = 51954117 }, - { url = "https://files.pythonhosted.org/packages/29/88/f4e9145da0417b3d2c12035a8492b35ff4a3dbc653e614fcfb51d9dedb38/pyarrow-24.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:38be1808cdd068605b787e6ca9119b27eb275a0234e50212c3492331680c3b1e", size = 28001155 }, - { url = "https://files.pythonhosted.org/packages/79/4f/46a49a63f43526da895b1a45bbb51d5baf8e4d77159f8528fc3e5490007f/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:418e48ce50a45a6a6c73c454677203a9c75c966cb1e92ca3370959185f197a05", size = 35250387 }, - { url = "https://files.pythonhosted.org/packages/a0/da/d5e0cd5ef00796922404806d5f00325cdadc3441ce2c13fe7115f2df9a64/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:2f16197705a230a78270cdd4ea8a1d57e86b2fdcbc34a1f6aebc72e65c986f9a", size = 36797102 }, - { url = "https://files.pythonhosted.org/packages/34/c7/5904145b0a593a05236c882933d439b5720f0a145381179063722fbfc123/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:fb24ac194bfc5e86839d7dcd52092ee31e5fe6733fe11f5e3b06ef0812b20072", size = 45745118 }, - { url = "https://files.pythonhosted.org/packages/13/d3/cca42fe166d1c6e4d5b80e530b7949104d10e17508a90ae202dac205ce2a/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:9700ebd9a51f5895ce75ff4ac4b3c47a7d4b42bc618be8e713e5d56bacf5f931", size = 48844765 }, - { url = "https://files.pythonhosted.org/packages/b0/49/942c3b79878ba928324d1e17c274ed84581db8c0a749b24bcf4cbdf15bd3/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d8ddd2768da81d3ee08cfea9b597f4abb4e8e1dc8ae7e204b608d23a0d3ab699", size = 49471890 }, - { url = "https://files.pythonhosted.org/packages/76/97/ff71431000a75d84135a1ace5ca4ba11726a231a8007bbb320a4c54075d5/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:61a3d7eaa97a14768b542f3d284dc6400dd2470d9f080708b13cd46b6ae18136", size = 51932250 }, - { url = "https://files.pythonhosted.org/packages/51/be/6f79d55816d5c22557cf27533543d5d70dfe692adfbee4b99f2760674f38/pyarrow-24.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:c91d00057f23b8d353039520dc3a6c09d8608164c692e9f59a175a42b2ae0c19", size = 28131282 }, -] - -[[package]] -name = "pydantic" -version = "2.13.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "annotated-types" }, - { name = "pydantic-core" }, - { name = "typing-extensions" }, - { name = "typing-inspection" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl", hash = "sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba", size = 472262 }, -] - -[[package]] -name = "pydantic-core" -version = "2.46.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/9d/56/921726b776ace8d8f5db44c4ef961006580d91dc52b803c489fafd1aa249/pydantic_core-2.46.4.tar.gz", hash = "sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1", size = 471464 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ce/8c/af022f0af448d7747c5154288d46b5f2bc5f17366eaa0e23e9aa04d59f3b/pydantic_core-2.46.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:3245406455a5d98187ec35530fd772b1d799b26667980872c8d4614991e2c4a2", size = 2106158 }, - { url = "https://files.pythonhosted.org/packages/19/95/6195171e385007300f0f5574592e467c568becce2d937a0b6804f218bc49/pydantic_core-2.46.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:962ccbab7b642487b1d8b7df90ef677e03134cf1fd8880bf698649b22a69371f", size = 1951724 }, - { url = "https://files.pythonhosted.org/packages/8e/bc/f47d1ff9cbb1620e1b5b697eef06010035735f07820180e74178226b27b3/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8233f2947cf85404441fd7e0085f53b10c93e0ee78611099b5c7237e36aacbf7", size = 1975742 }, - { url = "https://files.pythonhosted.org/packages/5b/11/9b9a5b0306345664a2da6410877af6e8082481b5884b3ddd78d47c6013ce/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3a233125ac121aa3ffba9a2b59edfc4a985a76092dc8279586ab4b71390875e7", size = 2052418 }, - { url = "https://files.pythonhosted.org/packages/f1/b7/a65fec226f5d78fc39f4a13c4cc0c768c22b113438f60c14adc9d2865038/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b712b53160b79a5850310b912a5ef8e57e56947c8ad690c227f5c9d7e561712", size = 2232274 }, - { url = "https://files.pythonhosted.org/packages/68/f0/92039db98b907ef49269a8271f67db9cb78ae2fc68062ef7e4e77adb5f61/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9401557acd873c3a7f3eb9383edef8ac4968f9510e340f4808d427e75667e7b4", size = 2309940 }, - { url = "https://files.pythonhosted.org/packages/5f/97/2aab507d3d00ca626e8e57c1eac6a79e4e5fbcc63eb99733ff55d1717f65/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:926c9541b14b12b1681dca8a0b75feb510b06c6341b70a8e500c2fdcff837cce", size = 2094516 }, - { url = "https://files.pythonhosted.org/packages/22/37/a8aca44d40d737dde2bc05b3c6c07dff0de07ce6f82e9f3167aeaf4d5dea/pydantic_core-2.46.4-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:56cb4851bcaf3d117eddcef4fe66afd750a50274b0da8e22be256d10e5611987", size = 2136854 }, - { url = "https://files.pythonhosted.org/packages/24/99/fcef1b79238c06a8cbec70819ac722ba76e02bc8ada9b0fd66eba40da01b/pydantic_core-2.46.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c68fcd102d71ea85c5b2dfac3f4f8476eff42a9e078fd5faefff6d145063536b", size = 2180306 }, - { url = "https://files.pythonhosted.org/packages/ae/6c/fc44000918855b42779d007ae63b0532794739027b2f417321cddbc44f6a/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:b2f69dec1725e79a012d920df1707de5caf7ed5e08f3be4435e25803efc47458", size = 2190044 }, - { url = "https://files.pythonhosted.org/packages/6b/65/d9cadc9f1920d7a127ad2edba16c1db7916e59719285cd6c94600b0080ba/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:8d0820e8192167f80d88d64038e609c31452eeca865b4e1d9950a27a4609b00b", size = 2329133 }, - { url = "https://files.pythonhosted.org/packages/d0/cf/c873d91679f3a30bcf5e7ac280ce5573483e72295307685120d0d5ad3416/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fbdb89b3e1c94a30cc5edfce477c6e6a5dc4d8f84665b455c27582f211a1c72c", size = 2374464 }, - { url = "https://files.pythonhosted.org/packages/47/bd/6f2fc8188f31bf10590f1e98e7b306336161fac930a8c514cd7bd828c7dc/pydantic_core-2.46.4-cp312-cp312-win32.whl", hash = "sha256:9aa768456404a8bf48a4406685ac2bec8e72b62c69313734fa3b73cf33b3a894", size = 1974823 }, - { url = "https://files.pythonhosted.org/packages/40/8c/985c1d41ea1107c2534abd9870e4ed5c8e7669b5c308297835c001e7a1c4/pydantic_core-2.46.4-cp312-cp312-win_amd64.whl", hash = "sha256:e9c26f834c65f5752f3f06cb08cb86a913ceb7274d0db6e267808a708b46bc89", size = 2072919 }, - { url = "https://files.pythonhosted.org/packages/c4/ba/f463d006e0c47373ca7ec5e1a261c59dc01ef4d62b2657af925fb0deee3a/pydantic_core-2.46.4-cp312-cp312-win_arm64.whl", hash = "sha256:4fc73cb559bdb54b1134a706a2802a4cddd27a0633f5abb7e53056268751ac6a", size = 2027604 }, - { url = "https://files.pythonhosted.org/packages/51/a2/5d30b469c5267a17b39dec53208222f76a8d351dfac4af661888c5aee77d/pydantic_core-2.46.4-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:5d5902252db0d3cedf8d4a1bc68f70eeb430f7e4c7104c8c476753519b423008", size = 2106306 }, - { url = "https://files.pythonhosted.org/packages/c1/81/4fa520eaffa8bd7d1525e644cd6d39e7d60b1592bc5b516693c7340b50f1/pydantic_core-2.46.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c94f0688e7b8d0a67abf40e57a7eaaecd17cc9586706a31b76c031f63df052b4", size = 1951906 }, - { url = "https://files.pythonhosted.org/packages/03/d5/fd02da45b659668b05923b17ba3a0100a0a3d5541e3bd8fcc4ecb711309e/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f027324c56cd5406ca49c124b0db10e56c69064fec039acc571c29020cc87c76", size = 1976802 }, - { url = "https://files.pythonhosted.org/packages/21/f2/95727e1368be3d3ed485eaab7adbd7dda408f33f7a36e8b48e0144002b91/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e739fee756ba1010f8bcccb534252e85a35fe45ae92c295a06059ce58b74ccd3", size = 2052446 }, - { url = "https://files.pythonhosted.org/packages/9c/86/5d99feea3f77c7234b8718075b23db11532773c1a0dbd9b9490215dc2eeb/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9d56801be94b86a9da183e5f3766e6310752b99ff647e38b09a9500d88e46e76", size = 2232757 }, - { url = "https://files.pythonhosted.org/packages/d2/3a/508ac615935ef7588cf6d9e9b91309fdc2da751af865e02a9098de88258c/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2412e734dcb48da14d4e4006b82b46b74f2518b8a26ee7e58c6844a6cd6d03c4", size = 2309275 }, - { url = "https://files.pythonhosted.org/packages/07/f8/41db9de19d7987d6b04715a02b3b40aea467000275d9d758ffaa31af7d50/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9551187363ffc0de2a00b2e47c25aeaeb1020b69b668762966df15fc5659dd5a", size = 2094467 }, - { url = "https://files.pythonhosted.org/packages/2c/e2/f35033184cb11d0052daf4416e8e10a502ea2ac006fc4f459aee872727d1/pydantic_core-2.46.4-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:0186750b482eefa11d7f435892b09c5c606193ef3375bcf94aa00ae6bfb66262", size = 2134417 }, - { url = "https://files.pythonhosted.org/packages/7e/7b/6ceeb1cc90e193862f444ebe373d8fdf613f0a82572dde03fb10734c6c71/pydantic_core-2.46.4-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5855698a4856556d86e8e6cd8434bc3ac0314ee8e12089ae0e143f64c6256e4e", size = 2179782 }, - { url = "https://files.pythonhosted.org/packages/5a/f2/c8d7773ede6af08036423a00ae0ceffce266c3c52a096c435d68c896083f/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:cbaf13819775b7f769bf4a1f066cb6df7a28d4480081a589828ef190226881cd", size = 2188782 }, - { url = "https://files.pythonhosted.org/packages/59/31/0c864784e31f09f05cdd87606f08923b9c9e7f6e51dd27f20f62f975ce9f/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:633147d34cf4550417f12e2b1a0383973bdf5cdfde212cb09e9a581cf10820be", size = 2328334 }, - { url = "https://files.pythonhosted.org/packages/c2/eb/4f6c8a41efa30baa755590f4141abf3a8c370fab610915733e74134a7270/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:82cf5301172168103724d49a1444d3378cb20cdee30b116a1bd6031236298a5d", size = 2372986 }, - { url = "https://files.pythonhosted.org/packages/5b/24/b375a480d53113860c299764bfe9f349a3dc9108b3adc0d7f0d786492ebf/pydantic_core-2.46.4-cp313-cp313-win32.whl", hash = "sha256:9fa8ae11da9e2b3126c6426f147e0fba88d96d65921799bb30c6abd1cb2c97fb", size = 1973693 }, - { url = "https://files.pythonhosted.org/packages/7e/e8/cff247591966f2d22ec8c003cd7587e27b7ba7b81ab2fb888e3ab75dc285/pydantic_core-2.46.4-cp313-cp313-win_amd64.whl", hash = "sha256:6b3ace8194b0e5204818c92802dcdca7fc6d88aabbb799d7c795540d9cd6d292", size = 2071819 }, - { url = "https://files.pythonhosted.org/packages/c6/1a/f4aee670d5670e9e148e0c82c7db98d780be566c6e6a97ee8035528ca0b3/pydantic_core-2.46.4-cp313-cp313-win_arm64.whl", hash = "sha256:184c081504d17f1c1066e430e117142b2c77d9448a97f7b65c6ac9fd9aee238d", size = 2027411 }, - { url = "https://files.pythonhosted.org/packages/8d/74/228a26ddad29c6672b805d9fd78e8d251cd04004fa7eed0e622096cd0250/pydantic_core-2.46.4-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:428e04521a40150c85216fc8b85e8d39fece235a9cf5e383761238c7fa9b96fb", size = 2102079 }, - { url = "https://files.pythonhosted.org/packages/ad/1f/8970b150a4b4365623ae00fc88603491f763c627311ae8031e3111356d6e/pydantic_core-2.46.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:23ace664830ee0bfe014a0c7bc248b1f7f25ed7ad103852c317624a1083af462", size = 1952179 }, - { url = "https://files.pythonhosted.org/packages/95/30/5211a831ae054928054b2f79731661087a2bc5c01e825c672b3a4a8f1b3e/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce5c1d2a8b27468f433ca974829c44060b8097eedc39933e3c206a90ee49c4a9", size = 1978926 }, - { url = "https://files.pythonhosted.org/packages/57/e9/689668733b1eb67adeef047db3c2e8788fcf65a7fd9c9e2b46b7744fe245/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7283d57845ecf5a163403eb0702dfc220cc4fbdd18919cb5ccea4f95ee1cdab4", size = 2046785 }, - { url = "https://files.pythonhosted.org/packages/60/d9/6715260422ff50a2109878fd24d948a6c3446bb2664f34ee78cd972b3acd/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8daafc69c93ee8a0204506a3b6b30f586ef54028f52aeeeb5c4cfc5184fd5914", size = 2228733 }, - { url = "https://files.pythonhosted.org/packages/18/ae/fdb2f64316afca925640f8e70bb1a564b0ec2721c1389e25b8eb4bf9a299/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd2213145bcc2ba85884d0ac63d222fece9209678f77b9b4d76f054c561adb28", size = 2307534 }, - { url = "https://files.pythonhosted.org/packages/89/1d/8eff589b45bb8190a9d12c49cfad0f176a5cbd1534908a6b5125e2886239/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a5f930472650a82629163023e630d160863fce524c616f4e5186e5de9d9a49b", size = 2099732 }, - { url = "https://files.pythonhosted.org/packages/06/d5/ee5a3366637fee41dee51a1fc91562dcf12ddbc68fda34e6b253da2324bb/pydantic_core-2.46.4-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:c1b3f518abeca3aa13c712fd202306e145abf59a18b094a6bafb2d2bbf59192c", size = 2129627 }, - { url = "https://files.pythonhosted.org/packages/94/33/2414be571d2c6a6c4d08be21f9292b6d3fdb08949a97b6dfe985017821db/pydantic_core-2.46.4-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1a7dd0b3ee80d90150e3495a3a13ac34dbcbfd4f012996a6a1d8900e91b5c0fb", size = 2179141 }, - { url = "https://files.pythonhosted.org/packages/7b/79/7daa95be995be0eecc4cf75064cb33f9bbbfe3fe0158caf2f0d4a996a5c7/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:3fb702cd90b0446a3a1c5e470bfa0dd23c0233b676a9099ddcc964fa6ca13898", size = 2184325 }, - { url = "https://files.pythonhosted.org/packages/9f/cb/d0a382f5c0de8a222dc61c65348e0ce831b1f68e0a018450d31c2cace3a5/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:b8458003118a712e66286df6a707db01c52c0f52f7db8e4a38f0da1d3b94fc4e", size = 2323990 }, - { url = "https://files.pythonhosted.org/packages/05/db/d9ba624cc4a5aced1598e88c04fdbd8310c8a69b9d38b9a3d39ce3a61ed7/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:372429a130e469c9cd698925ce5fc50940b7a1336b0d82038e63d5bbc4edc519", size = 2369978 }, - { url = "https://files.pythonhosted.org/packages/f2/20/d15df15ba918c423461905802bfd2981c3af0bfa0e40d05e13edbfa48bc3/pydantic_core-2.46.4-cp314-cp314-win32.whl", hash = "sha256:85bb3611ff1802f3ee7fdd7dbff26b56f343fb432d57a4728fdd49b6ef35e2f4", size = 1966354 }, - { url = "https://files.pythonhosted.org/packages/fc/b6/6b8de4c0a7d7ab3004c439c80c5c1e0a3e8d78bbae19379b01960383d9e5/pydantic_core-2.46.4-cp314-cp314-win_amd64.whl", hash = "sha256:811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac", size = 2072238 }, - { url = "https://files.pythonhosted.org/packages/32/36/51eb763beec1f4cf59b1db243a7dcc39cbb41230f050a09b9d69faaf0a48/pydantic_core-2.46.4-cp314-cp314-win_arm64.whl", hash = "sha256:bfec22eab3c8cc2ceec0248aec886624116dc079afa027ecc8ad4a7e62010f8a", size = 2018251 }, - { url = "https://files.pythonhosted.org/packages/e8/91/855af51d625b23aa987116a19e231d2aaef9c4a415273ddc189b79a45fee/pydantic_core-2.46.4-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:af8244b2bef6aaad6d92cda81372de7f8c8d36c9f0c3ea36e827c60e7d9467a0", size = 2099593 }, - { url = "https://files.pythonhosted.org/packages/fb/1b/8784a54c65edb5f49f0a14d6977cf1b209bba85a4c77445b255c2de58ab3/pydantic_core-2.46.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5a4330cdbc57162e4b3aa303f588ba752257694c9c9be3e7ebb11b4aca659b5d", size = 1935226 }, - { url = "https://files.pythonhosted.org/packages/e8/e7/1955d28d1afc56dd4b3ad7cc0cf39df1b9852964cf16e5d13912756d6d6b/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29c61fc04a3d840155ff08e475a04809278972fe6aef51e2720554e96367e34b", size = 1974605 }, - { url = "https://files.pythonhosted.org/packages/93/e2/3fedbf0ba7a22850e6e9fd78117f1c0f10f950182344d8a6c535d468fdd8/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c50f2528cf200c5eed56faf3f4e22fcd5f38c157a8b78576e6ba3168ec35f000", size = 2030777 }, - { url = "https://files.pythonhosted.org/packages/f8/61/46be275fcaaba0b4f5b9669dd852267ce1ff616592dccf7a7845588df091/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0cbe8b01f948de4286c74cdd6c667aceb38f5c1e26f0693b3983d9d74887c65e", size = 2236641 }, - { url = "https://files.pythonhosted.org/packages/60/db/12e93e46a8bac9988be3c016860f83293daea8c716c029c9ace279036f2f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:617d7e2ca7dcb8c5cf6bcb8c59b8832c94b36196bbf1cbd1bfb56ed341905edd", size = 2286404 }, - { url = "https://files.pythonhosted.org/packages/e2/4a/4d8b19008f38d31c53b8219cfedc2e3d5de5fe99d90076b7e767de29274f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7027560ee92211647d0d34e3f7cd6f50da56399d26a9c8ad0da286d3869a53f3", size = 2109219 }, - { url = "https://files.pythonhosted.org/packages/88/70/3cbc40978fefb7bb09c6708d40d4ad1a5d70fd7213c3d17f971de868ec1f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:f99626688942fb746e545232e7726926f3be91b5975f8b55327665fafda991c7", size = 2110594 }, - { url = "https://files.pythonhosted.org/packages/9d/20/b8d36736216e29491125531685b2f9e61aa5b4b2599893f8268551da3338/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff", size = 2159542 }, - { url = "https://files.pythonhosted.org/packages/1d/a2/367df868eb584dacf6bf82a389272406d7178e301c4ac82545ab98bc2dd9/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:97e7cf2be5c77b7d1a9713a05605d49460d02c6078d38d8bef3cbe323c548424", size = 2168146 }, - { url = "https://files.pythonhosted.org/packages/c1/b8/4460f77f7e201893f649a29ab355dddd3beee8a97bcb1a320db414f9a06e/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:3bf92c5d0e00fefaab325a4d27828fe6b6e2a21848686b5b60d2d9eeb09d76c6", size = 2306309 }, - { url = "https://files.pythonhosted.org/packages/64/c4/be2639293acd87dc8ddbcec41a73cee9b2ebf996fe6d892a1a74e88ad3f7/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:3ecbc122d18468d06ca279dc26a8c2e2d5acb10943bb35e36ae92096dc3b5565", size = 2369736 }, - { url = "https://files.pythonhosted.org/packages/30/a6/9f9f380dbb301f67023bf8f707aaa75daadf84f7152d95c410fd7e81d994/pydantic_core-2.46.4-cp314-cp314t-win32.whl", hash = "sha256:e846ae7835bf0703ae43f534ab79a867146dadd59dc9ca5c8b53d5c8f7c9ef02", size = 1955575 }, - { url = "https://files.pythonhosted.org/packages/40/1f/f1eb9eb350e795d1af8586289746f5c5677d16043040d63710e22abc43c9/pydantic_core-2.46.4-cp314-cp314t-win_amd64.whl", hash = "sha256:2108ba5c1c1eca18030634489dc544844144ee36357f2f9f780b93e7ddbb44b5", size = 2051624 }, - { url = "https://files.pythonhosted.org/packages/f6/d2/42dd53d0a85c27606f316d3aa5d2869c4e8470a5ed6dec30e4a1abe19192/pydantic_core-2.46.4-cp314-cp314t-win_arm64.whl", hash = "sha256:4fcbe087dbc2068af7eda3aa87634eba216dbda64d1ae73c8684b621d33f6596", size = 2017325 }, -] - -[[package]] -name = "pygments" -version = "2.20.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151 }, -] - -[[package]] -name = "pypdf" -version = "6.11.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/bf/58/6dd97d78a4b17a7a6b9d1c6ad23895abc41f0fdc49c553cc05bdfdcc36d0/pypdf-6.11.0.tar.gz", hash = "sha256:062b51c81b0910e6d2755e99e1c5547a0a23b7d0a32322af66240d8edcfabe87", size = 6453975 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/07/b1/68feb7eb3b99f0c020b414234825f4a5d70e0126c18d933770e8c93a35fc/pypdf-6.11.0-py3-none-any.whl", hash = "sha256:769394d5756d5b304c9b6bef88b54b1816b328e7e6fc9254e625529a15ed4ab8", size = 338819 }, -] - -[[package]] -name = "pytest" -version = "9.0.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, - { name = "iniconfig" }, - { name = "packaging" }, - { name = "pluggy" }, - { name = "pygments" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249 }, -] - -[[package]] -name = "pytest-asyncio" -version = "1.3.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "pytest" }, - { name = "typing-extensions", marker = "python_full_version < '3.13'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/90/2c/8af215c0f776415f3590cac4f9086ccefd6fd463befeae41cd4d3f193e5a/pytest_asyncio-1.3.0.tar.gz", hash = "sha256:d7f52f36d231b80ee124cd216ffb19369aa168fc10095013c6b014a34d3ee9e5", size = 50087 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e5/35/f8b19922b6a25bc0880171a2f1a003eaeb93657475193ab516fd87cac9da/pytest_asyncio-1.3.0-py3-none-any.whl", hash = "sha256:611e26147c7f77640e6d0a92a38ed17c3e9848063698d5c93d5aa7aa11cebff5", size = 15075 }, -] - -[[package]] -name = "python-dateutil" -version = "2.9.0.post0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "six" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 }, -] - -[[package]] -name = "python-dotenv" -version = "1.2.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101 }, -] - -[[package]] -name = "pytz" -version = "2026.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ff/46/dd499ec9038423421951e4fad73051febaa13d2df82b4064f87af8b8c0c3/pytz-2026.2.tar.gz", hash = "sha256:0e60b47b29f21574376f218fe21abc009894a2321ea16c6754f3cad6eb7cdd6a", size = 320861 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/dd/96da98f892250475bdf2328112d7468abdd4acc7b902b6af23f4ed958ea0/pytz-2026.2-py2.py3-none-any.whl", hash = "sha256:04156e608bee23d3792fd45c94ae47fae1036688e75032eea2e3bf0323d1f126", size = 510141 }, -] - -[[package]] -name = "pyyaml" -version = "6.0.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063 }, - { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973 }, - { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116 }, - { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011 }, - { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870 }, - { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089 }, - { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181 }, - { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658 }, - { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003 }, - { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344 }, - { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669 }, - { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252 }, - { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081 }, - { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159 }, - { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626 }, - { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613 }, - { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115 }, - { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427 }, - { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090 }, - { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246 }, - { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814 }, - { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809 }, - { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454 }, - { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355 }, - { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175 }, - { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228 }, - { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194 }, - { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429 }, - { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912 }, - { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108 }, - { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641 }, - { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901 }, - { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132 }, - { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261 }, - { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272 }, - { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923 }, - { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062 }, - { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341 }, -] - -[[package]] -name = "regex" -version = "2026.5.9" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/dc/0e/49aee608ad09480e7fd276898c99ec6192985fa331abe4eb3a986094490b/regex-2026.5.9.tar.gz", hash = "sha256:a8234aa23ec39894bfe4a3f1b85616a7032481964a13ac6fc9f10de4f6fca270", size = 416074 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/50/9b/6550044bc44e17c84d312c031c2ec42fbdb6a4ec4e29093be3a172d08772/regex-2026.5.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:57eeeb05db7979413dec5438f2db21d7ecbba787cde7a711df1a6f6df672aa06", size = 490451 }, - { url = "https://files.pythonhosted.org/packages/1e/95/fc7ba4303b5a0f92446a12ee6778ef2c6c799233f5060042a31bf390cfe9/regex-2026.5.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:398c521292f4c7fb807001dcd54694d3a1fcafc179a36ad9cc56f98df85930b6", size = 292112 }, - { url = "https://files.pythonhosted.org/packages/54/4b/ee27938d1b2c443e89a9a10e00d2d19aa5ee300cd3d61140644e93bb083e/regex-2026.5.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f7a7c26137296beba7784de6eba69c6a93a63ccebc385e4962fe67e267a91225", size = 289599 }, - { url = "https://files.pythonhosted.org/packages/d8/dd/ba103dc19614e25f3880800ca67ce093d6e21b325d72b8383c7bf906e9fa/regex-2026.5.9-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6441cc660d76107934a09c22167200839a0e89604a6297f78a974e66e931d2c0", size = 796732 }, - { url = "https://files.pythonhosted.org/packages/cf/e7/f035b4fd858b050b0080bf302968dc0f59ba34e391872d54936758e6844e/regex-2026.5.9-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:91328f1c23d47595ca3ef0a7557fa129c5a23404b775c770697d2f35b33e0107", size = 865440 }, - { url = "https://files.pythonhosted.org/packages/0a/51/8cd301ecc899aea28124357f729f4272f44de7806fc7ca02490bfbe253e8/regex-2026.5.9-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:93a7860539414dddaefba2b40f8771765ae17949d4c7182b876ce429e11a8309", size = 912329 }, - { url = "https://files.pythonhosted.org/packages/cc/1e/3fbe2fa1e8cebd62f3bb7d3321cff1640aca2e240b51d9bd624aad949260/regex-2026.5.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dd2810d22146b6d838acc5ec15602cb6b47920aa4e33015df3868eedfd20bab8", size = 801239 }, - { url = "https://files.pythonhosted.org/packages/17/2f/6f6008682bf2cf98040a0d3153a8e557b6ab728d7713d045cee4ce544ab8/regex-2026.5.9-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:daff2bdbaf1d23e52fdff7c0b7bc2048b68f978df6a4d107ac981f94caef2e66", size = 777054 }, - { url = "https://files.pythonhosted.org/packages/19/2b/eee0d20a6842ba04df4b8847a920b57ef56853f14ef85405473e586b605a/regex-2026.5.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4eeb011098fcb77af513dcef521a3dbecbf8849b1e38940759d293b7a93f5026", size = 785098 }, - { url = "https://files.pythonhosted.org/packages/4a/98/6fc1e6410feefb92159edaed5041992bfe390e8d26c721865434acbca558/regex-2026.5.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:ea9c8ecfa1b73c73b626534d6626e5340d429630943672b8480724f44e84b962", size = 860095 }, - { url = "https://files.pythonhosted.org/packages/18/a3/bd855e0f2cb1a978ecf6fa6bb69632dd9c3f6ea3b81cde62fde14c9daec7/regex-2026.5.9-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:cd2846168eb9ee3c513902bc8225409cb1caab31d04728b145171fa1625d9621", size = 765762 }, - { url = "https://files.pythonhosted.org/packages/dc/66/0ae8c092e60b14c79d24f8e0b7f0aea5bfbffdcab00b5483d13404d3c3a5/regex-2026.5.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:39617fb0cde9c0e6306dc70e3bfc096f3da793219879f7ae7aa341a69fbdcf6d", size = 852100 }, - { url = "https://files.pythonhosted.org/packages/21/de/8dfde60fc1b21c946a893ba273403b72617edb261370cb1087099a83f088/regex-2026.5.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fd03c4f0e33280d15cae17159b899245d6b7c53d21def19b263b39655061f5ce", size = 789479 }, - { url = "https://files.pythonhosted.org/packages/c3/1c/bdcc98f9a4af4fdd166c74941174619ccff4726d3ce32faa8e9a2ecd38dd/regex-2026.5.9-cp312-cp312-win32.whl", hash = "sha256:164eba9b755ea6f244b0d881196fbc1fac09714e9782c9e2732b813142033c8e", size = 266699 }, - { url = "https://files.pythonhosted.org/packages/78/87/240d36864f9e48ace85f72e79ced97ceb7f27ce87739a947dcb834b4e6bc/regex-2026.5.9-cp312-cp312-win_amd64.whl", hash = "sha256:86f40a5d6444db30a125c9c9177e6b25dad981cbc37451fd838f145e6edac92e", size = 277783 }, - { url = "https://files.pythonhosted.org/packages/4f/b5/7b30f312b0669dff5beebe5b0989dc2d1a312b1a44fab852199c387a5b96/regex-2026.5.9-cp312-cp312-win_arm64.whl", hash = "sha256:96f5f58b54a063d7ea9dca08e1cf57bfe10499c4d579ee672da284f57f5f0070", size = 270513 }, - { url = "https://files.pythonhosted.org/packages/aa/da/797e91ecec6f84135da778ddce78c20e0af5d2a15c26f87a81bc3eadb6db/regex-2026.5.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:d626b84406444b165fc0ba981604edea39f0588ff1f92baa23fe50799ea9afdb", size = 490303 }, - { url = "https://files.pythonhosted.org/packages/44/da/bf30abaaa737b58f4a4b8c4a03659e02fd92092c822e0197ed9e0daab917/regex-2026.5.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d7bdc0ab8f3dd7e1b4f9ab88634e13374669db86bb3c72e8292f07ae313f539f", size = 292019 }, - { url = "https://files.pythonhosted.org/packages/2d/e7/d0eaf5713828417b9e5648cf81fa9bacd4961f6ab98c380c2034f8716e35/regex-2026.5.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a8820737949116ffff55fe18f9fc644530063ba6ebfcb8314239416e78f1347c", size = 289468 }, - { url = "https://files.pythonhosted.org/packages/d3/9b/b3fdd62b003baa1a9b593cd8c8699c9651c2e80cc21a5c715707983c42d7/regex-2026.5.9-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aa0fbdbac82cb3e4450d0ccde7d7a35607f4cb2dd9fba4b8b69bfaf8c9fa6aed", size = 796749 }, - { url = "https://files.pythonhosted.org/packages/d4/30/66ab84588765f5b4b271a9ca09ef7ce2b87caa95176ec3d2ad65d7bc4902/regex-2026.5.9-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:57e8915c7986aa33d25e4d3629cef711cd2863f2961b10409f0c04cb8b7d9020", size = 865445 }, - { url = "https://files.pythonhosted.org/packages/1a/89/f05169e8588aac365f35ffc7f3bc3184f095ef4cfded7cfaa3c7fd5dbd89/regex-2026.5.9-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:508f56a89ba9cb26e4168cbc37dbd60a28d82430a9e18ad1d25fe0883c314ca2", size = 912322 }, - { url = "https://files.pythonhosted.org/packages/30/e1/c93444052cf41581f3c884ab3fb5823daf0992f11cd4388d4275ca610558/regex-2026.5.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b6d189041f15691cfa2b6c4290448ec221244d225b3f5fe9e7771b34ffcdf6e2", size = 801269 }, - { url = "https://files.pythonhosted.org/packages/50/fe/0cf96b882f540e62e8b9956599798203d599c44cf4c77917ca27400ff69b/regex-2026.5.9-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e82db382b44d0111b22601c509c89f64434816c9e0eef9d1989cda8cc6ff1c04", size = 777085 }, - { url = "https://files.pythonhosted.org/packages/23/5c/d78d4924e7fc875557b9e9b768423925fdfaac5549d06da7810019a9bd26/regex-2026.5.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2acfb48634f64996b57f90f39afa692ff362162722581921fe92239a59960f3c", size = 785153 }, - { url = "https://files.pythonhosted.org/packages/bf/e0/5214774090e7b4524dcea3e3c4aa74141d43043f8beb49c1599db1c8b53a/regex-2026.5.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:d29eebfc9525db68cad3c97eedd7f754fa265aa5cd0cf4f863b2421e1b48fc9f", size = 860164 }, - { url = "https://files.pythonhosted.org/packages/6e/e1/4a57a83350319b1271f0d7a249b8672513ed928b237a741631270de6caea/regex-2026.5.9-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:debb893095e944091c16e641a6e33c1b0f4cb61ab945ec5afbf53ce7068834d8", size = 765731 }, - { url = "https://files.pythonhosted.org/packages/12/f4/499e74a20c156fc75836ee04a72a38d1a063978f600937f9760467beb1b0/regex-2026.5.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:d659eee77986549c9ea45b861c7567e44d6287c3dc9a4565478853f7b9fe2ff6", size = 852062 }, - { url = "https://files.pythonhosted.org/packages/5b/92/7eebc0d0a01e78629695f342ba17e0deaff8fb45e79cc0d7b98287da6e3e/regex-2026.5.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2efa205e6d98b24d1f3ab395c11aa15cdf10935bca283d0285e0499c284fba21", size = 789577 }, - { url = "https://files.pythonhosted.org/packages/05/a4/018e71f7d2ad48c1ebe6d3ae0026f9b7cb4802fd15c7cc02fdf724355102/regex-2026.5.9-cp313-cp313-win32.whl", hash = "sha256:f3844f134e834076677dd369976e9f5068679fcb8e50102fdf6b7ac96a3ec127", size = 266691 }, - { url = "https://files.pythonhosted.org/packages/e6/1d/861a93719fb9ee7dbfc3761b3797b7a3e112a5d42c6129459d2d741be9b5/regex-2026.5.9-cp313-cp313-win_amd64.whl", hash = "sha256:3527bb4942d2c14552155406cdedd906567456821848aed1cb4933a391bf5eca", size = 277747 }, - { url = "https://files.pythonhosted.org/packages/d9/c6/0a2436ae4da1ba76e51cb98943c6838a9a721faa40ebe2dce07694ae34e3/regex-2026.5.9-cp313-cp313-win_arm64.whl", hash = "sha256:56a33f191f17d8c417f99945ebdc1e691d3af9605d86ec68c7e54a57e3e17af6", size = 270500 }, - { url = "https://files.pythonhosted.org/packages/e8/e9/d21346f7b60ed58789371358ed66b09d00f832e1bd7c06e55d9da5679882/regex-2026.5.9-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:01f28d868834624c934b8d2e0aa1c8341337e37831f4a012f18a5afcba4cbaf3", size = 494172 }, - { url = "https://files.pythonhosted.org/packages/c4/43/fd1177a2032037c681baecdb3422ee4e1424aec4e4f470ef47793d325274/regex-2026.5.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:48036f6374aaa79eb3b754ec29c61d1c6b1606749d705a13f8854fa2539671f6", size = 293952 }, - { url = "https://files.pythonhosted.org/packages/f2/7d/9fbf919768368d3f8a4f6c692cf2aa61e482b2b81ec6a298ace4cbf02480/regex-2026.5.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b96350aa424e79d4fd6b567b344dcbe2b2d6bfc48dfe7717587e1fa6d43da6ff", size = 292314 }, - { url = "https://files.pythonhosted.org/packages/e2/6c/e41bfeecb589716843e7c4df09ba46ff2a42961457afece19059d85caeef/regex-2026.5.9-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8f3af7a4903c5c04a11a196a5aa75cdd7dd3f8508132f9fb3259d9f5908e3b88", size = 811681 }, - { url = "https://files.pythonhosted.org/packages/87/83/a5c1c525fba0aa656e88ad0face0b1829788ef4c2fb6b26df58aa1151b84/regex-2026.5.9-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7e87577720152d2caae19fe2baaf1f8d5ca12091e9e229f03915c37d1e4b9178", size = 871135 }, - { url = "https://files.pythonhosted.org/packages/18/d4/80882e799e440dd878b0979cbebf8fa4d54624a332c83037c7a701649e3f/regex-2026.5.9-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:c8b9b9d294cfea3cd19c718ade7cc93492b2c4991abd9a68d0b3477ae6d8e100", size = 917265 }, - { url = "https://files.pythonhosted.org/packages/ae/ff/8db60211e2286e396aad7dc7725356c502bff0901ea05bd6cdc2e1a042b9/regex-2026.5.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:728d8bfd28a8845c8b6bc5dc7ce010453d206396786c0765c2740cb65f37791e", size = 816311 }, - { url = "https://files.pythonhosted.org/packages/4c/47/742ef579c61730f8d268e5cf1f9ce0e37e2ea041ad0f5644724f2378e463/regex-2026.5.9-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:7e30b874d341fac767d7df5a0870540541c2c054b80cfaac116e8d367a8a7ff2", size = 785498 }, - { url = "https://files.pythonhosted.org/packages/7f/ab/cb0999802dcb0fb95b1ab005e8d4163d8afdd67efc2cb6b6630ac13f8cb1/regex-2026.5.9-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:fd190e88a895a8901325fad284a3f74ea52b1da8525b76cc811fa9b1edf0ce2b", size = 801348 }, - { url = "https://files.pythonhosted.org/packages/7d/62/8ca59a24c55bc34d166eefaf3717bd77772f329fdbf984d86581e0a3571c/regex-2026.5.9-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:8e76e8161ad00694cfce6767d5dea860c6391ac5b83e5c3a39661e696f11fc7e", size = 866493 }, - { url = "https://files.pythonhosted.org/packages/8d/3d/30f2ae62cef3278bb5bb821f467277a55fb73f01032cf85997e15e8289a8/regex-2026.5.9-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ddda5340e6c01a293027dd46232fa79eaff1b48058ce7a98f572b6445b088041", size = 772811 }, - { url = "https://files.pythonhosted.org/packages/d8/ae/7d2089bcd78ad0c0161bc684339df50032acb438a7bd3305e7ddb1193cec/regex-2026.5.9-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:205109e96b3cf5adf8f4cd62bedde9487feb282b9497a3535451e5a24cd706a0", size = 856584 }, - { url = "https://files.pythonhosted.org/packages/a9/29/92ff47f75990131ea4f24ba17819e5a9d141e10819807e09addd73409af6/regex-2026.5.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dfbe4579b9f08036aa7d101d1835437a20783574ac66327e6b29b4018a138081", size = 803453 }, - { url = "https://files.pythonhosted.org/packages/04/99/eff29f1037dcab36702c9ee5d6858cf1ce2336ea8ea2987f64245b99ea5e/regex-2026.5.9-cp313-cp313t-win32.whl", hash = "sha256:ed2c9e8068b614c574d8d30e543d617cf5379b0535d46f97ef00e904745a08b5", size = 269951 }, - { url = "https://files.pythonhosted.org/packages/0e/9d/8870b8981d27b22cda77bb26a5ac7ebfa9c7d9e0dea195a834a82380e748/regex-2026.5.9-cp313-cp313t-win_amd64.whl", hash = "sha256:b46b0f094dc1d3b90356c85a0bd2c9bafc4a6a190b9d6f8ddd5a033b6e088ed4", size = 281240 }, - { url = "https://files.pythonhosted.org/packages/72/b1/3379415e8f135c13ac551353397cc4fe97b4978f3cac73c5fcbcded548b8/regex-2026.5.9-cp313-cp313t-win_arm64.whl", hash = "sha256:872acc074bd29ffc9913ecdfedf6ea77502312ca44a4aa0d3779089c6069d8de", size = 272383 }, - { url = "https://files.pythonhosted.org/packages/13/3e/9c3cd292d8808b3645a2ce517e200179b6d0e903f176300bd8b542e14de5/regex-2026.5.9-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:1bd7587a2948b4085195d5a3374eaf4a425dc3e55784c038175355ecf3bbbf8a", size = 490376 }, - { url = "https://files.pythonhosted.org/packages/60/70/d43ee8a2ca0a8b68d167f21658b85520ac0574617c7f320367c5047f7556/regex-2026.5.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:dea2e88e1cce4522496cce630e11e67b98b7076620bc4336c3f674bc21a375f4", size = 291964 }, - { url = "https://files.pythonhosted.org/packages/21/91/9d50b433828d8e74196904e168a43abf1e6e88b2a15d47ed742456720c37/regex-2026.5.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2099f7e7ff7b6aa3192312650a56e91cc091e49d50b04e4f6f8b6e28b3b27f1c", size = 289682 }, - { url = "https://files.pythonhosted.org/packages/3e/d2/b835e3cafbb9d977736912436259ff551d60919f7d7b3d37d46659c63564/regex-2026.5.9-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecd353045824e4477562a2ac718c25799cdaaa41f7aa925a806a8a3e6848a5b9", size = 796996 }, - { url = "https://files.pythonhosted.org/packages/2c/a6/9f992d00019166b9de01c546dd4549bc679f2a68df11b877740b0760b7c2/regex-2026.5.9-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:65c8c8c37377794bd5b2f3ebe51919042bf17aec802e23c833d89782ed0c78af", size = 866089 }, - { url = "https://files.pythonhosted.org/packages/e0/08/4d32af657e049b19cb62b02e46e38fe1518797bfb2203ee93a510b21b0dc/regex-2026.5.9-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5b73ab8afcf66c622db143d1c6fda4e58e4d537ee4f125229ad47b1ab80f34c0", size = 911530 }, - { url = "https://files.pythonhosted.org/packages/d9/27/2af43dd1dc201d1fecefda64a45f4ad0995855b92724f795a777b402ee69/regex-2026.5.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0de5cf193997384ed2ca6f1cd4f78055b255d93d82d5a8cd6ba0d11c10b167e4", size = 800643 }, - { url = "https://files.pythonhosted.org/packages/a4/dd/23a249047013b5321d4a60c4d2437462086f601b061776a525e5fba2a59f/regex-2026.5.9-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d641a8c9a61618047796d572a39a79b26167b0411d2c3031937b2fe2d081e2cf", size = 777223 }, - { url = "https://files.pythonhosted.org/packages/94/6a/e85ed9538cd19586d0465076a4578a12e093ce776d15f3f8ce92733a8dd6/regex-2026.5.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:24b2355ef5cc9aa5b8f07d17704face1c166fdcc2290fa7bd6e6c925655a8346", size = 785760 }, - { url = "https://files.pythonhosted.org/packages/2a/c4/f25473209438638e947c55f9156fd8f236f74169229028cc99116380868e/regex-2026.5.9-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:a24852d3c29ad9e47593593d8a247c44ccc3d0548ef12c822d6ed0810affe676", size = 860891 }, - { url = "https://files.pythonhosted.org/packages/f9/f7/f4f86e3c74419c37370e91f150ae0c2ef7d34b2e0e4cdd5da046a02e4022/regex-2026.5.9-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:916714069da19329ef7de197dcbc77bb3104145c7c2c864dbfbe318f46b88b14", size = 765891 }, - { url = "https://files.pythonhosted.org/packages/26/70/704d8e13765939146b1cd0ef4e2feb71d7929727d2290f026eed10095955/regex-2026.5.9-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:fa411799ca8da32a8d38d020a88faa5b6f91657d284761352940ecf9f7c3bbdd", size = 851380 }, - { url = "https://files.pythonhosted.org/packages/26/29/1a13582a8460038edc38e49f64ceb0dd7c60f5caba77571f4bf6601965d9/regex-2026.5.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1e6da47d679b7010ef27556b6e0f99771b744936db1792a10ceac6547ae1503e", size = 789350 }, - { url = "https://files.pythonhosted.org/packages/73/56/3dcafe34fc72e271d62ad9a291801e88a1457bb251c132f15fcc2e5aad1a/regex-2026.5.9-cp314-cp314-win32.whl", hash = "sha256:98bd73080e8756255137e1bd3f3f00295bbc5aa383c0e0f973920e9134d7c4ad", size = 272130 }, - { url = "https://files.pythonhosted.org/packages/d0/9c/02eebf0be95efe416c664db7fb8b6b05b7a0b06a7544f2884f2558b0526f/regex-2026.5.9-cp314-cp314-win_amd64.whl", hash = "sha256:ff8d372ac2acdc048d1c19916f27ee61bc5722728458ba6ca5052f2c72d51763", size = 280999 }, - { url = "https://files.pythonhosted.org/packages/70/5a/1dd1abee76cb7a846a0bcf42fdc87e5720c3c33c24f3e37814310a513d9f/regex-2026.5.9-cp314-cp314-win_arm64.whl", hash = "sha256:e1d93bf647916292e8edcec150c07ddf3dc50179ccaf770c04a7f9e452155372", size = 273500 }, - { url = "https://files.pythonhosted.org/packages/86/c1/c5f619b0057a7965cb78ec559c1d7a45ce8c99a35bea95483d64959a93d9/regex-2026.5.9-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:83d0ee4a57d1c87cb549e195ec300b8f0ec3a82eba66d835e4e2ed8634fe4499", size = 494269 }, - { url = "https://files.pythonhosted.org/packages/05/2c/5d01f1aee33de4bbe60c8452945bfc8477ca7c5ae4450f6bfe711036cb36/regex-2026.5.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:d3d7eb5c9a7f6df82ed3cfac9beb93882a5cbcb5b8b157b56cb2b3b276574ac1", size = 293954 }, - { url = "https://files.pythonhosted.org/packages/7a/fe/e8988b2ae2108c6ef71bd4aa8d87fbe257976dd0810e826cd75f701c68b6/regex-2026.5.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:075160bf16658e16d35233300b8453aac25de4cbea808d22348b6979668e924d", size = 292405 }, - { url = "https://files.pythonhosted.org/packages/79/34/d2b0937faa7859263f7f0a3c6b103a1296306be6952dc173d0154e9a2f49/regex-2026.5.9-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45375819235558a4ff1c4971dc32881f022613abdb180128f5cb4768c1765a1c", size = 811855 }, - { url = "https://files.pythonhosted.org/packages/80/fe/daf53a47457a8486db66c66c01ceb9c2303eecee3f87197f1e77eb1a736d/regex-2026.5.9-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ead4b163ac30a29574510cd4b3e2e985ac5290c05fc7095557d6a5f403fc31b5", size = 871189 }, - { url = "https://files.pythonhosted.org/packages/1c/75/058fc4470cbfbf57d800aff1a0022b929a3f9fa553ee10a0cdf2070eb31f/regex-2026.5.9-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8c6e4218fbdfbcd4f6c19efca40930d24a621bf4b48cb76bc6640543bd28ef20", size = 917485 }, - { url = "https://files.pythonhosted.org/packages/88/e7/179cfda3a28bc843b5c6cfe7f79f23489c791ed95f151083803660878432/regex-2026.5.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6351571c8a42b505eb555c0dc47d740d0fb66977dc142919eea6f4325b7c56a0", size = 816369 }, - { url = "https://files.pythonhosted.org/packages/41/90/6f0cc422071688266d344fca8462d787cba0a2c144acb25721f9a61ec265/regex-2026.5.9-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:002205cafd2a9e78c6290c7d1df277bf3277b3b7a30e0b4bb0dac2e2e3f7cb2d", size = 785869 }, - { url = "https://files.pythonhosted.org/packages/02/67/a31f1760f09c27b251ef39e9beb541f462cf977381d067faa764c2c0e393/regex-2026.5.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8abd33fef90b2a9efac5557d6033ca82d1195ed3a15fea5af15ba7b463c6a63b", size = 801427 }, - { url = "https://files.pythonhosted.org/packages/e3/c4/1a80654597b6bc1e1ea0494824c31200e8a956abe290afae9b19a166a148/regex-2026.5.9-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:31037c82eccb44b7ea2e9e221d7c01429430e989a1f4b91ea5a855f6017b509a", size = 866482 }, - { url = "https://files.pythonhosted.org/packages/d1/11/960724e06482c08466ff5611e242e86f80062949cdf6b4b9cc317b9dd93d/regex-2026.5.9-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:5604dfd046dc37eca90250fc3be938b076c8059fa772ac0ed6f499b0f0fb0415", size = 773022 }, - { url = "https://files.pythonhosted.org/packages/50/a8/a9979c3e7918280e93159ebcab5ef1a65116dd4f3bd6091be0eae4a126e8/regex-2026.5.9-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0e1b1b4e496afbb24f4a62aba855ee4f88f25578927697b340702e48c9ee6bc2", size = 856642 }, - { url = "https://files.pythonhosted.org/packages/fe/d4/a9b732f2f0072c0ab12227483abb24fffcb9f73f8a2b203df0a6d0434735/regex-2026.5.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:be3372b9df6ddecff6486d37e19095a7b4973137caf5512407a89f4455361f41", size = 803552 }, - { url = "https://files.pythonhosted.org/packages/d5/fe/1b3113817447a1d4155e4ac76d2e072f42c0bcba2f43fa8a0e756ea2cd91/regex-2026.5.9-cp314-cp314t-win32.whl", hash = "sha256:3ddd90103f9e5c471c49c7852ecc1fe27c7e45eb99e977aefe7caa4e779f4f58", size = 275746 }, - { url = "https://files.pythonhosted.org/packages/92/73/93d42045302636c91f2e5ef588b65b84b01428f28ec77de256b1dfdfbe5c/regex-2026.5.9-cp314-cp314t-win_amd64.whl", hash = "sha256:ca518ed29c46eecba6010b15f1b9a479314d2de409536e71b6a13aa04e3b8a77", size = 285685 }, - { url = "https://files.pythonhosted.org/packages/da/80/35b4c33c804a165a7f55289afda3ea9e3eb6d15800341a2d66455c0f1f30/regex-2026.5.9-cp314-cp314t-win_arm64.whl", hash = "sha256:5e41809d2683fcde7d5a8c87a6567ba1fb1ce0de9f31bff578de00a4b2d76daa", size = 275713 }, -] - -[[package]] -name = "reportlab" -version = "4.5.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "charset-normalizer" }, - { name = "pillow" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/4d/3f/b3861b7e40c9d66f4a04e018958d681d16b948bfd1963c962d43a8c23f66/reportlab-4.5.1.tar.gz", hash = "sha256:9fdf68f4de9171ec66acb4a5feed8f8ca2af43479e707a6fbb0daa75d88e5494", size = 3939748 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a7/45/ea7fad10122440de6e845568d106bffdc456ca0e8a1d8ae10b46016087e4/reportlab-4.5.1-py3-none-any.whl", hash = "sha256:06fce8cb56c83307cfa4909cdf4e6a2ddbb44e5d6ef4d2edca896d7e9769f091", size = 1957812 }, -] - -[[package]] -name = "requests" -version = "2.34.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "certifi" }, - { name = "charset-normalizer" }, - { name = "idna" }, - { name = "urllib3" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/43/b8/7a707d60fea4c49094e40262cc0e2ca6c768cca21587e34d3f705afec47e/requests-2.34.0.tar.gz", hash = "sha256:7d62fe92f50eb82c529b0916bb445afa1531a566fc8f35ffdc64446e771b856a", size = 142436 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ef/e6/e300fce5fe83c30520607a015dabd985df3251e188d234bfe9492e17a389/requests-2.34.0-py3-none-any.whl", hash = "sha256:917520a21b767485ce7c588f4ebb917c436b24a31231b44228715eaeb5a52c60", size = 73021 }, -] - -[[package]] -name = "respx" -version = "0.23.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "httpx" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/43/98/4e55c9c486404ec12373708d015ebce157966965a5ebe7f28ff2c784d41b/respx-0.23.1.tar.gz", hash = "sha256:242dcc6ce6b5b9bf621f5870c82a63997e8e82bc7c947f9ffe272b8f3dd5a780", size = 29243 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1d/4a/221da6ca167db45693d8d26c7dc79ccfc978a440251bf6721c9aaf251ac0/respx-0.23.1-py2.py3-none-any.whl", hash = "sha256:b18004b029935384bccfa6d7d9d74b4ec9af73a081cc28600fffc0447f4b8c1a", size = 25557 }, -] - -[[package]] -name = "rich" -version = "15.0.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "markdown-it-py" }, - { name = "pygments" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/c0/8f/0722ca900cc807c13a6a0c696dacf35430f72e0ec571c4275d2371fca3e9/rich-15.0.0.tar.gz", hash = "sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36", size = 230680 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/82/3b/64d4899d73f91ba49a8c18a8ff3f0ea8f1c1d75481760df8c68ef5235bf5/rich-15.0.0-py3-none-any.whl", hash = "sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb", size = 310654 }, -] - -[[package]] -name = "ruff" -version = "0.15.12" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/99/43/3291f1cc9106f4c63bdce7a8d0df5047fe8422a75b091c16b5e9355e0b11/ruff-0.15.12.tar.gz", hash = "sha256:ecea26adb26b4232c0c2ca19ccbc0083a68344180bba2a600605538ce51a40a6", size = 4643852 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c3/6e/e78ffb61d4686f3d96ba3df2c801161843746dcbcbb17a1e927d4829312b/ruff-0.15.12-py3-none-linux_armv6l.whl", hash = "sha256:f86f176e188e94d6bdbc09f09bfd9dc729059ad93d0e7390b5a73efe19f8861c", size = 10640713 }, - { url = "https://files.pythonhosted.org/packages/ae/08/a317bc231fb9e7b93e4ef3089501e51922ff88d6936ce5cf870c4fe55419/ruff-0.15.12-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:e3bcd123364c3770b8e1b7baaf343cc99a35f197c5c6e8af79015c666c423a6c", size = 11069267 }, - { url = "https://files.pythonhosted.org/packages/aa/a4/f828e9718d3dce1f5f11c39c4f65afd32783c8b2aebb2e3d259e492c47bd/ruff-0.15.12-py3-none-macosx_11_0_arm64.whl", hash = "sha256:fe87510d000220aa1ed530d4448a7c696a0cae1213e5ec30e5874287b66557b5", size = 10397182 }, - { url = "https://files.pythonhosted.org/packages/71/e0/3310fc6d1b5e1fdea22bf3b1b807c7e187b581021b0d7d4514cccdb5fb71/ruff-0.15.12-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:84a1630093121375a3e2a95b4a6dc7b59e2b4ee76216e32d81aae550a832d002", size = 10758012 }, - { url = "https://files.pythonhosted.org/packages/11/c1/a606911aee04c324ddaa883ae418f3569792fd3c4a10c50e0dd0a2311e1e/ruff-0.15.12-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fb129f40f114f089ebe0ca56c0d251cf2061b17651d464bb6478dc01e69f11f5", size = 10447479 }, - { url = "https://files.pythonhosted.org/packages/9d/68/4201e8444f0894f21ab4aeeaee68aa4f10b51613514a20d80bd628d57e88/ruff-0.15.12-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b0c862b172d695db7598426b8af465e7e9ac00a3ea2a3630ee67eb82e366aaa6", size = 11234040 }, - { url = "https://files.pythonhosted.org/packages/34/ff/8a6d6cf4ccc23fd67060874e832c18919d1557a0611ebef03fdb01fff11e/ruff-0.15.12-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2849ea9f3484c3aca43a82f484210370319e7170df4dfe4843395ddf6c57bc33", size = 12087377 }, - { url = "https://files.pythonhosted.org/packages/85/f6/c669cf73f5152f623d34e69866a46d5e6185816b19fcd5b6dd8a2d299922/ruff-0.15.12-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9e77c7e51c07fe396826d5969a5b846d9cd4c402535835fb6e21ce8b28fef847", size = 11367784 }, - { url = "https://files.pythonhosted.org/packages/e8/39/c61d193b8a1daaa8977f7dea9e8d8ba866e02ea7b65d32f6861693aa4c12/ruff-0.15.12-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83b2f4f2f3b1026b5fb449b467d9264bf22067b600f7b6f41fc5958909f449d0", size = 11344088 }, - { url = "https://files.pythonhosted.org/packages/c2/8d/49afab3645e31e12c590acb6d3b5b69d7aab5b81926dbaf7461f9441f37a/ruff-0.15.12-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:9ba3b8f1afd7e2e43d8943e55f249e13f9682fde09711644a6e7290eb4f3e339", size = 11271770 }, - { url = "https://files.pythonhosted.org/packages/46/06/33f41fe94403e2b755481cdfb9b7ef3e4e0ed031c4581124658d935d52b4/ruff-0.15.12-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:e852ba9fdc890655e1d78f2df1499efbe0e54126bd405362154a75e2bde159c5", size = 10719355 }, - { url = "https://files.pythonhosted.org/packages/0d/59/18aa4e014debbf559670e4048e39260a85c7fcee84acfd761ac01e7b8d35/ruff-0.15.12-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:dd8aed930da53780d22fc70bdf84452c843cf64f8cb4eb38984319c24c5cd5fd", size = 10462758 }, - { url = "https://files.pythonhosted.org/packages/25/e7/cc9f16fd0f3b5fddcbd7ec3d6ae30c8f3fde1047f32a4093a98d633c6570/ruff-0.15.12-py3-none-musllinux_1_2_i686.whl", hash = "sha256:01da3988d225628b709493d7dc67c3b9b12c0210016b08690ef9bd27970b262b", size = 10953498 }, - { url = "https://files.pythonhosted.org/packages/72/7a/a9ba7f98c7a575978698f4230c5e8cc54bbc761af34f560818f933dafa0c/ruff-0.15.12-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:9cae0f92bd5700d1213188b31cd3bdd2b315361296d10b96b8e2337d3d11f53e", size = 11447765 }, - { url = "https://files.pythonhosted.org/packages/ea/f9/0ae446942c846b8266059ad8a30702a35afae55f5cdc54c5adf8d7afdc27/ruff-0.15.12-py3-none-win32.whl", hash = "sha256:d0185894e038d7043ba8fd6aee7499ece6462dc0ea9f1e260c7451807c714c20", size = 10657277 }, - { url = "https://files.pythonhosted.org/packages/33/f1/9614e03e1cdcbf9437570b5400ced8a720b5db22b28d8e0f1bda429f660d/ruff-0.15.12-py3-none-win_amd64.whl", hash = "sha256:c87a162d61ab3adca47c03f7f717c68672edec7d1b5499e652331780fe74950d", size = 11837758 }, - { url = "https://files.pythonhosted.org/packages/c0/98/6beb4b351e472e5f4c4613f7c35a5290b8be2497e183825310c4c3a3984b/ruff-0.15.12-py3-none-win_arm64.whl", hash = "sha256:a538f7a82d061cee7be55542aca1d86d1393d55d81d4fcc314370f4340930d4f", size = 11120821 }, -] - -[[package]] -name = "scikit-learn" -version = "1.8.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "joblib" }, - { name = "numpy" }, - { name = "scipy" }, - { name = "threadpoolctl" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/0e/d4/40988bf3b8e34feec1d0e6a051446b1f66225f8529b9309becaeef62b6c4/scikit_learn-1.8.0.tar.gz", hash = "sha256:9bccbb3b40e3de10351f8f5068e105d0f4083b1a65fa07b6634fbc401a6287fd", size = 7335585 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/90/74/e6a7cc4b820e95cc38cf36cd74d5aa2b42e8ffc2d21fe5a9a9c45c1c7630/scikit_learn-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:5fb63362b5a7ddab88e52b6dbb47dac3fd7dafeee740dc6c8d8a446ddedade8e", size = 8548242 }, - { url = "https://files.pythonhosted.org/packages/49/d8/9be608c6024d021041c7f0b3928d4749a706f4e2c3832bbede4fb4f58c95/scikit_learn-1.8.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:5025ce924beccb28298246e589c691fe1b8c1c96507e6d27d12c5fadd85bfd76", size = 8079075 }, - { url = "https://files.pythonhosted.org/packages/dd/47/f187b4636ff80cc63f21cd40b7b2d177134acaa10f6bb73746130ee8c2e5/scikit_learn-1.8.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4496bb2cf7a43ce1a2d7524a79e40bc5da45cf598dbf9545b7e8316ccba47bb4", size = 8660492 }, - { url = "https://files.pythonhosted.org/packages/97/74/b7a304feb2b49df9fafa9382d4d09061a96ee9a9449a7cbea7988dda0828/scikit_learn-1.8.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0bcfe4d0d14aec44921545fd2af2338c7471de9cb701f1da4c9d85906ab847a", size = 8931904 }, - { url = "https://files.pythonhosted.org/packages/9f/c4/0ab22726a04ede56f689476b760f98f8f46607caecff993017ac1b64aa5d/scikit_learn-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:35c007dedb2ffe38fe3ee7d201ebac4a2deccd2408e8621d53067733e3c74809", size = 8019359 }, - { url = "https://files.pythonhosted.org/packages/24/90/344a67811cfd561d7335c1b96ca21455e7e472d281c3c279c4d3f2300236/scikit_learn-1.8.0-cp312-cp312-win_arm64.whl", hash = "sha256:8c497fff237d7b4e07e9ef1a640887fa4fb765647f86fbe00f969ff6280ce2bb", size = 7641898 }, - { url = "https://files.pythonhosted.org/packages/03/aa/e22e0768512ce9255eba34775be2e85c2048da73da1193e841707f8f039c/scikit_learn-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0d6ae97234d5d7079dc0040990a6f7aeb97cb7fa7e8945f1999a429b23569e0a", size = 8513770 }, - { url = "https://files.pythonhosted.org/packages/58/37/31b83b2594105f61a381fc74ca19e8780ee923be2d496fcd8d2e1147bd99/scikit_learn-1.8.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:edec98c5e7c128328124a029bceb09eda2d526997780fef8d65e9a69eead963e", size = 8044458 }, - { url = "https://files.pythonhosted.org/packages/2d/5a/3f1caed8765f33eabb723596666da4ebbf43d11e96550fb18bdec42b467b/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:74b66d8689d52ed04c271e1329f0c61635bcaf5b926db9b12d58914cdc01fe57", size = 8610341 }, - { url = "https://files.pythonhosted.org/packages/38/cf/06896db3f71c75902a8e9943b444a56e727418f6b4b4a90c98c934f51ed4/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8fdf95767f989b0cfedb85f7ed8ca215d4be728031f56ff5a519ee1e3276dc2e", size = 8900022 }, - { url = "https://files.pythonhosted.org/packages/1c/f9/9b7563caf3ec8873e17a31401858efab6b39a882daf6c1bfa88879c0aa11/scikit_learn-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:2de443b9373b3b615aec1bb57f9baa6bb3a9bd093f1269ba95c17d870422b271", size = 7989409 }, - { url = "https://files.pythonhosted.org/packages/49/bd/1f4001503650e72c4f6009ac0c4413cb17d2d601cef6f71c0453da2732fc/scikit_learn-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:eddde82a035681427cbedded4e6eff5e57fa59216c2e3e90b10b19ab1d0a65c3", size = 7619760 }, - { url = "https://files.pythonhosted.org/packages/d2/7d/a630359fc9dcc95496588c8d8e3245cc8fd81980251079bc09c70d41d951/scikit_learn-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7cc267b6108f0a1499a734167282c00c4ebf61328566b55ef262d48e9849c735", size = 8826045 }, - { url = "https://files.pythonhosted.org/packages/cc/56/a0c86f6930cfcd1c7054a2bc417e26960bb88d32444fe7f71d5c2cfae891/scikit_learn-1.8.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:fe1c011a640a9f0791146011dfd3c7d9669785f9fed2b2a5f9e207536cf5c2fd", size = 8420324 }, - { url = "https://files.pythonhosted.org/packages/46/1e/05962ea1cebc1cf3876667ecb14c283ef755bf409993c5946ade3b77e303/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72358cce49465d140cc4e7792015bb1f0296a9742d5622c67e31399b75468b9e", size = 8680651 }, - { url = "https://files.pythonhosted.org/packages/fe/56/a85473cd75f200c9759e3a5f0bcab2d116c92a8a02ee08ccd73b870f8bb4/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:80832434a6cc114f5219211eec13dcbc16c2bac0e31ef64c6d346cde3cf054cb", size = 8925045 }, - { url = "https://files.pythonhosted.org/packages/cc/b7/64d8cfa896c64435ae57f4917a548d7ac7a44762ff9802f75a79b77cb633/scikit_learn-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ee787491dbfe082d9c3013f01f5991658b0f38aa8177e4cd4bf434c58f551702", size = 8507994 }, - { url = "https://files.pythonhosted.org/packages/5e/37/e192ea709551799379958b4c4771ec507347027bb7c942662c7fbeba31cb/scikit_learn-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf97c10a3f5a7543f9b88cbf488d33d175e9146115a451ae34568597ba33dcde", size = 7869518 }, - { url = "https://files.pythonhosted.org/packages/24/05/1af2c186174cc92dcab2233f327336058c077d38f6fe2aceb08e6ab4d509/scikit_learn-1.8.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:c22a2da7a198c28dd1a6e1136f19c830beab7fdca5b3e5c8bba8394f8a5c45b3", size = 8528667 }, - { url = "https://files.pythonhosted.org/packages/a8/25/01c0af38fe969473fb292bba9dc2b8f9b451f3112ff242c647fee3d0dfe7/scikit_learn-1.8.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:6b595b07a03069a2b1740dc08c2299993850ea81cce4fe19b2421e0c970de6b7", size = 8066524 }, - { url = "https://files.pythonhosted.org/packages/be/ce/a0623350aa0b68647333940ee46fe45086c6060ec604874e38e9ab7d8e6c/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:29ffc74089f3d5e87dfca4c2c8450f88bdc61b0fc6ed5d267f3988f19a1309f6", size = 8657133 }, - { url = "https://files.pythonhosted.org/packages/b8/cb/861b41341d6f1245e6ca80b1c1a8c4dfce43255b03df034429089ca2a2c5/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fb65db5d7531bccf3a4f6bec3462223bea71384e2cda41da0f10b7c292b9e7c4", size = 8923223 }, - { url = "https://files.pythonhosted.org/packages/76/18/a8def8f91b18cd1ba6e05dbe02540168cb24d47e8dcf69e8d00b7da42a08/scikit_learn-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:56079a99c20d230e873ea40753102102734c5953366972a71d5cb39a32bc40c6", size = 8096518 }, - { url = "https://files.pythonhosted.org/packages/d1/77/482076a678458307f0deb44e29891d6022617b2a64c840c725495bee343f/scikit_learn-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:3bad7565bc9cf37ce19a7c0d107742b320c1285df7aab1a6e2d28780df167242", size = 7754546 }, - { url = "https://files.pythonhosted.org/packages/2d/d1/ef294ca754826daa043b2a104e59960abfab4cf653891037d19dd5b6f3cf/scikit_learn-1.8.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:4511be56637e46c25721e83d1a9cea9614e7badc7040c4d573d75fbe257d6fd7", size = 8848305 }, - { url = "https://files.pythonhosted.org/packages/5b/e2/b1f8b05138ee813b8e1a4149f2f0d289547e60851fd1bb268886915adbda/scikit_learn-1.8.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:a69525355a641bf8ef136a7fa447672fb54fe8d60cab5538d9eb7c6438543fb9", size = 8432257 }, - { url = "https://files.pythonhosted.org/packages/26/11/c32b2138a85dcb0c99f6afd13a70a951bfdff8a6ab42d8160522542fb647/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c2656924ec73e5939c76ac4c8b026fc203b83d8900362eb2599d8aee80e4880f", size = 8678673 }, - { url = "https://files.pythonhosted.org/packages/c7/57/51f2384575bdec454f4fe4e7a919d696c9ebce914590abf3e52d47607ab8/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:15fc3b5d19cc2be65404786857f2e13c70c83dd4782676dd6814e3b89dc8f5b9", size = 8922467 }, - { url = "https://files.pythonhosted.org/packages/35/4d/748c9e2872637a57981a04adc038dacaa16ba8ca887b23e34953f0b3f742/scikit_learn-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:00d6f1d66fbcf4eba6e356e1420d33cc06c70a45bb1363cd6f6a8e4ebbbdece2", size = 8774395 }, - { url = "https://files.pythonhosted.org/packages/60/22/d7b2ebe4704a5e50790ba089d5c2ae308ab6bb852719e6c3bd4f04c3a363/scikit_learn-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:f28dd15c6bb0b66ba09728cf09fd8736c304be29409bd8445a080c1280619e8c", size = 8002647 }, -] - -[[package]] -name = "scipy" -version = "1.17.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/35/48/b992b488d6f299dbe3f11a20b24d3dda3d46f1a635ede1c46b5b17a7b163/scipy-1.17.1-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:35c3a56d2ef83efc372eaec584314bd0ef2e2f0d2adb21c55e6ad5b344c0dcb8", size = 31610954 }, - { url = "https://files.pythonhosted.org/packages/b2/02/cf107b01494c19dc100f1d0b7ac3cc08666e96ba2d64db7626066cee895e/scipy-1.17.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76", size = 28172662 }, - { url = "https://files.pythonhosted.org/packages/cf/a9/599c28631bad314d219cf9ffd40e985b24d603fc8a2f4ccc5ae8419a535b/scipy-1.17.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:cc90d2e9c7e5c7f1a482c9875007c095c3194b1cfedca3c2f3291cdc2bc7c086", size = 20344366 }, - { url = "https://files.pythonhosted.org/packages/35/f5/906eda513271c8deb5af284e5ef0206d17a96239af79f9fa0aebfe0e36b4/scipy-1.17.1-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:c80be5ede8f3f8eded4eff73cc99a25c388ce98e555b17d31da05287015ffa5b", size = 22704017 }, - { url = "https://files.pythonhosted.org/packages/da/34/16f10e3042d2f1d6b66e0428308ab52224b6a23049cb2f5c1756f713815f/scipy-1.17.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21", size = 32927842 }, - { url = "https://files.pythonhosted.org/packages/01/8e/1e35281b8ab6d5d72ebe9911edcdffa3f36b04ed9d51dec6dd140396e220/scipy-1.17.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458", size = 35235890 }, - { url = "https://files.pythonhosted.org/packages/c5/5c/9d7f4c88bea6e0d5a4f1bc0506a53a00e9fcb198de372bfe4d3652cef482/scipy-1.17.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb", size = 35003557 }, - { url = "https://files.pythonhosted.org/packages/65/94/7698add8f276dbab7a9de9fb6b0e02fc13ee61d51c7c3f85ac28b65e1239/scipy-1.17.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea", size = 37625856 }, - { url = "https://files.pythonhosted.org/packages/a2/84/dc08d77fbf3d87d3ee27f6a0c6dcce1de5829a64f2eae85a0ecc1f0daa73/scipy-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:41b71f4a3a4cab9d366cd9065b288efc4d4f3c0b37a91a8e0947fb5bd7f31d87", size = 36549682 }, - { url = "https://files.pythonhosted.org/packages/bc/98/fe9ae9ffb3b54b62559f52dedaebe204b408db8109a8c66fdd04869e6424/scipy-1.17.1-cp312-cp312-win_arm64.whl", hash = "sha256:f4115102802df98b2b0db3cce5cb9b92572633a1197c77b7553e5203f284a5b3", size = 24547340 }, - { url = "https://files.pythonhosted.org/packages/76/27/07ee1b57b65e92645f219b37148a7e7928b82e2b5dbeccecb4dff7c64f0b/scipy-1.17.1-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:5e3c5c011904115f88a39308379c17f91546f77c1667cea98739fe0fccea804c", size = 31590199 }, - { url = "https://files.pythonhosted.org/packages/ec/ae/db19f8ab842e9b724bf5dbb7db29302a91f1e55bc4d04b1025d6d605a2c5/scipy-1.17.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f", size = 28154001 }, - { url = "https://files.pythonhosted.org/packages/5b/58/3ce96251560107b381cbd6e8413c483bbb1228a6b919fa8652b0d4090e7f/scipy-1.17.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d", size = 20325719 }, - { url = "https://files.pythonhosted.org/packages/b2/83/15087d945e0e4d48ce2377498abf5ad171ae013232ae31d06f336e64c999/scipy-1.17.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:4b400bdc6f79fa02a4d86640310dde87a21fba0c979efff5248908c6f15fad1b", size = 22683595 }, - { url = "https://files.pythonhosted.org/packages/b4/e0/e58fbde4a1a594c8be8114eb4aac1a55bcd6587047efc18a61eb1f5c0d30/scipy-1.17.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6", size = 32896429 }, - { url = "https://files.pythonhosted.org/packages/f5/5f/f17563f28ff03c7b6799c50d01d5d856a1d55f2676f537ca8d28c7f627cd/scipy-1.17.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464", size = 35203952 }, - { url = "https://files.pythonhosted.org/packages/8d/a5/9afd17de24f657fdfe4df9a3f1ea049b39aef7c06000c13db1530d81ccca/scipy-1.17.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950", size = 34979063 }, - { url = "https://files.pythonhosted.org/packages/8b/13/88b1d2384b424bf7c924f2038c1c409f8d88bb2a8d49d097861dd64a57b2/scipy-1.17.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369", size = 37598449 }, - { url = "https://files.pythonhosted.org/packages/35/e5/d6d0e51fc888f692a35134336866341c08655d92614f492c6860dc45bb2c/scipy-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:37425bc9175607b0268f493d79a292c39f9d001a357bebb6b88fdfaff13f6448", size = 36510943 }, - { url = "https://files.pythonhosted.org/packages/2a/fd/3be73c564e2a01e690e19cc618811540ba5354c67c8680dce3281123fb79/scipy-1.17.1-cp313-cp313-win_arm64.whl", hash = "sha256:5cf36e801231b6a2059bf354720274b7558746f3b1a4efb43fcf557ccd484a87", size = 24545621 }, - { url = "https://files.pythonhosted.org/packages/6f/6b/17787db8b8114933a66f9dcc479a8272e4b4da75fe03b0c282f7b0ade8cd/scipy-1.17.1-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a", size = 31936708 }, - { url = "https://files.pythonhosted.org/packages/38/2e/524405c2b6392765ab1e2b722a41d5da33dc5c7b7278184a8ad29b6cb206/scipy-1.17.1-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0", size = 28570135 }, - { url = "https://files.pythonhosted.org/packages/fd/c3/5bd7199f4ea8556c0c8e39f04ccb014ac37d1468e6cfa6a95c6b3562b76e/scipy-1.17.1-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:2ceb2d3e01c5f1d83c4189737a42d9cb2fc38a6eeed225e7515eef71ad301dce", size = 20741977 }, - { url = "https://files.pythonhosted.org/packages/d9/b8/8ccd9b766ad14c78386599708eb745f6b44f08400a5fd0ade7cf89b6fc93/scipy-1.17.1-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6", size = 23029601 }, - { url = "https://files.pythonhosted.org/packages/6d/a0/3cb6f4d2fb3e17428ad2880333cac878909ad1a89f678527b5328b93c1d4/scipy-1.17.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e", size = 33019667 }, - { url = "https://files.pythonhosted.org/packages/f3/c3/2d834a5ac7bf3a0c806ad1508efc02dda3c8c61472a56132d7894c312dea/scipy-1.17.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475", size = 35264159 }, - { url = "https://files.pythonhosted.org/packages/4d/77/d3ed4becfdbd217c52062fafe35a72388d1bd82c2d0ba5ca19d6fcc93e11/scipy-1.17.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50", size = 35102771 }, - { url = "https://files.pythonhosted.org/packages/bd/12/d19da97efde68ca1ee5538bb261d5d2c062f0c055575128f11a2730e3ac1/scipy-1.17.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca", size = 37665910 }, - { url = "https://files.pythonhosted.org/packages/06/1c/1172a88d507a4baaf72c5a09bb6c018fe2ae0ab622e5830b703a46cc9e44/scipy-1.17.1-cp313-cp313t-win_amd64.whl", hash = "sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c", size = 36562980 }, - { url = "https://files.pythonhosted.org/packages/70/b0/eb757336e5a76dfa7911f63252e3b7d1de00935d7705cf772db5b45ec238/scipy-1.17.1-cp313-cp313t-win_arm64.whl", hash = "sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49", size = 24856543 }, - { url = "https://files.pythonhosted.org/packages/cf/83/333afb452af6f0fd70414dc04f898647ee1423979ce02efa75c3b0f2c28e/scipy-1.17.1-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717", size = 31584510 }, - { url = "https://files.pythonhosted.org/packages/ed/a6/d05a85fd51daeb2e4ea71d102f15b34fedca8e931af02594193ae4fd25f7/scipy-1.17.1-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:45abad819184f07240d8a696117a7aacd39787af9e0b719d00285549ed19a1e9", size = 28170131 }, - { url = "https://files.pythonhosted.org/packages/db/7b/8624a203326675d7746a254083a187398090a179335b2e4a20e2ddc46e83/scipy-1.17.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:3fd1fcdab3ea951b610dc4cef356d416d5802991e7e32b5254828d342f7b7e0b", size = 20342032 }, - { url = "https://files.pythonhosted.org/packages/c9/35/2c342897c00775d688d8ff3987aced3426858fd89d5a0e26e020b660b301/scipy-1.17.1-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866", size = 22678766 }, - { url = "https://files.pythonhosted.org/packages/ef/f2/7cdb8eb308a1a6ae1e19f945913c82c23c0c442a462a46480ce487fdc0ac/scipy-1.17.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350", size = 32957007 }, - { url = "https://files.pythonhosted.org/packages/0b/2e/7eea398450457ecb54e18e9d10110993fa65561c4f3add5e8eccd2b9cd41/scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118", size = 35221333 }, - { url = "https://files.pythonhosted.org/packages/d9/77/5b8509d03b77f093a0d52e606d3c4f79e8b06d1d38c441dacb1e26cacf46/scipy-1.17.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068", size = 35042066 }, - { url = "https://files.pythonhosted.org/packages/f9/df/18f80fb99df40b4070328d5ae5c596f2f00fffb50167e31439e932f29e7d/scipy-1.17.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118", size = 37612763 }, - { url = "https://files.pythonhosted.org/packages/4b/39/f0e8ea762a764a9dc52aa7dabcfad51a354819de1f0d4652b6a1122424d6/scipy-1.17.1-cp314-cp314-win_amd64.whl", hash = "sha256:3877ac408e14da24a6196de0ddcace62092bfc12a83823e92e49e40747e52c19", size = 37290984 }, - { url = "https://files.pythonhosted.org/packages/7c/56/fe201e3b0f93d1a8bcf75d3379affd228a63d7e2d80ab45467a74b494947/scipy-1.17.1-cp314-cp314-win_arm64.whl", hash = "sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293", size = 25192877 }, - { url = "https://files.pythonhosted.org/packages/96/ad/f8c414e121f82e02d76f310f16db9899c4fcde36710329502a6b2a3c0392/scipy-1.17.1-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6", size = 31949750 }, - { url = "https://files.pythonhosted.org/packages/7c/b0/c741e8865d61b67c81e255f4f0a832846c064e426636cd7de84e74d209be/scipy-1.17.1-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1", size = 28585858 }, - { url = "https://files.pythonhosted.org/packages/ed/1b/3985219c6177866628fa7c2595bfd23f193ceebbe472c98a08824b9466ff/scipy-1.17.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39", size = 20757723 }, - { url = "https://files.pythonhosted.org/packages/c0/19/2a04aa25050d656d6f7b9e7b685cc83d6957fb101665bfd9369ca6534563/scipy-1.17.1-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca", size = 23043098 }, - { url = "https://files.pythonhosted.org/packages/86/f1/3383beb9b5d0dbddd030335bf8a8b32d4317185efe495374f134d8be6cce/scipy-1.17.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad", size = 33030397 }, - { url = "https://files.pythonhosted.org/packages/41/68/8f21e8a65a5a03f25a79165ec9d2b28c00e66dc80546cf5eb803aeeff35b/scipy-1.17.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a", size = 35281163 }, - { url = "https://files.pythonhosted.org/packages/84/8d/c8a5e19479554007a5632ed7529e665c315ae7492b4f946b0deb39870e39/scipy-1.17.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4", size = 35116291 }, - { url = "https://files.pythonhosted.org/packages/52/52/e57eceff0e342a1f50e274264ed47497b59e6a4e3118808ee58ddda7b74a/scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2", size = 37682317 }, - { url = "https://files.pythonhosted.org/packages/11/2f/b29eafe4a3fbc3d6de9662b36e028d5f039e72d345e05c250e121a230dd4/scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484", size = 37345327 }, - { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165 }, -] - -[[package]] -name = "setuptools" -version = "82.0.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/4f/db/cfac1baf10650ab4d1c111714410d2fbb77ac5a616db26775db562c8fab2/setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9", size = 1152316 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9d/76/f789f7a86709c6b087c5a2f52f911838cad707cc613162401badc665acfe/setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb", size = 1006223 }, -] - -[[package]] -name = "shellingham" -version = "1.5.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755 }, -] - -[[package]] -name = "six" -version = "1.17.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 }, -] - -[[package]] -name = "sqlalchemy" -version = "2.0.49" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/09/45/461788f35e0364a8da7bda51a1fe1b09762d0c32f12f63727998d85a873b/sqlalchemy-2.0.49.tar.gz", hash = "sha256:d15950a57a210e36dd4cec1aac22787e2a4d57ba9318233e2ef8b2daf9ff2d5f", size = 9898221 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/49/b3/2de412451330756aaaa72d27131db6dde23995efe62c941184e15242a5fa/sqlalchemy-2.0.49-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4bbccb45260e4ff1b7db0be80a9025bb1e6698bdb808b83fff0000f7a90b2c0b", size = 2157681 }, - { url = "https://files.pythonhosted.org/packages/50/84/b2a56e2105bd11ebf9f0b93abddd748e1a78d592819099359aa98134a8bf/sqlalchemy-2.0.49-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fb37f15714ec2652d574f021d479e78cd4eb9d04396dca36568fdfffb3487982", size = 3338976 }, - { url = "https://files.pythonhosted.org/packages/2c/fa/65fcae2ed62f84ab72cf89536c7c3217a156e71a2c111b1305ab6f0690e2/sqlalchemy-2.0.49-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb9ec6436a820a4c006aad1ac351f12de2f2dbdaad171692ee457a02429b672", size = 3351937 }, - { url = "https://files.pythonhosted.org/packages/f8/2f/6fd118563572a7fe475925742eb6b3443b2250e346a0cc27d8d408e73773/sqlalchemy-2.0.49-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8d6efc136f44a7e8bc8088507eaabbb8c2b55b3dbb63fe102c690da0ddebe55e", size = 3281646 }, - { url = "https://files.pythonhosted.org/packages/c5/d7/410f4a007c65275b9cf82354adb4bb8ba587b176d0a6ee99caa16fe638f8/sqlalchemy-2.0.49-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e06e617e3d4fd9e51d385dfe45b077a41e9d1b033a7702551e3278ac597dc750", size = 3316695 }, - { url = "https://files.pythonhosted.org/packages/d9/95/81f594aa60ded13273a844539041ccf1e66c5a7bed0a8e27810a3b52d522/sqlalchemy-2.0.49-cp312-cp312-win32.whl", hash = "sha256:83101a6930332b87653886c01d1ee7e294b1fe46a07dd9a2d2b4f91bcc88eec0", size = 2117483 }, - { url = "https://files.pythonhosted.org/packages/47/9e/fd90114059175cac64e4fafa9bf3ac20584384d66de40793ae2e2f26f3bb/sqlalchemy-2.0.49-cp312-cp312-win_amd64.whl", hash = "sha256:618a308215b6cececb6240b9abde545e3acdabac7ae3e1d4e666896bf5ba44b4", size = 2144494 }, - { url = "https://files.pythonhosted.org/packages/ae/81/81755f50eb2478eaf2049728491d4ea4f416c1eb013338682173259efa09/sqlalchemy-2.0.49-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:df2d441bacf97022e81ad047e1597552eb3f83ca8a8f1a1fdd43cd7fe3898120", size = 2154547 }, - { url = "https://files.pythonhosted.org/packages/a2/bc/3494270da80811d08bcfa247404292428c4fe16294932bce5593f215cad9/sqlalchemy-2.0.49-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8e20e511dc15265fb433571391ba313e10dd8ea7e509d51686a51313b4ac01a2", size = 3280782 }, - { url = "https://files.pythonhosted.org/packages/cd/f5/038741f5e747a5f6ea3e72487211579d8cbea5eb9827a9cbd61d0108c4bd/sqlalchemy-2.0.49-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47604cb2159f8bbd5a1ab48a714557156320f20871ee64d550d8bf2683d980d3", size = 3297156 }, - { url = "https://files.pythonhosted.org/packages/88/50/a6af0ff9dc954b43a65ca9b5367334e45d99684c90a3d3413fc19a02d43c/sqlalchemy-2.0.49-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:22d8798819f86720bc646ab015baff5ea4c971d68121cb36e2ebc2ee43ead2b7", size = 3228832 }, - { url = "https://files.pythonhosted.org/packages/bc/d1/5f6bdad8de0bf546fc74370939621396515e0cdb9067402d6ba1b8afbe9a/sqlalchemy-2.0.49-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9b1c058c171b739e7c330760044803099c7fff11511e3ab3573e5327116a9c33", size = 3267000 }, - { url = "https://files.pythonhosted.org/packages/f7/30/ad62227b4a9819a5e1c6abff77c0f614fa7c9326e5a3bdbee90f7139382b/sqlalchemy-2.0.49-cp313-cp313-win32.whl", hash = "sha256:a143af2ea6672f2af3f44ed8f9cd020e9cc34c56f0e8db12019d5d9ecf41cb3b", size = 2115641 }, - { url = "https://files.pythonhosted.org/packages/17/3a/7215b1b7d6d49dc9a87211be44562077f5f04f9bb5a59552c1c8e2d98173/sqlalchemy-2.0.49-cp313-cp313-win_amd64.whl", hash = "sha256:12b04d1db2663b421fe072d638a138460a51d5a862403295671c4f3987fb9148", size = 2141498 }, - { url = "https://files.pythonhosted.org/packages/28/4b/52a0cb2687a9cd1648252bb257be5a1ba2c2ded20ba695c65756a55a15a4/sqlalchemy-2.0.49-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:24bd94bb301ec672d8f0623eba9226cc90d775d25a0c92b5f8e4965d7f3a1518", size = 3560807 }, - { url = "https://files.pythonhosted.org/packages/8c/d8/fda95459204877eed0458550d6c7c64c98cc50c2d8d618026737de9ed41a/sqlalchemy-2.0.49-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a51d3db74ba489266ef55c7a4534eb0b8db9a326553df481c11e5d7660c8364d", size = 3527481 }, - { url = "https://files.pythonhosted.org/packages/ff/0a/2aac8b78ac6487240cf7afef8f203ca783e8796002dc0cf65c4ee99ff8bb/sqlalchemy-2.0.49-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:55250fe61d6ebfd6934a272ee16ef1244e0f16b7af6cd18ab5b1fc9f08631db0", size = 3468565 }, - { url = "https://files.pythonhosted.org/packages/a5/3d/ce71cfa82c50a373fd2148b3c870be05027155ce791dc9a5dcf439790b8b/sqlalchemy-2.0.49-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:46796877b47034b559a593d7e4b549aba151dae73f9e78212a3478161c12ab08", size = 3477769 }, - { url = "https://files.pythonhosted.org/packages/d5/e8/0a9f5c1f7c6f9ca480319bf57c2d7423f08d31445974167a27d14483c948/sqlalchemy-2.0.49-cp313-cp313t-win32.whl", hash = "sha256:9c4969a86e41454f2858256c39bdfb966a20961e9b58bf8749b65abf447e9a8d", size = 2143319 }, - { url = "https://files.pythonhosted.org/packages/0e/51/fb5240729fbec73006e137c4f7a7918ffd583ab08921e6ff81a999d6517a/sqlalchemy-2.0.49-cp313-cp313t-win_amd64.whl", hash = "sha256:b9870d15ef00e4d0559ae10ee5bc71b654d1f20076dbe8bc7ed19b4c0625ceba", size = 2175104 }, - { url = "https://files.pythonhosted.org/packages/55/33/bf28f618c0a9597d14e0b9ee7d1e0622faff738d44fe986ee287cdf1b8d0/sqlalchemy-2.0.49-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:233088b4b99ebcbc5258c755a097aa52fbf90727a03a5a80781c4b9c54347a2e", size = 2156356 }, - { url = "https://files.pythonhosted.org/packages/d1/a7/5f476227576cb8644650eff68cc35fa837d3802b997465c96b8340ced1e2/sqlalchemy-2.0.49-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:57ca426a48eb2c682dae8204cd89ea8ab7031e2675120a47924fabc7caacbc2a", size = 3276486 }, - { url = "https://files.pythonhosted.org/packages/2e/84/efc7c0bf3a1c5eef81d397f6fddac855becdbb11cb38ff957888603014a7/sqlalchemy-2.0.49-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685e93e9c8f399b0c96a624799820176312f5ceef958c0f88215af4013d29066", size = 3281479 }, - { url = "https://files.pythonhosted.org/packages/91/68/bb406fa4257099c67bd75f3f2261b129c63204b9155de0d450b37f004698/sqlalchemy-2.0.49-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9e0400fa22f79acc334d9a6b185dc00a44a8e6578aa7e12d0ddcd8434152b187", size = 3226269 }, - { url = "https://files.pythonhosted.org/packages/67/84/acb56c00cca9f251f437cb49e718e14f7687505749ea9255d7bd8158a6df/sqlalchemy-2.0.49-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a05977bffe9bffd2229f477fa75eabe3192b1b05f408961d1bebff8d1cd4d401", size = 3248260 }, - { url = "https://files.pythonhosted.org/packages/56/19/6a20ea25606d1efd7bd1862149bb2a22d1451c3f851d23d887969201633f/sqlalchemy-2.0.49-cp314-cp314-win32.whl", hash = "sha256:0f2fa354ba106eafff2c14b0cc51f22801d1e8b2e4149342023bd6f0955de5f5", size = 2118463 }, - { url = "https://files.pythonhosted.org/packages/cf/4f/8297e4ed88e80baa1f5aa3c484a0ee29ef3c69c7582f206c916973b75057/sqlalchemy-2.0.49-cp314-cp314-win_amd64.whl", hash = "sha256:77641d299179c37b89cf2343ca9972c88bb6eef0d5fc504a2f86afd15cd5adf5", size = 2144204 }, - { url = "https://files.pythonhosted.org/packages/1f/33/95e7216df810c706e0cd3655a778604bbd319ed4f43333127d465a46862d/sqlalchemy-2.0.49-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c1dc3368794d522f43914e03312202523cc89692f5389c32bea0233924f8d977", size = 3565474 }, - { url = "https://files.pythonhosted.org/packages/0c/a4/ed7b18d8ccf7f954a83af6bb73866f5bc6f5636f44c7731fbb741f72cc4f/sqlalchemy-2.0.49-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7c821c47ecfe05cc32140dcf8dc6fd5d21971c86dbd56eabfe5ba07a64910c01", size = 3530567 }, - { url = "https://files.pythonhosted.org/packages/73/a3/20faa869c7e21a827c4a2a42b41353a54b0f9f5e96df5087629c306df71e/sqlalchemy-2.0.49-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9c04bff9a5335eb95c6ecf1c117576a0aa560def274876fd156cfe5510fccc61", size = 3474282 }, - { url = "https://files.pythonhosted.org/packages/b7/50/276b9a007aa0764304ad467eceb70b04822dc32092492ee5f322d559a4dc/sqlalchemy-2.0.49-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:7f605a456948c35260e7b2a39f8952a26f077fd25653c37740ed186b90aaa68a", size = 3480406 }, - { url = "https://files.pythonhosted.org/packages/e5/c3/c80fcdb41905a2df650c2a3e0337198b6848876e63d66fe9188ef9003d24/sqlalchemy-2.0.49-cp314-cp314t-win32.whl", hash = "sha256:6270d717b11c5476b0cbb21eedc8d4dbb7d1a956fd6c15a23e96f197a6193158", size = 2149151 }, - { url = "https://files.pythonhosted.org/packages/05/52/9f1a62feab6ed368aff068524ff414f26a6daebc7361861035ae00b05530/sqlalchemy-2.0.49-cp314-cp314t-win_amd64.whl", hash = "sha256:275424295f4256fd301744b8f335cff367825d270f155d522b30c7bf49903ee7", size = 2184178 }, - { url = "https://files.pythonhosted.org/packages/e5/30/8519fdde58a7bdf155b714359791ad1dc018b47d60269d5d160d311fdc36/sqlalchemy-2.0.49-py3-none-any.whl", hash = "sha256:ec44cfa7ef1a728e88ad41674de50f6db8cfdb3e2af84af86e0041aaf02d43d0", size = 1942158 }, -] - -[package.optional-dependencies] -asyncio = [ - { name = "greenlet" }, -] - -[[package]] -name = "surfsense-evals" -version = "0.1.0" -source = { editable = "." } -dependencies = [ - { name = "azure-ai-documentintelligence" }, - { name = "datasets" }, - { name = "httpx" }, - { name = "httpx-sse" }, - { name = "huggingface-hub" }, - { name = "llama-cloud-services" }, - { name = "numpy" }, - { name = "pillow" }, - { name = "pyarrow" }, - { name = "pydantic" }, - { name = "pypdf" }, - { name = "python-dotenv" }, - { name = "reportlab" }, - { name = "rich" }, - { name = "scikit-learn" }, - { name = "scipy" }, - { name = "tqdm" }, - { name = "trafilatura" }, -] - -[package.optional-dependencies] -dev = [ - { name = "pytest" }, - { name = "pytest-asyncio" }, - { name = "respx" }, - { name = "ruff" }, -] - -[package.metadata] -requires-dist = [ - { name = "azure-ai-documentintelligence", specifier = ">=1.0.2" }, - { name = "datasets", specifier = ">=2.21.0" }, - { name = "httpx", specifier = ">=0.27.0" }, - { name = "httpx-sse", specifier = ">=0.4.0" }, - { name = "huggingface-hub", specifier = ">=0.24.0" }, - { name = "llama-cloud-services", specifier = ">=0.6.25" }, - { name = "numpy", specifier = ">=1.26.0" }, - { name = "pillow", specifier = ">=10.0.0" }, - { name = "pyarrow", specifier = ">=15.0.0" }, - { name = "pydantic", specifier = ">=2.6.0" }, - { name = "pypdf", specifier = ">=5.1.0" }, - { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" }, - { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.0" }, - { name = "python-dotenv", specifier = ">=1.0.0" }, - { name = "reportlab", specifier = ">=4.0.0" }, - { name = "respx", marker = "extra == 'dev'", specifier = ">=0.21.0" }, - { name = "rich", specifier = ">=13.7.0" }, - { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.5.0" }, - { name = "scikit-learn", specifier = ">=1.4.0" }, - { name = "scipy", specifier = ">=1.12.0" }, - { name = "tqdm", specifier = ">=4.66.0" }, - { name = "trafilatura", specifier = ">=1.12.0" }, -] -provides-extras = ["dev"] - -[[package]] -name = "tenacity" -version = "9.1.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/47/c6/ee486fd809e357697ee8a44d3d69222b344920433d3b6666ccd9b374630c/tenacity-9.1.4.tar.gz", hash = "sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a", size = 49413 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d7/c1/eb8f9debc45d3b7918a32ab756658a0904732f75e555402972246b0b8e71/tenacity-9.1.4-py3-none-any.whl", hash = "sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55", size = 28926 }, -] - -[[package]] -name = "threadpoolctl" -version = "3.6.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638 }, -] - -[[package]] -name = "tiktoken" -version = "0.12.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "regex" }, - { name = "requests" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/7d/ab/4d017d0f76ec3171d469d80fc03dfbb4e48a4bcaddaa831b31d526f05edc/tiktoken-0.12.0.tar.gz", hash = "sha256:b18ba7ee2b093863978fcb14f74b3707cdc8d4d4d3836853ce7ec60772139931", size = 37806 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a4/85/be65d39d6b647c79800fd9d29241d081d4eeb06271f383bb87200d74cf76/tiktoken-0.12.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b97f74aca0d78a1ff21b8cd9e9925714c15a9236d6ceacf5c7327c117e6e21e8", size = 1050728 }, - { url = "https://files.pythonhosted.org/packages/4a/42/6573e9129bc55c9bf7300b3a35bef2c6b9117018acca0dc760ac2d93dffe/tiktoken-0.12.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2b90f5ad190a4bb7c3eb30c5fa32e1e182ca1ca79f05e49b448438c3e225a49b", size = 994049 }, - { url = "https://files.pythonhosted.org/packages/66/c5/ed88504d2f4a5fd6856990b230b56d85a777feab84e6129af0822f5d0f70/tiktoken-0.12.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:65b26c7a780e2139e73acc193e5c63ac754021f160df919add909c1492c0fb37", size = 1129008 }, - { url = "https://files.pythonhosted.org/packages/f4/90/3dae6cc5436137ebd38944d396b5849e167896fc2073da643a49f372dc4f/tiktoken-0.12.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:edde1ec917dfd21c1f2f8046b86348b0f54a2c0547f68149d8600859598769ad", size = 1152665 }, - { url = "https://files.pythonhosted.org/packages/a3/fe/26df24ce53ffde419a42f5f53d755b995c9318908288c17ec3f3448313a3/tiktoken-0.12.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:35a2f8ddd3824608b3d650a000c1ef71f730d0c56486845705a8248da00f9fe5", size = 1194230 }, - { url = "https://files.pythonhosted.org/packages/20/cc/b064cae1a0e9fac84b0d2c46b89f4e57051a5f41324e385d10225a984c24/tiktoken-0.12.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:83d16643edb7fa2c99eff2ab7733508aae1eebb03d5dfc46f5565862810f24e3", size = 1254688 }, - { url = "https://files.pythonhosted.org/packages/81/10/b8523105c590c5b8349f2587e2fdfe51a69544bd5a76295fc20f2374f470/tiktoken-0.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:ffc5288f34a8bc02e1ea7047b8d041104791d2ddbf42d1e5fa07822cbffe16bd", size = 878694 }, - { url = "https://files.pythonhosted.org/packages/00/61/441588ee21e6b5cdf59d6870f86beb9789e532ee9718c251b391b70c68d6/tiktoken-0.12.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:775c2c55de2310cc1bc9a3ad8826761cbdc87770e586fd7b6da7d4589e13dab3", size = 1050802 }, - { url = "https://files.pythonhosted.org/packages/1f/05/dcf94486d5c5c8d34496abe271ac76c5b785507c8eae71b3708f1ad9b45a/tiktoken-0.12.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a01b12f69052fbe4b080a2cfb867c4de12c704b56178edf1d1d7b273561db160", size = 993995 }, - { url = "https://files.pythonhosted.org/packages/a0/70/5163fe5359b943f8db9946b62f19be2305de8c3d78a16f629d4165e2f40e/tiktoken-0.12.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:01d99484dc93b129cd0964f9d34eee953f2737301f18b3c7257bf368d7615baa", size = 1128948 }, - { url = "https://files.pythonhosted.org/packages/0c/da/c028aa0babf77315e1cef357d4d768800c5f8a6de04d0eac0f377cb619fa/tiktoken-0.12.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:4a1a4fcd021f022bfc81904a911d3df0f6543b9e7627b51411da75ff2fe7a1be", size = 1151986 }, - { url = "https://files.pythonhosted.org/packages/a0/5a/886b108b766aa53e295f7216b509be95eb7d60b166049ce2c58416b25f2a/tiktoken-0.12.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:981a81e39812d57031efdc9ec59fa32b2a5a5524d20d4776574c4b4bd2e9014a", size = 1194222 }, - { url = "https://files.pythonhosted.org/packages/f4/f8/4db272048397636ac7a078d22773dd2795b1becee7bc4922fe6207288d57/tiktoken-0.12.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9baf52f84a3f42eef3ff4e754a0db79a13a27921b457ca9832cf944c6be4f8f3", size = 1255097 }, - { url = "https://files.pythonhosted.org/packages/8e/32/45d02e2e0ea2be3a9ed22afc47d93741247e75018aac967b713b2941f8ea/tiktoken-0.12.0-cp313-cp313-win_amd64.whl", hash = "sha256:b8a0cd0c789a61f31bf44851defbd609e8dd1e2c8589c614cc1060940ef1f697", size = 879117 }, - { url = "https://files.pythonhosted.org/packages/ce/76/994fc868f88e016e6d05b0da5ac24582a14c47893f4474c3e9744283f1d5/tiktoken-0.12.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:d5f89ea5680066b68bcb797ae85219c72916c922ef0fcdd3480c7d2315ffff16", size = 1050309 }, - { url = "https://files.pythonhosted.org/packages/f6/b8/57ef1456504c43a849821920d582a738a461b76a047f352f18c0b26c6516/tiktoken-0.12.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b4e7ed1c6a7a8a60a3230965bdedba8cc58f68926b835e519341413370e0399a", size = 993712 }, - { url = "https://files.pythonhosted.org/packages/72/90/13da56f664286ffbae9dbcfadcc625439142675845baa62715e49b87b68b/tiktoken-0.12.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:fc530a28591a2d74bce821d10b418b26a094bf33839e69042a6e86ddb7a7fb27", size = 1128725 }, - { url = "https://files.pythonhosted.org/packages/05/df/4f80030d44682235bdaecd7346c90f67ae87ec8f3df4a3442cb53834f7e4/tiktoken-0.12.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:06a9f4f49884139013b138920a4c393aa6556b2f8f536345f11819389c703ebb", size = 1151875 }, - { url = "https://files.pythonhosted.org/packages/22/1f/ae535223a8c4ef4c0c1192e3f9b82da660be9eb66b9279e95c99288e9dab/tiktoken-0.12.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:04f0e6a985d95913cabc96a741c5ffec525a2c72e9df086ff17ebe35985c800e", size = 1194451 }, - { url = "https://files.pythonhosted.org/packages/78/a7/f8ead382fce0243cb625c4f266e66c27f65ae65ee9e77f59ea1653b6d730/tiktoken-0.12.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:0ee8f9ae00c41770b5f9b0bb1235474768884ae157de3beb5439ca0fd70f3e25", size = 1253794 }, - { url = "https://files.pythonhosted.org/packages/93/e0/6cc82a562bc6365785a3ff0af27a2a092d57c47d7a81d9e2295d8c36f011/tiktoken-0.12.0-cp313-cp313t-win_amd64.whl", hash = "sha256:dc2dd125a62cb2b3d858484d6c614d136b5b848976794edfb63688d539b8b93f", size = 878777 }, - { url = "https://files.pythonhosted.org/packages/72/05/3abc1db5d2c9aadc4d2c76fa5640134e475e58d9fbb82b5c535dc0de9b01/tiktoken-0.12.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:a90388128df3b3abeb2bfd1895b0681412a8d7dc644142519e6f0a97c2111646", size = 1050188 }, - { url = "https://files.pythonhosted.org/packages/e3/7b/50c2f060412202d6c95f32b20755c7a6273543b125c0985d6fa9465105af/tiktoken-0.12.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:da900aa0ad52247d8794e307d6446bd3cdea8e192769b56276695d34d2c9aa88", size = 993978 }, - { url = "https://files.pythonhosted.org/packages/14/27/bf795595a2b897e271771cd31cb847d479073497344c637966bdf2853da1/tiktoken-0.12.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:285ba9d73ea0d6171e7f9407039a290ca77efcdb026be7769dccc01d2c8d7fff", size = 1129271 }, - { url = "https://files.pythonhosted.org/packages/f5/de/9341a6d7a8f1b448573bbf3425fa57669ac58258a667eb48a25dfe916d70/tiktoken-0.12.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:d186a5c60c6a0213f04a7a802264083dea1bbde92a2d4c7069e1a56630aef830", size = 1151216 }, - { url = "https://files.pythonhosted.org/packages/75/0d/881866647b8d1be4d67cb24e50d0c26f9f807f994aa1510cb9ba2fe5f612/tiktoken-0.12.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:604831189bd05480f2b885ecd2d1986dc7686f609de48208ebbbddeea071fc0b", size = 1194860 }, - { url = "https://files.pythonhosted.org/packages/b3/1e/b651ec3059474dab649b8d5b69f5c65cd8fcd8918568c1935bd4136c9392/tiktoken-0.12.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:8f317e8530bb3a222547b85a58583238c8f74fd7a7408305f9f63246d1a0958b", size = 1254567 }, - { url = "https://files.pythonhosted.org/packages/80/57/ce64fd16ac390fafde001268c364d559447ba09b509181b2808622420eec/tiktoken-0.12.0-cp314-cp314-win_amd64.whl", hash = "sha256:399c3dd672a6406719d84442299a490420b458c44d3ae65516302a99675888f3", size = 921067 }, - { url = "https://files.pythonhosted.org/packages/ac/a4/72eed53e8976a099539cdd5eb36f241987212c29629d0a52c305173e0a68/tiktoken-0.12.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:c2c714c72bc00a38ca969dae79e8266ddec999c7ceccd603cc4f0d04ccd76365", size = 1050473 }, - { url = "https://files.pythonhosted.org/packages/e6/d7/0110b8f54c008466b19672c615f2168896b83706a6611ba6e47313dbc6e9/tiktoken-0.12.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cbb9a3ba275165a2cb0f9a83f5d7025afe6b9d0ab01a22b50f0e74fee2ad253e", size = 993855 }, - { url = "https://files.pythonhosted.org/packages/5f/77/4f268c41a3957c418b084dd576ea2fad2e95da0d8e1ab705372892c2ca22/tiktoken-0.12.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:dfdfaa5ffff8993a3af94d1125870b1d27aed7cb97aa7eb8c1cefdbc87dbee63", size = 1129022 }, - { url = "https://files.pythonhosted.org/packages/4e/2b/fc46c90fe5028bd094cd6ee25a7db321cb91d45dc87531e2bdbb26b4867a/tiktoken-0.12.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:584c3ad3d0c74f5269906eb8a659c8bfc6144a52895d9261cdaf90a0ae5f4de0", size = 1150736 }, - { url = "https://files.pythonhosted.org/packages/28/c0/3c7a39ff68022ddfd7d93f3337ad90389a342f761c4d71de99a3ccc57857/tiktoken-0.12.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:54c891b416a0e36b8e2045b12b33dd66fb34a4fe7965565f1b482da50da3e86a", size = 1194908 }, - { url = "https://files.pythonhosted.org/packages/ab/0d/c1ad6f4016a3968c048545f5d9b8ffebf577774b2ede3e2e352553b685fe/tiktoken-0.12.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5edb8743b88d5be814b1a8a8854494719080c28faaa1ccbef02e87354fe71ef0", size = 1253706 }, - { url = "https://files.pythonhosted.org/packages/af/df/c7891ef9d2712ad774777271d39fdef63941ffba0a9d59b7ad1fd2765e57/tiktoken-0.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f61c0aea5565ac82e2ec50a05e02a6c44734e91b51c10510b084ea1b8e633a71", size = 920667 }, -] - -[[package]] -name = "tinytag" -version = "2.2.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/96/59/8a8cb2331e2602b53e4dc06960f57d1387a2b18e7efd24e5f9cb60ea4925/tinytag-2.2.1.tar.gz", hash = "sha256:e6d06610ebe7cd66fd07be2d3b9495914ab32654a5e47657bb8cd44c2484523c", size = 38214 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ce/34/d50e338631baaf65ec5396e70085e5de0b52b24b28db1ffbc1c6e82190dc/tinytag-2.2.1-py3-none-any.whl", hash = "sha256:ed8b1e6d25367937e3321e054f4974f9abfde1a3e0a538824c87da377130c2b6", size = 32927 }, -] - -[[package]] -name = "tld" -version = "0.13.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/5c/5d/76b4383ac4e5b5e254e50c09807b3e13820bed6d6c11cd540264988d6802/tld-0.13.2.tar.gz", hash = "sha256:d983fa92b9d717400742fca844e29d5e18271079c7bcfabf66d01b39b4a14345", size = 467175 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/90/39a85a4b63c84213e78b3c17d22e1bf45328acf8ebb33ef93be30d0a3911/tld-0.13.2-py2.py3-none-any.whl", hash = "sha256:9b8fdbdb880e7ba65b216a4937f2c94c49a7226723783d5838fc958ac76f4e0c", size = 296743 }, -] - -[[package]] -name = "tqdm" -version = "4.67.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374 }, -] - -[[package]] -name = "trafilatura" -version = "2.0.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "certifi" }, - { name = "charset-normalizer" }, - { name = "courlan" }, - { name = "htmldate" }, - { name = "justext" }, - { name = "lxml" }, - { name = "urllib3" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/06/25/e3ebeefdebfdfae8c4a4396f5a6ea51fc6fa0831d63ce338e5090a8003dc/trafilatura-2.0.0.tar.gz", hash = "sha256:ceb7094a6ecc97e72fea73c7dba36714c5c5b577b6470e4520dca893706d6247", size = 253404 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8a/b6/097367f180b6383a3581ca1b86fcae284e52075fa941d1232df35293363c/trafilatura-2.0.0-py3-none-any.whl", hash = "sha256:77eb5d1e993747f6f20938e1de2d840020719735690c840b9a1024803a4cd51d", size = 132557 }, -] - -[[package]] -name = "typer" -version = "0.25.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "annotated-doc" }, - { name = "click" }, - { name = "rich" }, - { name = "shellingham" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/e4/51/9aed62104cea109b820bbd6c14245af756112017d309da813ef107d42e7e/typer-0.25.1.tar.gz", hash = "sha256:9616eb8853a09ffeabab1698952f33c6f29ffdbceb4eaeecf571880e8d7664cc", size = 122276 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/3f/f9/2b3ff4e56e5fa7debfaf9eb135d0da96f3e9a1d5b27222223c7296336e5f/typer-0.25.1-py3-none-any.whl", hash = "sha256:75caa44ed46a03fb2dab8808753ffacdbfea88495e74c85a28c5eefcf5f39c89", size = 58409 }, -] - -[[package]] -name = "typing-extensions" -version = "4.15.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614 }, -] - -[[package]] -name = "typing-inspect" -version = "0.9.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "mypy-extensions" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/dc/74/1789779d91f1961fa9438e9a8710cdae6bd138c80d7303996933d117264a/typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78", size = 13825 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/65/f3/107a22063bf27bdccf2024833d3445f4eea42b2e598abfbd46f6a63b6cb0/typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f", size = 8827 }, -] - -[[package]] -name = "typing-inspection" -version = "0.4.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611 }, -] - -[[package]] -name = "tzdata" -version = "2026.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz", hash = "sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10", size = 198254 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321 }, -] - -[[package]] -name = "tzlocal" -version = "5.3.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "tzdata", marker = "sys_platform == 'win32'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/8b/2e/c14812d3d4d9cd1773c6be938f89e5735a1f11a9f184ac3639b93cef35d5/tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd", size = 30761 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c2/14/e2a54fabd4f08cd7af1c07030603c3356b74da07f7cc056e600436edfa17/tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d", size = 18026 }, -] - -[[package]] -name = "urllib3" -version = "2.7.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087 }, -] - -[[package]] -name = "wrapt" -version = "2.1.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2e/64/925f213fdcbb9baeb1530449ac71a4d57fc361c053d06bf78d0c5c7cd80c/wrapt-2.1.2.tar.gz", hash = "sha256:3996a67eecc2c68fd47b4e3c564405a5777367adfd9b8abb58387b63ee83b21e", size = 81678 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4c/b6/1db817582c49c7fcbb7df6809d0f515af29d7c2fbf57eb44c36e98fb1492/wrapt-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ff2aad9c4cda28a8f0653fc2d487596458c2a3f475e56ba02909e950a9efa6a9", size = 61255 }, - { url = "https://files.pythonhosted.org/packages/a2/16/9b02a6b99c09227c93cd4b73acc3678114154ec38da53043c0ddc1fba0dc/wrapt-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6433ea84e1cfacf32021d2a4ee909554ade7fd392caa6f7c13f1f4bf7b8e8748", size = 61848 }, - { url = "https://files.pythonhosted.org/packages/af/aa/ead46a88f9ec3a432a4832dfedb84092fc35af2d0ba40cd04aea3889f247/wrapt-2.1.2-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:c20b757c268d30d6215916a5fa8461048d023865d888e437fab451139cad6c8e", size = 121433 }, - { url = "https://files.pythonhosted.org/packages/3a/9f/742c7c7cdf58b59085a1ee4b6c37b013f66ac33673a7ef4aaed5e992bc33/wrapt-2.1.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:79847b83eb38e70d93dc392c7c5b587efe65b3e7afcc167aa8abd5d60e8761c8", size = 123013 }, - { url = "https://files.pythonhosted.org/packages/e8/44/2c3dd45d53236b7ed7c646fcf212251dc19e48e599debd3926b52310fafb/wrapt-2.1.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f8fba1bae256186a83d1875b2b1f4e2d1242e8fac0f58ec0d7e41b26967b965c", size = 117326 }, - { url = "https://files.pythonhosted.org/packages/74/e2/b17d66abc26bd96f89dec0ecd0ef03da4a1286e6ff793839ec431b9fae57/wrapt-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e3d3b35eedcf5f7d022291ecd7533321c4775f7b9cd0050a31a68499ba45757c", size = 121444 }, - { url = "https://files.pythonhosted.org/packages/3c/62/e2977843fdf9f03daf1586a0ff49060b1b2fc7ff85a7ea82b6217c1ae36e/wrapt-2.1.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:6f2c5390460de57fa9582bc8a1b7a6c86e1a41dfad74c5225fc07044c15cc8d1", size = 116237 }, - { url = "https://files.pythonhosted.org/packages/88/dd/27fc67914e68d740bce512f11734aec08696e6b17641fef8867c00c949fc/wrapt-2.1.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7dfa9f2cf65d027b951d05c662cc99ee3bd01f6e4691ed39848a7a5fffc902b2", size = 120563 }, - { url = "https://files.pythonhosted.org/packages/ec/9f/b750b3692ed2ef4705cb305bd68858e73010492b80e43d2a4faa5573cbe7/wrapt-2.1.2-cp312-cp312-win32.whl", hash = "sha256:eba8155747eb2cae4a0b913d9ebd12a1db4d860fc4c829d7578c7b989bd3f2f0", size = 58198 }, - { url = "https://files.pythonhosted.org/packages/8e/b2/feecfe29f28483d888d76a48f03c4c4d8afea944dbee2b0cd3380f9df032/wrapt-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:1c51c738d7d9faa0b3601708e7e2eda9bf779e1b601dce6c77411f2a1b324a63", size = 60441 }, - { url = "https://files.pythonhosted.org/packages/44/e1/e328f605d6e208547ea9fd120804fcdec68536ac748987a68c47c606eea8/wrapt-2.1.2-cp312-cp312-win_arm64.whl", hash = "sha256:c8e46ae8e4032792eb2f677dbd0d557170a8e5524d22acc55199f43efedd39bf", size = 58836 }, - { url = "https://files.pythonhosted.org/packages/4c/7a/d936840735c828b38d26a854e85d5338894cda544cb7a85a9d5b8b9c4df7/wrapt-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:787fd6f4d67befa6fe2abdffcbd3de2d82dfc6fb8a6d850407c53332709d030b", size = 61259 }, - { url = "https://files.pythonhosted.org/packages/5e/88/9a9b9a90ac8ca11c2fdb6a286cb3a1fc7dd774c00ed70929a6434f6bc634/wrapt-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4bdf26e03e6d0da3f0e9422fd36bcebf7bc0eeb55fdf9c727a09abc6b9fe472e", size = 61851 }, - { url = "https://files.pythonhosted.org/packages/03/a9/5b7d6a16fd6533fed2756900fc8fc923f678179aea62ada6d65c92718c00/wrapt-2.1.2-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:bbac24d879aa22998e87f6b3f481a5216311e7d53c7db87f189a7a0266dafffb", size = 121446 }, - { url = "https://files.pythonhosted.org/packages/45/bb/34c443690c847835cfe9f892be78c533d4f32366ad2888972c094a897e39/wrapt-2.1.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:16997dfb9d67addc2e3f41b62a104341e80cac52f91110dece393923c0ebd5ca", size = 123056 }, - { url = "https://files.pythonhosted.org/packages/93/b9/ff205f391cb708f67f41ea148545f2b53ff543a7ac293b30d178af4d2271/wrapt-2.1.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:162e4e2ba7542da9027821cb6e7c5e068d64f9a10b5f15512ea28e954893a267", size = 117359 }, - { url = "https://files.pythonhosted.org/packages/1f/3d/1ea04d7747825119c3c9a5e0874a40b33594ada92e5649347c457d982805/wrapt-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f29c827a8d9936ac320746747a016c4bc66ef639f5cd0d32df24f5eacbf9c69f", size = 121479 }, - { url = "https://files.pythonhosted.org/packages/78/cc/ee3a011920c7a023b25e8df26f306b2484a531ab84ca5c96260a73de76c0/wrapt-2.1.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:a9dd9813825f7ecb018c17fd147a01845eb330254dff86d3b5816f20f4d6aaf8", size = 116271 }, - { url = "https://files.pythonhosted.org/packages/98/fd/e5ff7ded41b76d802cf1191288473e850d24ba2e39a6ec540f21ae3b57cb/wrapt-2.1.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6f8dbdd3719e534860d6a78526aafc220e0241f981367018c2875178cf83a413", size = 120573 }, - { url = "https://files.pythonhosted.org/packages/47/c5/242cae3b5b080cd09bacef0591691ba1879739050cc7c801ff35c8886b66/wrapt-2.1.2-cp313-cp313-win32.whl", hash = "sha256:5c35b5d82b16a3bc6e0a04349b606a0582bc29f573786aebe98e0c159bc48db6", size = 58205 }, - { url = "https://files.pythonhosted.org/packages/12/69/c358c61e7a50f290958809b3c61ebe8b3838ea3e070d7aac9814f95a0528/wrapt-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:f8bc1c264d8d1cf5b3560a87bbdd31131573eb25f9f9447bb6252b8d4c44a3a1", size = 60452 }, - { url = "https://files.pythonhosted.org/packages/8e/66/c8a6fcfe321295fd8c0ab1bd685b5a01462a9b3aa2f597254462fc2bc975/wrapt-2.1.2-cp313-cp313-win_arm64.whl", hash = "sha256:3beb22f674550d5634642c645aba4c72a2c66fb185ae1aebe1e955fae5a13baf", size = 58842 }, - { url = "https://files.pythonhosted.org/packages/da/55/9c7052c349106e0b3f17ae8db4b23a691a963c334de7f9dbd60f8f74a831/wrapt-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0fc04bc8664a8bc4c8e00b37b5355cffca2535209fba1abb09ae2b7c76ddf82b", size = 63075 }, - { url = "https://files.pythonhosted.org/packages/09/a8/ce7b4006f7218248dd71b7b2b732d0710845a0e49213b18faef64811ffef/wrapt-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a9b9d50c9af998875a1482a038eb05755dfd6fe303a313f6a940bb53a83c3f18", size = 63719 }, - { url = "https://files.pythonhosted.org/packages/e4/e5/2ca472e80b9e2b7a17f106bb8f9df1db11e62101652ce210f66935c6af67/wrapt-2.1.2-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:2d3ff4f0024dd224290c0eabf0240f1bfc1f26363431505fb1b0283d3b08f11d", size = 152643 }, - { url = "https://files.pythonhosted.org/packages/36/42/30f0f2cefca9d9cbf6835f544d825064570203c3e70aa873d8ae12e23791/wrapt-2.1.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3278c471f4468ad544a691b31bb856374fbdefb7fee1a152153e64019379f015", size = 158805 }, - { url = "https://files.pythonhosted.org/packages/bb/67/d08672f801f604889dcf58f1a0b424fe3808860ede9e03affc1876b295af/wrapt-2.1.2-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a8914c754d3134a3032601c6984db1c576e6abaf3fc68094bb8ab1379d75ff92", size = 145990 }, - { url = "https://files.pythonhosted.org/packages/68/a7/fd371b02e73babec1de6ade596e8cd9691051058cfdadbfd62a5898f3295/wrapt-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ff95d4264e55839be37bafe1536db2ab2de19da6b65f9244f01f332b5286cfbf", size = 155670 }, - { url = "https://files.pythonhosted.org/packages/86/2d/9fe0095dfdb621009f40117dcebf41d7396c2c22dca6eac779f4c007b86c/wrapt-2.1.2-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:76405518ca4e1b76fbb1b9f686cff93aebae03920cc55ceeec48ff9f719c5f67", size = 144357 }, - { url = "https://files.pythonhosted.org/packages/0e/b6/ec7b4a254abbe4cde9fa15c5d2cca4518f6b07d0f1b77d4ee9655e30280e/wrapt-2.1.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c0be8b5a74c5824e9359b53e7e58bef71a729bacc82e16587db1c4ebc91f7c5a", size = 150269 }, - { url = "https://files.pythonhosted.org/packages/6e/6b/2fabe8ebf148f4ee3c782aae86a795cc68ffe7d432ef550f234025ce0cfa/wrapt-2.1.2-cp313-cp313t-win32.whl", hash = "sha256:f01277d9a5fc1862f26f7626da9cf443bebc0abd2f303f41c5e995b15887dabd", size = 59894 }, - { url = "https://files.pythonhosted.org/packages/ca/fb/9ba66fc2dedc936de5f8073c0217b5d4484e966d87723415cc8262c5d9c2/wrapt-2.1.2-cp313-cp313t-win_amd64.whl", hash = "sha256:84ce8f1c2104d2f6daa912b1b5b039f331febfeee74f8042ad4e04992bd95c8f", size = 63197 }, - { url = "https://files.pythonhosted.org/packages/c0/1c/012d7423c95d0e337117723eb8ecf73c622ce15a97847e84cf3f8f26cd7e/wrapt-2.1.2-cp313-cp313t-win_arm64.whl", hash = "sha256:a93cd767e37faeddbe07d8fc4212d5cba660af59bdb0f6372c93faaa13e6e679", size = 60363 }, - { url = "https://files.pythonhosted.org/packages/39/25/e7ea0b417db02bb796182a5316398a75792cd9a22528783d868755e1f669/wrapt-2.1.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:1370e516598854e5b4366e09ce81e08bfe94d42b0fd569b88ec46cc56d9164a9", size = 61418 }, - { url = "https://files.pythonhosted.org/packages/ec/0f/fa539e2f6a770249907757eaeb9a5ff4deb41c026f8466c1c6d799088a9b/wrapt-2.1.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:6de1a3851c27e0bd6a04ca993ea6f80fc53e6c742ee1601f486c08e9f9b900a9", size = 61914 }, - { url = "https://files.pythonhosted.org/packages/53/37/02af1867f5b1441aaeda9c82deed061b7cd1372572ddcd717f6df90b5e93/wrapt-2.1.2-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:de9f1a2bbc5ac7f6012ec24525bdd444765a2ff64b5985ac6e0692144838542e", size = 120417 }, - { url = "https://files.pythonhosted.org/packages/c3/b7/0138a6238c8ba7476c77cf786a807f871672b37f37a422970342308276e7/wrapt-2.1.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:970d57ed83fa040d8b20c52fe74a6ae7e3775ae8cff5efd6a81e06b19078484c", size = 122797 }, - { url = "https://files.pythonhosted.org/packages/e1/ad/819ae558036d6a15b7ed290d5b14e209ca795dd4da9c58e50c067d5927b0/wrapt-2.1.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3969c56e4563c375861c8df14fa55146e81ac11c8db49ea6fb7f2ba58bc1ff9a", size = 117350 }, - { url = "https://files.pythonhosted.org/packages/8b/2d/afc18dc57a4600a6e594f77a9ae09db54f55ba455440a54886694a84c71b/wrapt-2.1.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:57d7c0c980abdc5f1d98b11a2aa3bb159790add80258c717fa49a99921456d90", size = 121223 }, - { url = "https://files.pythonhosted.org/packages/b9/5b/5ec189b22205697bc56eb3b62aed87a1e0423e9c8285d0781c7a83170d15/wrapt-2.1.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:776867878e83130c7a04237010463372e877c1c994d449ca6aaafeab6aab2586", size = 116287 }, - { url = "https://files.pythonhosted.org/packages/f7/2d/f84939a7c9b5e6cdd8a8d0f6a26cabf36a0f7e468b967720e8b0cd2bdf69/wrapt-2.1.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:fab036efe5464ec3291411fabb80a7a39e2dd80bae9bcbeeca5087fdfa891e19", size = 119593 }, - { url = "https://files.pythonhosted.org/packages/0b/fe/ccd22a1263159c4ac811ab9374c061bcb4a702773f6e06e38de5f81a1bdc/wrapt-2.1.2-cp314-cp314-win32.whl", hash = "sha256:e6ed62c82ddf58d001096ae84ce7f833db97ae2263bff31c9b336ba8cfe3f508", size = 58631 }, - { url = "https://files.pythonhosted.org/packages/65/0a/6bd83be7bff2e7efaac7b4ac9748da9d75a34634bbbbc8ad077d527146df/wrapt-2.1.2-cp314-cp314-win_amd64.whl", hash = "sha256:467e7c76315390331c67073073d00662015bb730c566820c9ca9b54e4d67fd04", size = 60875 }, - { url = "https://files.pythonhosted.org/packages/6c/c0/0b3056397fe02ff80e5a5d72d627c11eb885d1ca78e71b1a5c1e8c7d45de/wrapt-2.1.2-cp314-cp314-win_arm64.whl", hash = "sha256:da1f00a557c66225d53b095a97eace0fc5349e3bfda28fa34ffae238978ee575", size = 59164 }, - { url = "https://files.pythonhosted.org/packages/71/ed/5d89c798741993b2371396eb9d4634f009ff1ad8a6c78d366fe2883ea7a6/wrapt-2.1.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:62503ffbc2d3a69891cf29beeaccdb4d5e0a126e2b6a851688d4777e01428dbb", size = 63163 }, - { url = "https://files.pythonhosted.org/packages/c6/8c/05d277d182bf36b0a13d6bd393ed1dec3468a25b59d01fba2dd70fe4d6ae/wrapt-2.1.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c7e6cd120ef837d5b6f860a6ea3745f8763805c418bb2f12eeb1fa6e25f22d22", size = 63723 }, - { url = "https://files.pythonhosted.org/packages/f4/27/6c51ec1eff4413c57e72d6106bb8dec6f0c7cdba6503d78f0fa98767bcc9/wrapt-2.1.2-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:3769a77df8e756d65fbc050333f423c01ae012b4f6731aaf70cf2bef61b34596", size = 152652 }, - { url = "https://files.pythonhosted.org/packages/db/4c/d7dd662d6963fc7335bfe29d512b02b71cdfa23eeca7ab3ac74a67505deb/wrapt-2.1.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a76d61a2e851996150ba0f80582dd92a870643fa481f3b3846f229de88caf044", size = 158807 }, - { url = "https://files.pythonhosted.org/packages/b4/4d/1e5eea1a78d539d346765727422976676615814029522c76b87a95f6bcdd/wrapt-2.1.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:6f97edc9842cf215312b75fe737ee7c8adda75a89979f8e11558dfff6343cc4b", size = 146061 }, - { url = "https://files.pythonhosted.org/packages/89/bc/62cabea7695cd12a288023251eeefdcb8465056ddaab6227cb78a2de005b/wrapt-2.1.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:4006c351de6d5007aa33a551f600404ba44228a89e833d2fadc5caa5de8edfbf", size = 155667 }, - { url = "https://files.pythonhosted.org/packages/e9/99/6f2888cd68588f24df3a76572c69c2de28287acb9e1972bf0c83ce97dbc1/wrapt-2.1.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:a9372fc3639a878c8e7d87e1556fa209091b0a66e912c611e3f833e2c4202be2", size = 144392 }, - { url = "https://files.pythonhosted.org/packages/40/51/1dfc783a6c57971614c48e361a82ca3b6da9055879952587bc99fe1a7171/wrapt-2.1.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3144b027ff30cbd2fca07c0a87e67011adb717eb5f5bd8496325c17e454257a3", size = 150296 }, - { url = "https://files.pythonhosted.org/packages/6c/38/cbb8b933a0201076c1f64fc42883b0023002bdc14a4964219154e6ff3350/wrapt-2.1.2-cp314-cp314t-win32.whl", hash = "sha256:3b8d15e52e195813efe5db8cec156eebe339aaf84222f4f4f051a6c01f237ed7", size = 60539 }, - { url = "https://files.pythonhosted.org/packages/82/dd/e5176e4b241c9f528402cebb238a36785a628179d7d8b71091154b3e4c9e/wrapt-2.1.2-cp314-cp314t-win_amd64.whl", hash = "sha256:08ffa54146a7559f5b8df4b289b46d963a8e74ed16ba3687f99896101a3990c5", size = 63969 }, - { url = "https://files.pythonhosted.org/packages/5c/99/79f17046cf67e4a95b9987ea129632ba8bcec0bc81f3fb3d19bdb0bd60cd/wrapt-2.1.2-cp314-cp314t-win_arm64.whl", hash = "sha256:72aaa9d0d8e4ed0e2e98019cea47a21f823c9dd4b43c7b77bba6679ffcca6a00", size = 60554 }, - { url = "https://files.pythonhosted.org/packages/1a/c7/8528ac2dfa2c1e6708f647df7ae144ead13f0a31146f43c7264b4942bf12/wrapt-2.1.2-py3-none-any.whl", hash = "sha256:b8fd6fa2b2c4e7621808f8c62e8317f4aae56e59721ad933bac5239d913cf0e8", size = 43993 }, -] - -[[package]] -name = "xxhash" -version = "3.7.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/24/2f/e183a1b407002f5af81822bee18b61cdb94b8670208ef34734d8d2b8ebe9/xxhash-3.7.0.tar.gz", hash = "sha256:6cc4eefbb542a5d6ffd6d70ea9c502957c925e800f998c5630ecc809d6702bae", size = 82022 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f2/8a/51a14cdef4728c6c2337db8a7d8704422cc65676d9199d77215464c880af/xxhash-3.7.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:082c87bfdd2b9f457606c7a4a53457f4c4b48b0cdc48de0277f4349d79bb3d7a", size = 33357 }, - { url = "https://files.pythonhosted.org/packages/b9/1b/0c2c933809421ffd9bf42b59315552c143c755db5d9a816b2f1ae273e884/xxhash-3.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5e7ce913b61f35b0c1c839a49ac9c8e75dd8d860150688aed353b0ce1bf409d8", size = 30869 }, - { url = "https://files.pythonhosted.org/packages/03/a8/89d5fdd6ee12d70ba99451de46dd0e8010167468dcd913ec855653f4dd50/xxhash-3.7.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3beb1de3b1e9694fcdd853e570ee64c631c7062435d2f8c69c1adf809bc086f0", size = 194100 }, - { url = "https://files.pythonhosted.org/packages/87/ee/2f9f2ed993e77206d1e66991290a1ebe22e843351ca3ebec8e49e01ba186/xxhash-3.7.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f3e7b689c3bce16699efcf736066f5c6cc4472c3840fe4b22bd8279daf4abdac", size = 212977 }, - { url = "https://files.pythonhosted.org/packages/de/60/5a91644615a9e9d4e42c2e9925f1908e3a24e4e691d9de7340d565bea024/xxhash-3.7.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:a6545e6b409e3d5cbafc850fb84c55a1ca26ed15a6b11e3bf07a0e0cd84517c8", size = 236373 }, - { url = "https://files.pythonhosted.org/packages/22/c0/f3a9384eaaed9d14d4d062a5d953aa0da489bfe9747877aa994caa87cd0b/xxhash-3.7.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:31ab1461c77a11461d703c88eb949e132a1c6515933cf675d97ec680f4bd18de", size = 212229 }, - { url = "https://files.pythonhosted.org/packages/2e/67/02f07a9fd79726804190f2172c4894c3ed9a4ebccaca05653c84beb58025/xxhash-3.7.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7c4d596b7676f811172687ec567cbafb9e4dea2f9be1bbb4f622410cb7f40f40", size = 445462 }, - { url = "https://files.pythonhosted.org/packages/40/37/558f5a90c0672fc9b4402dc25d87ac5b7406616e8969430c9ca4e52ee74d/xxhash-3.7.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13805f0461cba0a857924e70ff91ae6d52d2598f79a884e788db80532614a4a1", size = 193932 }, - { url = "https://files.pythonhosted.org/packages/d5/90/aaa09cd58661d32044dbbad7df55bbe22a623032b810e7ed3b8c569a2a6f/xxhash-3.7.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1d398f372496152f1c6933a33566373f8d1b37b98b8c9d608fa6edc0976f23b2", size = 284807 }, - { url = "https://files.pythonhosted.org/packages/d6/f3/53df3719ab127a02c174f0c1c74924fcd110866e89c966bc7909cfa8fa84/xxhash-3.7.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d610aa62cdb7d4d497740741772a24a794903bf3e79eaa51d2e800082abe11e5", size = 210445 }, - { url = "https://files.pythonhosted.org/packages/72/33/d219975c0e8b6fa2eb9ccd486fe47e21bf1847985b878dd2fbc3126e0d5c/xxhash-3.7.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:073c23900a9fbf3d26616c17c830db28af9803677cd5b33aea3224d824111514", size = 241273 }, - { url = "https://files.pythonhosted.org/packages/3e/50/49b1afe610eb3964cedcb90a4d4c3d46a261ee8669cbd4f060652619ae3c/xxhash-3.7.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:418a463c3e6a590c0cdc890f8be19adb44a8c8acd175ca5b2a6de77e61d0b386", size = 197950 }, - { url = "https://files.pythonhosted.org/packages/c6/75/5f42a1a4c78717d906a4b6a140c6dbf837ab1f547a54d23c4e2903310936/xxhash-3.7.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:03f8ff4474ee61c845758ce00711d7087a770d77efb36f7e74a6e867301000b8", size = 210709 }, - { url = "https://files.pythonhosted.org/packages/8a/85/237e446c25abced71e9c53d269f2cef5bab8a82b3f88a12e00c5368e7368/xxhash-3.7.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:44fba4a5f1d179b7ddc7b3dc40f56f9209046421679b57025d4d8821b376fd8d", size = 275345 }, - { url = "https://files.pythonhosted.org/packages/62/34/c2c26c0a6a9cc739bc2a5f0ae03ba8b87deb12b8bce35f7ac495e790dc6d/xxhash-3.7.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:31e3516a0f829d06ded4a2c0f3c7c5561993256bfa1c493975fb9dc7bfa828a1", size = 414056 }, - { url = "https://files.pythonhosted.org/packages/a0/aa/5c58e9bc8071b8afd8dcf297ff362f723c4892168faba149f19904132bf4/xxhash-3.7.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b59ee2ac81de57771a09ecad09191e840a1d2fae1ef684208320591055768f83", size = 191485 }, - { url = "https://files.pythonhosted.org/packages/d4/69/a929cf9d1e2e65a48b818cdce72cb6b69eab2e6877f21436d0a1942aff43/xxhash-3.7.0-cp312-cp312-win32.whl", hash = "sha256:74bbd92f8c7fcc397ba0a11bfdc106bc72ad7f11e3a60277753f87e7532b4d81", size = 30671 }, - { url = "https://files.pythonhosted.org/packages/b9/1b/104b41a8947f4e1d4a66ce1e628eea752f37d1890bfd7453559ca7a3d950/xxhash-3.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:7bd7bc82dd4f185f28f35193c2e968ef46131628e3cac62f639dadf321cba4d1", size = 31514 }, - { url = "https://files.pythonhosted.org/packages/98/a0/1fd0ea1f1b886d9e7c73f0397571e22333a7d79e31da6d7127c2a4a71d75/xxhash-3.7.0-cp312-cp312-win_arm64.whl", hash = "sha256:7d7148180ec99ba36585b42c8c5de25e9b40191613bc4be68909b4d25a77a852", size = 27761 }, - { url = "https://files.pythonhosted.org/packages/c1/ca/d5174b4c36d10f64d4ca7050563138c5a599efb01a765858ddefc9c1202a/xxhash-3.7.0-cp313-cp313-android_21_arm64_v8a.whl", hash = "sha256:4b6d6b33f141158692bd4eafbb96edbc5aa0dabdb593a962db01a91983d4f8fa", size = 36813 }, - { url = "https://files.pythonhosted.org/packages/41/d0/abc6c9d347ba1f1e1e1d98125d0881a0452c7f9a76a9dd03a7b5d2197f23/xxhash-3.7.0-cp313-cp313-android_21_x86_64.whl", hash = "sha256:845d347df254d6c619f616afa921331bada8614b8d373d58725c663ba97c3605", size = 35121 }, - { url = "https://files.pythonhosted.org/packages/bf/11/4cc834eb3d79f2f2b3a6ef7324195208bcdfbdcf7534d2b17267aa5f3a8f/xxhash-3.7.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:fddbbb69a6fff4f421e7a0d1fa28f894b20112e9e3fab306af451e2dfd0e459b", size = 29624 }, - { url = "https://files.pythonhosted.org/packages/23/83/e97d3e7b635fe73a1dfb1e91f805324dd6d930bb42041cbf18f183bc0b6d/xxhash-3.7.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:54876a4e45101cec2bf8f31a973cda073a23e2e108538dad224ba07f85f22487", size = 30638 }, - { url = "https://files.pythonhosted.org/packages/f4/40/d84951d80c35db1f4c40a29a64a8520eea5d56e764c603906b4fe763580f/xxhash-3.7.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:0c72fe9c7e3d6dfd7f1e21e224a877917fa09c465694ba4e06464b9511b65544", size = 33323 }, - { url = "https://files.pythonhosted.org/packages/89/cc/c7dc6558d97e9ab023f663d69ab28b340ed9bf4d2d94f2c259cf896bb354/xxhash-3.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a6d73a830b17ef49bc04e00182bd839164c1b3c59c127cd7c54fcb10c7ed8ee8", size = 33362 }, - { url = "https://files.pythonhosted.org/packages/2a/6e/46b84017b1301d54091430353d4ad5901654a3e0871649877a416f7f1644/xxhash-3.7.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:91c3b07cf3362086d8f126c6aecd8e5e9396ad8b2f2219ea7e49a8250c318acd", size = 30874 }, - { url = "https://files.pythonhosted.org/packages/df/5e/8f9158e3ab906ad3fec51e09b5ea0093e769f12207bfa42a368ca204e7ab/xxhash-3.7.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:50e879ebbac351c81565ca108db766d7832f5b8b6a5b14b8c0151f7190028e3d", size = 194185 }, - { url = "https://files.pythonhosted.org/packages/f3/29/a804ded9f5d3d3758292678d23e7528b08fda7b7e750688d08b052322475/xxhash-3.7.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:921c14e93817842dd0dd9f372890a0f0c72e534650b6ab13c5be5cd0db11d47e", size = 213033 }, - { url = "https://files.pythonhosted.org/packages/8b/91/1ce5a7d2fdc975267320e2c78fc1cecfe7ab735ccbcf6993ec5dd541cb2c/xxhash-3.7.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e64a7c9d7dfca3e0fafcbc5e455519090706a3e36e95d655cec3e04e79f95aaa", size = 236140 }, - { url = "https://files.pythonhosted.org/packages/34/04/fd595a4fd8617b05fa27bd9b684ecb4985bfed27917848eea85d54036d06/xxhash-3.7.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2220af08163baf5fa36c2b8af079dc2cbe6e66ae061385267f9472362dfd53c6", size = 212291 }, - { url = "https://files.pythonhosted.org/packages/03/fb/f1a379cbc372ae5b9f4ab36154c48a849ca6ebe3ac477067a57865bf3bc6/xxhash-3.7.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f14bb8b22a4a91325813e3d553b8963c10cf8c756cff65ee50c194431296c655", size = 445532 }, - { url = "https://files.pythonhosted.org/packages/65/59/172424b79f8cfd4b6d8a122b2193e6b8ad4b11f7159bb3b6f9b3191329bb/xxhash-3.7.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:496736f86a9bedaf64b0dc70e3539d0766df01c71ea22032698e88f3f04a1ce9", size = 193990 }, - { url = "https://files.pythonhosted.org/packages/b9/19/aeac22161d953f139f07ba5586cb4a17c5b7b6dff985122803bb12933500/xxhash-3.7.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0ff71596bd79816975b3de7130ab1ff4541410285a3c084584eeb1c8239996fd", size = 284876 }, - { url = "https://files.pythonhosted.org/packages/77/d5/4fd0b59e7a02242953da05ff679fbb961b0a4368eac97a217e11dae110c1/xxhash-3.7.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1ad86695c19b1d46fe106925db3c7a37f16be37669dcf58dcc70a9dd6e324676", size = 210495 }, - { url = "https://files.pythonhosted.org/packages/aa/fb/976a3165c728c7faf74aa1b5ab3cf6a85e6d731612894741840524c7d28c/xxhash-3.7.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:970f9f8c50961d639cbd0d988c96f80ddf66006de93641719282c4fe7a87c5e6", size = 241331 }, - { url = "https://files.pythonhosted.org/packages/4a/2c/6763d5901d53ac9e6ba296e5717ae599025c9d268396e8faa8b4b0a8e0ac/xxhash-3.7.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:5886ad85e9e347911783760a1d16cb6b393e8f9e3b52c982568226cb56927bdc", size = 198037 }, - { url = "https://files.pythonhosted.org/packages/61/2b/876e722d533833f5f9a83473e6ba993e48745701096944e77bbecf29b2c3/xxhash-3.7.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:6e934bbae1e0ec74e27d5f0d7f37ef547ce5ff9f0a7e63fb39e559fc99526734", size = 210744 }, - { url = "https://files.pythonhosted.org/packages/21/e6/d7e7baef7ce24166b4668d3c48557bb35a23b92ecadcac7e7718d099ab69/xxhash-3.7.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:3b6b3d28228af044ebcded71c4a3dd86e1dbd7e2f4645bf40f7b5da65bb5fb5a", size = 275406 }, - { url = "https://files.pythonhosted.org/packages/92/fe/198b3763b2e01ca908f2154969a2352ec99bda892b574a11a9a151c5ede4/xxhash-3.7.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:6be4d70d9ab76c9f324ead9c01af6ff52c324745ea0c3731682a0cf99720f1fe", size = 414125 }, - { url = "https://files.pythonhosted.org/packages/3a/6d/019a11affd5a5499137cacca53808659964785439855b5aa40dfd3412916/xxhash-3.7.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:151d7520838d4465461a0b7f4ae488b3b00de16183dd3214c1a6b14bf89d7fb6", size = 191555 }, - { url = "https://files.pythonhosted.org/packages/76/21/b96d58568df2d01533244c3e0e5cbdd0c8b2b25c4bec4d72f19259a292d7/xxhash-3.7.0-cp313-cp313-win32.whl", hash = "sha256:d798c1e291bffb8e37b5bbe0dda77fc767cd19e89cadaf66e6ed5d0ff88c9fe6", size = 30668 }, - { url = "https://files.pythonhosted.org/packages/99/57/d849a8d3afa1f8f4bc6a831cd89f49f9706fbbad94d2975d6140a171988c/xxhash-3.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:875811ba23c543b1a1c3143c926e43996eb27ebb8f52d3500744aa608c275aed", size = 31524 }, - { url = "https://files.pythonhosted.org/packages/81/52/bacc753e92dee78b058af8dcef0a50815f5f860986c664a92d75f965b6a5/xxhash-3.7.0-cp313-cp313-win_arm64.whl", hash = "sha256:54a675cb300dda83d71daae2a599389d22db8021a0f8db0dd659e14626eb3ecc", size = 27768 }, - { url = "https://files.pythonhosted.org/packages/1c/47/ddbd683b7fc7e592c1a8d9d65f73ce9ab513f082b3967eee2baf549b8fc6/xxhash-3.7.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a3b19a42111c4057c1547a4a1396a53961dca576a0f6b82bfa88a2d1561764b2", size = 33576 }, - { url = "https://files.pythonhosted.org/packages/07/f2/36d3310161db7f72efb4562aadde0ed429f1d0531782dd6345b12d2da527/xxhash-3.7.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8f4608a06e4d61b7a3425665a46d00e0579122e1a2fae97a0c52953a3aad9aa3", size = 31123 }, - { url = "https://files.pythonhosted.org/packages/0d/3f/75937a5c69556ed213021e43cbedd84c8e0279d0d74e7d41a255d84ba4b1/xxhash-3.7.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:ad37c7792479e49cf96c1ab25517d7003fe0d93687a772ba19a097d235bbe41e", size = 196491 }, - { url = "https://files.pythonhosted.org/packages/22/29/f10d7ff8c7a733d4403a43b9de18c8fabc005f98cec054644f04418659ee/xxhash-3.7.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dc026e3b89d98e30a8288c95cb696e77d150b3f0fb7a51f73dcd49ee6b5577fa", size = 215793 }, - { url = "https://files.pythonhosted.org/packages/8b/fd/778f60aa295f58907938f030a8b514611f391405614a525cccd2ffc00eb5/xxhash-3.7.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c9b31ab1f28b078a6a1ac1a54eb35e7d5390deddd56870d0be3a0a733d1c321c", size = 237993 }, - { url = "https://files.pythonhosted.org/packages/70/f5/736db5de387b4a540e37a05b84b40dc58a1ce974bfd2b4e5754ce29b68c3/xxhash-3.7.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3bb5fd680c038fd5229e44e9c493782f90df9bef632fd0499d442374688ff70b", size = 214887 }, - { url = "https://files.pythonhosted.org/packages/4d/aa/09a095f22fdb9a27fbb716841fbff52119721f9ca4261952d07a912f7839/xxhash-3.7.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:030c0fd688fce3569fbb49a2feefd4110cbb0b650186fb4610759ecfac677548", size = 448407 }, - { url = "https://files.pythonhosted.org/packages/74/8a/b745efeeca9e34a91c26fdc97ad8514c43d5a81ac78565cba80a1353870a/xxhash-3.7.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5b1bde10324f4c31812ae0d0502e92d916ae8917cad7209353f122b8b8f610c3", size = 196119 }, - { url = "https://files.pythonhosted.org/packages/8a/5c/0cfceb024af90c191f665c7933b1f318ee234f4797858383bebd1881d52f/xxhash-3.7.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:503722d52a615f2604f5e7611de7d43878df010dc0053094ef91cb9a9ac3d987", size = 286751 }, - { url = "https://files.pythonhosted.org/packages/0b/0a/0793e405dc3cf8f4ebe2c1acec1e4e4608cd9e7e50ea691dabbc2a95ccbb/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c72500a3b6d6c30ebfc135035bcace9eb5884f2dc220804efcaaba43e9f611dd", size = 212961 }, - { url = "https://files.pythonhosted.org/packages/0c/7e/721118ffc63bfff94aa565bcf2555a820f9f4bdb0f001e0d609bdfad70de/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:43475925a766d01ca8cd9a857fd87f3d50406983c8506a4c07c4df12adcc867f", size = 243703 }, - { url = "https://files.pythonhosted.org/packages/6e/18/16f6267160488b8276fd3d449d425712512add292ba545c1b6946bfdb7dd/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:8d09dfd2ab135b985daf868b594315ebe11ad86cd9fea46e6c69f19b28f7d25a", size = 200894 }, - { url = "https://files.pythonhosted.org/packages/2d/94/80ba841287fd97e3e9cac1d228788c8ef623746f570404961eec748ecb5c/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c50269d0055ac1faecfd559886d2cbe4b730de236585aba0e873f9d9dadbe585", size = 213357 }, - { url = "https://files.pythonhosted.org/packages/a1/7e/106d4067130c59f1e18a55ffadcd876d8c68534883a1e02685b29d3d8153/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:1910df4756a5ab58cfad8744fc2d0f23926e3efcc346ee76e87b974abab922f4", size = 277600 }, - { url = "https://files.pythonhosted.org/packages/c5/86/a081dd30da71d720b2612a792bfd55e45fa9a07ac76a0507f60487473c25/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:d006faf3b491957efcb433489be3c149efe4787b7063d5cddb8ddaefdc60e0c1", size = 416980 }, - { url = "https://files.pythonhosted.org/packages/35/29/1a95221a029a3c1293773869e1ab47b07cbbdd82444a42809e8c60156626/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:abb65b4e947e958f7b3b0d71db3ce447d1bc5f37f5eab871ce7223bda8768a04", size = 193840 }, - { url = "https://files.pythonhosted.org/packages/c5/e0/db909dd0823285de2286f67e10ee4d81e96ad35d7d8e964ecb07fccd8af9/xxhash-3.7.0-cp313-cp313t-win32.whl", hash = "sha256:178959906cb1716a1ce08e0d69c82886c70a15a6f2790fc084fdd146ca30cd49", size = 30966 }, - { url = "https://files.pythonhosted.org/packages/7b/ff/d705b15b22f21ee106adce239cb65d35067a158c630b240270f09b17c2e6/xxhash-3.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2524a1e20d4c231d13b50f7cf39e44265b055669a64a7a4b9a2a44faa03f19b6", size = 31784 }, - { url = "https://files.pythonhosted.org/packages/a2/1f/b2cf83c3638fd0588e0b17f22e5a9400bdfb1a3e3755324ac0aee2250b88/xxhash-3.7.0-cp313-cp313t-win_arm64.whl", hash = "sha256:37d994d0ffe81ef087bb330d392caa809bb5853c77e22ea3f71db024a0543dba", size = 27932 }, - { url = "https://files.pythonhosted.org/packages/0e/cc/431db584f6fbb9312e40a173af027644e5580d39df1f73603cbb9dca4d6b/xxhash-3.7.0-cp314-cp314-android_24_arm64_v8a.whl", hash = "sha256:8c5fcfd806c335bfa2adf1cd0b3110a44fc7b6995c3a648c27489bae85801465", size = 36644 }, - { url = "https://files.pythonhosted.org/packages/bc/01/255ec513e0a705d1f9a61413e78dfce4e3235203f0ed525a24c2b4b56345/xxhash-3.7.0-cp314-cp314-android_24_x86_64.whl", hash = "sha256:506a0b488f190f0a06769575e30caf71615c898ed93ab18b0dbcb6dec5c3713c", size = 35003 }, - { url = "https://files.pythonhosted.org/packages/68/70/c55fc33c93445b44d8fc5a17b41ed99e3cebe92bcf8396809e63fc9a1165/xxhash-3.7.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:ec68dbba21532c0173a9872298e65c89749f7c9d21538c3a78b5bb6105871568", size = 29655 }, - { url = "https://files.pythonhosted.org/packages/c2/72/ff8de73df000d74467d12a59ce6d6e2b2a368b978d41ab7b1fba5ed442be/xxhash-3.7.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:fa77e7ec1450d415d20129961814787c9abd9a07f98872f070b1fe96c5084611", size = 30664 }, - { url = "https://files.pythonhosted.org/packages/b6/91/08416d9bd9bc3bf39d831abe8a5631ac2db5141dfd6fe81c3fe59a1f9264/xxhash-3.7.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:fe32736295ea38e43e7d9424053c8c47c9f64fecfc7c895fb3da9b30b131c9ee", size = 33317 }, - { url = "https://files.pythonhosted.org/packages/0e/3b/86b1caa4dee10a99f4bf9521e623359341c5e50d05158fa10c275b2bd079/xxhash-3.7.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:ab9dd2c83c4bbd63e422181a76f13502d049d3ddcac9a1bdc29196263d692bb8", size = 33457 }, - { url = "https://files.pythonhosted.org/packages/ed/38/98ea14ad1517e1461292a65906951458d520689782bfbae111050145bdba/xxhash-3.7.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:3afec3a336a2286601a437cb07562ab0227685e6fbb9ec17e8c18457ff348ecf", size = 30894 }, - { url = "https://files.pythonhosted.org/packages/61/a2/074654d0b893606541199993c7db70067d9fc63b748e0d60020a52a1bd36/xxhash-3.7.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:565df64437a9390f84465dcca33e7377114c7ede8d05cd2cf20081f831ea788e", size = 194409 }, - { url = "https://files.pythonhosted.org/packages/e2/26/6d2a1afc468189f77ca28c32e1c83e1b9da1178231e05641dbc1b350e332/xxhash-3.7.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:12eca820a5d558633d423bf8bb78ce72a55394823f64089247f788a7e0ae691e", size = 213135 }, - { url = "https://files.pythonhosted.org/packages/8e/0e/d8aecf95e09c42547453137be74d2f7b8b14e08f5177fa2fab6144a19061/xxhash-3.7.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f262b8f7599516567e070abf607b9af649052b2c4bd6f9be02b0cb41b7024805", size = 236379 }, - { url = "https://files.pythonhosted.org/packages/f2/74/8140e8210536b3dd0cc816c4faaeb5ba6e63e8125ab25af4bcddd6a037b3/xxhash-3.7.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1598916cb197681e03e601901e4ab96a9a963de398c59d0964f8a6f44a2b361", size = 212447 }, - { url = "https://files.pythonhosted.org/packages/a0/d2/462001d2903b4bee5a5689598a0a55e5e7cd1ac7f4247a5545cff10d3ebb/xxhash-3.7.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:322b2f0622230f526aeb1738149948a7ae357a9e2ceb1383c6fd1fdaecdafa16", size = 445660 }, - { url = "https://files.pythonhosted.org/packages/23/09/2bd1ed7f8689b20e51727952cac8329d50c694dc32b2eba06ba5bc742b37/xxhash-3.7.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:24cc22070880cc57b830a65cde4e65fa884c6d9b28ae4803b5ee05911e7bafba", size = 194076 }, - { url = "https://files.pythonhosted.org/packages/c9/6e/692302cd0a5f4ac4e6289f37fa888dc2e1e07750b68fe3e4bfe939b8cea3/xxhash-3.7.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb5a888a968b2434abf9ecda357b5d43f10d7b5a6da6fdbbe036208473aff0e2", size = 284990 }, - { url = "https://files.pythonhosted.org/packages/05/d9/e54b159b3d9df7999d2a7c676ce7b323d1b5588a64f8f51ed8172567bd87/xxhash-3.7.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a999771ff97bec27d18341be4f3a36b163bb1ac41ec17bef6d2dabd84acd33c7", size = 210590 }, - { url = "https://files.pythonhosted.org/packages/50/93/0e0df1a3a196ced4ca71de76d65ead25d8e87bbfb87b64306ea47a40c00d/xxhash-3.7.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:ed4a6efe2dee1655adb73e7ad40c6aa955a6892422b1e3b95de6a34de56e3cbb", size = 241442 }, - { url = "https://files.pythonhosted.org/packages/9a/a9/d917a7a814e90b218f8a0d37967105eea91bf752c3303683c99a1f7bfc1f/xxhash-3.7.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:9fd17f14ac0faa12126c2f9ca774a8cf342957265ec3c8669c144e5e6cdb478c", size = 198356 }, - { url = "https://files.pythonhosted.org/packages/89/5e/f2ba1877c39469abbefc72991d6ebdcbd4c0880db01ae8cb1f553b0c537d/xxhash-3.7.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:05fd1254268c59b5cb2a029dfc204275e9fc52de2913f1e53aa8d01442c96b4d", size = 210898 }, - { url = "https://files.pythonhosted.org/packages/90/c6/be56b58e73de531f39a10de1355bb77ceb663900dc4bf2d6d3002a9c3f9e/xxhash-3.7.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:a2eae53197c6276d5b317f75a1be226bbf440c20b58bf525f36b5d0e1f657ca6", size = 275519 }, - { url = "https://files.pythonhosted.org/packages/92/e2/17ddc85d5765b9c709f192009ed8f5a1fc876f4eb35bba7c307b5b1169f9/xxhash-3.7.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:bfe6f92e3522dcbe8c4281efd74fa7542a336cb00b0e3272c4ec0edabeaeaf67", size = 414191 }, - { url = "https://files.pythonhosted.org/packages/9c/42/85f5b79f4bf1ec7ba052491164adfd4f4e9519f5dc7246de4fbd64a1bd56/xxhash-3.7.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7ab9a49c410d8c6c786ab99e79c529938d894c01433130353dd0fe999111077a", size = 191604 }, - { url = "https://files.pythonhosted.org/packages/b8/d0/6127b623aa4cca18d8b7743592b048d689fd6c6e37ff26a22cddf6cd9d7f/xxhash-3.7.0-cp314-cp314-win32.whl", hash = "sha256:040ea63668f9185b92bc74942df09c7e65703deed71431333678fc6e739a9955", size = 31271 }, - { url = "https://files.pythonhosted.org/packages/64/4f/44fc4788568004c43921701cbc127f48218a1eede2c9aea231115323564d/xxhash-3.7.0-cp314-cp314-win_amd64.whl", hash = "sha256:2a61e2a3fb23c892496d587b470dee7fa1b58b248a187719c65ea8e94ec13257", size = 32284 }, - { url = "https://files.pythonhosted.org/packages/6d/77/18bb895eb60a49453d16e17d67990e5caff557c78eafc90ad4e2eabf4570/xxhash-3.7.0-cp314-cp314-win_arm64.whl", hash = "sha256:c7741c7524961d8c0cb4d4c21b28957ff731a3fd5b5cd8b856dc80a40e9e5acc", size = 28701 }, - { url = "https://files.pythonhosted.org/packages/45/a0/46f72244570c550fbbb7db1ef554183dd5ebe9136385f30e032b781ae8f6/xxhash-3.7.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:fc84bf7aa7592f31ec63a3e7b11d624f468a3f19f5238cec7282a42e838ab1d7", size = 33646 }, - { url = "https://files.pythonhosted.org/packages/4a/3a/453846a7eceea11e75def361eed01ec6a0205b9822c19927ed364ccae7cc/xxhash-3.7.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9f1563fdc8abfc389748e6932c7e4e99c89a53e4ec37d4563c24fc06f5e5644b", size = 31125 }, - { url = "https://files.pythonhosted.org/packages/bd/3e/49434aba738885d512f9e486db1bdd19db28dfa40372b56da26ef7a4e738/xxhash-3.7.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:2d415f18becf6f153046ab6adc97da77e3643a0ee205dae61c4012604113a020", size = 196633 }, - { url = "https://files.pythonhosted.org/packages/a4/e9/006cb6127baeb9f8abe6d15e62faa01349f09b34e2bfd65175b2422d026b/xxhash-3.7.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bb16aa13ed175bc9be5c2491ba031b85a9b51c4ed90e0b3d4ebe63cf3fb54f8e", size = 215899 }, - { url = "https://files.pythonhosted.org/packages/27/e4/cc57d72e66df0ae29b914335f1c6dcf61e8f3746ddf0ae3c471aa4f15e00/xxhash-3.7.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f9fd595f1e5941b3d7863e4774e4b30caa6731fc34b9277da032295aa5656ee5", size = 238116 }, - { url = "https://files.pythonhosted.org/packages/af/78/3531d4a3fd8a0038cc6be1f265a69c1b3587f557a10b677dd736de2202c1/xxhash-3.7.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1295325c5a98d552333fa53dc2b026b0ef0ec9c8e73ca3a952990b4c7d65d459", size = 215012 }, - { url = "https://files.pythonhosted.org/packages/b4/f6/259fb1eaaec921f59b17203b0daee69829761226d3b980d5191d7723dd83/xxhash-3.7.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3573a651d146912da9daa9e29e5fbc45994420daaa9ef1e2fa5823e1dc485513", size = 448534 }, - { url = "https://files.pythonhosted.org/packages/7b/16/a66d0eaf6a7e68532c07714361ddc904c663ec940f3b028c1ae4a21a7b9d/xxhash-3.7.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ec1e080a3d02d94ea9335bfab0e3374b877e25411422c18f51a943fa4b46381", size = 196217 }, - { url = "https://files.pythonhosted.org/packages/8d/ef/d2efc7fc51756dc52509109d1a25cefc859d74bc4b19a167b12dbd8c2786/xxhash-3.7.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:84415265192072d8638a3afc3c1bc5995e310570cd9acb54dc46d3939e364fe0", size = 286906 }, - { url = "https://files.pythonhosted.org/packages/fc/67/25decd1d4a4018582ec4db2a868a2b7e40640f4adb20dfeb19ac923aa825/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8d4dea659b57443989ef32f4295104fd6912c73d0bf26d1d148bb88a9f159b02", size = 213057 }, - { url = "https://files.pythonhosted.org/packages/0d/5d/17651eb29d06786cdc40c60ae3d27d645aa5d61d2eca6237a7ba0b94789b/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:05ece0fe4d9c9c2728912d1981ae1566cfc83a011571b24732cbf76e1fb70dca", size = 243886 }, - { url = "https://files.pythonhosted.org/packages/8a/d4/174d9cf7502243d586e6a9ae842b1ae23026620995114f85f1380e588bc9/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:fd880353cf1ffaf321bc18dd663e111976dbd0d3bbd8a66d58d2b470dfa7f396", size = 201015 }, - { url = "https://files.pythonhosted.org/packages/91/8c/2254e2d06c3ac5e6fe22eaf3da791b87ea823ae9f2c17b4af66755c5752d/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:4e15cc9e2817f6481160f930c62842b3ff419e20e13072bcbab12230943092bc", size = 213457 }, - { url = "https://files.pythonhosted.org/packages/79/a2/e3daa762545921173e3360f3b4ff7fc63c2d27359f7230ec1a7a74e117f6/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:90b9d1a8bd37d768ffc92a1f651ec69afc532a96fa1ac2ea7abbed5d630b3237", size = 277738 }, - { url = "https://files.pythonhosted.org/packages/e1/4c/e186da2c46b87f5204640e008d42730bf3c1ee9f0efb71ae1ebcdfeac681/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:157c49475b34ecea8809e51123d9769a534e139d1247942f7a4bc67710bb2533", size = 417127 }, - { url = "https://files.pythonhosted.org/packages/17/28/3798e15007a3712d0da3d3fe70f8e11916569858b5cc371053bc26270832/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5a6ddec83325685e729ca119d1f5c518ec39294212ecd770e60693cdc5f7eb79", size = 193962 }, - { url = "https://files.pythonhosted.org/packages/ad/95/a26baa93b5241fd7630998816a4ec47a5a0bad193b3f8fc8f3593e1a4a67/xxhash-3.7.0-cp314-cp314t-win32.whl", hash = "sha256:a04a6cab47e2166435aaf5b9e5ee41d1532cc8300efdef87f2a4d0acb7db19ed", size = 31643 }, - { url = "https://files.pythonhosted.org/packages/44/36/5454f13c447e395f9b06a3e91274c59f503d31fad84e1836efe3bdb71f6a/xxhash-3.7.0-cp314-cp314t-win_amd64.whl", hash = "sha256:8653dd7c2eda020545bb2c71c7f7039b53fe7434d0fc1a0a9deb79ab3f1a4fc1", size = 32522 }, - { url = "https://files.pythonhosted.org/packages/74/35/698e7e3ff38e22992ea24870a511d8762474fb6783627a2910ff22a185c2/xxhash-3.7.0-cp314-cp314t-win_arm64.whl", hash = "sha256:468f0fc114faaa4b36699f8e328bbc3bb11dc418ba94ac52c26dd736d4b6c637", size = 28807 }, -] - -[[package]] -name = "yarl" -version = "1.23.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "idna" }, - { name = "multidict" }, - { name = "propcache" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/23/6e/beb1beec874a72f23815c1434518bfc4ed2175065173fb138c3705f658d4/yarl-1.23.0.tar.gz", hash = "sha256:53b1ea6ca88ebd4420379c330aea57e258408dd0df9af0992e5de2078dc9f5d5", size = 194676 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/88/8a/94615bc31022f711add374097ad4144d569e95ff3c38d39215d07ac153a0/yarl-1.23.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:1932b6b8bba8d0160a9d1078aae5838a66039e8832d41d2992daa9a3a08f7860", size = 124737 }, - { url = "https://files.pythonhosted.org/packages/e3/6f/c6554045d59d64052698add01226bc867b52fe4a12373415d7991fdca95d/yarl-1.23.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:411225bae281f114067578891bc75534cfb3d92a3b4dfef7a6ca78ba354e6069", size = 87029 }, - { url = "https://files.pythonhosted.org/packages/19/2a/725ecc166d53438bc88f76822ed4b1e3b10756e790bafd7b523fe97c322d/yarl-1.23.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:13a563739ae600a631c36ce096615fe307f131344588b0bc0daec108cdb47b25", size = 86310 }, - { url = "https://files.pythonhosted.org/packages/99/30/58260ed98e6ff7f90ba84442c1ddd758c9170d70327394a6227b310cd60f/yarl-1.23.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cbf44c5cb4a7633d078788e1b56387e3d3cf2b8139a3be38040b22d6c3221c8", size = 97587 }, - { url = "https://files.pythonhosted.org/packages/76/0a/8b08aac08b50682e65759f7f8dde98ae8168f72487e7357a5d684c581ef9/yarl-1.23.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:53ad387048f6f09a8969631e4de3f1bf70c50e93545d64af4f751b2498755072", size = 92528 }, - { url = "https://files.pythonhosted.org/packages/52/07/0b7179101fe5f8385ec6c6bb5d0cb9f76bd9fb4a769591ab6fb5cdbfc69a/yarl-1.23.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4a59ba56f340334766f3a4442e0efd0af895fae9e2b204741ef885c446b3a1a8", size = 105339 }, - { url = "https://files.pythonhosted.org/packages/d3/8a/36d82869ab5ec829ca8574dfcb92b51286fcfb1e9c7a73659616362dc880/yarl-1.23.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:803a3c3ce4acc62eaf01eaca1208dcf0783025ef27572c3336502b9c232005e7", size = 105061 }, - { url = "https://files.pythonhosted.org/packages/66/3e/868e5c3364b6cee19ff3e1a122194fa4ce51def02c61023970442162859e/yarl-1.23.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a3d2bff8f37f8d0f96c7ec554d16945050d54462d6e95414babaa18bfafc7f51", size = 100132 }, - { url = "https://files.pythonhosted.org/packages/cf/26/9c89acf82f08a52cb52d6d39454f8d18af15f9d386a23795389d1d423823/yarl-1.23.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c75eb09e8d55bceb4367e83496ff8ef2bc7ea6960efb38e978e8073ea59ecb67", size = 99289 }, - { url = "https://files.pythonhosted.org/packages/6f/54/5b0db00d2cb056922356104468019c0a132e89c8d3ab67d8ede9f4483d2a/yarl-1.23.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:877b0738624280e34c55680d6054a307aa94f7d52fa0e3034a9cc6e790871da7", size = 96950 }, - { url = "https://files.pythonhosted.org/packages/f6/40/10fa93811fd439341fad7e0718a86aca0de9548023bbb403668d6555acab/yarl-1.23.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:b5405bb8f0e783a988172993cfc627e4d9d00432d6bbac65a923041edacf997d", size = 93960 }, - { url = "https://files.pythonhosted.org/packages/bc/d2/8ae2e6cd77d0805f4526e30ec43b6f9a3dfc542d401ac4990d178e4bf0cf/yarl-1.23.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:1c3a3598a832590c5a3ce56ab5576361b5688c12cb1d39429cf5dba30b510760", size = 104703 }, - { url = "https://files.pythonhosted.org/packages/2f/0c/b3ceacf82c3fe21183ce35fa2acf5320af003d52bc1fcf5915077681142e/yarl-1.23.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:8419ebd326430d1cbb7efb5292330a2cf39114e82df5cc3d83c9a0d5ebeaf2f2", size = 98325 }, - { url = "https://files.pythonhosted.org/packages/9d/e0/12900edd28bdab91a69bd2554b85ad7b151f64e8b521fe16f9ad2f56477a/yarl-1.23.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:be61f6fff406ca40e3b1d84716fde398fc08bc63dd96d15f3a14230a0973ed86", size = 105067 }, - { url = "https://files.pythonhosted.org/packages/15/61/74bb1182cf79c9bbe4eb6b1f14a57a22d7a0be5e9cedf8e2d5c2086474c3/yarl-1.23.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3ceb13c5c858d01321b5d9bb65e4cf37a92169ea470b70fec6f236b2c9dd7e34", size = 100285 }, - { url = "https://files.pythonhosted.org/packages/69/7f/cd5ef733f2550de6241bd8bd8c3febc78158b9d75f197d9c7baa113436af/yarl-1.23.0-cp312-cp312-win32.whl", hash = "sha256:fffc45637bcd6538de8b85f51e3df3223e4ad89bccbfca0481c08c7fc8b7ed7d", size = 82359 }, - { url = "https://files.pythonhosted.org/packages/f5/be/25216a49daeeb7af2bec0db22d5e7df08ed1d7c9f65d78b14f3b74fd72fc/yarl-1.23.0-cp312-cp312-win_amd64.whl", hash = "sha256:f69f57305656a4852f2a7203efc661d8c042e6cc67f7acd97d8667fb448a426e", size = 87674 }, - { url = "https://files.pythonhosted.org/packages/d2/35/aeab955d6c425b227d5b7247eafb24f2653fedc32f95373a001af5dfeb9e/yarl-1.23.0-cp312-cp312-win_arm64.whl", hash = "sha256:6e87a6e8735b44816e7db0b2fbc9686932df473c826b0d9743148432e10bb9b9", size = 81879 }, - { url = "https://files.pythonhosted.org/packages/9a/4b/a0a6e5d0ee8a2f3a373ddef8a4097d74ac901ac363eea1440464ccbe0898/yarl-1.23.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:16c6994ac35c3e74fb0ae93323bf8b9c2a9088d55946109489667c510a7d010e", size = 123796 }, - { url = "https://files.pythonhosted.org/packages/67/b6/8925d68af039b835ae876db5838e82e76ec87b9782ecc97e192b809c4831/yarl-1.23.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4a42e651629dafb64fd5b0286a3580613702b5809ad3f24934ea87595804f2c5", size = 86547 }, - { url = "https://files.pythonhosted.org/packages/ae/50/06d511cc4b8e0360d3c94af051a768e84b755c5eb031b12adaaab6dec6e5/yarl-1.23.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7c6b9461a2a8b47c65eef63bb1c76a4f1c119618ffa99ea79bc5bb1e46c5821b", size = 85854 }, - { url = "https://files.pythonhosted.org/packages/c4/f4/4e30b250927ffdab4db70da08b9b8d2194d7c7b400167b8fbeca1e4701ca/yarl-1.23.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2569b67d616eab450d262ca7cb9f9e19d2f718c70a8b88712859359d0ab17035", size = 98351 }, - { url = "https://files.pythonhosted.org/packages/86/fc/4118c5671ea948208bdb1492d8b76bdf1453d3e73df051f939f563e7dcc5/yarl-1.23.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e9d9a4d06d3481eab79803beb4d9bd6f6a8e781ec078ac70d7ef2dcc29d1bea5", size = 92711 }, - { url = "https://files.pythonhosted.org/packages/56/11/1ed91d42bd9e73c13dc9e7eb0dd92298d75e7ac4dd7f046ad0c472e231cd/yarl-1.23.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f514f6474e04179d3d33175ed3f3e31434d3130d42ec153540d5b157deefd735", size = 106014 }, - { url = "https://files.pythonhosted.org/packages/ce/c9/74e44e056a23fbc33aca71779ef450ca648a5bc472bdad7a82339918f818/yarl-1.23.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fda207c815b253e34f7e1909840fd14299567b1c0eb4908f8c2ce01a41265401", size = 105557 }, - { url = "https://files.pythonhosted.org/packages/66/fe/b1e10b08d287f518994f1e2ff9b6d26f0adeecd8dd7d533b01bab29a3eda/yarl-1.23.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34b6cf500e61c90f305094911f9acc9c86da1a05a7a3f5be9f68817043f486e4", size = 101559 }, - { url = "https://files.pythonhosted.org/packages/72/59/c5b8d94b14e3d3c2a9c20cb100119fd534ab5a14b93673ab4cc4a4141ea5/yarl-1.23.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d7504f2b476d21653e4d143f44a175f7f751cd41233525312696c76aa3dbb23f", size = 100502 }, - { url = "https://files.pythonhosted.org/packages/77/4f/96976cb54cbfc5c9fd73ed4c51804f92f209481d1fb190981c0f8a07a1d7/yarl-1.23.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:578110dd426f0d209d1509244e6d4a3f1a3e9077655d98c5f22583d63252a08a", size = 98027 }, - { url = "https://files.pythonhosted.org/packages/63/6e/904c4f476471afdbad6b7e5b70362fb5810e35cd7466529a97322b6f5556/yarl-1.23.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:609d3614d78d74ebe35f54953c5bbd2ac647a7ddb9c30a5d877580f5e86b22f2", size = 95369 }, - { url = "https://files.pythonhosted.org/packages/9d/40/acfcdb3b5f9d68ef499e39e04d25e141fe90661f9d54114556cf83be8353/yarl-1.23.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:4966242ec68afc74c122f8459abd597afd7d8a60dc93d695c1334c5fd25f762f", size = 105565 }, - { url = "https://files.pythonhosted.org/packages/5e/c6/31e28f3a6ba2869c43d124f37ea5260cac9c9281df803c354b31f4dd1f3c/yarl-1.23.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:e0fd068364a6759bc794459f0a735ab151d11304346332489c7972bacbe9e72b", size = 99813 }, - { url = "https://files.pythonhosted.org/packages/08/1f/6f65f59e72d54aa467119b63fc0b0b1762eff0232db1f4720cd89e2f4a17/yarl-1.23.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:39004f0ad156da43e86aa71f44e033de68a44e5a31fc53507b36dd253970054a", size = 105632 }, - { url = "https://files.pythonhosted.org/packages/a3/c4/18b178a69935f9e7a338127d5b77d868fdc0f0e49becd286d51b3a18c61d/yarl-1.23.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e5723c01a56c5028c807c701aa66722916d2747ad737a046853f6c46f4875543", size = 101895 }, - { url = "https://files.pythonhosted.org/packages/8f/54/f5b870b5505663911dba950a8e4776a0dbd51c9c54c0ae88e823e4b874a0/yarl-1.23.0-cp313-cp313-win32.whl", hash = "sha256:1b6b572edd95b4fa8df75de10b04bc81acc87c1c7d16bcdd2035b09d30acc957", size = 82356 }, - { url = "https://files.pythonhosted.org/packages/7a/84/266e8da36879c6edcd37b02b547e2d9ecdfea776be49598e75696e3316e1/yarl-1.23.0-cp313-cp313-win_amd64.whl", hash = "sha256:baaf55442359053c7d62f6f8413a62adba3205119bcb6f49594894d8be47e5e3", size = 87515 }, - { url = "https://files.pythonhosted.org/packages/00/fd/7e1c66efad35e1649114fa13f17485f62881ad58edeeb7f49f8c5e748bf9/yarl-1.23.0-cp313-cp313-win_arm64.whl", hash = "sha256:fb4948814a2a98e3912505f09c9e7493b1506226afb1f881825368d6fb776ee3", size = 81785 }, - { url = "https://files.pythonhosted.org/packages/9c/fc/119dd07004f17ea43bb91e3ece6587759edd7519d6b086d16bfbd3319982/yarl-1.23.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:aecfed0b41aa72b7881712c65cf764e39ce2ec352324f5e0837c7048d9e6daaa", size = 130719 }, - { url = "https://files.pythonhosted.org/packages/e6/0d/9f2348502fbb3af409e8f47730282cd6bc80dec6630c1e06374d882d6eb2/yarl-1.23.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a41bcf68efd19073376eb8cf948b8d9be0af26256403e512bb18f3966f1f9120", size = 89690 }, - { url = "https://files.pythonhosted.org/packages/50/93/e88f3c80971b42cfc83f50a51b9d165a1dbf154b97005f2994a79f212a07/yarl-1.23.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cde9a2ecd91668bcb7f077c4966d8ceddb60af01b52e6e3e2680e4cf00ad1a59", size = 89851 }, - { url = "https://files.pythonhosted.org/packages/1c/07/61c9dd8ba8f86473263b4036f70fb594c09e99c0d9737a799dfd8bc85651/yarl-1.23.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5023346c4ee7992febc0068e7593de5fa2bf611848c08404b35ebbb76b1b0512", size = 95874 }, - { url = "https://files.pythonhosted.org/packages/9e/e9/f9ff8ceefba599eac6abddcfb0b3bee9b9e636e96dbf54342a8577252379/yarl-1.23.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:d1009abedb49ae95b136a8904a3f71b342f849ffeced2d3747bf29caeda218c4", size = 88710 }, - { url = "https://files.pythonhosted.org/packages/eb/78/0231bfcc5d4c8eec220bc2f9ef82cb4566192ea867a7c5b4148f44f6cbcd/yarl-1.23.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a8d00f29b42f534cc8aa3931cfe773b13b23e561e10d2b26f27a8d309b0e82a1", size = 101033 }, - { url = "https://files.pythonhosted.org/packages/cd/9b/30ea5239a61786f18fd25797151a17fbb3be176977187a48d541b5447dd4/yarl-1.23.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:95451e6ce06c3e104556d73b559f5da6c34a069b6b62946d3ad66afcd51642ea", size = 100817 }, - { url = "https://files.pythonhosted.org/packages/62/e2/a4980481071791bc83bce2b7a1a1f7adcabfa366007518b4b845e92eeee3/yarl-1.23.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:531ef597132086b6cf96faa7c6c1dcd0361dd5f1694e5cc30375907b9b7d3ea9", size = 97482 }, - { url = "https://files.pythonhosted.org/packages/e5/1e/304a00cf5f6100414c4b5a01fc7ff9ee724b62158a08df2f8170dfc72a2d/yarl-1.23.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:88f9fb0116fbfcefcab70f85cf4b74a2b6ce5d199c41345296f49d974ddb4123", size = 95949 }, - { url = "https://files.pythonhosted.org/packages/68/03/093f4055ed4cae649ac53bca3d180bd37102e9e11d048588e9ab0c0108d0/yarl-1.23.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:e7b0460976dc75cb87ad9cc1f9899a4b97751e7d4e77ab840fc9b6d377b8fd24", size = 95839 }, - { url = "https://files.pythonhosted.org/packages/b9/28/4c75ebb108f322aa8f917ae10a8ffa4f07cae10a8a627b64e578617df6a0/yarl-1.23.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:115136c4a426f9da976187d238e84139ff6b51a20839aa6e3720cd1026d768de", size = 90696 }, - { url = "https://files.pythonhosted.org/packages/23/9c/42c2e2dd91c1a570402f51bdf066bfdb1241c2240ba001967bad778e77b7/yarl-1.23.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:ead11956716a940c1abc816b7df3fa2b84d06eaed8832ca32f5c5e058c65506b", size = 100865 }, - { url = "https://files.pythonhosted.org/packages/74/05/1bcd60a8a0a914d462c305137246b6f9d167628d73568505fce3f1cb2e65/yarl-1.23.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:fe8f8f5e70e6dbdfca9882cd9deaac058729bcf323cf7a58660901e55c9c94f6", size = 96234 }, - { url = "https://files.pythonhosted.org/packages/90/b2/f52381aac396d6778ce516b7bc149c79e65bfc068b5de2857ab69eeea3b7/yarl-1.23.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:a0e317df055958a0c1e79e5d2aa5a5eaa4a6d05a20d4b0c9c3f48918139c9fc6", size = 100295 }, - { url = "https://files.pythonhosted.org/packages/e5/e8/638bae5bbf1113a659b2435d8895474598afe38b4a837103764f603aba56/yarl-1.23.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6f0fd84de0c957b2d280143522c4f91a73aada1923caee763e24a2b3fda9f8a5", size = 97784 }, - { url = "https://files.pythonhosted.org/packages/80/25/a3892b46182c586c202629fc2159aa13975d3741d52ebd7347fd501d48d5/yarl-1.23.0-cp313-cp313t-win32.whl", hash = "sha256:93a784271881035ab4406a172edb0faecb6e7d00f4b53dc2f55919d6c9688595", size = 88313 }, - { url = "https://files.pythonhosted.org/packages/43/68/8c5b36aa5178900b37387937bc2c2fe0e9505537f713495472dcf6f6fccc/yarl-1.23.0-cp313-cp313t-win_amd64.whl", hash = "sha256:dd00607bffbf30250fe108065f07453ec124dbf223420f57f5e749b04295e090", size = 94932 }, - { url = "https://files.pythonhosted.org/packages/c6/cc/d79ba8292f51f81f4dc533a8ccfb9fc6992cabf0998ed3245de7589dc07c/yarl-1.23.0-cp313-cp313t-win_arm64.whl", hash = "sha256:ac09d42f48f80c9ee1635b2fcaa819496a44502737660d3c0f2ade7526d29144", size = 84786 }, - { url = "https://files.pythonhosted.org/packages/90/98/b85a038d65d1b92c3903ab89444f48d3cee490a883477b716d7a24b1a78c/yarl-1.23.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:21d1b7305a71a15b4794b5ff22e8eef96ff4a6d7f9657155e5aa419444b28912", size = 124455 }, - { url = "https://files.pythonhosted.org/packages/39/54/bc2b45559f86543d163b6e294417a107bb87557609007c007ad889afec18/yarl-1.23.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:85610b4f27f69984932a7abbe52703688de3724d9f72bceb1cca667deff27474", size = 86752 }, - { url = "https://files.pythonhosted.org/packages/24/f9/e8242b68362bffe6fb536c8db5076861466fc780f0f1b479fc4ffbebb128/yarl-1.23.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:23f371bd662cf44a7630d4d113101eafc0cfa7518a2760d20760b26021454719", size = 86291 }, - { url = "https://files.pythonhosted.org/packages/ea/d8/d1cb2378c81dd729e98c716582b1ccb08357e8488e4c24714658cc6630e8/yarl-1.23.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4a80f77dc1acaaa61f0934176fccca7096d9b1ff08c8ba9cddf5ae034a24319", size = 99026 }, - { url = "https://files.pythonhosted.org/packages/0a/ff/7196790538f31debe3341283b5b0707e7feb947620fc5e8236ef28d44f72/yarl-1.23.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:bd654fad46d8d9e823afbb4f87c79160b5a374ed1ff5bde24e542e6ba8f41434", size = 92355 }, - { url = "https://files.pythonhosted.org/packages/c1/56/25d58c3eddde825890a5fe6aa1866228377354a3c39262235234ab5f616b/yarl-1.23.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:682bae25f0a0dd23a056739f23a134db9f52a63e2afd6bfb37ddc76292bbd723", size = 106417 }, - { url = "https://files.pythonhosted.org/packages/51/8a/882c0e7bc8277eb895b31bce0138f51a1ba551fc2e1ec6753ffc1e7c1377/yarl-1.23.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a82836cab5f197a0514235aaf7ffccdc886ccdaa2324bc0aafdd4ae898103039", size = 106422 }, - { url = "https://files.pythonhosted.org/packages/42/2b/fef67d616931055bf3d6764885990a3ac647d68734a2d6a9e1d13de437a2/yarl-1.23.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1c57676bdedc94cd3bc37724cf6f8cd2779f02f6aba48de45feca073e714fe52", size = 101915 }, - { url = "https://files.pythonhosted.org/packages/18/6a/530e16aebce27c5937920f3431c628a29a4b6b430fab3fd1c117b26ff3f6/yarl-1.23.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c7f8dc16c498ff06497c015642333219871effba93e4a2e8604a06264aca5c5c", size = 100690 }, - { url = "https://files.pythonhosted.org/packages/88/08/93749219179a45e27b036e03260fda05190b911de8e18225c294ac95bbc9/yarl-1.23.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5ee586fb17ff8f90c91cf73c6108a434b02d69925f44f5f8e0d7f2f260607eae", size = 98750 }, - { url = "https://files.pythonhosted.org/packages/d9/cf/ea424a004969f5d81a362110a6ac1496d79efdc6d50c2c4b2e3ea0fc2519/yarl-1.23.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:17235362f580149742739cc3828b80e24029d08cbb9c4bda0242c7b5bc610a8e", size = 94685 }, - { url = "https://files.pythonhosted.org/packages/e2/b7/14341481fe568e2b0408bcf1484c652accafe06a0ade9387b5d3fd9df446/yarl-1.23.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:0793e2bd0cf14234983bbb371591e6bea9e876ddf6896cdcc93450996b0b5c85", size = 106009 }, - { url = "https://files.pythonhosted.org/packages/0a/e6/5c744a9b54f4e8007ad35bce96fbc9218338e84812d36f3390cea616881a/yarl-1.23.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:3650dc2480f94f7116c364096bc84b1d602f44224ef7d5c7208425915c0475dd", size = 100033 }, - { url = "https://files.pythonhosted.org/packages/0c/23/e3bfc188d0b400f025bc49d99793d02c9abe15752138dcc27e4eaf0c4a9e/yarl-1.23.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f40e782d49630ad384db66d4d8b73ff4f1b8955dc12e26b09a3e3af064b3b9d6", size = 106483 }, - { url = "https://files.pythonhosted.org/packages/72/42/f0505f949a90b3f8b7a363d6cbdf398f6e6c58946d85c6d3a3bc70595b26/yarl-1.23.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:94f8575fbdf81749008d980c17796097e645574a3b8c28ee313931068dad14fe", size = 102175 }, - { url = "https://files.pythonhosted.org/packages/aa/65/b39290f1d892a9dd671d1c722014ca062a9c35d60885d57e5375db0404b5/yarl-1.23.0-cp314-cp314-win32.whl", hash = "sha256:c8aa34a5c864db1087d911a0b902d60d203ea3607d91f615acd3f3108ac32169", size = 83871 }, - { url = "https://files.pythonhosted.org/packages/a9/5b/9b92f54c784c26e2a422e55a8d2607ab15b7ea3349e28359282f84f01d43/yarl-1.23.0-cp314-cp314-win_amd64.whl", hash = "sha256:63e92247f383c85ab00dd0091e8c3fa331a96e865459f5ee80353c70a4a42d70", size = 89093 }, - { url = "https://files.pythonhosted.org/packages/e0/7d/8a84dc9381fd4412d5e7ff04926f9865f6372b4c2fd91e10092e65d29eb8/yarl-1.23.0-cp314-cp314-win_arm64.whl", hash = "sha256:70efd20be968c76ece7baa8dafe04c5be06abc57f754d6f36f3741f7aa7a208e", size = 83384 }, - { url = "https://files.pythonhosted.org/packages/dd/8d/d2fad34b1c08aa161b74394183daa7d800141aaaee207317e82c790b418d/yarl-1.23.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:9a18d6f9359e45722c064c97464ec883eb0e0366d33eda61cb19a244bf222679", size = 131019 }, - { url = "https://files.pythonhosted.org/packages/19/ff/33009a39d3ccf4b94d7d7880dfe17fb5816c5a4fe0096d9b56abceea9ac7/yarl-1.23.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:2803ed8b21ca47a43da80a6fd1ed3019d30061f7061daa35ac54f63933409412", size = 89894 }, - { url = "https://files.pythonhosted.org/packages/0c/f1/dab7ac5e7306fb79c0190766a3c00b4cb8d09a1f390ded68c85a5934faf5/yarl-1.23.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:394906945aa8b19fc14a61cf69743a868bb8c465efe85eee687109cc540b98f4", size = 89979 }, - { url = "https://files.pythonhosted.org/packages/aa/b1/08e95f3caee1fad6e65017b9f26c1d79877b502622d60e517de01e72f95d/yarl-1.23.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:71d006bee8397a4a89f469b8deb22469fe7508132d3c17fa6ed871e79832691c", size = 95943 }, - { url = "https://files.pythonhosted.org/packages/c0/cc/6409f9018864a6aa186c61175b977131f373f1988e198e031236916e87e4/yarl-1.23.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:62694e275c93d54f7ccedcfef57d42761b2aad5234b6be1f3e3026cae4001cd4", size = 88786 }, - { url = "https://files.pythonhosted.org/packages/76/40/cc22d1d7714b717fde2006fad2ced5efe5580606cb059ae42117542122f3/yarl-1.23.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31de1613658308efdb21ada98cbc86a97c181aa050ba22a808120bb5be3ab94", size = 101307 }, - { url = "https://files.pythonhosted.org/packages/8f/0d/476c38e85ddb4c6ec6b20b815bdd779aa386a013f3d8b85516feee55c8dc/yarl-1.23.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fb1e8b8d66c278b21d13b0a7ca22c41dd757a7c209c6b12c313e445c31dd3b28", size = 100904 }, - { url = "https://files.pythonhosted.org/packages/72/32/0abe4a76d59adf2081dcb0397168553ece4616ada1c54d1c49d8936c74f8/yarl-1.23.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50f9d8d531dfb767c565f348f33dd5139a6c43f5cbdf3f67da40d54241df93f6", size = 97728 }, - { url = "https://files.pythonhosted.org/packages/b7/35/7b30f4810fba112f60f5a43237545867504e15b1c7647a785fbaf588fac2/yarl-1.23.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:575aa4405a656e61a540f4a80eaa5260f2a38fff7bfdc4b5f611840d76e9e277", size = 95964 }, - { url = "https://files.pythonhosted.org/packages/2d/86/ed7a73ab85ef00e8bb70b0cb5421d8a2a625b81a333941a469a6f4022828/yarl-1.23.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:041b1a4cefacf65840b4e295c6985f334ba83c30607441ae3cf206a0eed1a2e4", size = 95882 }, - { url = "https://files.pythonhosted.org/packages/19/90/d56967f61a29d8498efb7afb651e0b2b422a1e9b47b0ab5f4e40a19b699b/yarl-1.23.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:d38c1e8231722c4ce40d7593f28d92b5fc72f3e9774fe73d7e800ec32299f63a", size = 90797 }, - { url = "https://files.pythonhosted.org/packages/72/00/8b8f76909259f56647adb1011d7ed8b321bcf97e464515c65016a47ecdf0/yarl-1.23.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:d53834e23c015ee83a99377db6e5e37d8484f333edb03bd15b4bc312cc7254fb", size = 101023 }, - { url = "https://files.pythonhosted.org/packages/ac/e2/cab11b126fb7d440281b7df8e9ddbe4851e70a4dde47a202b6642586b8d9/yarl-1.23.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:2e27c8841126e017dd2a054a95771569e6070b9ee1b133366d8b31beb5018a41", size = 96227 }, - { url = "https://files.pythonhosted.org/packages/c2/9b/2c893e16bfc50e6b2edf76c1a9eb6cb0c744346197e74c65e99ad8d634d0/yarl-1.23.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:76855800ac56f878847a09ce6dba727c93ca2d89c9e9d63002d26b916810b0a2", size = 100302 }, - { url = "https://files.pythonhosted.org/packages/28/ec/5498c4e3a6d5f1003beb23405671c2eb9cdbf3067d1c80f15eeafe301010/yarl-1.23.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e09fd068c2e169a7070d83d3bde728a4d48de0549f975290be3c108c02e499b4", size = 98202 }, - { url = "https://files.pythonhosted.org/packages/fe/c3/cd737e2d45e70717907f83e146f6949f20cc23cd4bf7b2688727763aa458/yarl-1.23.0-cp314-cp314t-win32.whl", hash = "sha256:73309162a6a571d4cbd3b6a1dcc703c7311843ae0d1578df6f09be4e98df38d4", size = 90558 }, - { url = "https://files.pythonhosted.org/packages/e1/19/3774d162f6732d1cfb0b47b4140a942a35ca82bb19b6db1f80e9e7bdc8f8/yarl-1.23.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4503053d296bc6e4cbd1fad61cf3b6e33b939886c4f249ba7c78b602214fabe2", size = 97610 }, - { url = "https://files.pythonhosted.org/packages/51/47/3fa2286c3cb162c71cdb34c4224d5745a1ceceb391b2bd9b19b668a8d724/yarl-1.23.0-cp314-cp314t-win_arm64.whl", hash = "sha256:44bb7bef4ea409384e3f8bc36c063d77ea1b8d4a5b2706956c0d6695f07dcc25", size = 86041 }, - { url = "https://files.pythonhosted.org/packages/69/68/c8739671f5699c7dc470580a4f821ef37c32c4cb0b047ce223a7f115757f/yarl-1.23.0-py3-none-any.whl", hash = "sha256:a2df6afe50dea8ae15fa34c9f824a3ee958d785fd5d089063d960bae1daa0a3f", size = 48288 }, -] diff --git a/surfsense_local/AGENTS.md b/surfsense_local/AGENTS.md new file mode 100644 index 0000000000..61afcca5da --- /dev/null +++ b/surfsense_local/AGENTS.md @@ -0,0 +1,23 @@ +# surfsense_local + +Electron desktop (`electron/`) plus bundled backend and Vite SPA (`frontend/`). No official Electron skill — this file is the desktop map. Root `AGENTS.md` owns organization. + +## Commands + +```bash +cd electron && pnpm dev +cd electron && pnpm test +cd electron && pnpm typecheck +./scripts/bump-version.sh +``` + +Version is `VERSION`. `scripts/bump-version.sh` writes it through the desktop package files. + +## Do + +- New modules: vertical slice, one responsibility per file. See root `AGENTS.md`. +- New behavior: `tdd` skill. + +## Do not + +- Do not rewrite the Electron shell to the new layout unless that is the task. diff --git a/surfsense_mcp/AGENTS.md b/surfsense_mcp/AGENTS.md new file mode 100644 index 0000000000..f79529b616 --- /dev/null +++ b/surfsense_mcp/AGENTS.md @@ -0,0 +1,24 @@ +# surfsense_mcp + +MCP server. Talks to the SurfSense backend over REST only. Does not import backend code. Root `AGENTS.md` owns organization. + +Layout: `mcp_server/features/` (scrapers, knowledge base, workspaces), `mcp_server/core/` (auth, client, transport). + +## Commands + +```bash +uv sync +uv run pytest +uv run surfsense-mcp +``` + +## Do + +- New tools: vertical slice under `mcp_server/features/`, one responsibility per file. +- New behavior: `tdd` skill. +- Adding a new MCP tool shape later: install Anthropic `mcp-builder` then, not now. + +## Do not + +- Do not import `surfsense_backend`. +- Do not rewrite existing feature packages unless that is the task. diff --git a/surfsense_obsidian/.editorconfig b/surfsense_obsidian/.editorconfig deleted file mode 100644 index 81f3ec3548..0000000000 --- a/surfsense_obsidian/.editorconfig +++ /dev/null @@ -1,10 +0,0 @@ -# top-most EditorConfig file -root = true - -[*] -charset = utf-8 -end_of_line = lf -insert_final_newline = true -indent_style = tab -indent_size = 4 -tab_width = 4 diff --git a/surfsense_obsidian/.gitignore b/surfsense_obsidian/.gitignore deleted file mode 100644 index 386ac2bdb9..0000000000 --- a/surfsense_obsidian/.gitignore +++ /dev/null @@ -1,22 +0,0 @@ -# vscode -.vscode - -# Intellij -*.iml -.idea - -# npm -node_modules - -# Don't include the compiled main.js file in the repo. -# They should be uploaded to GitHub releases instead. -main.js - -# Exclude sourcemaps -*.map - -# obsidian -data.json - -# Exclude macOS Finder (System Explorer) View States -.DS_Store diff --git a/surfsense_obsidian/.npmrc b/surfsense_obsidian/.npmrc deleted file mode 100644 index b9737525fd..0000000000 --- a/surfsense_obsidian/.npmrc +++ /dev/null @@ -1 +0,0 @@ -tag-version-prefix="" \ No newline at end of file diff --git a/surfsense_obsidian/AGENTS.md b/surfsense_obsidian/AGENTS.md deleted file mode 100644 index 3f4274ac69..0000000000 --- a/surfsense_obsidian/AGENTS.md +++ /dev/null @@ -1,251 +0,0 @@ -# Obsidian community plugin - -## Project overview - -- Target: Obsidian Community Plugin (TypeScript → bundled JavaScript). -- Entry point: `main.ts` compiled to `main.js` and loaded by Obsidian. -- Required release artifacts: `main.js`, `manifest.json`, and optional `styles.css`. - -## Environment & tooling - -- Node.js: use current LTS (Node 18+ recommended). -- **Package manager: npm** (required for this sample - `package.json` defines npm scripts and dependencies). -- **Bundler: esbuild** (required for this sample - `esbuild.config.mjs` and build scripts depend on it). Alternative bundlers like Rollup or webpack are acceptable for other projects if they bundle all external dependencies into `main.js`. -- Types: `obsidian` type definitions. - -**Note**: This sample project has specific technical dependencies on npm and esbuild. If you're creating a plugin from scratch, you can choose different tools, but you'll need to replace the build configuration accordingly. - -### Install - -```bash -npm install -``` - -### Dev (watch) - -```bash -npm run dev -``` - -### Production build - -```bash -npm run build -``` - -## Linting - -- To use eslint install eslint from terminal: `npm install -g eslint` -- To use eslint to analyze this project use this command: `eslint main.ts` -- eslint will then create a report with suggestions for code improvement by file and line number. -- If your source code is in a folder, such as `src`, you can use eslint with this command to analyze all files in that folder: `eslint ./src/` - -## File & folder conventions - -- **Organize code into multiple files**: Split functionality across separate modules rather than putting everything in `main.ts`. -- Source lives in `src/`. Keep `main.ts` small and focused on plugin lifecycle (loading, unloading, registering commands). -- **Example file structure**: - ``` - src/ - main.ts # Plugin entry point, lifecycle management - settings.ts # Settings interface and defaults - commands/ # Command implementations - command1.ts - command2.ts - ui/ # UI components, modals, views - modal.ts - view.ts - utils/ # Utility functions, helpers - helpers.ts - constants.ts - types.ts # TypeScript interfaces and types - ``` -- **Do not commit build artifacts**: Never commit `node_modules/`, `main.js`, or other generated files to version control. -- Keep the plugin small. Avoid large dependencies. Prefer browser-compatible packages. -- Generated output should be placed at the plugin root or `dist/` depending on your build setup. Release artifacts must end up at the top level of the plugin folder in the vault (`main.js`, `manifest.json`, `styles.css`). - -## Manifest rules (`manifest.json`) - -- Must include (non-exhaustive): - - `id` (plugin ID; for local dev it should match the folder name) - - `name` - - `version` (Semantic Versioning `x.y.z`) - - `minAppVersion` - - `description` - - `isDesktopOnly` (boolean) - - Optional: `author`, `authorUrl`, `fundingUrl` (string or map) -- Never change `id` after release. Treat it as stable API. -- Keep `minAppVersion` accurate when using newer APIs. -- Canonical requirements are coded here: https://github.com/obsidianmd/obsidian-releases/blob/master/.github/workflows/validate-plugin-entry.yml - -## Testing - -- Manual install for testing: copy `main.js`, `manifest.json`, `styles.css` (if any) to: - ``` - /.obsidian/plugins// - ``` -- Reload Obsidian and enable the plugin in **Settings → Community plugins**. - -## Commands & settings - -- Any user-facing commands should be added via `this.addCommand(...)`. -- If the plugin has configuration, provide a settings tab and sensible defaults. -- Persist settings using `this.loadData()` / `this.saveData()`. -- Use stable command IDs; avoid renaming once released. - -## Versioning & releases - -- Bump `version` in `manifest.json` (SemVer) and update `versions.json` to map plugin version → minimum app version. -- Create a GitHub release whose tag exactly matches `manifest.json`'s `version`. Do not use a leading `v`. -- Attach `manifest.json`, `main.js`, and `styles.css` (if present) to the release as individual assets. -- After the initial release, follow the process to add/update your plugin in the community catalog as required. - -## Security, privacy, and compliance - -Follow Obsidian's **Developer Policies** and **Plugin Guidelines**. In particular: - -- Default to local/offline operation. Only make network requests when essential to the feature. -- No hidden telemetry. If you collect optional analytics or call third-party services, require explicit opt-in and document clearly in `README.md` and in settings. -- Never execute remote code, fetch and eval scripts, or auto-update plugin code outside of normal releases. -- Minimize scope: read/write only what's necessary inside the vault. Do not access files outside the vault. -- Clearly disclose any external services used, data sent, and risks. -- Respect user privacy. Do not collect vault contents, filenames, or personal information unless absolutely necessary and explicitly consented. -- Avoid deceptive patterns, ads, or spammy notifications. -- Register and clean up all DOM, app, and interval listeners using the provided `register*` helpers so the plugin unloads safely. - -## UX & copy guidelines (for UI text, commands, settings) - -- Prefer sentence case for headings, buttons, and titles. -- Use clear, action-oriented imperatives in step-by-step copy. -- Use **bold** to indicate literal UI labels. Prefer "select" for interactions. -- Use arrow notation for navigation: **Settings → Community plugins**. -- Keep in-app strings short, consistent, and free of jargon. - -## Performance - -- Keep startup light. Defer heavy work until needed. -- Avoid long-running tasks during `onload`; use lazy initialization. -- Batch disk access and avoid excessive vault scans. -- Debounce/throttle expensive operations in response to file system events. - -## Coding conventions - -- TypeScript with `"strict": true` preferred. -- **Keep `main.ts` minimal**: Focus only on plugin lifecycle (onload, onunload, addCommand calls). Delegate all feature logic to separate modules. -- **Split large files**: If any file exceeds ~200-300 lines, consider breaking it into smaller, focused modules. -- **Use clear module boundaries**: Each file should have a single, well-defined responsibility. -- Bundle everything into `main.js` (no unbundled runtime deps). -- Avoid Node/Electron APIs if you want mobile compatibility; set `isDesktopOnly` accordingly. -- Prefer `async/await` over promise chains; handle errors gracefully. - -## Mobile - -- Where feasible, test on iOS and Android. -- Don't assume desktop-only behavior unless `isDesktopOnly` is `true`. -- Avoid large in-memory structures; be mindful of memory and storage constraints. - -## Agent do/don't - -**Do** -- Add commands with stable IDs (don't rename once released). -- Provide defaults and validation in settings. -- Write idempotent code paths so reload/unload doesn't leak listeners or intervals. -- Use `this.register*` helpers for everything that needs cleanup. - -**Don't** -- Introduce network calls without an obvious user-facing reason and documentation. -- Ship features that require cloud services without clear disclosure and explicit opt-in. -- Store or transmit vault contents unless essential and consented. - -## Common tasks - -### Organize code across multiple files - -**main.ts** (minimal, lifecycle only): -```ts -import { Plugin } from "obsidian"; -import { MySettings, DEFAULT_SETTINGS } from "./settings"; -import { registerCommands } from "./commands"; - -export default class MyPlugin extends Plugin { - settings: MySettings; - - async onload() { - this.settings = Object.assign({}, DEFAULT_SETTINGS, await this.loadData()); - registerCommands(this); - } -} -``` - -**settings.ts**: -```ts -export interface MySettings { - enabled: boolean; - apiKey: string; -} - -export const DEFAULT_SETTINGS: MySettings = { - enabled: true, - apiKey: "", -}; -``` - -**commands/index.ts**: -```ts -import { Plugin } from "obsidian"; -import { doSomething } from "./my-command"; - -export function registerCommands(plugin: Plugin) { - plugin.addCommand({ - id: "do-something", - name: "Do something", - callback: () => doSomething(plugin), - }); -} -``` - -### Add a command - -```ts -this.addCommand({ - id: "your-command-id", - name: "Do the thing", - callback: () => this.doTheThing(), -}); -``` - -### Persist settings - -```ts -interface MySettings { enabled: boolean } -const DEFAULT_SETTINGS: MySettings = { enabled: true }; - -async onload() { - this.settings = Object.assign({}, DEFAULT_SETTINGS, await this.loadData()); - await this.saveData(this.settings); -} -``` - -### Register listeners safely - -```ts -this.registerEvent(this.app.workspace.on("file-open", f => { /* ... */ })); -this.registerDomEvent(window, "resize", () => { /* ... */ }); -this.registerInterval(window.setInterval(() => { /* ... */ }, 1000)); -``` - -## Troubleshooting - -- Plugin doesn't load after build: ensure `main.js` and `manifest.json` are at the top level of the plugin folder under `/.obsidian/plugins//`. -- Build issues: if `main.js` is missing, run `npm run build` or `npm run dev` to compile your TypeScript source code. -- Commands not appearing: verify `addCommand` runs after `onload` and IDs are unique. -- Settings not persisting: ensure `loadData`/`saveData` are awaited and you re-render the UI after changes. -- Mobile-only issues: confirm you're not using desktop-only APIs; check `isDesktopOnly` and adjust. - -## References - -- Obsidian sample plugin: https://github.com/obsidianmd/obsidian-sample-plugin -- API documentation: https://docs.obsidian.md -- Developer policies: https://docs.obsidian.md/Developer+policies -- Plugin guidelines: https://docs.obsidian.md/Plugins/Releasing/Plugin+guidelines -- Style guide: https://help.obsidian.md/style-guide diff --git a/surfsense_obsidian/LICENSE b/surfsense_obsidian/LICENSE deleted file mode 100644 index 261eeb9e9f..0000000000 --- a/surfsense_obsidian/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/surfsense_obsidian/README.md b/surfsense_obsidian/README.md deleted file mode 100644 index 52d88ab901..0000000000 --- a/surfsense_obsidian/README.md +++ /dev/null @@ -1,145 +0,0 @@ -# SurfSense for Obsidian - -Sync your Obsidian vault to [SurfSense](https://github.com/MODSetter/SurfSense) -so your notes become searchable alongside the rest of your knowledge sources -(GitHub, Slack, Linear, Drive, web pages, etc.) from any SurfSense chat. - -The plugin runs inside Obsidian itself, on desktop and mobile, so it works -the same way for SurfSense Cloud and self-hosted deployments. There is no -server-side vault mount and no Electron-only path; everything goes over HTTPS. - -## What it does - -- Realtime sync as you create, edit, rename, or delete notes -- Initial scan + reconciliation against the server manifest on startup, - so vault edits made while the plugin was offline still show up -- Persistent upload queue, so a crash or offline window never loses changes -- Frontmatter, `[[wiki links]]`, `#tags`, headings, and resolved/unresolved - links are extracted and indexed -- Each chat citation links straight back into Obsidian via the - `obsidian://open?vault=…&file=…` deep link -- Multi-vault aware: each vault you enable the plugin in becomes its own - connector row in SurfSense, named after the vault - -## Install - -### Via [BRAT](https://github.com/TfTHacker/obsidian42-brat) (current) - -1. Install the BRAT community plugin. -2. Run **BRAT: Add a beta plugin for testing**. -3. Paste `MODSetter/SurfSense` and pick the latest release. -4. Enable **SurfSense** in *Settings → Community plugins*. - -### Manual sideload - -1. Download `main.js`, `manifest.json`, and `styles.css` from the latest - GitHub release tagged with the plugin version (e.g. `0.1.0`, with no `v` - prefix, matching the `version` field in `manifest.json`). -2. Copy them into `/.obsidian/plugins/surfsense/`. -3. Restart Obsidian and enable the plugin. - -### Community plugin store - -Submission to the official Obsidian community plugin store is in progress. -Once approved you will be able to install from *Settings → Community plugins* -inside Obsidian. - -## Configure - -Open **Settings → SurfSense** in Obsidian and fill in: - -| Setting | Value | -| --- | --- | -| Server URL | `https://surfsense.com` for SurfSense Cloud, or your self-hosted URL | -| API token | Create a personal access token from the *Connectors → Obsidian* dialog or *User settings → API access* in the SurfSense web app | -| Search space | Pick the workspace this vault should sync into | -| Vault name | Defaults to your Obsidian vault name; rename if you have multiple vaults | -| Sync mode | *Auto* (recommended) or *Manual* | -| Exclude patterns | Glob patterns of folders/files to skip (e.g. `.trash`, `_attachments`, `templates/**`) | -| Include attachments | Off by default; enable to sync non-`.md` files | - -The connector row appears automatically inside SurfSense the first time the -plugin successfully calls `/obsidian/connect`. You can manage or delete it -from *Connectors → Obsidian* in the web app. - -## Mobile - -The plugin works on Obsidian for iOS and Android. Sync runs whenever the -app is in the foreground and once more on app close. Mobile OSes -aggressively suspend background apps, so mobile sync is near-realtime rather -than instant. Desktop is the source of truth for live editing. - -## Privacy & safety - -The SurfSense backend qualifies as server-side telemetry under Obsidian's -[Developer policies](https://github.com/obsidianmd/obsidian-developer-docs/blob/main/en/Developer%20policies.md), -so here is the full list of what the plugin sends and stores. The -canonical SurfSense privacy policy lives at -; this section is the plugin-specific -addendum. - -**Sent on `/connect` (once per onload):** - -- `vault_id`: a random UUID minted in the plugin's `data.json` on first run -- `vault_name`: the Obsidian vault folder name -- `workspace_id`: the SurfSense workspace you picked - -**Sent per note on `/sync`, `/rename`, `/delete`:** - -- `path`, `name`, `extension` -- `content` (plain text of the note) -- `frontmatter`, `tags`, `headings`, resolved and unresolved links, - `embeds`, `aliases` -- `content_hash` (SHA-256 of the note body), `mtime`, `ctime` - -**Stored server-side per vault:** - -- One connector row keyed by `vault_id` with `{vault_name, source: "plugin", - last_connect_at}`. Nothing per-device, no plugin version, no analytics. -- One `documents` row per note (soft-deleted rather than hard-deleted so - existing chat citations remain valid). - -**What never leaves the plugin:** - -- No remote code loading, no `eval`, no analytics. -- All network traffic goes to your configured **Server URL** only. -- The `Authorization: Bearer …` header is set per-request with the token - you paste; the plugin never reads cookies or other Obsidian state. -- The plugin uses Obsidian's `requestUrl` (no `fetch`, no `node:http`, - no `node:https`) and Web Crypto for hashing, per Obsidian's mobile guidance. - -For retention, deletion, and contact details see -. - -## Development - -This plugin lives in [`surfsense_obsidian/`](.) inside the SurfSense -monorepo. To work on it locally: - -```sh -cd surfsense_obsidian -npm install -npm run dev # esbuild in watch mode → main.js -``` - -Symlink the folder into a test vault's `.obsidian/plugins/surfsense/`, -enable the plugin, then **Cmd+R** in Obsidian whenever `main.js` rebuilds. - -Lint: - -```sh -npm run lint -``` - -The release pipeline lives at -[`.github/workflows/release-obsidian-plugin.yml`](../.github/workflows/release-obsidian-plugin.yml) -in the repo root and is triggered by tags of the form `obsidian-v0.1.0`. -It verifies the tag matches `manifest.json`, builds the plugin, attaches -`main.js` + `manifest.json` + `styles.css` to a GitHub release tagged with -the bare version (e.g. `0.1.0`, the form BRAT and the Obsidian community -store look for), and mirrors `manifest.json` + `versions.json` to the repo -root so Obsidian's community plugin browser can discover them. - -## License - -[Apache-2.0](LICENSE), same as the rest of SurfSense. diff --git a/surfsense_obsidian/esbuild.config.mjs b/surfsense_obsidian/esbuild.config.mjs deleted file mode 100644 index 1c74a149e0..0000000000 --- a/surfsense_obsidian/esbuild.config.mjs +++ /dev/null @@ -1,49 +0,0 @@ -import esbuild from "esbuild"; -import process from "process"; -import { builtinModules } from 'node:module'; - -const banner = -`/* -THIS IS A GENERATED/BUNDLED FILE BY ESBUILD -if you want to view the source, please visit the github repository of this plugin -*/ -`; - -const prod = (process.argv[2] === "production"); - -const context = await esbuild.context({ - banner: { - js: banner, - }, - entryPoints: ["src/main.ts"], - bundle: true, - external: [ - "obsidian", - "electron", - "@codemirror/autocomplete", - "@codemirror/collab", - "@codemirror/commands", - "@codemirror/language", - "@codemirror/lint", - "@codemirror/search", - "@codemirror/state", - "@codemirror/view", - "@lezer/common", - "@lezer/highlight", - "@lezer/lr", - ...builtinModules], - format: "cjs", - target: "es2018", - logLevel: "info", - sourcemap: prod ? false : "inline", - treeShaking: true, - outfile: "main.js", - minify: prod, -}); - -if (prod) { - await context.rebuild(); - process.exit(0); -} else { - await context.watch(); -} diff --git a/surfsense_obsidian/eslint.config.mts b/surfsense_obsidian/eslint.config.mts deleted file mode 100644 index a2615ae6d4..0000000000 --- a/surfsense_obsidian/eslint.config.mts +++ /dev/null @@ -1,55 +0,0 @@ -import tseslint from 'typescript-eslint'; -import obsidianmd from "eslint-plugin-obsidianmd"; -import globals from "globals"; -import { globalIgnores } from "eslint/config"; - -export default tseslint.config( - { - languageOptions: { - globals: { - ...globals.browser, - }, - parserOptions: { - projectService: { - allowDefaultProject: [ - 'eslint.config.js', - 'manifest.json' - ] - }, - tsconfigRootDir: import.meta.dirname, - extraFileExtensions: ['.json'] - }, - }, - }, - ...obsidianmd.configs.recommended, - { - plugins: { obsidianmd }, - rules: { - "obsidianmd/ui/sentence-case": [ - "error", - { - brands: [ - "Surfsense", - "iOS", - "iPadOS", - "macOS", - "Windows", - "Android", - "Linux", - "Obsidian", - "Markdown", - ], - }, - ], - }, - }, - globalIgnores([ - "node_modules", - "dist", - "esbuild.config.mjs", - "eslint.config.js", - "version-bump.mjs", - "versions.json", - "main.js", - ]), -); diff --git a/surfsense_obsidian/manifest.json b/surfsense_obsidian/manifest.json deleted file mode 100644 index d03a5b6500..0000000000 --- a/surfsense_obsidian/manifest.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "id": "surfsense-obsidian", - "name": "SurfSense", - "version": "0.1.0", - "minAppVersion": "1.5.4", - "description": "Turn your vault into a searchable second brain with SurfSense.", - "author": "SurfSense", - "authorUrl": "https://www.surfsense.com", - "isDesktopOnly": false -} diff --git a/surfsense_obsidian/package-lock.json b/surfsense_obsidian/package-lock.json deleted file mode 100644 index e62b898854..0000000000 --- a/surfsense_obsidian/package-lock.json +++ /dev/null @@ -1,5170 +0,0 @@ -{ - "name": "surfsense-obsidian", - "version": "0.1.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "surfsense-obsidian", - "version": "0.1.0", - "license": "Apache-2.0", - "dependencies": { - "obsidian": "latest" - }, - "devDependencies": { - "@eslint/js": "9.30.1", - "@types/node": "^20.19.39", - "esbuild": "0.25.5", - "eslint-plugin-obsidianmd": "0.1.9", - "globals": "14.0.0", - "jiti": "2.6.1", - "tslib": "2.4.0", - "typescript": "^5.8.3", - "typescript-eslint": "8.35.1" - } - }, - "node_modules/@codemirror/state": { - "version": "6.5.0", - "resolved": "https://registry.npmjs.org/@codemirror/state/-/state-6.5.0.tgz", - "integrity": "sha512-MwBHVK60IiIHDcoMet78lxt6iw5gJOGSbNbOIVBHWVXIH4/Nq1+GQgLLGgI1KlnN86WDXsPudVaqYHKBIx7Eyw==", - "license": "MIT", - "peer": true, - "dependencies": { - "@marijn/find-cluster-break": "^1.0.0" - } - }, - "node_modules/@codemirror/view": { - "version": "6.38.6", - "resolved": "https://registry.npmjs.org/@codemirror/view/-/view-6.38.6.tgz", - "integrity": "sha512-qiS0z1bKs5WOvHIAC0Cybmv4AJSkAXgX5aD6Mqd2epSLlVJsQl8NG23jCVouIgkh4All/mrbdsf2UOLFnJw0tw==", - "license": "MIT", - "peer": true, - "dependencies": { - "@codemirror/state": "^6.5.0", - "crelt": "^1.0.6", - "style-mod": "^4.1.0", - "w3c-keyname": "^2.2.4" - } - }, - "node_modules/@esbuild/aix-ppc64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.25.5.tgz", - "integrity": "sha512-9o3TMmpmftaCMepOdA5k/yDw8SfInyzWWTjYTFCX3kPSDJMROQTb8jg+h9Cnwnmm1vOzvxN7gIfB5V2ewpjtGA==", - "cpu": [ - "ppc64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "aix" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/android-arm": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.25.5.tgz", - "integrity": "sha512-AdJKSPeEHgi7/ZhuIPtcQKr5RQdo6OO2IL87JkianiMYMPbCtot9fxPbrMiBADOWWm3T2si9stAiVsGbTQFkbA==", - "cpu": [ - "arm" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "android" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/android-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.25.5.tgz", - "integrity": "sha512-VGzGhj4lJO+TVGV1v8ntCZWJktV7SGCs3Pn1GRWI1SBFtRALoomm8k5E9Pmwg3HOAal2VDc2F9+PM/rEY6oIDg==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "android" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/android-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.25.5.tgz", - "integrity": "sha512-D2GyJT1kjvO//drbRT3Hib9XPwQeWd9vZoBJn+bu/lVsOZ13cqNdDeqIF/xQ5/VmWvMduP6AmXvylO/PIc2isw==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "android" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/darwin-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.25.5.tgz", - "integrity": "sha512-GtaBgammVvdF7aPIgH2jxMDdivezgFu6iKpmT+48+F8Hhg5J/sfnDieg0aeG/jfSvkYQU2/pceFPDKlqZzwnfQ==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "darwin" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/darwin-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.25.5.tgz", - "integrity": "sha512-1iT4FVL0dJ76/q1wd7XDsXrSW+oLoquptvh4CLR4kITDtqi2e/xwXwdCVH8hVHU43wgJdsq7Gxuzcs6Iq/7bxQ==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "darwin" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/freebsd-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.25.5.tgz", - "integrity": "sha512-nk4tGP3JThz4La38Uy/gzyXtpkPW8zSAmoUhK9xKKXdBCzKODMc2adkB2+8om9BDYugz+uGV7sLmpTYzvmz6Sw==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "freebsd" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/freebsd-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.25.5.tgz", - "integrity": "sha512-PrikaNjiXdR2laW6OIjlbeuCPrPaAl0IwPIaRv+SMV8CiM8i2LqVUHFC1+8eORgWyY7yhQY+2U2fA55mBzReaw==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "freebsd" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-arm": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.25.5.tgz", - "integrity": "sha512-cPzojwW2okgh7ZlRpcBEtsX7WBuqbLrNXqLU89GxWbNt6uIg78ET82qifUy3W6OVww6ZWobWub5oqZOVtwolfw==", - "cpu": [ - "arm" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.25.5.tgz", - "integrity": "sha512-Z9kfb1v6ZlGbWj8EJk9T6czVEjjq2ntSYLY2cw6pAZl4oKtfgQuS4HOq41M/BcoLPzrUbNd+R4BXFyH//nHxVg==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-ia32": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-ia32/-/linux-ia32-0.25.5.tgz", - "integrity": "sha512-sQ7l00M8bSv36GLV95BVAdhJ2QsIbCuCjh/uYrWiMQSUuV+LpXwIqhgJDcvMTj+VsQmqAHL2yYaasENvJ7CDKA==", - "cpu": [ - "ia32" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-loong64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-loong64/-/linux-loong64-0.25.5.tgz", - "integrity": "sha512-0ur7ae16hDUC4OL5iEnDb0tZHDxYmuQyhKhsPBV8f99f6Z9KQM02g33f93rNH5A30agMS46u2HP6qTdEt6Q1kg==", - "cpu": [ - "loong64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-mips64el": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-mips64el/-/linux-mips64el-0.25.5.tgz", - "integrity": "sha512-kB/66P1OsHO5zLz0i6X0RxlQ+3cu0mkxS3TKFvkb5lin6uwZ/ttOkP3Z8lfR9mJOBk14ZwZ9182SIIWFGNmqmg==", - "cpu": [ - "mips64el" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-ppc64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.25.5.tgz", - "integrity": "sha512-UZCmJ7r9X2fe2D6jBmkLBMQetXPXIsZjQJCjgwpVDz+YMcS6oFR27alkgGv3Oqkv07bxdvw7fyB71/olceJhkQ==", - "cpu": [ - "ppc64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-riscv64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.25.5.tgz", - "integrity": "sha512-kTxwu4mLyeOlsVIFPfQo+fQJAV9mh24xL+y+Bm6ej067sYANjyEw1dNHmvoqxJUCMnkBdKpvOn0Ahql6+4VyeA==", - "cpu": [ - "riscv64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-s390x": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-s390x/-/linux-s390x-0.25.5.tgz", - "integrity": "sha512-K2dSKTKfmdh78uJ3NcWFiqyRrimfdinS5ErLSn3vluHNeHVnBAFWC8a4X5N+7FgVE1EjXS1QDZbpqZBjfrqMTQ==", - "cpu": [ - "s390x" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/linux-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/linux-x64/-/linux-x64-0.25.5.tgz", - "integrity": "sha512-uhj8N2obKTE6pSZ+aMUbqq+1nXxNjZIIjCjGLfsWvVpy7gKCOL6rsY1MhRh9zLtUtAI7vpgLMK6DxjO8Qm9lJw==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/netbsd-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/netbsd-arm64/-/netbsd-arm64-0.25.5.tgz", - "integrity": "sha512-pwHtMP9viAy1oHPvgxtOv+OkduK5ugofNTVDilIzBLpoWAM16r7b/mxBvfpuQDpRQFMfuVr5aLcn4yveGvBZvw==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "netbsd" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/netbsd-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/netbsd-x64/-/netbsd-x64-0.25.5.tgz", - "integrity": "sha512-WOb5fKrvVTRMfWFNCroYWWklbnXH0Q5rZppjq0vQIdlsQKuw6mdSihwSo4RV/YdQ5UCKKvBy7/0ZZYLBZKIbwQ==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "netbsd" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/openbsd-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/openbsd-arm64/-/openbsd-arm64-0.25.5.tgz", - "integrity": "sha512-7A208+uQKgTxHd0G0uqZO8UjK2R0DDb4fDmERtARjSHWxqMTye4Erz4zZafx7Di9Cv+lNHYuncAkiGFySoD+Mw==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "openbsd" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/openbsd-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/openbsd-x64/-/openbsd-x64-0.25.5.tgz", - "integrity": "sha512-G4hE405ErTWraiZ8UiSoesH8DaCsMm0Cay4fsFWOOUcz8b8rC6uCvnagr+gnioEjWn0wC+o1/TAHt+It+MpIMg==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "openbsd" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/sunos-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/sunos-x64/-/sunos-x64-0.25.5.tgz", - "integrity": "sha512-l+azKShMy7FxzY0Rj4RCt5VD/q8mG/e+mDivgspo+yL8zW7qEwctQ6YqKX34DTEleFAvCIUviCFX1SDZRSyMQA==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "sunos" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/win32-arm64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/win32-arm64/-/win32-arm64-0.25.5.tgz", - "integrity": "sha512-O2S7SNZzdcFG7eFKgvwUEZ2VG9D/sn/eIiz8XRZ1Q/DO5a3s76Xv0mdBzVM5j5R639lXQmPmSo0iRpHqUUrsxw==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/win32-ia32": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/win32-ia32/-/win32-ia32-0.25.5.tgz", - "integrity": "sha512-onOJ02pqs9h1iMJ1PQphR+VZv8qBMQ77Klcsqv9CNW2w6yLqoURLcgERAIurY6QE63bbLuqgP9ATqajFLK5AMQ==", - "cpu": [ - "ia32" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/win32-x64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/win32-x64/-/win32-x64-0.25.5.tgz", - "integrity": "sha512-TXv6YnJ8ZMVdX+SXWVBo/0p8LTcrUYngpWjvm91TMjjBQii7Oz11Lw5lbDV5Y0TzuhSJHwiH4hEtC1I42mMS0g==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@eslint-community/eslint-utils": { - "version": "4.9.0", - "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.9.0.tgz", - "integrity": "sha512-ayVFHdtZ+hsq1t2Dy24wCmGXGe4q9Gu3smhLYALJrr473ZH27MsnSL+LKUlimp4BWJqMDMLmPpx/Q9R3OAlL4g==", - "dev": true, - "license": "MIT", - "dependencies": { - "eslint-visitor-keys": "^3.4.3" - }, - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - }, - "peerDependencies": { - "eslint": "^6.0.0 || ^7.0.0 || >=8.0.0" - } - }, - "node_modules/@eslint-community/eslint-utils/node_modules/eslint-visitor-keys": { - "version": "3.4.3", - "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz", - "integrity": "sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/@eslint-community/regexpp": { - "version": "4.12.2", - "resolved": "https://registry.npmjs.org/@eslint-community/regexpp/-/regexpp-4.12.2.tgz", - "integrity": "sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew==", - "dev": true, - "license": "MIT", - "engines": { - "node": "^12.0.0 || ^14.0.0 || >=16.0.0" - } - }, - "node_modules/@eslint/config-array": { - "version": "0.21.1", - "resolved": "https://registry.npmjs.org/@eslint/config-array/-/config-array-0.21.1.tgz", - "integrity": "sha512-aw1gNayWpdI/jSYVgzN5pL0cfzU02GT3NBpeT/DXbx1/1x7ZKxFPd9bwrzygx/qiwIQiJ1sw/zD8qY/kRvlGHA==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "@eslint/object-schema": "^2.1.7", - "debug": "^4.3.1", - "minimatch": "^3.1.2" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - } - }, - "node_modules/@eslint/config-helpers": { - "version": "0.4.2", - "resolved": "https://registry.npmjs.org/@eslint/config-helpers/-/config-helpers-0.4.2.tgz", - "integrity": "sha512-gBrxN88gOIf3R7ja5K9slwNayVcZgK6SOUORm2uBzTeIEfeVaIhOpCtTox3P6R7o2jLFwLFTLnC7kU/RGcYEgw==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "@eslint/core": "^0.17.0" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - } - }, - "node_modules/@eslint/core": { - "version": "0.17.0", - "resolved": "https://registry.npmjs.org/@eslint/core/-/core-0.17.0.tgz", - "integrity": "sha512-yL/sLrpmtDaFEiUj1osRP4TI2MDz1AddJL+jZ7KSqvBuliN4xqYY54IfdN8qD8Toa6g1iloph1fxQNkjOxrrpQ==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "@types/json-schema": "^7.0.15" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - } - }, - "node_modules/@eslint/eslintrc": { - "version": "3.3.1", - "resolved": "https://registry.npmjs.org/@eslint/eslintrc/-/eslintrc-3.3.1.tgz", - "integrity": "sha512-gtF186CXhIl1p4pJNGZw8Yc6RlshoePRvE0X91oPGb3vZ8pM3qOS9W9NGPat9LziaBV7XrJWGylNQXkGcnM3IQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "ajv": "^6.12.4", - "debug": "^4.3.2", - "espree": "^10.0.1", - "globals": "^14.0.0", - "ignore": "^5.2.0", - "import-fresh": "^3.2.1", - "js-yaml": "^4.1.0", - "minimatch": "^3.1.2", - "strip-json-comments": "^3.1.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/@eslint/js": { - "version": "9.30.1", - "resolved": "https://registry.npmjs.org/@eslint/js/-/js-9.30.1.tgz", - "integrity": "sha512-zXhuECFlyep42KZUhWjfvsmXGX39W8K8LFb8AWXM9gSV9dQB+MrJGLKvW6Zw0Ggnbpw0VHTtrhFXYe3Gym18jg==", - "dev": true, - "license": "MIT", - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://eslint.org/donate" - } - }, - "node_modules/@eslint/json": { - "version": "0.14.0", - "resolved": "https://registry.npmjs.org/@eslint/json/-/json-0.14.0.tgz", - "integrity": "sha512-rvR/EZtvUG3p9uqrSmcDJPYSH7atmWr0RnFWN6m917MAPx82+zQgPUmDu0whPFG6XTyM0vB/hR6c1Q63OaYtCQ==", - "dev": true, - "license": "Apache-2.0", - "peer": true, - "dependencies": { - "@eslint/core": "^0.17.0", - "@eslint/plugin-kit": "^0.4.1", - "@humanwhocodes/momoa": "^3.3.10", - "natural-compare": "^1.4.0" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - } - }, - "node_modules/@eslint/object-schema": { - "version": "2.1.7", - "resolved": "https://registry.npmjs.org/@eslint/object-schema/-/object-schema-2.1.7.tgz", - "integrity": "sha512-VtAOaymWVfZcmZbp6E2mympDIHvyjXs/12LqWYjVw6qjrfF+VK+fyG33kChz3nnK+SU5/NeHOqrTEHS8sXO3OA==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - } - }, - "node_modules/@eslint/plugin-kit": { - "version": "0.4.1", - "resolved": "https://registry.npmjs.org/@eslint/plugin-kit/-/plugin-kit-0.4.1.tgz", - "integrity": "sha512-43/qtrDUokr7LJqoF2c3+RInu/t4zfrpYdoSDfYyhg52rwLV6TnOvdG4fXm7IkSB3wErkcmJS9iEhjVtOSEjjA==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "@eslint/core": "^0.17.0", - "levn": "^0.4.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - } - }, - "node_modules/@humanfs/core": { - "version": "0.19.1", - "resolved": "https://registry.npmjs.org/@humanfs/core/-/core-0.19.1.tgz", - "integrity": "sha512-5DyQ4+1JEUzejeK1JGICcideyfUbGixgS9jNgex5nqkW+cY7WZhxBigmieN5Qnw9ZosSNVC9KQKyb+GUaGyKUA==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": ">=18.18.0" - } - }, - "node_modules/@humanfs/node": { - "version": "0.16.7", - "resolved": "https://registry.npmjs.org/@humanfs/node/-/node-0.16.7.tgz", - "integrity": "sha512-/zUx+yOsIrG4Y43Eh2peDeKCxlRt/gET6aHfaKpuq267qXdYDFViVHfMaLyygZOnl0kGWxFIgsBy8QFuTLUXEQ==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "@humanfs/core": "^0.19.1", - "@humanwhocodes/retry": "^0.4.0" - }, - "engines": { - "node": ">=18.18.0" - } - }, - "node_modules/@humanwhocodes/module-importer": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/@humanwhocodes/module-importer/-/module-importer-1.0.1.tgz", - "integrity": "sha512-bxveV4V8v5Yb4ncFTT3rPSgZBOpCkjfK0y4oVVVJwIuDVBRMDXrPyXRL988i5ap9m9bnyEEjWfm5WkBmtffLfA==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": ">=12.22" - }, - "funding": { - "type": "github", - "url": "https://github.com/sponsors/nzakas" - } - }, - "node_modules/@humanwhocodes/momoa": { - "version": "3.3.10", - "resolved": "https://registry.npmjs.org/@humanwhocodes/momoa/-/momoa-3.3.10.tgz", - "integrity": "sha512-KWiFQpSAqEIyrTXko3hFNLeQvSK8zXlJQzhhxsyVn58WFRYXST99b3Nqnu+ttOtjds2Pl2grUHGpe2NzhPynuQ==", - "dev": true, - "license": "Apache-2.0", - "peer": true, - "engines": { - "node": ">=18" - } - }, - "node_modules/@humanwhocodes/retry": { - "version": "0.4.3", - "resolved": "https://registry.npmjs.org/@humanwhocodes/retry/-/retry-0.4.3.tgz", - "integrity": "sha512-bV0Tgo9K4hfPCek+aMAn81RppFKv2ySDQeMoSZuvTASywNTnVJCArCZE2FWqpvIatKu7VMRLWlR1EazvVhDyhQ==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": ">=18.18" - }, - "funding": { - "type": "github", - "url": "https://github.com/sponsors/nzakas" - } - }, - "node_modules/@marijn/find-cluster-break": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/@marijn/find-cluster-break/-/find-cluster-break-1.0.2.tgz", - "integrity": "sha512-l0h88YhZFyKdXIFNfSWpyjStDjGHwZ/U7iobcK1cQQD8sejsONdQtTVU+1wVN1PBw40PiiHB1vA5S7VTfQiP9g==", - "license": "MIT", - "peer": true - }, - "node_modules/@microsoft/eslint-plugin-sdl": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@microsoft/eslint-plugin-sdl/-/eslint-plugin-sdl-1.1.0.tgz", - "integrity": "sha512-dxdNHOemLnBhfY3eByrujX9KyLigcNtW8sU+axzWv5nLGcsSBeKW2YYyTpfPo1hV8YPOmIGnfA4fZHyKVtWqBQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "eslint-plugin-n": "17.10.3", - "eslint-plugin-react": "7.37.3", - "eslint-plugin-security": "1.4.0" - }, - "engines": { - "node": ">=18.0.0" - }, - "peerDependencies": { - "eslint": "^9" - } - }, - "node_modules/@microsoft/eslint-plugin-sdl/node_modules/eslint-plugin-security": { - "version": "1.4.0", - "resolved": "https://registry.npmjs.org/eslint-plugin-security/-/eslint-plugin-security-1.4.0.tgz", - "integrity": "sha512-xlS7P2PLMXeqfhyf3NpqbvbnW04kN8M9NtmhpR3XGyOvt/vNKS7XPXT5EDbwKW9vCjWH4PpfQvgD/+JgN0VJKA==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "safe-regex": "^1.1.0" - } - }, - "node_modules/@microsoft/eslint-plugin-sdl/node_modules/safe-regex": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/safe-regex/-/safe-regex-1.1.0.tgz", - "integrity": "sha512-aJXcif4xnaNUzvUuC5gcb46oTS7zvg4jpMTnuqtrEPlR3vFr4pxtdTwaF1Qs3Enjn9HK+ZlwQui+a7z0SywIzg==", - "dev": true, - "license": "MIT", - "dependencies": { - "ret": "~0.1.10" - } - }, - "node_modules/@nodelib/fs.scandir": { - "version": "2.1.5", - "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz", - "integrity": "sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==", - "dev": true, - "license": "MIT", - "dependencies": { - "@nodelib/fs.stat": "2.0.5", - "run-parallel": "^1.1.9" - }, - "engines": { - "node": ">= 8" - } - }, - "node_modules/@nodelib/fs.stat": { - "version": "2.0.5", - "resolved": "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz", - "integrity": "sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 8" - } - }, - "node_modules/@nodelib/fs.walk": { - "version": "1.2.8", - "resolved": "https://registry.npmjs.org/@nodelib/fs.walk/-/fs.walk-1.2.8.tgz", - "integrity": "sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@nodelib/fs.scandir": "2.1.5", - "fastq": "^1.6.0" - }, - "engines": { - "node": ">= 8" - } - }, - "node_modules/@pkgr/core": { - "version": "0.1.2", - "resolved": "https://registry.npmjs.org/@pkgr/core/-/core-0.1.2.tgz", - "integrity": "sha512-fdDH1LSGfZdTH2sxdpVMw31BanV28K/Gry0cVFxaNP77neJSkd82mM8ErPNYs9e+0O7SdHBLTDzDgwUuy18RnQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": "^12.20.0 || ^14.18.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/unts" - } - }, - "node_modules/@rtsao/scc": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@rtsao/scc/-/scc-1.1.0.tgz", - "integrity": "sha512-zt6OdqaDoOnJ1ZYsCYGt9YmWzDXl4vQdKTyJev62gFhRGKdx7mcT54V9KIjg+d2wi9EXsPvAPKe7i7WjfVWB8g==", - "dev": true, - "license": "MIT" - }, - "node_modules/@types/codemirror": { - "version": "5.60.8", - "resolved": "https://registry.npmjs.org/@types/codemirror/-/codemirror-5.60.8.tgz", - "integrity": "sha512-VjFgDF/eB+Aklcy15TtOTLQeMjTo07k7KAjql8OK5Dirr7a6sJY4T1uVBDuTVG9VEmn1uUsohOpYnVfgC6/jyw==", - "license": "MIT", - "dependencies": { - "@types/tern": "*" - } - }, - "node_modules/@types/eslint": { - "version": "8.56.2", - "resolved": "https://registry.npmjs.org/@types/eslint/-/eslint-8.56.2.tgz", - "integrity": "sha512-uQDwm1wFHmbBbCZCqAlq6Do9LYwByNZHWzXppSnay9SuwJ+VRbjkbLABer54kcPnMSlG6Fdiy2yaFXm/z9Z5gw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@types/estree": "*", - "@types/json-schema": "*" - } - }, - "node_modules/@types/estree": { - "version": "1.0.8", - "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.8.tgz", - "integrity": "sha512-dWHzHa2WqEXI/O1E9OjrocMTKJl2mSrEolh1Iomrv6U+JuNwaHXsXx9bLu5gG7BUWFIN0skIQJQ/L1rIex4X6w==", - "license": "MIT" - }, - "node_modules/@types/json-schema": { - "version": "7.0.15", - "resolved": "https://registry.npmjs.org/@types/json-schema/-/json-schema-7.0.15.tgz", - "integrity": "sha512-5+fP8P8MFNC+AyZCDxrB2pkZFPGzqQWUzpSeuuVLvm8VMcorNYavBqoFcxK8bQz4Qsbn4oUEEem4wDLfcysGHA==", - "dev": true, - "license": "MIT" - }, - "node_modules/@types/json5": { - "version": "0.0.29", - "resolved": "https://registry.npmjs.org/@types/json5/-/json5-0.0.29.tgz", - "integrity": "sha512-dRLjCWHYg4oaA77cxO64oO+7JwCwnIzkZPdrrC71jQmQtlhM556pwKo5bUzqvZndkVbeFLIIi+9TC40JNF5hNQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/@types/node": { - "version": "20.19.39", - "resolved": "https://registry.npmjs.org/@types/node/-/node-20.19.39.tgz", - "integrity": "sha512-orrrD74MBUyK8jOAD/r0+lfa1I2MO6I+vAkmAWzMYbCcgrN4lCrmK52gRFQq/JRxfYPfonkr4b0jcY7Olqdqbw==", - "dev": true, - "license": "MIT", - "dependencies": { - "undici-types": "~6.21.0" - } - }, - "node_modules/@types/node/node_modules/undici-types": { - "version": "6.21.0", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", - "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/@types/tern": { - "version": "0.23.9", - "resolved": "https://registry.npmjs.org/@types/tern/-/tern-0.23.9.tgz", - "integrity": "sha512-ypzHFE/wBzh+BlH6rrBgS5I/Z7RD21pGhZ2rltb/+ZrVM1awdZwjx7hE5XfuYgHWk9uvV5HLZN3SloevCAp3Bw==", - "license": "MIT", - "dependencies": { - "@types/estree": "*" - } - }, - "node_modules/@typescript-eslint/eslint-plugin": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/eslint-plugin/-/eslint-plugin-8.35.1.tgz", - "integrity": "sha512-9XNTlo7P7RJxbVeICaIIIEipqxLKguyh+3UbXuT2XQuFp6d8VOeDEGuz5IiX0dgZo8CiI6aOFLg4e8cF71SFVg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@eslint-community/regexpp": "^4.10.0", - "@typescript-eslint/scope-manager": "8.35.1", - "@typescript-eslint/type-utils": "8.35.1", - "@typescript-eslint/utils": "8.35.1", - "@typescript-eslint/visitor-keys": "8.35.1", - "graphemer": "^1.4.0", - "ignore": "^7.0.0", - "natural-compare": "^1.4.0", - "ts-api-utils": "^2.1.0" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "@typescript-eslint/parser": "^8.35.1", - "eslint": "^8.57.0 || ^9.0.0", - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/eslint-plugin/node_modules/ignore": { - "version": "7.0.5", - "resolved": "https://registry.npmjs.org/ignore/-/ignore-7.0.5.tgz", - "integrity": "sha512-Hs59xBNfUIunMFgWAbGX5cq6893IbWg4KnrjbYwX3tx0ztorVgTDA6B2sxf8ejHJ4wz8BqGUMYlnzNBer5NvGg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 4" - } - }, - "node_modules/@typescript-eslint/parser": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/parser/-/parser-8.35.1.tgz", - "integrity": "sha512-3MyiDfrfLeK06bi/g9DqJxP5pV74LNv4rFTyvGDmT3x2p1yp1lOd+qYZfiRPIOf/oON+WRZR5wxxuF85qOar+w==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/scope-manager": "8.35.1", - "@typescript-eslint/types": "8.35.1", - "@typescript-eslint/typescript-estree": "8.35.1", - "@typescript-eslint/visitor-keys": "8.35.1", - "debug": "^4.3.4" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "eslint": "^8.57.0 || ^9.0.0", - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/project-service": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/project-service/-/project-service-8.35.1.tgz", - "integrity": "sha512-VYxn/5LOpVxADAuP3NrnxxHYfzVtQzLKeldIhDhzC8UHaiQvYlXvKuVho1qLduFbJjjy5U5bkGwa3rUGUb1Q6Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/tsconfig-utils": "^8.35.1", - "@typescript-eslint/types": "^8.35.1", - "debug": "^4.3.4" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/scope-manager": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/scope-manager/-/scope-manager-8.35.1.tgz", - "integrity": "sha512-s/Bpd4i7ht2934nG+UoSPlYXd08KYz3bmjLEb7Ye1UVob0d1ENiT3lY8bsCmik4RqfSbPw9xJJHbugpPpP5JUg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/types": "8.35.1", - "@typescript-eslint/visitor-keys": "8.35.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - } - }, - "node_modules/@typescript-eslint/tsconfig-utils": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/tsconfig-utils/-/tsconfig-utils-8.35.1.tgz", - "integrity": "sha512-K5/U9VmT9dTHoNowWZpz+/TObS3xqC5h0xAIjXPw+MNcKV9qg6eSatEnmeAwkjHijhACH0/N7bkhKvbt1+DXWQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/type-utils": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/type-utils/-/type-utils-8.35.1.tgz", - "integrity": "sha512-HOrUBlfVRz5W2LIKpXzZoy6VTZzMu2n8q9C2V/cFngIC5U1nStJgv0tMV4sZPzdf4wQm9/ToWUFPMN9Vq9VJQQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/typescript-estree": "8.35.1", - "@typescript-eslint/utils": "8.35.1", - "debug": "^4.3.4", - "ts-api-utils": "^2.1.0" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "eslint": "^8.57.0 || ^9.0.0", - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/types": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/types/-/types-8.35.1.tgz", - "integrity": "sha512-q/O04vVnKHfrrhNAscndAn1tuQhIkwqnaW+eu5waD5IPts2eX1dgJxgqcPx5BX109/qAz7IG6VrEPTOYKCNfRQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - } - }, - "node_modules/@typescript-eslint/typescript-estree": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/typescript-estree/-/typescript-estree-8.35.1.tgz", - "integrity": "sha512-Vvpuvj4tBxIka7cPs6Y1uvM7gJgdF5Uu9F+mBJBPY4MhvjrjWGK4H0lVgLJd/8PWZ23FTqsaJaLEkBCFUk8Y9g==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/project-service": "8.35.1", - "@typescript-eslint/tsconfig-utils": "8.35.1", - "@typescript-eslint/types": "8.35.1", - "@typescript-eslint/visitor-keys": "8.35.1", - "debug": "^4.3.4", - "fast-glob": "^3.3.2", - "is-glob": "^4.0.3", - "minimatch": "^9.0.4", - "semver": "^7.6.0", - "ts-api-utils": "^2.1.0" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/typescript-estree/node_modules/brace-expansion": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz", - "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "balanced-match": "^1.0.0" - } - }, - "node_modules/@typescript-eslint/typescript-estree/node_modules/minimatch": { - "version": "9.0.5", - "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-9.0.5.tgz", - "integrity": "sha512-G6T0ZX48xgozx7587koeX9Ys2NYy6Gmv//P89sEte9V9whIapMNF4idKxnW2QtCcLiTWlb/wfCabAtAFWhhBow==", - "dev": true, - "license": "ISC", - "dependencies": { - "brace-expansion": "^2.0.1" - }, - "engines": { - "node": ">=16 || 14 >=14.17" - }, - "funding": { - "url": "https://github.com/sponsors/isaacs" - } - }, - "node_modules/@typescript-eslint/typescript-estree/node_modules/semver": { - "version": "7.7.3", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz", - "integrity": "sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/@typescript-eslint/utils": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/utils/-/utils-8.35.1.tgz", - "integrity": "sha512-lhnwatFmOFcazAsUm3ZnZFpXSxiwoa1Lj50HphnDe1Et01NF4+hrdXONSUHIcbVu2eFb1bAf+5yjXkGVkXBKAQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "@eslint-community/eslint-utils": "^4.7.0", - "@typescript-eslint/scope-manager": "8.35.1", - "@typescript-eslint/types": "8.35.1", - "@typescript-eslint/typescript-estree": "8.35.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "eslint": "^8.57.0 || ^9.0.0", - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/@typescript-eslint/visitor-keys": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/@typescript-eslint/visitor-keys/-/visitor-keys-8.35.1.tgz", - "integrity": "sha512-VRwixir4zBWCSTP/ljEo091lbpypz57PoeAQ9imjG+vbeof9LplljsL1mos4ccG6H9IjfrVGM359RozUnuFhpw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/types": "8.35.1", - "eslint-visitor-keys": "^4.2.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - } - }, - "node_modules/acorn": { - "version": "8.15.0", - "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz", - "integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==", - "dev": true, - "license": "MIT", - "bin": { - "acorn": "bin/acorn" - }, - "engines": { - "node": ">=0.4.0" - } - }, - "node_modules/acorn-jsx": { - "version": "5.3.2", - "resolved": "https://registry.npmjs.org/acorn-jsx/-/acorn-jsx-5.3.2.tgz", - "integrity": "sha512-rq9s+JNhf0IChjtDXxllJ7g41oZk5SlXtp0LHwyA5cejwn7vKmKp4pPri6YEePv2PU65sAsegbXtIinmDFDXgQ==", - "dev": true, - "license": "MIT", - "peerDependencies": { - "acorn": "^6.0.0 || ^7.0.0 || ^8.0.0" - } - }, - "node_modules/ajv": { - "version": "6.12.6", - "resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz", - "integrity": "sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==", - "dev": true, - "license": "MIT", - "dependencies": { - "fast-deep-equal": "^3.1.1", - "fast-json-stable-stringify": "^2.0.0", - "json-schema-traverse": "^0.4.1", - "uri-js": "^4.2.2" - }, - "funding": { - "type": "github", - "url": "https://github.com/sponsors/epoberezkin" - } - }, - "node_modules/ansi-styles": { - "version": "4.3.0", - "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz", - "integrity": "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==", - "dev": true, - "license": "MIT", - "dependencies": { - "color-convert": "^2.0.1" - }, - "engines": { - "node": ">=8" - }, - "funding": { - "url": "https://github.com/chalk/ansi-styles?sponsor=1" - } - }, - "node_modules/argparse": { - "version": "2.0.1", - "resolved": "https://registry.npmjs.org/argparse/-/argparse-2.0.1.tgz", - "integrity": "sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q==", - "dev": true, - "license": "Python-2.0" - }, - "node_modules/array-buffer-byte-length": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/array-buffer-byte-length/-/array-buffer-byte-length-1.0.2.tgz", - "integrity": "sha512-LHE+8BuR7RYGDKvnrmcuSq3tDcKv9OFEXQt/HpbZhY7V6h0zlUXutnAD82GiFx9rdieCMjkvtcsPqBwgUl1Iiw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "is-array-buffer": "^3.0.5" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/array-includes": { - "version": "3.1.9", - "resolved": "https://registry.npmjs.org/array-includes/-/array-includes-3.1.9.tgz", - "integrity": "sha512-FmeCCAenzH0KH381SPT5FZmiA/TmpndpcaShhfgEN9eCVjnFBqq3l1xrI42y8+PPLI6hypzou4GXw00WHmPBLQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.4", - "define-properties": "^1.2.1", - "es-abstract": "^1.24.0", - "es-object-atoms": "^1.1.1", - "get-intrinsic": "^1.3.0", - "is-string": "^1.1.1", - "math-intrinsics": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/array.prototype.findlast": { - "version": "1.2.5", - "resolved": "https://registry.npmjs.org/array.prototype.findlast/-/array.prototype.findlast-1.2.5.tgz", - "integrity": "sha512-CVvd6FHg1Z3POpBLxO6E6zr+rSKEQ9L6rZHAaY7lLfhKsWYUBBOuMs0e9o24oopj6H+geRCX0YJ+TJLBK2eHyQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.7", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.2", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.0.0", - "es-shim-unscopables": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/array.prototype.findlastindex": { - "version": "1.2.6", - "resolved": "https://registry.npmjs.org/array.prototype.findlastindex/-/array.prototype.findlastindex-1.2.6.tgz", - "integrity": "sha512-F/TKATkzseUExPlfvmwQKGITM3DGTK+vkAsCZoDc5daVygbJBnjEUCbgkAvVFsgfXfX4YIqZ/27G3k3tdXrTxQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.4", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.9", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.1.1", - "es-shim-unscopables": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/array.prototype.flat": { - "version": "1.3.3", - "resolved": "https://registry.npmjs.org/array.prototype.flat/-/array.prototype.flat-1.3.3.tgz", - "integrity": "sha512-rwG/ja1neyLqCuGZ5YYrznA62D4mZXg0i1cIskIUKSiqF3Cje9/wXAls9B9s1Wa2fomMsIv8czB8jZcPmxCXFg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.5", - "es-shim-unscopables": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/array.prototype.flatmap": { - "version": "1.3.3", - "resolved": "https://registry.npmjs.org/array.prototype.flatmap/-/array.prototype.flatmap-1.3.3.tgz", - "integrity": "sha512-Y7Wt51eKJSyi80hFrJCePGGNo5ktJCslFuboqJsbf57CCPcm5zztluPlc4/aD8sWsKvlwatezpV4U1efk8kpjg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.5", - "es-shim-unscopables": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/array.prototype.tosorted": { - "version": "1.1.4", - "resolved": "https://registry.npmjs.org/array.prototype.tosorted/-/array.prototype.tosorted-1.1.4.tgz", - "integrity": "sha512-p6Fx8B7b7ZhL/gmUsAy0D15WhvDccw3mnGNbZpi3pmeJdxtWsj2jEaI4Y6oo3XiHfzuSgPwKc04MYt6KgvC/wA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.7", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.3", - "es-errors": "^1.3.0", - "es-shim-unscopables": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/arraybuffer.prototype.slice": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/arraybuffer.prototype.slice/-/arraybuffer.prototype.slice-1.0.4.tgz", - "integrity": "sha512-BNoCY6SXXPQ7gF2opIP4GBE+Xw7U+pHMYKuzjgCN3GwiaIR09UUeKfheyIry77QtrCBlC0KK0q5/TER/tYh3PQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "array-buffer-byte-length": "^1.0.1", - "call-bind": "^1.0.8", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.5", - "es-errors": "^1.3.0", - "get-intrinsic": "^1.2.6", - "is-array-buffer": "^3.0.4" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/async-function": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/async-function/-/async-function-1.0.0.tgz", - "integrity": "sha512-hsU18Ae8CDTR6Kgu9DYf0EbCr/a5iGL0rytQDobUcdpYOKokk8LEjVphnXkDkgpi0wYVsqrXuP0bZxJaTqdgoA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/available-typed-arrays": { - "version": "1.0.7", - "resolved": "https://registry.npmjs.org/available-typed-arrays/-/available-typed-arrays-1.0.7.tgz", - "integrity": "sha512-wvUjBtSGN7+7SjNpq/9M2Tg350UZD3q62IFZLbRAR1bSMlCo1ZaeW+BJ+D090e4hIIZLBcTDWe4Mh4jvUDajzQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "possible-typed-array-names": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/balanced-match": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.2.tgz", - "integrity": "sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==", - "dev": true, - "license": "MIT" - }, - "node_modules/brace-expansion": { - "version": "1.1.12", - "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz", - "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==", - "dev": true, - "license": "MIT", - "dependencies": { - "balanced-match": "^1.0.0", - "concat-map": "0.0.1" - } - }, - "node_modules/braces": { - "version": "3.0.3", - "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz", - "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==", - "dev": true, - "license": "MIT", - "dependencies": { - "fill-range": "^7.1.1" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/call-bind": { - "version": "1.0.8", - "resolved": "https://registry.npmjs.org/call-bind/-/call-bind-1.0.8.tgz", - "integrity": "sha512-oKlSFMcMwpUg2ednkhQ454wfWiU/ul3CkJe/PEHcTKuiX6RpbehUiFMXu13HalGZxfUwCQzZG747YXBn1im9ww==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind-apply-helpers": "^1.0.0", - "es-define-property": "^1.0.0", - "get-intrinsic": "^1.2.4", - "set-function-length": "^1.2.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/call-bind-apply-helpers": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/call-bind-apply-helpers/-/call-bind-apply-helpers-1.0.2.tgz", - "integrity": "sha512-Sp1ablJ0ivDkSzjcaJdxEunN5/XvksFJ2sMBFfq6x0ryhQV/2b/KwFe21cMpmHtPOSij8K99/wSfoEuTObmuMQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "function-bind": "^1.1.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/call-bound": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/call-bound/-/call-bound-1.0.4.tgz", - "integrity": "sha512-+ys997U96po4Kx/ABpBCqhA9EuxJaQWDQg7295H4hBphv3IZg0boBKuwYpt4YXp6MZ5AmZQnU/tyMTlRpaSejg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind-apply-helpers": "^1.0.2", - "get-intrinsic": "^1.3.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/callsites": { - "version": "3.1.0", - "resolved": "https://registry.npmjs.org/callsites/-/callsites-3.1.0.tgz", - "integrity": "sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6" - } - }, - "node_modules/chalk": { - "version": "4.1.2", - "resolved": "https://registry.npmjs.org/chalk/-/chalk-4.1.2.tgz", - "integrity": "sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==", - "dev": true, - "license": "MIT", - "dependencies": { - "ansi-styles": "^4.1.0", - "supports-color": "^7.1.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/chalk/chalk?sponsor=1" - } - }, - "node_modules/color-convert": { - "version": "2.0.1", - "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz", - "integrity": "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "color-name": "~1.1.4" - }, - "engines": { - "node": ">=7.0.0" - } - }, - "node_modules/color-name": { - "version": "1.1.4", - "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz", - "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==", - "dev": true, - "license": "MIT" - }, - "node_modules/concat-map": { - "version": "0.0.1", - "resolved": "https://registry.npmjs.org/concat-map/-/concat-map-0.0.1.tgz", - "integrity": "sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==", - "dev": true, - "license": "MIT" - }, - "node_modules/crelt": { - "version": "1.0.6", - "resolved": "https://registry.npmjs.org/crelt/-/crelt-1.0.6.tgz", - "integrity": "sha512-VQ2MBenTq1fWZUH9DJNGti7kKv6EeAuYr3cLwxUWhIu1baTaXh4Ib5W2CqHVqib4/MqbYGJqiL3Zb8GJZr3l4g==", - "license": "MIT", - "peer": true - }, - "node_modules/cross-spawn": { - "version": "7.0.6", - "resolved": "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.6.tgz", - "integrity": "sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==", - "dev": true, - "license": "MIT", - "dependencies": { - "path-key": "^3.1.0", - "shebang-command": "^2.0.0", - "which": "^2.0.1" - }, - "engines": { - "node": ">= 8" - } - }, - "node_modules/data-view-buffer": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/data-view-buffer/-/data-view-buffer-1.0.2.tgz", - "integrity": "sha512-EmKO5V3OLXh1rtK2wgXRansaK1/mtVdTUEiEI0W8RkvgT05kfxaH29PliLnpLP73yYO6142Q72QNa8Wx/A5CqQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "es-errors": "^1.3.0", - "is-data-view": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/data-view-byte-length": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/data-view-byte-length/-/data-view-byte-length-1.0.2.tgz", - "integrity": "sha512-tuhGbE6CfTM9+5ANGf+oQb72Ky/0+s3xKUpHvShfiz2RxMFgFPjsXuRLBVMtvMs15awe45SRb83D6wH4ew6wlQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "es-errors": "^1.3.0", - "is-data-view": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/inspect-js" - } - }, - "node_modules/data-view-byte-offset": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/data-view-byte-offset/-/data-view-byte-offset-1.0.1.tgz", - "integrity": "sha512-BS8PfmtDGnrgYdOonGZQdLZslWIeCGFP9tpan0hi1Co2Zr2NKADsvGYA8XxuG/4UWgJ6Cjtv+YJnB6MM69QGlQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "es-errors": "^1.3.0", - "is-data-view": "^1.0.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/debug": { - "version": "4.4.3", - "resolved": "https://registry.npmjs.org/debug/-/debug-4.4.3.tgz", - "integrity": "sha512-RGwwWnwQvkVfavKVt22FGLw+xYSdzARwm0ru6DhTVA3umU5hZc28V3kO4stgYryrTlLpuvgI9GiijltAjNbcqA==", - "dev": true, - "license": "MIT", - "dependencies": { - "ms": "^2.1.3" - }, - "engines": { - "node": ">=6.0" - }, - "peerDependenciesMeta": { - "supports-color": { - "optional": true - } - } - }, - "node_modules/deep-is": { - "version": "0.1.4", - "resolved": "https://registry.npmjs.org/deep-is/-/deep-is-0.1.4.tgz", - "integrity": "sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/define-data-property": { - "version": "1.1.4", - "resolved": "https://registry.npmjs.org/define-data-property/-/define-data-property-1.1.4.tgz", - "integrity": "sha512-rBMvIzlpA8v6E+SJZoo++HAYqsLrkg7MSfIinMPFhmkorw7X+dOXVJQs+QT69zGkzMyfDnIMN2Wid1+NbL3T+A==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-define-property": "^1.0.0", - "es-errors": "^1.3.0", - "gopd": "^1.0.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/define-properties": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/define-properties/-/define-properties-1.2.1.tgz", - "integrity": "sha512-8QmQKqEASLd5nx0U1B1okLElbUuuttJ/AnYmRXbbbGDWh6uS208EjD4Xqq/I9wK7u0v6O08XhTWnt5XtEbR6Dg==", - "dev": true, - "license": "MIT", - "dependencies": { - "define-data-property": "^1.0.1", - "has-property-descriptors": "^1.0.0", - "object-keys": "^1.1.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/doctrine": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/doctrine/-/doctrine-2.1.0.tgz", - "integrity": "sha512-35mSku4ZXK0vfCuHEDAwt55dg2jNajHZ1odvF+8SSr82EsZY4QmXfuWso8oEd8zRhVObSN18aM0CjSdoBX7zIw==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "esutils": "^2.0.2" - }, - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/dunder-proto": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/dunder-proto/-/dunder-proto-1.0.1.tgz", - "integrity": "sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind-apply-helpers": "^1.0.1", - "es-errors": "^1.3.0", - "gopd": "^1.2.0" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/empathic": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/empathic/-/empathic-2.0.0.tgz", - "integrity": "sha512-i6UzDscO/XfAcNYD75CfICkmfLedpyPDdozrLMmQc5ORaQcdMoc21OnlEylMIqI7U8eniKrPMxxtj8k0vhmJhA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=14" - } - }, - "node_modules/enhanced-resolve": { - "version": "5.18.3", - "resolved": "https://registry.npmjs.org/enhanced-resolve/-/enhanced-resolve-5.18.3.tgz", - "integrity": "sha512-d4lC8xfavMeBjzGr2vECC3fsGXziXZQyJxD868h2M/mBI3PwAuODxAkLkq5HYuvrPYcUtiLzsTo8U3PgX3Ocww==", - "dev": true, - "license": "MIT", - "dependencies": { - "graceful-fs": "^4.2.4", - "tapable": "^2.2.0" - }, - "engines": { - "node": ">=10.13.0" - } - }, - "node_modules/es-abstract": { - "version": "1.24.0", - "resolved": "https://registry.npmjs.org/es-abstract/-/es-abstract-1.24.0.tgz", - "integrity": "sha512-WSzPgsdLtTcQwm4CROfS5ju2Wa1QQcVeT37jFjYzdFz1r9ahadC8B8/a4qxJxM+09F18iumCdRmlr96ZYkQvEg==", - "dev": true, - "license": "MIT", - "dependencies": { - "array-buffer-byte-length": "^1.0.2", - "arraybuffer.prototype.slice": "^1.0.4", - "available-typed-arrays": "^1.0.7", - "call-bind": "^1.0.8", - "call-bound": "^1.0.4", - "data-view-buffer": "^1.0.2", - "data-view-byte-length": "^1.0.2", - "data-view-byte-offset": "^1.0.1", - "es-define-property": "^1.0.1", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.1.1", - "es-set-tostringtag": "^2.1.0", - "es-to-primitive": "^1.3.0", - "function.prototype.name": "^1.1.8", - "get-intrinsic": "^1.3.0", - "get-proto": "^1.0.1", - "get-symbol-description": "^1.1.0", - "globalthis": "^1.0.4", - "gopd": "^1.2.0", - "has-property-descriptors": "^1.0.2", - "has-proto": "^1.2.0", - "has-symbols": "^1.1.0", - "hasown": "^2.0.2", - "internal-slot": "^1.1.0", - "is-array-buffer": "^3.0.5", - "is-callable": "^1.2.7", - "is-data-view": "^1.0.2", - "is-negative-zero": "^2.0.3", - "is-regex": "^1.2.1", - "is-set": "^2.0.3", - "is-shared-array-buffer": "^1.0.4", - "is-string": "^1.1.1", - "is-typed-array": "^1.1.15", - "is-weakref": "^1.1.1", - "math-intrinsics": "^1.1.0", - "object-inspect": "^1.13.4", - "object-keys": "^1.1.1", - "object.assign": "^4.1.7", - "own-keys": "^1.0.1", - "regexp.prototype.flags": "^1.5.4", - "safe-array-concat": "^1.1.3", - "safe-push-apply": "^1.0.0", - "safe-regex-test": "^1.1.0", - "set-proto": "^1.0.0", - "stop-iteration-iterator": "^1.1.0", - "string.prototype.trim": "^1.2.10", - "string.prototype.trimend": "^1.0.9", - "string.prototype.trimstart": "^1.0.8", - "typed-array-buffer": "^1.0.3", - "typed-array-byte-length": "^1.0.3", - "typed-array-byte-offset": "^1.0.4", - "typed-array-length": "^1.0.7", - "unbox-primitive": "^1.1.0", - "which-typed-array": "^1.1.19" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/es-define-property": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.1.tgz", - "integrity": "sha512-e3nRfgfUZ4rNGL232gUgX06QNyyez04KdjFrF+LTRoOXmrOgFKDg4BCdsjW8EnT69eqdYGmRpJwiPVYNrCaW3g==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/es-errors": { - "version": "1.3.0", - "resolved": "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz", - "integrity": "sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/es-iterator-helpers": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/es-iterator-helpers/-/es-iterator-helpers-1.2.1.tgz", - "integrity": "sha512-uDn+FE1yrDzyC0pCo961B2IHbdM8y/ACZsKD4dG6WqrjV53BADjwa7D+1aom2rsNVfLyDgU/eigvlJGJ08OQ4w==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.3", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.6", - "es-errors": "^1.3.0", - "es-set-tostringtag": "^2.0.3", - "function-bind": "^1.1.2", - "get-intrinsic": "^1.2.6", - "globalthis": "^1.0.4", - "gopd": "^1.2.0", - "has-property-descriptors": "^1.0.2", - "has-proto": "^1.2.0", - "has-symbols": "^1.1.0", - "internal-slot": "^1.1.0", - "iterator.prototype": "^1.1.4", - "safe-array-concat": "^1.1.3" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/es-object-atoms": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/es-object-atoms/-/es-object-atoms-1.1.1.tgz", - "integrity": "sha512-FGgH2h8zKNim9ljj7dankFPcICIK9Cp5bm+c2gQSYePhpaG5+esrLODihIorn+Pe6FGJzWhXQotPv73jTaldXA==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/es-set-tostringtag": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/es-set-tostringtag/-/es-set-tostringtag-2.1.0.tgz", - "integrity": "sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "get-intrinsic": "^1.2.6", - "has-tostringtag": "^1.0.2", - "hasown": "^2.0.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/es-shim-unscopables": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/es-shim-unscopables/-/es-shim-unscopables-1.1.0.tgz", - "integrity": "sha512-d9T8ucsEhh8Bi1woXCf+TIKDIROLG5WCkxg8geBCbvk22kzwC5G2OnXVMO6FUsvQlgUUXQ2itephWDLqDzbeCw==", - "dev": true, - "license": "MIT", - "dependencies": { - "hasown": "^2.0.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/es-to-primitive": { - "version": "1.3.0", - "resolved": "https://registry.npmjs.org/es-to-primitive/-/es-to-primitive-1.3.0.tgz", - "integrity": "sha512-w+5mJ3GuFL+NjVtJlvydShqE1eN3h3PbI7/5LAsYJP/2qtuMXjfL2LpHSRqo4b4eSF5K/DH1JXKUAHSB2UW50g==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-callable": "^1.2.7", - "is-date-object": "^1.0.5", - "is-symbol": "^1.0.4" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/esbuild": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.25.5.tgz", - "integrity": "sha512-P8OtKZRv/5J5hhz0cUAdu/cLuPIKXpQl1R9pZtvmHWQvrAUVd0UNIPT4IB4W3rNOqVO0rlqHmCIbSwxh/c9yUQ==", - "dev": true, - "hasInstallScript": true, - "license": "MIT", - "bin": { - "esbuild": "bin/esbuild" - }, - "engines": { - "node": ">=18" - }, - "optionalDependencies": { - "@esbuild/aix-ppc64": "0.25.5", - "@esbuild/android-arm": "0.25.5", - "@esbuild/android-arm64": "0.25.5", - "@esbuild/android-x64": "0.25.5", - "@esbuild/darwin-arm64": "0.25.5", - "@esbuild/darwin-x64": "0.25.5", - "@esbuild/freebsd-arm64": "0.25.5", - "@esbuild/freebsd-x64": "0.25.5", - "@esbuild/linux-arm": "0.25.5", - "@esbuild/linux-arm64": "0.25.5", - "@esbuild/linux-ia32": "0.25.5", - "@esbuild/linux-loong64": "0.25.5", - "@esbuild/linux-mips64el": "0.25.5", - "@esbuild/linux-ppc64": "0.25.5", - "@esbuild/linux-riscv64": "0.25.5", - "@esbuild/linux-s390x": "0.25.5", - "@esbuild/linux-x64": "0.25.5", - "@esbuild/netbsd-arm64": "0.25.5", - "@esbuild/netbsd-x64": "0.25.5", - "@esbuild/openbsd-arm64": "0.25.5", - "@esbuild/openbsd-x64": "0.25.5", - "@esbuild/sunos-x64": "0.25.5", - "@esbuild/win32-arm64": "0.25.5", - "@esbuild/win32-ia32": "0.25.5", - "@esbuild/win32-x64": "0.25.5" - } - }, - "node_modules/escape-string-regexp": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-4.0.0.tgz", - "integrity": "sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/eslint": { - "version": "9.39.1", - "resolved": "https://registry.npmjs.org/eslint/-/eslint-9.39.1.tgz", - "integrity": "sha512-BhHmn2yNOFA9H9JmmIVKJmd288g9hrVRDkdoIgRCRuSySRUHH7r/DI6aAXW9T1WwUuY3DFgrcaqB+deURBLR5g==", - "dev": true, - "license": "MIT", - "dependencies": { - "@eslint-community/eslint-utils": "^4.8.0", - "@eslint-community/regexpp": "^4.12.1", - "@eslint/config-array": "^0.21.1", - "@eslint/config-helpers": "^0.4.2", - "@eslint/core": "^0.17.0", - "@eslint/eslintrc": "^3.3.1", - "@eslint/js": "9.39.1", - "@eslint/plugin-kit": "^0.4.1", - "@humanfs/node": "^0.16.6", - "@humanwhocodes/module-importer": "^1.0.1", - "@humanwhocodes/retry": "^0.4.2", - "@types/estree": "^1.0.6", - "ajv": "^6.12.4", - "chalk": "^4.0.0", - "cross-spawn": "^7.0.6", - "debug": "^4.3.2", - "escape-string-regexp": "^4.0.0", - "eslint-scope": "^8.4.0", - "eslint-visitor-keys": "^4.2.1", - "espree": "^10.4.0", - "esquery": "^1.5.0", - "esutils": "^2.0.2", - "fast-deep-equal": "^3.1.3", - "file-entry-cache": "^8.0.0", - "find-up": "^5.0.0", - "glob-parent": "^6.0.2", - "ignore": "^5.2.0", - "imurmurhash": "^0.1.4", - "is-glob": "^4.0.0", - "json-stable-stringify-without-jsonify": "^1.0.1", - "lodash.merge": "^4.6.2", - "minimatch": "^3.1.2", - "natural-compare": "^1.4.0", - "optionator": "^0.9.3" - }, - "bin": { - "eslint": "bin/eslint.js" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://eslint.org/donate" - }, - "peerDependencies": { - "jiti": "*" - }, - "peerDependenciesMeta": { - "jiti": { - "optional": true - } - } - }, - "node_modules/eslint-compat-utils": { - "version": "0.5.1", - "resolved": "https://registry.npmjs.org/eslint-compat-utils/-/eslint-compat-utils-0.5.1.tgz", - "integrity": "sha512-3z3vFexKIEnjHE3zCMRo6fn/e44U7T1khUjg+Hp0ZQMCigh28rALD0nPFBcGZuiLC5rLZa2ubQHDRln09JfU2Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "semver": "^7.5.4" - }, - "engines": { - "node": ">=12" - }, - "peerDependencies": { - "eslint": ">=6.0.0" - } - }, - "node_modules/eslint-compat-utils/node_modules/semver": { - "version": "7.7.3", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz", - "integrity": "sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/eslint-import-resolver-node": { - "version": "0.3.9", - "resolved": "https://registry.npmjs.org/eslint-import-resolver-node/-/eslint-import-resolver-node-0.3.9.tgz", - "integrity": "sha512-WFj2isz22JahUv+B788TlO3N6zL3nNJGU8CcZbPZvVEkBPaJdCV4vy5wyghty5ROFbCRnm132v8BScu5/1BQ8g==", - "dev": true, - "license": "MIT", - "dependencies": { - "debug": "^3.2.7", - "is-core-module": "^2.13.0", - "resolve": "^1.22.4" - } - }, - "node_modules/eslint-import-resolver-node/node_modules/debug": { - "version": "3.2.7", - "resolved": "https://registry.npmjs.org/debug/-/debug-3.2.7.tgz", - "integrity": "sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "ms": "^2.1.1" - } - }, - "node_modules/eslint-module-utils": { - "version": "2.12.1", - "resolved": "https://registry.npmjs.org/eslint-module-utils/-/eslint-module-utils-2.12.1.tgz", - "integrity": "sha512-L8jSWTze7K2mTg0vos/RuLRS5soomksDPoJLXIslC7c8Wmut3bx7CPpJijDcBZtxQ5lrbUdM+s0OlNbz0DCDNw==", - "dev": true, - "license": "MIT", - "dependencies": { - "debug": "^3.2.7" - }, - "engines": { - "node": ">=4" - }, - "peerDependenciesMeta": { - "eslint": { - "optional": true - } - } - }, - "node_modules/eslint-module-utils/node_modules/debug": { - "version": "3.2.7", - "resolved": "https://registry.npmjs.org/debug/-/debug-3.2.7.tgz", - "integrity": "sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "ms": "^2.1.1" - } - }, - "node_modules/eslint-plugin-depend": { - "version": "1.3.1", - "resolved": "https://registry.npmjs.org/eslint-plugin-depend/-/eslint-plugin-depend-1.3.1.tgz", - "integrity": "sha512-1uo2rFAr9vzNrCYdp7IBZRB54LiyVxfaIso0R6/QV3t6Dax6DTbW/EV2Hktf0f4UtmGHK8UyzJWI382pwW04jw==", - "dev": true, - "license": "MIT", - "dependencies": { - "empathic": "^2.0.0", - "module-replacements": "^2.8.0", - "semver": "^7.6.3" - } - }, - "node_modules/eslint-plugin-depend/node_modules/semver": { - "version": "7.7.3", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz", - "integrity": "sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/eslint-plugin-es-x": { - "version": "7.8.0", - "resolved": "https://registry.npmjs.org/eslint-plugin-es-x/-/eslint-plugin-es-x-7.8.0.tgz", - "integrity": "sha512-7Ds8+wAAoV3T+LAKeu39Y5BzXCrGKrcISfgKEqTS4BDN8SFEDQd0S43jiQ8vIa3wUKD07qitZdfzlenSi8/0qQ==", - "dev": true, - "funding": [ - "https://github.com/sponsors/ota-meshi", - "https://opencollective.com/eslint" - ], - "license": "MIT", - "dependencies": { - "@eslint-community/eslint-utils": "^4.1.2", - "@eslint-community/regexpp": "^4.11.0", - "eslint-compat-utils": "^0.5.1" - }, - "engines": { - "node": "^14.18.0 || >=16.0.0" - }, - "peerDependencies": { - "eslint": ">=8" - } - }, - "node_modules/eslint-plugin-import": { - "version": "2.32.0", - "resolved": "https://registry.npmjs.org/eslint-plugin-import/-/eslint-plugin-import-2.32.0.tgz", - "integrity": "sha512-whOE1HFo/qJDyX4SnXzP4N6zOWn79WhnCUY/iDR0mPfQZO8wcYE4JClzI2oZrhBnnMUCBCHZhO6VQyoBU95mZA==", - "dev": true, - "license": "MIT", - "dependencies": { - "@rtsao/scc": "^1.1.0", - "array-includes": "^3.1.9", - "array.prototype.findlastindex": "^1.2.6", - "array.prototype.flat": "^1.3.3", - "array.prototype.flatmap": "^1.3.3", - "debug": "^3.2.7", - "doctrine": "^2.1.0", - "eslint-import-resolver-node": "^0.3.9", - "eslint-module-utils": "^2.12.1", - "hasown": "^2.0.2", - "is-core-module": "^2.16.1", - "is-glob": "^4.0.3", - "minimatch": "^3.1.2", - "object.fromentries": "^2.0.8", - "object.groupby": "^1.0.3", - "object.values": "^1.2.1", - "semver": "^6.3.1", - "string.prototype.trimend": "^1.0.9", - "tsconfig-paths": "^3.15.0" - }, - "engines": { - "node": ">=4" - }, - "peerDependencies": { - "eslint": "^2 || ^3 || ^4 || ^5 || ^6 || ^7.2.0 || ^8 || ^9" - } - }, - "node_modules/eslint-plugin-import/node_modules/debug": { - "version": "3.2.7", - "resolved": "https://registry.npmjs.org/debug/-/debug-3.2.7.tgz", - "integrity": "sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "ms": "^2.1.1" - } - }, - "node_modules/eslint-plugin-json-schema-validator": { - "version": "5.1.0", - "resolved": "https://registry.npmjs.org/eslint-plugin-json-schema-validator/-/eslint-plugin-json-schema-validator-5.1.0.tgz", - "integrity": "sha512-ZmVyxRIjm58oqe2kTuy90PpmZPrrKvOjRPXKzq8WCgRgAkidCgm5X8domL2KSfadZ3QFAmifMgGTcVNhZ5ez2g==", - "dev": true, - "license": "MIT", - "dependencies": { - "@eslint-community/eslint-utils": "^4.3.0", - "ajv": "^8.0.0", - "debug": "^4.3.1", - "eslint-compat-utils": "^0.5.0", - "json-schema-migrate": "^2.0.0", - "jsonc-eslint-parser": "^2.0.0", - "minimatch": "^8.0.0", - "synckit": "^0.9.0", - "toml-eslint-parser": "^0.9.0", - "tunnel-agent": "^0.6.0", - "yaml-eslint-parser": "^1.0.0" - }, - "engines": { - "node": "^14.18.0 || >=16.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/ota-meshi" - }, - "peerDependencies": { - "eslint": ">=6.0.0" - } - }, - "node_modules/eslint-plugin-json-schema-validator/node_modules/ajv": { - "version": "8.17.1", - "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.17.1.tgz", - "integrity": "sha512-B/gBuNg5SiMTrPkC+A2+cW0RszwxYmn6VYxB/inlBStS5nx6xHIt/ehKRhIMhqusl7a8LjQoZnjCs5vhwxOQ1g==", - "dev": true, - "license": "MIT", - "dependencies": { - "fast-deep-equal": "^3.1.3", - "fast-uri": "^3.0.1", - "json-schema-traverse": "^1.0.0", - "require-from-string": "^2.0.2" - }, - "funding": { - "type": "github", - "url": "https://github.com/sponsors/epoberezkin" - } - }, - "node_modules/eslint-plugin-json-schema-validator/node_modules/brace-expansion": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz", - "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "balanced-match": "^1.0.0" - } - }, - "node_modules/eslint-plugin-json-schema-validator/node_modules/json-schema-traverse": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz", - "integrity": "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==", - "dev": true, - "license": "MIT" - }, - "node_modules/eslint-plugin-json-schema-validator/node_modules/minimatch": { - "version": "8.0.4", - "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-8.0.4.tgz", - "integrity": "sha512-W0Wvr9HyFXZRGIDgCicunpQ299OKXs9RgZfaukz4qAW/pJhcpUfupc9c+OObPOFueNy8VSrZgEmDtk6Kh4WzDA==", - "dev": true, - "license": "ISC", - "dependencies": { - "brace-expansion": "^2.0.1" - }, - "engines": { - "node": ">=16 || 14 >=14.17" - }, - "funding": { - "url": "https://github.com/sponsors/isaacs" - } - }, - "node_modules/eslint-plugin-n": { - "version": "17.10.3", - "resolved": "https://registry.npmjs.org/eslint-plugin-n/-/eslint-plugin-n-17.10.3.tgz", - "integrity": "sha512-ySZBfKe49nQZWR1yFaA0v/GsH6Fgp8ah6XV0WDz6CN8WO0ek4McMzb7A2xnf4DCYV43frjCygvb9f/wx7UUxRw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@eslint-community/eslint-utils": "^4.4.0", - "enhanced-resolve": "^5.17.0", - "eslint-plugin-es-x": "^7.5.0", - "get-tsconfig": "^4.7.0", - "globals": "^15.8.0", - "ignore": "^5.2.4", - "minimatch": "^9.0.5", - "semver": "^7.5.3" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - }, - "peerDependencies": { - "eslint": ">=8.23.0" - } - }, - "node_modules/eslint-plugin-n/node_modules/brace-expansion": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz", - "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "balanced-match": "^1.0.0" - } - }, - "node_modules/eslint-plugin-n/node_modules/globals": { - "version": "15.15.0", - "resolved": "https://registry.npmjs.org/globals/-/globals-15.15.0.tgz", - "integrity": "sha512-7ACyT3wmyp3I61S4fG682L0VA2RGD9otkqGJIwNUMF1SWUombIIk+af1unuDYgMm082aHYwD+mzJvv9Iu8dsgg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=18" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/eslint-plugin-n/node_modules/minimatch": { - "version": "9.0.5", - "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-9.0.5.tgz", - "integrity": "sha512-G6T0ZX48xgozx7587koeX9Ys2NYy6Gmv//P89sEte9V9whIapMNF4idKxnW2QtCcLiTWlb/wfCabAtAFWhhBow==", - "dev": true, - "license": "ISC", - "dependencies": { - "brace-expansion": "^2.0.1" - }, - "engines": { - "node": ">=16 || 14 >=14.17" - }, - "funding": { - "url": "https://github.com/sponsors/isaacs" - } - }, - "node_modules/eslint-plugin-n/node_modules/semver": { - "version": "7.7.3", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz", - "integrity": "sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/eslint-plugin-obsidianmd": { - "version": "0.1.9", - "resolved": "https://registry.npmjs.org/eslint-plugin-obsidianmd/-/eslint-plugin-obsidianmd-0.1.9.tgz", - "integrity": "sha512-/gyo5vky3Y7re4BtT/8MQbHU5Wes4o6VRqas3YmXE7aTCnMsdV0kfzV1GDXJN9Hrsc9UQPoeKUMiapKL0aGE4g==", - "dev": true, - "license": "MIT", - "dependencies": { - "@microsoft/eslint-plugin-sdl": "^1.1.0", - "@types/eslint": "8.56.2", - "@types/node": "20.12.12", - "eslint": ">=9.0.0 <10.0.0", - "eslint-plugin-depend": "1.3.1", - "eslint-plugin-import": "^2.31.0", - "eslint-plugin-json-schema-validator": "5.1.0", - "eslint-plugin-security": "2.1.1", - "globals": "14.0.0", - "obsidian": "1.8.7", - "typescript": "5.4.5" - }, - "bin": { - "eslint-plugin-obsidian": "dist/lib/index.js" - }, - "engines": { - "node": ">= 18" - }, - "peerDependencies": { - "@eslint/js": "^9.30.1", - "@eslint/json": "0.14.0", - "eslint": ">=9.0.0 <10.0.0", - "obsidian": "1.8.7", - "typescript-eslint": "^8.35.1" - } - }, - "node_modules/eslint-plugin-obsidianmd/node_modules/@types/node": { - "version": "20.12.12", - "resolved": "https://registry.npmjs.org/@types/node/-/node-20.12.12.tgz", - "integrity": "sha512-eWLDGF/FOSPtAvEqeRAQ4C8LSA7M1I7i0ky1I8U7kD1J5ITyW3AsRhQrKVoWf5pFKZ2kILsEGJhsI9r93PYnOw==", - "dev": true, - "license": "MIT", - "dependencies": { - "undici-types": "~5.26.4" - } - }, - "node_modules/eslint-plugin-obsidianmd/node_modules/obsidian": { - "version": "1.8.7", - "resolved": "https://registry.npmjs.org/obsidian/-/obsidian-1.8.7.tgz", - "integrity": "sha512-h4bWwNFAGRXlMlMAzdEiIM2ppTGlrh7uGOJS6w4gClrsjc+ei/3YAtU2VdFUlCiPuTHpY4aBpFJJW75S1Tl/JA==", - "dev": true, - "license": "MIT", - "dependencies": { - "@types/codemirror": "5.60.8", - "moment": "2.29.4" - }, - "peerDependencies": { - "@codemirror/state": "^6.0.0", - "@codemirror/view": "^6.0.0" - } - }, - "node_modules/eslint-plugin-obsidianmd/node_modules/typescript": { - "version": "5.4.5", - "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.4.5.tgz", - "integrity": "sha512-vcI4UpRgg81oIRUFwR0WSIHKt11nJ7SAVlYNIu+QpqeyXP+gpQJy/Z4+F0aGxSE4MqwjyXvW/TzgkLAx2AGHwQ==", - "dev": true, - "license": "Apache-2.0", - "bin": { - "tsc": "bin/tsc", - "tsserver": "bin/tsserver" - }, - "engines": { - "node": ">=14.17" - } - }, - "node_modules/eslint-plugin-react": { - "version": "7.37.3", - "resolved": "https://registry.npmjs.org/eslint-plugin-react/-/eslint-plugin-react-7.37.3.tgz", - "integrity": "sha512-DomWuTQPFYZwF/7c9W2fkKkStqZmBd3uugfqBYLdkZ3Hii23WzZuOLUskGxB8qkSKqftxEeGL1TB2kMhrce0jA==", - "dev": true, - "license": "MIT", - "dependencies": { - "array-includes": "^3.1.8", - "array.prototype.findlast": "^1.2.5", - "array.prototype.flatmap": "^1.3.3", - "array.prototype.tosorted": "^1.1.4", - "doctrine": "^2.1.0", - "es-iterator-helpers": "^1.2.1", - "estraverse": "^5.3.0", - "hasown": "^2.0.2", - "jsx-ast-utils": "^2.4.1 || ^3.0.0", - "minimatch": "^3.1.2", - "object.entries": "^1.1.8", - "object.fromentries": "^2.0.8", - "object.values": "^1.2.1", - "prop-types": "^15.8.1", - "resolve": "^2.0.0-next.5", - "semver": "^6.3.1", - "string.prototype.matchall": "^4.0.12", - "string.prototype.repeat": "^1.0.0" - }, - "engines": { - "node": ">=4" - }, - "peerDependencies": { - "eslint": "^3 || ^4 || ^5 || ^6 || ^7 || ^8 || ^9.7" - } - }, - "node_modules/eslint-plugin-react/node_modules/resolve": { - "version": "2.0.0-next.5", - "resolved": "https://registry.npmjs.org/resolve/-/resolve-2.0.0-next.5.tgz", - "integrity": "sha512-U7WjGVG9sH8tvjW5SmGbQuui75FiyjAX72HX15DwBBwF9dNiQZRQAg9nnPhYy+TUnE0+VcrttuvNI8oSxZcocA==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-core-module": "^2.13.0", - "path-parse": "^1.0.7", - "supports-preserve-symlinks-flag": "^1.0.0" - }, - "bin": { - "resolve": "bin/resolve" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/eslint-plugin-security": { - "version": "2.1.1", - "resolved": "https://registry.npmjs.org/eslint-plugin-security/-/eslint-plugin-security-2.1.1.tgz", - "integrity": "sha512-7cspIGj7WTfR3EhaILzAPcfCo5R9FbeWvbgsPYWivSurTBKW88VQxtP3c4aWMG9Hz/GfJlJVdXEJ3c8LqS+u2w==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "safe-regex": "^2.1.1" - } - }, - "node_modules/eslint-scope": { - "version": "8.4.0", - "resolved": "https://registry.npmjs.org/eslint-scope/-/eslint-scope-8.4.0.tgz", - "integrity": "sha512-sNXOfKCn74rt8RICKMvJS7XKV/Xk9kA7DyJr8mJik3S7Cwgy3qlkkmyS2uQB3jiJg6VNdZd/pDBJu0nvG2NlTg==", - "dev": true, - "license": "BSD-2-Clause", - "dependencies": { - "esrecurse": "^4.3.0", - "estraverse": "^5.2.0" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/eslint-visitor-keys": { - "version": "4.2.1", - "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-4.2.1.tgz", - "integrity": "sha512-Uhdk5sfqcee/9H/rCOJikYz67o0a2Tw2hGRPOG2Y1R2dg7brRe1uG0yaNQDHu+TO/uQPF/5eCapvYSmHUjt7JQ==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/eslint/node_modules/@eslint/js": { - "version": "9.39.1", - "resolved": "https://registry.npmjs.org/@eslint/js/-/js-9.39.1.tgz", - "integrity": "sha512-S26Stp4zCy88tH94QbBv3XCuzRQiZ9yXofEILmglYTh/Ug/a9/umqvgFtYBAo3Lp0nsI/5/qH1CCrbdK3AP1Tw==", - "dev": true, - "license": "MIT", - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://eslint.org/donate" - } - }, - "node_modules/espree": { - "version": "10.4.0", - "resolved": "https://registry.npmjs.org/espree/-/espree-10.4.0.tgz", - "integrity": "sha512-j6PAQ2uUr79PZhBjP5C5fhl8e39FmRnOjsD5lGnWrFU8i2G776tBK7+nP8KuQUTTyAZUwfQqXAgrVH5MbH9CYQ==", - "dev": true, - "license": "BSD-2-Clause", - "dependencies": { - "acorn": "^8.15.0", - "acorn-jsx": "^5.3.2", - "eslint-visitor-keys": "^4.2.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/esquery": { - "version": "1.6.0", - "resolved": "https://registry.npmjs.org/esquery/-/esquery-1.6.0.tgz", - "integrity": "sha512-ca9pw9fomFcKPvFLXhBKUK90ZvGibiGOvRJNbjljY7s7uq/5YO4BOzcYtJqExdx99rF6aAcnRxHmcUHcz6sQsg==", - "dev": true, - "license": "BSD-3-Clause", - "dependencies": { - "estraverse": "^5.1.0" - }, - "engines": { - "node": ">=0.10" - } - }, - "node_modules/esrecurse": { - "version": "4.3.0", - "resolved": "https://registry.npmjs.org/esrecurse/-/esrecurse-4.3.0.tgz", - "integrity": "sha512-KmfKL3b6G+RXvP8N1vr3Tq1kL/oCFgn2NYXEtqP8/L3pKapUA4G8cFVaoF3SU323CD4XypR/ffioHmkti6/Tag==", - "dev": true, - "license": "BSD-2-Clause", - "dependencies": { - "estraverse": "^5.2.0" - }, - "engines": { - "node": ">=4.0" - } - }, - "node_modules/estraverse": { - "version": "5.3.0", - "resolved": "https://registry.npmjs.org/estraverse/-/estraverse-5.3.0.tgz", - "integrity": "sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==", - "dev": true, - "license": "BSD-2-Clause", - "engines": { - "node": ">=4.0" - } - }, - "node_modules/esutils": { - "version": "2.0.3", - "resolved": "https://registry.npmjs.org/esutils/-/esutils-2.0.3.tgz", - "integrity": "sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==", - "dev": true, - "license": "BSD-2-Clause", - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/fast-deep-equal": { - "version": "3.1.3", - "resolved": "https://registry.npmjs.org/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz", - "integrity": "sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==", - "dev": true, - "license": "MIT" - }, - "node_modules/fast-glob": { - "version": "3.3.3", - "resolved": "https://registry.npmjs.org/fast-glob/-/fast-glob-3.3.3.tgz", - "integrity": "sha512-7MptL8U0cqcFdzIzwOTHoilX9x5BrNqye7Z/LuC7kCMRio1EMSyqRK3BEAUD7sXRq4iT4AzTVuZdhgQ2TCvYLg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@nodelib/fs.stat": "^2.0.2", - "@nodelib/fs.walk": "^1.2.3", - "glob-parent": "^5.1.2", - "merge2": "^1.3.0", - "micromatch": "^4.0.8" - }, - "engines": { - "node": ">=8.6.0" - } - }, - "node_modules/fast-glob/node_modules/glob-parent": { - "version": "5.1.2", - "resolved": "https://registry.npmjs.org/glob-parent/-/glob-parent-5.1.2.tgz", - "integrity": "sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==", - "dev": true, - "license": "ISC", - "dependencies": { - "is-glob": "^4.0.1" - }, - "engines": { - "node": ">= 6" - } - }, - "node_modules/fast-json-stable-stringify": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz", - "integrity": "sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw==", - "dev": true, - "license": "MIT" - }, - "node_modules/fast-levenshtein": { - "version": "2.0.6", - "resolved": "https://registry.npmjs.org/fast-levenshtein/-/fast-levenshtein-2.0.6.tgz", - "integrity": "sha512-DCXu6Ifhqcks7TZKY3Hxp3y6qphY5SJZmrWMDrKcERSOXWQdMhU9Ig/PYrzyw/ul9jOIyh0N4M0tbC5hodg8dw==", - "dev": true, - "license": "MIT" - }, - "node_modules/fast-uri": { - "version": "3.1.0", - "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.0.tgz", - "integrity": "sha512-iPeeDKJSWf4IEOasVVrknXpaBV0IApz/gp7S2bb7Z4Lljbl2MGJRqInZiUrQwV16cpzw/D3S5j5Julj/gT52AA==", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/fastify" - }, - { - "type": "opencollective", - "url": "https://opencollective.com/fastify" - } - ], - "license": "BSD-3-Clause" - }, - "node_modules/fastq": { - "version": "1.19.1", - "resolved": "https://registry.npmjs.org/fastq/-/fastq-1.19.1.tgz", - "integrity": "sha512-GwLTyxkCXjXbxqIhTsMI2Nui8huMPtnxg7krajPJAjnEG/iiOS7i+zCtWGZR9G0NBKbXKh6X9m9UIsYX/N6vvQ==", - "dev": true, - "license": "ISC", - "dependencies": { - "reusify": "^1.0.4" - } - }, - "node_modules/file-entry-cache": { - "version": "8.0.0", - "resolved": "https://registry.npmjs.org/file-entry-cache/-/file-entry-cache-8.0.0.tgz", - "integrity": "sha512-XXTUwCvisa5oacNGRP9SfNtYBNAMi+RPwBFmblZEF7N7swHYQS6/Zfk7SRwx4D5j3CH211YNRco1DEMNVfZCnQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "flat-cache": "^4.0.0" - }, - "engines": { - "node": ">=16.0.0" - } - }, - "node_modules/fill-range": { - "version": "7.1.1", - "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz", - "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==", - "dev": true, - "license": "MIT", - "dependencies": { - "to-regex-range": "^5.0.1" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/find-up": { - "version": "5.0.0", - "resolved": "https://registry.npmjs.org/find-up/-/find-up-5.0.0.tgz", - "integrity": "sha512-78/PXT1wlLLDgTzDs7sjq9hzz0vXD+zn+7wypEe4fXQxCmdmqfGsEPQxmiCSQI3ajFV91bVSsvNtrJRiW6nGng==", - "dev": true, - "license": "MIT", - "dependencies": { - "locate-path": "^6.0.0", - "path-exists": "^4.0.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/flat-cache": { - "version": "4.0.1", - "resolved": "https://registry.npmjs.org/flat-cache/-/flat-cache-4.0.1.tgz", - "integrity": "sha512-f7ccFPK3SXFHpx15UIGyRJ/FJQctuKZ0zVuN3frBo4HnK3cay9VEW0R6yPYFHC0AgqhukPzKjq22t5DmAyqGyw==", - "dev": true, - "license": "MIT", - "dependencies": { - "flatted": "^3.2.9", - "keyv": "^4.5.4" - }, - "engines": { - "node": ">=16" - } - }, - "node_modules/flatted": { - "version": "3.3.3", - "resolved": "https://registry.npmjs.org/flatted/-/flatted-3.3.3.tgz", - "integrity": "sha512-GX+ysw4PBCz0PzosHDepZGANEuFCMLrnRTiEy9McGjmkCQYwRq4A/X786G/fjM/+OjsWSU1ZrY5qyARZmO/uwg==", - "dev": true, - "license": "ISC" - }, - "node_modules/for-each": { - "version": "0.3.5", - "resolved": "https://registry.npmjs.org/for-each/-/for-each-0.3.5.tgz", - "integrity": "sha512-dKx12eRCVIzqCxFGplyFKJMPvLEWgmNtUrpTiJIR5u97zEhRG8ySrtboPHZXx7daLxQVrl643cTzbab2tkQjxg==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-callable": "^1.2.7" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/function-bind": { - "version": "1.1.2", - "resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.2.tgz", - "integrity": "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==", - "dev": true, - "license": "MIT", - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/function.prototype.name": { - "version": "1.1.8", - "resolved": "https://registry.npmjs.org/function.prototype.name/-/function.prototype.name-1.1.8.tgz", - "integrity": "sha512-e5iwyodOHhbMr/yNrc7fDYG4qlbIvI5gajyzPnb5TCwyhjApznQh1BMFou9b30SevY43gCJKXycoCBjMbsuW0Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.3", - "define-properties": "^1.2.1", - "functions-have-names": "^1.2.3", - "hasown": "^2.0.2", - "is-callable": "^1.2.7" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/functions-have-names": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/functions-have-names/-/functions-have-names-1.2.3.tgz", - "integrity": "sha512-xckBUXyTIqT97tq2x2AMb+g163b5JFysYk0x4qxNFwbfQkmNZoiRHb6sPzI9/QV33WeuvVYBUIiD4NzNIyqaRQ==", - "dev": true, - "license": "MIT", - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/generator-function": { - "version": "2.0.1", - "resolved": "https://registry.npmjs.org/generator-function/-/generator-function-2.0.1.tgz", - "integrity": "sha512-SFdFmIJi+ybC0vjlHN0ZGVGHc3lgE0DxPAT0djjVg+kjOnSqclqmj0KQ7ykTOLP6YxoqOvuAODGdcHJn+43q3g==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/get-intrinsic": { - "version": "1.3.0", - "resolved": "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.3.0.tgz", - "integrity": "sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind-apply-helpers": "^1.0.2", - "es-define-property": "^1.0.1", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.1.1", - "function-bind": "^1.1.2", - "get-proto": "^1.0.1", - "gopd": "^1.2.0", - "has-symbols": "^1.1.0", - "hasown": "^2.0.2", - "math-intrinsics": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/get-proto": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/get-proto/-/get-proto-1.0.1.tgz", - "integrity": "sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==", - "dev": true, - "license": "MIT", - "dependencies": { - "dunder-proto": "^1.0.1", - "es-object-atoms": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/get-symbol-description": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/get-symbol-description/-/get-symbol-description-1.1.0.tgz", - "integrity": "sha512-w9UMqWwJxHNOvoNzSJ2oPF5wvYcvP7jUvYzhp67yEhTi17ZDBBC1z9pTdGuzjD+EFIqLSYRweZjqfiPzQ06Ebg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "es-errors": "^1.3.0", - "get-intrinsic": "^1.2.6" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/get-tsconfig": { - "version": "4.13.0", - "resolved": "https://registry.npmjs.org/get-tsconfig/-/get-tsconfig-4.13.0.tgz", - "integrity": "sha512-1VKTZJCwBrvbd+Wn3AOgQP/2Av+TfTCOlE4AcRJE72W1ksZXbAx8PPBR9RzgTeSPzlPMHrbANMH3LbltH73wxQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "resolve-pkg-maps": "^1.0.0" - }, - "funding": { - "url": "https://github.com/privatenumber/get-tsconfig?sponsor=1" - } - }, - "node_modules/glob-parent": { - "version": "6.0.2", - "resolved": "https://registry.npmjs.org/glob-parent/-/glob-parent-6.0.2.tgz", - "integrity": "sha512-XxwI8EOhVQgWp6iDL+3b0r86f4d6AX6zSU55HfB4ydCEuXLXc5FcYeOu+nnGftS4TEju/11rt4KJPTMgbfmv4A==", - "dev": true, - "license": "ISC", - "dependencies": { - "is-glob": "^4.0.3" - }, - "engines": { - "node": ">=10.13.0" - } - }, - "node_modules/globals": { - "version": "14.0.0", - "resolved": "https://registry.npmjs.org/globals/-/globals-14.0.0.tgz", - "integrity": "sha512-oahGvuMGQlPw/ivIYBjVSrWAfWLBeku5tpPE2fOPLi+WHffIWbuh2tCjhyQhTBPMf5E9jDEH4FOmTYgYwbKwtQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=18" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/globalthis": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/globalthis/-/globalthis-1.0.4.tgz", - "integrity": "sha512-DpLKbNU4WylpxJykQujfCcwYWiV/Jhm50Goo0wrVILAv5jOr9d+H+UR3PhSCD2rCCEIg0uc+G+muBTwD54JhDQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "define-properties": "^1.2.1", - "gopd": "^1.0.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/gopd": { - "version": "1.2.0", - "resolved": "https://registry.npmjs.org/gopd/-/gopd-1.2.0.tgz", - "integrity": "sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/graceful-fs": { - "version": "4.2.11", - "resolved": "https://registry.npmjs.org/graceful-fs/-/graceful-fs-4.2.11.tgz", - "integrity": "sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==", - "dev": true, - "license": "ISC" - }, - "node_modules/graphemer": { - "version": "1.4.0", - "resolved": "https://registry.npmjs.org/graphemer/-/graphemer-1.4.0.tgz", - "integrity": "sha512-EtKwoO6kxCL9WO5xipiHTZlSzBm7WLT627TqC/uVRd0HKmq8NXyebnNYxDoBi7wt8eTWrUrKXCOVaFq9x1kgag==", - "dev": true, - "license": "MIT" - }, - "node_modules/has-bigints": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/has-bigints/-/has-bigints-1.1.0.tgz", - "integrity": "sha512-R3pbpkcIqv2Pm3dUwgjclDRVmWpTJW2DcMzcIhEXEx1oh/CEMObMm3KLmRJOdvhM7o4uQBnwr8pzRK2sJWIqfg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/has-flag": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz", - "integrity": "sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/has-property-descriptors": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz", - "integrity": "sha512-55JNKuIW+vq4Ke1BjOTjM2YctQIvCT7GFzHwmfZPGo5wnrgkid0YQtnAleFSqumZm4az3n2BS+erby5ipJdgrg==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-define-property": "^1.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/has-proto": { - "version": "1.2.0", - "resolved": "https://registry.npmjs.org/has-proto/-/has-proto-1.2.0.tgz", - "integrity": "sha512-KIL7eQPfHQRC8+XluaIw7BHUwwqL19bQn4hzNgdr+1wXoU0KKj6rufu47lhY7KbJR2C6T6+PfyN0Ea7wkSS+qQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "dunder-proto": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/has-symbols": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/has-symbols/-/has-symbols-1.1.0.tgz", - "integrity": "sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/has-tostringtag": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/has-tostringtag/-/has-tostringtag-1.0.2.tgz", - "integrity": "sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw==", - "dev": true, - "license": "MIT", - "dependencies": { - "has-symbols": "^1.0.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/hasown": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/hasown/-/hasown-2.0.2.tgz", - "integrity": "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "function-bind": "^1.1.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/ignore": { - "version": "5.3.2", - "resolved": "https://registry.npmjs.org/ignore/-/ignore-5.3.2.tgz", - "integrity": "sha512-hsBTNUqQTDwkWtcdYI2i06Y/nUBEsNEDJKjWdigLvegy8kDuJAS8uRlpkkcQpyEXL0Z/pjDy5HBmMjRCJ2gq+g==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 4" - } - }, - "node_modules/import-fresh": { - "version": "3.3.1", - "resolved": "https://registry.npmjs.org/import-fresh/-/import-fresh-3.3.1.tgz", - "integrity": "sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "parent-module": "^1.0.0", - "resolve-from": "^4.0.0" - }, - "engines": { - "node": ">=6" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/imurmurhash": { - "version": "0.1.4", - "resolved": "https://registry.npmjs.org/imurmurhash/-/imurmurhash-0.1.4.tgz", - "integrity": "sha512-JmXMZ6wuvDmLiHEml9ykzqO6lwFbof0GG4IkcGaENdCRDDmMVnny7s5HsIgHCbaq0w2MyPhDqkhTUgS2LU2PHA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.8.19" - } - }, - "node_modules/internal-slot": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/internal-slot/-/internal-slot-1.1.0.tgz", - "integrity": "sha512-4gd7VpWNQNB4UKKCFFVcp1AVv+FMOgs9NKzjHKusc8jTMhd5eL1NqQqOpE0KzMds804/yHlglp3uxgluOqAPLw==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "hasown": "^2.0.2", - "side-channel": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/is-array-buffer": { - "version": "3.0.5", - "resolved": "https://registry.npmjs.org/is-array-buffer/-/is-array-buffer-3.0.5.tgz", - "integrity": "sha512-DDfANUiiG2wC1qawP66qlTugJeL5HyzMpfr8lLK+jMQirGzNod0B12cFB/9q838Ru27sBwfw78/rdoU7RERz6A==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.3", - "get-intrinsic": "^1.2.6" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-async-function": { - "version": "2.1.1", - "resolved": "https://registry.npmjs.org/is-async-function/-/is-async-function-2.1.1.tgz", - "integrity": "sha512-9dgM/cZBnNvjzaMYHVoxxfPj2QXt22Ev7SuuPrs+xav0ukGB0S6d4ydZdEiM48kLx5kDV+QBPrpVnFyefL8kkQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "async-function": "^1.0.0", - "call-bound": "^1.0.3", - "get-proto": "^1.0.1", - "has-tostringtag": "^1.0.2", - "safe-regex-test": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-bigint": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/is-bigint/-/is-bigint-1.1.0.tgz", - "integrity": "sha512-n4ZT37wG78iz03xPRKJrHTdZbe3IicyucEtdRsV5yglwc3GyUfbAfpSeD0FJ41NbUNSt5wbhqfp1fS+BgnvDFQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "has-bigints": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-boolean-object": { - "version": "1.2.2", - "resolved": "https://registry.npmjs.org/is-boolean-object/-/is-boolean-object-1.2.2.tgz", - "integrity": "sha512-wa56o2/ElJMYqjCjGkXri7it5FbebW5usLw/nPmCMs5DeZ7eziSYZhSmPRn0txqeW4LnAmQQU7FgqLpsEFKM4A==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "has-tostringtag": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-callable": { - "version": "1.2.7", - "resolved": "https://registry.npmjs.org/is-callable/-/is-callable-1.2.7.tgz", - "integrity": "sha512-1BC0BVFhS/p0qtw6enp8e+8OD0UrK0oFLztSjNzhcKA3WDuJxxAPXzPuPtKkjEY9UUoEWlX/8fgKeu2S8i9JTA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-core-module": { - "version": "2.16.1", - "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.16.1.tgz", - "integrity": "sha512-UfoeMA6fIJ8wTYFEUjelnaGI67v6+N7qXJEvQuIGa99l4xsCruSYOVSQ0uPANn4dAzm8lkYPaKLrrijLq7x23w==", - "dev": true, - "license": "MIT", - "dependencies": { - "hasown": "^2.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-data-view": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/is-data-view/-/is-data-view-1.0.2.tgz", - "integrity": "sha512-RKtWF8pGmS87i2D6gqQu/l7EYRlVdfzemCJN/P3UOs//x1QE7mfhvzHIApBTRf7axvT6DMGwSwBXYCT0nfB9xw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "get-intrinsic": "^1.2.6", - "is-typed-array": "^1.1.13" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-date-object": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/is-date-object/-/is-date-object-1.1.0.tgz", - "integrity": "sha512-PwwhEakHVKTdRNVOw+/Gyh0+MzlCl4R6qKvkhuvLtPMggI1WAHt9sOwZxQLSGpUaDnrdyDsomoRgNnCfKNSXXg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "has-tostringtag": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-extglob": { - "version": "2.1.1", - "resolved": "https://registry.npmjs.org/is-extglob/-/is-extglob-2.1.1.tgz", - "integrity": "sha512-SbKbANkN603Vi4jEZv49LeVJMn4yGwsbzZworEoyEiutsN3nJYdbO36zfhGJ6QEDpOZIFkDtnq5JRxmvl3jsoQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/is-finalizationregistry": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/is-finalizationregistry/-/is-finalizationregistry-1.1.1.tgz", - "integrity": "sha512-1pC6N8qWJbWoPtEjgcL2xyhQOP491EQjeUo3qTKcmV8YSDDJrOepfG8pcC7h/QgnQHYSv0mJ3Z/ZWxmatVrysg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-generator-function": { - "version": "1.1.2", - "resolved": "https://registry.npmjs.org/is-generator-function/-/is-generator-function-1.1.2.tgz", - "integrity": "sha512-upqt1SkGkODW9tsGNG5mtXTXtECizwtS2kA161M+gJPc1xdb/Ax629af6YrTwcOeQHbewrPNlE5Dx7kzvXTizA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.4", - "generator-function": "^2.0.0", - "get-proto": "^1.0.1", - "has-tostringtag": "^1.0.2", - "safe-regex-test": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-glob": { - "version": "4.0.3", - "resolved": "https://registry.npmjs.org/is-glob/-/is-glob-4.0.3.tgz", - "integrity": "sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-extglob": "^2.1.1" - }, - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/is-map": { - "version": "2.0.3", - "resolved": "https://registry.npmjs.org/is-map/-/is-map-2.0.3.tgz", - "integrity": "sha512-1Qed0/Hr2m+YqxnM09CjA2d/i6YZNfF6R2oRAOj36eUdS6qIV/huPJNSEpKbupewFs+ZsJlxsjjPbc0/afW6Lw==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-negative-zero": { - "version": "2.0.3", - "resolved": "https://registry.npmjs.org/is-negative-zero/-/is-negative-zero-2.0.3.tgz", - "integrity": "sha512-5KoIu2Ngpyek75jXodFvnafB6DJgr3u8uuK0LEZJjrU19DrMD3EVERaR8sjz8CCGgpZvxPl9SuE1GMVPFHx1mw==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-number": { - "version": "7.0.0", - "resolved": "https://registry.npmjs.org/is-number/-/is-number-7.0.0.tgz", - "integrity": "sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.12.0" - } - }, - "node_modules/is-number-object": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/is-number-object/-/is-number-object-1.1.1.tgz", - "integrity": "sha512-lZhclumE1G6VYD8VHe35wFaIif+CTy5SJIi5+3y4psDgWu4wPDoBhF8NxUOinEc7pHgiTsT6MaBb92rKhhD+Xw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "has-tostringtag": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-regex": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/is-regex/-/is-regex-1.2.1.tgz", - "integrity": "sha512-MjYsKHO5O7mCsmRGxWcLWheFqN9DJ/2TmngvjKXihe6efViPqc274+Fx/4fYj/r03+ESvBdTXK0V6tA3rgez1g==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "gopd": "^1.2.0", - "has-tostringtag": "^1.0.2", - "hasown": "^2.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-set": { - "version": "2.0.3", - "resolved": "https://registry.npmjs.org/is-set/-/is-set-2.0.3.tgz", - "integrity": "sha512-iPAjerrse27/ygGLxw+EBR9agv9Y6uLeYVJMu+QNCoouJ1/1ri0mGrcWpfCqFZuzzx3WjtwxG098X+n4OuRkPg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-shared-array-buffer": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/is-shared-array-buffer/-/is-shared-array-buffer-1.0.4.tgz", - "integrity": "sha512-ISWac8drv4ZGfwKl5slpHG9OwPNty4jOWPRIhBpxOoD+hqITiwuipOQ2bNthAzwA3B4fIjO4Nln74N0S9byq8A==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-string": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/is-string/-/is-string-1.1.1.tgz", - "integrity": "sha512-BtEeSsoaQjlSPBemMQIrY1MY0uM6vnS1g5fmufYOtnxLGUZM2178PKbhsk7Ffv58IX+ZtcvoGwccYsh0PglkAA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "has-tostringtag": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-symbol": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/is-symbol/-/is-symbol-1.1.1.tgz", - "integrity": "sha512-9gGx6GTtCQM73BgmHQXfDmLtfjjTUDSyoxTCbp5WtoixAhfgsDirWIcVQ/IHpvI5Vgd5i/J5F7B9cN/WlVbC/w==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "has-symbols": "^1.1.0", - "safe-regex-test": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-typed-array": { - "version": "1.1.15", - "resolved": "https://registry.npmjs.org/is-typed-array/-/is-typed-array-1.1.15.tgz", - "integrity": "sha512-p3EcsicXjit7SaskXHs1hA91QxgTw46Fv6EFKKGS5DRFLD8yKnohjF3hxoju94b/OcMZoQukzpPpBE9uLVKzgQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "which-typed-array": "^1.1.16" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-weakmap": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/is-weakmap/-/is-weakmap-2.0.2.tgz", - "integrity": "sha512-K5pXYOm9wqY1RgjpL3YTkF39tni1XajUIkawTLUo9EZEVUFga5gSQJF8nNS7ZwJQ02y+1YCNYcMh+HIf1ZqE+w==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-weakref": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/is-weakref/-/is-weakref-1.1.1.tgz", - "integrity": "sha512-6i9mGWSlqzNMEqpCp93KwRS1uUOodk2OJ6b+sq7ZPDSy2WuI5NFIxp/254TytR8ftefexkWn5xNiHUNpPOfSew==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/is-weakset": { - "version": "2.0.4", - "resolved": "https://registry.npmjs.org/is-weakset/-/is-weakset-2.0.4.tgz", - "integrity": "sha512-mfcwb6IzQyOKTs84CQMrOwW4gQcaTOAWJ0zzJCl2WSPDrWk/OzDaImWFH3djXhb24g4eudZfLRozAvPGw4d9hQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "get-intrinsic": "^1.2.6" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/isarray": { - "version": "2.0.5", - "resolved": "https://registry.npmjs.org/isarray/-/isarray-2.0.5.tgz", - "integrity": "sha512-xHjhDr3cNBK0BzdUJSPXZntQUx/mwMS5Rw4A7lPJ90XGAO6ISP/ePDNuo0vhqOZU+UD5JoodwCAAoZQd3FeAKw==", - "dev": true, - "license": "MIT" - }, - "node_modules/isexe": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/isexe/-/isexe-2.0.0.tgz", - "integrity": "sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw==", - "dev": true, - "license": "ISC" - }, - "node_modules/iterator.prototype": { - "version": "1.1.5", - "resolved": "https://registry.npmjs.org/iterator.prototype/-/iterator.prototype-1.1.5.tgz", - "integrity": "sha512-H0dkQoCa3b2VEeKQBOxFph+JAbcrQdE7KC0UkqwpLmv2EC4P41QXP+rqo9wYodACiG5/WM5s9oDApTU8utwj9g==", - "dev": true, - "license": "MIT", - "dependencies": { - "define-data-property": "^1.1.4", - "es-object-atoms": "^1.0.0", - "get-intrinsic": "^1.2.6", - "get-proto": "^1.0.0", - "has-symbols": "^1.1.0", - "set-function-name": "^2.0.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/jiti": { - "version": "2.6.1", - "resolved": "https://registry.npmjs.org/jiti/-/jiti-2.6.1.tgz", - "integrity": "sha512-ekilCSN1jwRvIbgeg/57YFh8qQDNbwDb9xT/qu2DAHbFFZUicIl4ygVaAvzveMhMVr3LnpSKTNnwt8PoOfmKhQ==", - "dev": true, - "license": "MIT", - "bin": { - "jiti": "lib/jiti-cli.mjs" - } - }, - "node_modules/js-tokens": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz", - "integrity": "sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/js-yaml": { - "version": "4.1.1", - "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.1.tgz", - "integrity": "sha512-qQKT4zQxXl8lLwBtHMWwaTcGfFOZviOJet3Oy/xmGk2gZH677CJM9EvtfdSkgWcATZhj/55JZ0rmy3myCT5lsA==", - "dev": true, - "license": "MIT", - "dependencies": { - "argparse": "^2.0.1" - }, - "bin": { - "js-yaml": "bin/js-yaml.js" - } - }, - "node_modules/json-buffer": { - "version": "3.0.1", - "resolved": "https://registry.npmjs.org/json-buffer/-/json-buffer-3.0.1.tgz", - "integrity": "sha512-4bV5BfR2mqfQTJm+V5tPPdf+ZpuhiIvTuAB5g8kcrXOZpTT/QwwVRWBywX1ozr6lEuPdbHxwaJlm9G6mI2sfSQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/json-schema-migrate": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/json-schema-migrate/-/json-schema-migrate-2.0.0.tgz", - "integrity": "sha512-r38SVTtojDRp4eD6WsCqiE0eNDt4v1WalBXb9cyZYw9ai5cGtBwzRNWjHzJl38w6TxFkXAIA7h+fyX3tnrAFhQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "ajv": "^8.0.0" - } - }, - "node_modules/json-schema-migrate/node_modules/ajv": { - "version": "8.17.1", - "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.17.1.tgz", - "integrity": "sha512-B/gBuNg5SiMTrPkC+A2+cW0RszwxYmn6VYxB/inlBStS5nx6xHIt/ehKRhIMhqusl7a8LjQoZnjCs5vhwxOQ1g==", - "dev": true, - "license": "MIT", - "dependencies": { - "fast-deep-equal": "^3.1.3", - "fast-uri": "^3.0.1", - "json-schema-traverse": "^1.0.0", - "require-from-string": "^2.0.2" - }, - "funding": { - "type": "github", - "url": "https://github.com/sponsors/epoberezkin" - } - }, - "node_modules/json-schema-migrate/node_modules/json-schema-traverse": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz", - "integrity": "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==", - "dev": true, - "license": "MIT" - }, - "node_modules/json-schema-traverse": { - "version": "0.4.1", - "resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz", - "integrity": "sha512-xbbCH5dCYU5T8LcEhhuh7HJ88HXuW3qsI3Y0zOZFKfZEHcpWiHU/Jxzk629Brsab/mMiHQti9wMP+845RPe3Vg==", - "dev": true, - "license": "MIT" - }, - "node_modules/json-stable-stringify-without-jsonify": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/json-stable-stringify-without-jsonify/-/json-stable-stringify-without-jsonify-1.0.1.tgz", - "integrity": "sha512-Bdboy+l7tA3OGW6FjyFHWkP5LuByj1Tk33Ljyq0axyzdk9//JSi2u3fP1QSmd1KNwq6VOKYGlAu87CisVir6Pw==", - "dev": true, - "license": "MIT" - }, - "node_modules/json5": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/json5/-/json5-1.0.2.tgz", - "integrity": "sha512-g1MWMLBiz8FKi1e4w0UyVL3w+iJceWAFBAaBnnGKOpNa5f8TLktkbre1+s6oICydWAm+HRUGTmI+//xv2hvXYA==", - "dev": true, - "license": "MIT", - "dependencies": { - "minimist": "^1.2.0" - }, - "bin": { - "json5": "lib/cli.js" - } - }, - "node_modules/jsonc-eslint-parser": { - "version": "2.4.1", - "resolved": "https://registry.npmjs.org/jsonc-eslint-parser/-/jsonc-eslint-parser-2.4.1.tgz", - "integrity": "sha512-uuPNLJkKN8NXAlZlQ6kmUF9qO+T6Kyd7oV4+/7yy8Jz6+MZNyhPq8EdLpdfnPVzUC8qSf1b4j1azKaGnFsjmsw==", - "dev": true, - "license": "MIT", - "dependencies": { - "acorn": "^8.5.0", - "eslint-visitor-keys": "^3.0.0", - "espree": "^9.0.0", - "semver": "^7.3.5" - }, - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/ota-meshi" - } - }, - "node_modules/jsonc-eslint-parser/node_modules/eslint-visitor-keys": { - "version": "3.4.3", - "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz", - "integrity": "sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/jsonc-eslint-parser/node_modules/espree": { - "version": "9.6.1", - "resolved": "https://registry.npmjs.org/espree/-/espree-9.6.1.tgz", - "integrity": "sha512-oruZaFkjorTpF32kDSI5/75ViwGeZginGGy2NoOSg3Q9bnwlnmDm4HLnkl0RE3n+njDXR037aY1+x58Z/zFdwQ==", - "dev": true, - "license": "BSD-2-Clause", - "dependencies": { - "acorn": "^8.9.0", - "acorn-jsx": "^5.3.2", - "eslint-visitor-keys": "^3.4.1" - }, - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/jsonc-eslint-parser/node_modules/semver": { - "version": "7.7.3", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz", - "integrity": "sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/jsx-ast-utils": { - "version": "3.3.5", - "resolved": "https://registry.npmjs.org/jsx-ast-utils/-/jsx-ast-utils-3.3.5.tgz", - "integrity": "sha512-ZZow9HBI5O6EPgSJLUb8n2NKgmVWTwCvHGwFuJlMjvLFqlGG6pjirPhtdsseaLZjSibD8eegzmYpUZwoIlj2cQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "array-includes": "^3.1.6", - "array.prototype.flat": "^1.3.1", - "object.assign": "^4.1.4", - "object.values": "^1.1.6" - }, - "engines": { - "node": ">=4.0" - } - }, - "node_modules/keyv": { - "version": "4.5.4", - "resolved": "https://registry.npmjs.org/keyv/-/keyv-4.5.4.tgz", - "integrity": "sha512-oxVHkHR/EJf2CNXnWxRLW6mg7JyCCUcG0DtEGmL2ctUo1PNTin1PUil+r/+4r5MpVgC/fn1kjsx7mjSujKqIpw==", - "dev": true, - "license": "MIT", - "dependencies": { - "json-buffer": "3.0.1" - } - }, - "node_modules/levn": { - "version": "0.4.1", - "resolved": "https://registry.npmjs.org/levn/-/levn-0.4.1.tgz", - "integrity": "sha512-+bT2uH4E5LGE7h/n3evcS/sQlJXCpIp6ym8OWJ5eV6+67Dsql/LaaT7qJBAt2rzfoa/5QBGBhxDix1dMt2kQKQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "prelude-ls": "^1.2.1", - "type-check": "~0.4.0" - }, - "engines": { - "node": ">= 0.8.0" - } - }, - "node_modules/locate-path": { - "version": "6.0.0", - "resolved": "https://registry.npmjs.org/locate-path/-/locate-path-6.0.0.tgz", - "integrity": "sha512-iPZK6eYjbxRu3uB4/WZ3EsEIMJFMqAoopl3R+zuq0UjcAm/MO6KCweDgPfP3elTztoKP3KtnVHxTn2NHBSDVUw==", - "dev": true, - "license": "MIT", - "dependencies": { - "p-locate": "^5.0.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/lodash.merge": { - "version": "4.6.2", - "resolved": "https://registry.npmjs.org/lodash.merge/-/lodash.merge-4.6.2.tgz", - "integrity": "sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/loose-envify": { - "version": "1.4.0", - "resolved": "https://registry.npmjs.org/loose-envify/-/loose-envify-1.4.0.tgz", - "integrity": "sha512-lyuxPGr/Wfhrlem2CL/UcnUc1zcqKAImBDzukY7Y5F/yQiNdko6+fRLevlw1HgMySw7f611UIY408EtxRSoK3Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "js-tokens": "^3.0.0 || ^4.0.0" - }, - "bin": { - "loose-envify": "cli.js" - } - }, - "node_modules/math-intrinsics": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz", - "integrity": "sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/merge2": { - "version": "1.4.1", - "resolved": "https://registry.npmjs.org/merge2/-/merge2-1.4.1.tgz", - "integrity": "sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 8" - } - }, - "node_modules/micromatch": { - "version": "4.0.8", - "resolved": "https://registry.npmjs.org/micromatch/-/micromatch-4.0.8.tgz", - "integrity": "sha512-PXwfBhYu0hBCPw8Dn0E+WDYb7af3dSLVWKi3HGv84IdF4TyFoC0ysxFd0Goxw7nSv4T/PzEJQxsYsEiFCKo2BA==", - "dev": true, - "license": "MIT", - "dependencies": { - "braces": "^3.0.3", - "picomatch": "^2.3.1" - }, - "engines": { - "node": ">=8.6" - } - }, - "node_modules/minimatch": { - "version": "3.1.2", - "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz", - "integrity": "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==", - "dev": true, - "license": "ISC", - "dependencies": { - "brace-expansion": "^1.1.7" - }, - "engines": { - "node": "*" - } - }, - "node_modules/minimist": { - "version": "1.2.8", - "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.8.tgz", - "integrity": "sha512-2yyAR8qBkN3YuheJanUpWC5U3bb5osDywNB8RzDVlDwDHbocAJveqqj1u8+SVD7jkWT4yvsHCpWqqWqAxb0zCA==", - "dev": true, - "license": "MIT", - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/module-replacements": { - "version": "2.10.1", - "resolved": "https://registry.npmjs.org/module-replacements/-/module-replacements-2.10.1.tgz", - "integrity": "sha512-qkKuLpMHDqRSM676OPL7HUpCiiP3NSxgf8NNR1ga2h/iJLNKTsOSjMEwrcT85DMSti2vmOqxknOVBGWj6H6etQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/moment": { - "version": "2.29.4", - "resolved": "https://registry.npmjs.org/moment/-/moment-2.29.4.tgz", - "integrity": "sha512-5LC9SOxjSc2HF6vO2CyuTDNivEdoz2IvyJJGj6X8DJ0eFyfszE0QiEd+iXmBvUP3WHxSjFH/vIsA0EN00cgr8w==", - "license": "MIT", - "engines": { - "node": "*" - } - }, - "node_modules/ms": { - "version": "2.1.3", - "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.3.tgz", - "integrity": "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==", - "dev": true, - "license": "MIT" - }, - "node_modules/natural-compare": { - "version": "1.4.0", - "resolved": "https://registry.npmjs.org/natural-compare/-/natural-compare-1.4.0.tgz", - "integrity": "sha512-OWND8ei3VtNC9h7V60qff3SVobHr996CTwgxubgyQYEpg290h9J0buyECNNJexkFm5sOajh5G116RYA1c8ZMSw==", - "dev": true, - "license": "MIT" - }, - "node_modules/object-assign": { - "version": "4.1.1", - "resolved": "https://registry.npmjs.org/object-assign/-/object-assign-4.1.1.tgz", - "integrity": "sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/object-inspect": { - "version": "1.13.4", - "resolved": "https://registry.npmjs.org/object-inspect/-/object-inspect-1.13.4.tgz", - "integrity": "sha512-W67iLl4J2EXEGTbfeHCffrjDfitvLANg0UlX3wFUUSTx92KXRFegMHUVgSqE+wvhAbi4WqjGg9czysTV2Epbew==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/object-keys": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/object-keys/-/object-keys-1.1.1.tgz", - "integrity": "sha512-NuAESUOUMrlIXOfHKzD6bpPu3tYt3xvjNdRIQ+FeT0lNb4K8WR70CaDxhuNguS2XG+GjkyMwOzsN5ZktImfhLA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/object.assign": { - "version": "4.1.7", - "resolved": "https://registry.npmjs.org/object.assign/-/object.assign-4.1.7.tgz", - "integrity": "sha512-nK28WOo+QIjBkDduTINE4JkF/UJJKyf2EJxvJKfblDpyg0Q+pkOHNTL0Qwy6NP6FhE/EnzV73BxxqcJaXY9anw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.3", - "define-properties": "^1.2.1", - "es-object-atoms": "^1.0.0", - "has-symbols": "^1.1.0", - "object-keys": "^1.1.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/object.entries": { - "version": "1.1.9", - "resolved": "https://registry.npmjs.org/object.entries/-/object.entries-1.1.9.tgz", - "integrity": "sha512-8u/hfXFRBD1O0hPUjioLhoWFHRmt6tKA4/vZPyckBr18l1KE9uHrFaFaUi8MDRTpi4uak2goyPTSNJLXX2k2Hw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.4", - "define-properties": "^1.2.1", - "es-object-atoms": "^1.1.1" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/object.fromentries": { - "version": "2.0.8", - "resolved": "https://registry.npmjs.org/object.fromentries/-/object.fromentries-2.0.8.tgz", - "integrity": "sha512-k6E21FzySsSK5a21KRADBd/NGneRegFO5pLHfdQLpRDETUNJueLXs3WCzyQ3tFRDYgbq3KHGXfTbi2bs8WQ6rQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.7", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.2", - "es-object-atoms": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/object.groupby": { - "version": "1.0.3", - "resolved": "https://registry.npmjs.org/object.groupby/-/object.groupby-1.0.3.tgz", - "integrity": "sha512-+Lhy3TQTuzXI5hevh8sBGqbmurHbbIjAi0Z4S63nthVLmLxfbj4T54a4CfZrXIrt9iP4mVAPYMo/v99taj3wjQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.7", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/object.values": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/object.values/-/object.values-1.2.1.tgz", - "integrity": "sha512-gXah6aZrcUxjWg2zR2MwouP2eHlCBzdV4pygudehaKXSGW4v2AsRQUK+lwwXhii6KFZcunEnmSUoYp5CXibxtA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.3", - "define-properties": "^1.2.1", - "es-object-atoms": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/obsidian": { - "version": "1.10.3", - "resolved": "https://registry.npmjs.org/obsidian/-/obsidian-1.10.3.tgz", - "integrity": "sha512-VP+ZSxNMG7y6Z+sU9WqLvJAskCfkFrTz2kFHWmmzis+C+4+ELjk/sazwcTHrHXNZlgCeo8YOlM6SOrAFCynNew==", - "license": "MIT", - "dependencies": { - "@types/codemirror": "5.60.8", - "moment": "2.29.4" - }, - "peerDependencies": { - "@codemirror/state": "6.5.0", - "@codemirror/view": "6.38.6" - } - }, - "node_modules/optionator": { - "version": "0.9.4", - "resolved": "https://registry.npmjs.org/optionator/-/optionator-0.9.4.tgz", - "integrity": "sha512-6IpQ7mKUxRcZNLIObR0hz7lxsapSSIYNZJwXPGeF0mTVqGKFIXj1DQcMoT22S3ROcLyY/rz0PWaWZ9ayWmad9g==", - "dev": true, - "license": "MIT", - "dependencies": { - "deep-is": "^0.1.3", - "fast-levenshtein": "^2.0.6", - "levn": "^0.4.1", - "prelude-ls": "^1.2.1", - "type-check": "^0.4.0", - "word-wrap": "^1.2.5" - }, - "engines": { - "node": ">= 0.8.0" - } - }, - "node_modules/own-keys": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/own-keys/-/own-keys-1.0.1.tgz", - "integrity": "sha512-qFOyK5PjiWZd+QQIh+1jhdb9LpxTF0qs7Pm8o5QHYZ0M3vKqSqzsZaEB6oWlxZ+q2sJBMI/Ktgd2N5ZwQoRHfg==", - "dev": true, - "license": "MIT", - "dependencies": { - "get-intrinsic": "^1.2.6", - "object-keys": "^1.1.1", - "safe-push-apply": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/p-limit": { - "version": "3.1.0", - "resolved": "https://registry.npmjs.org/p-limit/-/p-limit-3.1.0.tgz", - "integrity": "sha512-TYOanM3wGwNGsZN2cVTYPArw454xnXj5qmWF1bEoAc4+cU/ol7GVh7odevjp1FNHduHc3KZMcFduxU5Xc6uJRQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "yocto-queue": "^0.1.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/p-locate": { - "version": "5.0.0", - "resolved": "https://registry.npmjs.org/p-locate/-/p-locate-5.0.0.tgz", - "integrity": "sha512-LaNjtRWUBY++zB5nE/NwcaoMylSPk+S+ZHNB1TzdbMJMny6dynpAGt7X/tl/QYq3TIeE6nxHppbo2LGymrG5Pw==", - "dev": true, - "license": "MIT", - "dependencies": { - "p-limit": "^3.0.2" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/parent-module": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/parent-module/-/parent-module-1.0.1.tgz", - "integrity": "sha512-GQ2EWRpQV8/o+Aw8YqtfZZPfNRWZYkbidE9k5rpl/hC3vtHHBfGm2Ifi6qWV+coDGkrUKZAxE3Lot5kcsRlh+g==", - "dev": true, - "license": "MIT", - "dependencies": { - "callsites": "^3.0.0" - }, - "engines": { - "node": ">=6" - } - }, - "node_modules/path-exists": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/path-exists/-/path-exists-4.0.0.tgz", - "integrity": "sha512-ak9Qy5Q7jYb2Wwcey5Fpvg2KoAc/ZIhLSLOSBmRmygPsGwkVVt0fZa0qrtMz+m6tJTAHfZQ8FnmB4MG4LWy7/w==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/path-key": { - "version": "3.1.1", - "resolved": "https://registry.npmjs.org/path-key/-/path-key-3.1.1.tgz", - "integrity": "sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/path-parse": { - "version": "1.0.7", - "resolved": "https://registry.npmjs.org/path-parse/-/path-parse-1.0.7.tgz", - "integrity": "sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==", - "dev": true, - "license": "MIT" - }, - "node_modules/picomatch": { - "version": "2.3.1", - "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-2.3.1.tgz", - "integrity": "sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8.6" - }, - "funding": { - "url": "https://github.com/sponsors/jonschlinkert" - } - }, - "node_modules/possible-typed-array-names": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/possible-typed-array-names/-/possible-typed-array-names-1.1.0.tgz", - "integrity": "sha512-/+5VFTchJDoVj3bhoqi6UeymcD00DAwb1nJwamzPvHEszJ4FpF6SNNbUbOS8yI56qHzdV8eK0qEfOSiodkTdxg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/prelude-ls": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/prelude-ls/-/prelude-ls-1.2.1.tgz", - "integrity": "sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.8.0" - } - }, - "node_modules/prop-types": { - "version": "15.8.1", - "resolved": "https://registry.npmjs.org/prop-types/-/prop-types-15.8.1.tgz", - "integrity": "sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg==", - "dev": true, - "license": "MIT", - "dependencies": { - "loose-envify": "^1.4.0", - "object-assign": "^4.1.1", - "react-is": "^16.13.1" - } - }, - "node_modules/punycode": { - "version": "2.3.1", - "resolved": "https://registry.npmjs.org/punycode/-/punycode-2.3.1.tgz", - "integrity": "sha512-vYt7UD1U9Wg6138shLtLOvdAu+8DsC/ilFtEVHcH+wydcSpNE20AfSOduf6MkRFahL5FY7X1oU7nKVZFtfq8Fg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6" - } - }, - "node_modules/queue-microtask": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/queue-microtask/-/queue-microtask-1.2.3.tgz", - "integrity": "sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/feross" - }, - { - "type": "patreon", - "url": "https://www.patreon.com/feross" - }, - { - "type": "consulting", - "url": "https://feross.org/support" - } - ], - "license": "MIT" - }, - "node_modules/react-is": { - "version": "16.13.1", - "resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz", - "integrity": "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==", - "dev": true, - "license": "MIT" - }, - "node_modules/reflect.getprototypeof": { - "version": "1.0.10", - "resolved": "https://registry.npmjs.org/reflect.getprototypeof/-/reflect.getprototypeof-1.0.10.tgz", - "integrity": "sha512-00o4I+DVrefhv+nX0ulyi3biSHCPDe+yLv5o/p6d/UVlirijB8E16FtfwSAi4g3tcqrQ4lRAqQSoFEZJehYEcw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.9", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.0.0", - "get-intrinsic": "^1.2.7", - "get-proto": "^1.0.1", - "which-builtin-type": "^1.2.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/regexp-tree": { - "version": "0.1.27", - "resolved": "https://registry.npmjs.org/regexp-tree/-/regexp-tree-0.1.27.tgz", - "integrity": "sha512-iETxpjK6YoRWJG5o6hXLwvjYAoW+FEZn9os0PD/b6AP6xQwsa/Y7lCVgIixBbUPMfhu+i2LtdeAqVTgGlQarfA==", - "dev": true, - "license": "MIT", - "bin": { - "regexp-tree": "bin/regexp-tree" - } - }, - "node_modules/regexp.prototype.flags": { - "version": "1.5.4", - "resolved": "https://registry.npmjs.org/regexp.prototype.flags/-/regexp.prototype.flags-1.5.4.tgz", - "integrity": "sha512-dYqgNSZbDwkaJ2ceRd9ojCGjBq+mOm9LmtXnAnEGyHhN/5R7iDW2TRw3h+o/jCFxus3P2LfWIIiwowAjANm7IA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "define-properties": "^1.2.1", - "es-errors": "^1.3.0", - "get-proto": "^1.0.1", - "gopd": "^1.2.0", - "set-function-name": "^2.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/require-from-string": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/require-from-string/-/require-from-string-2.0.2.tgz", - "integrity": "sha512-Xf0nWe6RseziFMu+Ap9biiUbmplq6S9/p+7w7YXP/JBHhrUDDUhwa+vANyubuqfZWTveU//DYVGsDG7RKL/vEw==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/resolve": { - "version": "1.22.11", - "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.11.tgz", - "integrity": "sha512-RfqAvLnMl313r7c9oclB1HhUEAezcpLjz95wFH4LVuhk9JF/r22qmVP9AMmOU4vMX7Q8pN8jwNg/CSpdFnMjTQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-core-module": "^2.16.1", - "path-parse": "^1.0.7", - "supports-preserve-symlinks-flag": "^1.0.0" - }, - "bin": { - "resolve": "bin/resolve" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/resolve-from": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/resolve-from/-/resolve-from-4.0.0.tgz", - "integrity": "sha512-pb/MYmXstAkysRFx8piNI1tGFNQIFA3vkE3Gq4EuA1dF6gHp/+vgZqsCGJapvy8N3Q+4o7FwvquPJcnZ7RYy4g==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=4" - } - }, - "node_modules/resolve-pkg-maps": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/resolve-pkg-maps/-/resolve-pkg-maps-1.0.0.tgz", - "integrity": "sha512-seS2Tj26TBVOC2NIc2rOe2y2ZO7efxITtLZcGSOnHHNOQ7CkiUBfw0Iw2ck6xkIhPwLhKNLS8BO+hEpngQlqzw==", - "dev": true, - "license": "MIT", - "funding": { - "url": "https://github.com/privatenumber/resolve-pkg-maps?sponsor=1" - } - }, - "node_modules/ret": { - "version": "0.1.15", - "resolved": "https://registry.npmjs.org/ret/-/ret-0.1.15.tgz", - "integrity": "sha512-TTlYpa+OL+vMMNG24xSlQGEJ3B/RzEfUlLct7b5G/ytav+wPrplCpVMFuwzXbkecJrb6IYo1iFb0S9v37754mg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.12" - } - }, - "node_modules/reusify": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/reusify/-/reusify-1.1.0.tgz", - "integrity": "sha512-g6QUff04oZpHs0eG5p83rFLhHeV00ug/Yf9nZM6fLeUrPguBTkTQOdpAWWspMh55TZfVQDPaN3NQJfbVRAxdIw==", - "dev": true, - "license": "MIT", - "engines": { - "iojs": ">=1.0.0", - "node": ">=0.10.0" - } - }, - "node_modules/run-parallel": { - "version": "1.2.0", - "resolved": "https://registry.npmjs.org/run-parallel/-/run-parallel-1.2.0.tgz", - "integrity": "sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA==", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/feross" - }, - { - "type": "patreon", - "url": "https://www.patreon.com/feross" - }, - { - "type": "consulting", - "url": "https://feross.org/support" - } - ], - "license": "MIT", - "dependencies": { - "queue-microtask": "^1.2.2" - } - }, - "node_modules/safe-array-concat": { - "version": "1.1.3", - "resolved": "https://registry.npmjs.org/safe-array-concat/-/safe-array-concat-1.1.3.tgz", - "integrity": "sha512-AURm5f0jYEOydBj7VQlVvDrjeFgthDdEF5H1dP+6mNpoXOMo1quQqJ4wvJDyRZ9+pO3kGWoOdmV08cSv2aJV6Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.2", - "get-intrinsic": "^1.2.6", - "has-symbols": "^1.1.0", - "isarray": "^2.0.5" - }, - "engines": { - "node": ">=0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/safe-buffer": { - "version": "5.2.1", - "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.2.1.tgz", - "integrity": "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/feross" - }, - { - "type": "patreon", - "url": "https://www.patreon.com/feross" - }, - { - "type": "consulting", - "url": "https://feross.org/support" - } - ], - "license": "MIT" - }, - "node_modules/safe-push-apply": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/safe-push-apply/-/safe-push-apply-1.0.0.tgz", - "integrity": "sha512-iKE9w/Z7xCzUMIZqdBsp6pEQvwuEebH4vdpjcDWnyzaI6yl6O9FHvVpmGelvEHNsoY6wGblkxR6Zty/h00WiSA==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "isarray": "^2.0.5" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/safe-regex": { - "version": "2.1.1", - "resolved": "https://registry.npmjs.org/safe-regex/-/safe-regex-2.1.1.tgz", - "integrity": "sha512-rx+x8AMzKb5Q5lQ95Zoi6ZbJqwCLkqi3XuJXp5P3rT8OEc6sZCJG5AE5dU3lsgRr/F4Bs31jSlVN+j5KrsGu9A==", - "dev": true, - "license": "MIT", - "dependencies": { - "regexp-tree": "~0.1.1" - } - }, - "node_modules/safe-regex-test": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/safe-regex-test/-/safe-regex-test-1.1.0.tgz", - "integrity": "sha512-x/+Cz4YrimQxQccJf5mKEbIa1NzeCRNI5Ecl/ekmlYaampdNLPalVyIcCZNNH3MvmqBugV5TMYZXv0ljslUlaw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "es-errors": "^1.3.0", - "is-regex": "^1.2.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/semver": { - "version": "6.3.1", - "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz", - "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - } - }, - "node_modules/set-function-length": { - "version": "1.2.2", - "resolved": "https://registry.npmjs.org/set-function-length/-/set-function-length-1.2.2.tgz", - "integrity": "sha512-pgRc4hJ4/sNjWCSS9AmnS40x3bNMDTknHgL5UaMBTMyJnU90EgWh1Rz+MC9eFu4BuN/UwZjKQuY/1v3rM7HMfg==", - "dev": true, - "license": "MIT", - "dependencies": { - "define-data-property": "^1.1.4", - "es-errors": "^1.3.0", - "function-bind": "^1.1.2", - "get-intrinsic": "^1.2.4", - "gopd": "^1.0.1", - "has-property-descriptors": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/set-function-name": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/set-function-name/-/set-function-name-2.0.2.tgz", - "integrity": "sha512-7PGFlmtwsEADb0WYyvCMa1t+yke6daIG4Wirafur5kcf+MhUnPms1UeR0CKQdTZD81yESwMHbtn+TR+dMviakQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "define-data-property": "^1.1.4", - "es-errors": "^1.3.0", - "functions-have-names": "^1.2.3", - "has-property-descriptors": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/set-proto": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/set-proto/-/set-proto-1.0.0.tgz", - "integrity": "sha512-RJRdvCo6IAnPdsvP/7m6bsQqNnn1FCBX5ZNtFL98MmFF/4xAIJTIg1YbHW5DC2W5SKZanrC6i4HsJqlajw/dZw==", - "dev": true, - "license": "MIT", - "dependencies": { - "dunder-proto": "^1.0.1", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/shebang-command": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz", - "integrity": "sha512-kHxr2zZpYtdmrN1qDjrrX/Z1rR1kG8Dx+gkpK1G4eXmvXswmcE1hTWBWYUzlraYw1/yZp6YuDY77YtvbN0dmDA==", - "dev": true, - "license": "MIT", - "dependencies": { - "shebang-regex": "^3.0.0" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/shebang-regex": { - "version": "3.0.0", - "resolved": "https://registry.npmjs.org/shebang-regex/-/shebang-regex-3.0.0.tgz", - "integrity": "sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/side-channel": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.1.0.tgz", - "integrity": "sha512-ZX99e6tRweoUXqR+VBrslhda51Nh5MTQwou5tnUDgbtyM0dBgmhEDtWGP/xbKn6hqfPRHujUNwz5fy/wbbhnpw==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "object-inspect": "^1.13.3", - "side-channel-list": "^1.0.0", - "side-channel-map": "^1.0.1", - "side-channel-weakmap": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/side-channel-list": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/side-channel-list/-/side-channel-list-1.0.0.tgz", - "integrity": "sha512-FCLHtRD/gnpCiCHEiJLOwdmFP+wzCmDEkc9y7NsYxeF4u7Btsn1ZuwgwJGxImImHicJArLP4R0yX4c2KCrMrTA==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "object-inspect": "^1.13.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/side-channel-map": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/side-channel-map/-/side-channel-map-1.0.1.tgz", - "integrity": "sha512-VCjCNfgMsby3tTdo02nbjtM/ewra6jPHmpThenkTYh8pG9ucZ/1P8So4u4FGBek/BjpOVsDCMoLA/iuBKIFXRA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "es-errors": "^1.3.0", - "get-intrinsic": "^1.2.5", - "object-inspect": "^1.13.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/side-channel-weakmap": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/side-channel-weakmap/-/side-channel-weakmap-1.0.2.tgz", - "integrity": "sha512-WPS/HvHQTYnHisLo9McqBHOJk2FkHO/tlpvldyrnem4aeQp4hai3gythswg6p01oSoTl58rcpiFAjF2br2Ak2A==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "es-errors": "^1.3.0", - "get-intrinsic": "^1.2.5", - "object-inspect": "^1.13.3", - "side-channel-map": "^1.0.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/stop-iteration-iterator": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/stop-iteration-iterator/-/stop-iteration-iterator-1.1.0.tgz", - "integrity": "sha512-eLoXW/DHyl62zxY4SCaIgnRhuMr6ri4juEYARS8E6sCEqzKpOiE521Ucofdx+KnDZl5xmvGYaaKCk5FEOxJCoQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "es-errors": "^1.3.0", - "internal-slot": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/string.prototype.matchall": { - "version": "4.0.12", - "resolved": "https://registry.npmjs.org/string.prototype.matchall/-/string.prototype.matchall-4.0.12.tgz", - "integrity": "sha512-6CC9uyBL+/48dYizRf7H7VAYCMCNTBeM78x/VTUe9bFEaxBepPJDa1Ow99LqI/1yF7kuy7Q3cQsYMrcjGUcskA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.3", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.6", - "es-errors": "^1.3.0", - "es-object-atoms": "^1.0.0", - "get-intrinsic": "^1.2.6", - "gopd": "^1.2.0", - "has-symbols": "^1.1.0", - "internal-slot": "^1.1.0", - "regexp.prototype.flags": "^1.5.3", - "set-function-name": "^2.0.2", - "side-channel": "^1.1.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/string.prototype.repeat": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/string.prototype.repeat/-/string.prototype.repeat-1.0.0.tgz", - "integrity": "sha512-0u/TldDbKD8bFCQ/4f5+mNRrXwZ8hg2w7ZR8wa16e8z9XpePWl3eGEcUD0OXpEH/VJH/2G3gjUtR3ZOiBe2S/w==", - "dev": true, - "license": "MIT", - "dependencies": { - "define-properties": "^1.1.3", - "es-abstract": "^1.17.5" - } - }, - "node_modules/string.prototype.trim": { - "version": "1.2.10", - "resolved": "https://registry.npmjs.org/string.prototype.trim/-/string.prototype.trim-1.2.10.tgz", - "integrity": "sha512-Rs66F0P/1kedk5lyYyH9uBzuiI/kNRmwJAR9quK6VOtIpZ2G+hMZd+HQbbv25MgCA6gEffoMZYxlTod4WcdrKA==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.2", - "define-data-property": "^1.1.4", - "define-properties": "^1.2.1", - "es-abstract": "^1.23.5", - "es-object-atoms": "^1.0.0", - "has-property-descriptors": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/string.prototype.trimend": { - "version": "1.0.9", - "resolved": "https://registry.npmjs.org/string.prototype.trimend/-/string.prototype.trimend-1.0.9.tgz", - "integrity": "sha512-G7Ok5C6E/j4SGfyLCloXTrngQIQU3PWtXGst3yM7Bea9FRURf1S42ZHlZZtsNque2FN2PoUhfZXYLNWwEr4dLQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "call-bound": "^1.0.2", - "define-properties": "^1.2.1", - "es-object-atoms": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/string.prototype.trimstart": { - "version": "1.0.8", - "resolved": "https://registry.npmjs.org/string.prototype.trimstart/-/string.prototype.trimstart-1.0.8.tgz", - "integrity": "sha512-UXSH262CSZY1tfu3G3Secr6uGLCFVPMhIqHjlgCUtCCcgihYc/xKs9djMTMUOb2j1mVSeU8EU6NWc/iQKU6Gfg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.7", - "define-properties": "^1.2.1", - "es-object-atoms": "^1.0.0" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/strip-bom": { - "version": "3.0.0", - "resolved": "https://registry.npmjs.org/strip-bom/-/strip-bom-3.0.0.tgz", - "integrity": "sha512-vavAMRXOgBVNF6nyEEmL3DBK19iRpDcoIwW+swQ+CbGiu7lju6t+JklA1MHweoWtadgt4ISVUsXLyDq34ddcwA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=4" - } - }, - "node_modules/strip-json-comments": { - "version": "3.1.1", - "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-3.1.1.tgz", - "integrity": "sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/style-mod": { - "version": "4.1.3", - "resolved": "https://registry.npmjs.org/style-mod/-/style-mod-4.1.3.tgz", - "integrity": "sha512-i/n8VsZydrugj3Iuzll8+x/00GH2vnYsk1eomD8QiRrSAeW6ItbCQDtfXCeJHd0iwiNagqjQkvpvREEPtW3IoQ==", - "license": "MIT", - "peer": true - }, - "node_modules/supports-color": { - "version": "7.2.0", - "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz", - "integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==", - "dev": true, - "license": "MIT", - "dependencies": { - "has-flag": "^4.0.0" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/supports-preserve-symlinks-flag": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz", - "integrity": "sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/synckit": { - "version": "0.9.3", - "resolved": "https://registry.npmjs.org/synckit/-/synckit-0.9.3.tgz", - "integrity": "sha512-JJoOEKTfL1urb1mDoEblhD9NhEbWmq9jHEMEnxoC4ujUaZ4itA8vKgwkFAyNClgxplLi9tsUKX+EduK0p/l7sg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@pkgr/core": "^0.1.0", - "tslib": "^2.6.2" - }, - "engines": { - "node": "^14.18.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/unts" - } - }, - "node_modules/synckit/node_modules/tslib": { - "version": "2.8.1", - "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz", - "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==", - "dev": true, - "license": "0BSD" - }, - "node_modules/tapable": { - "version": "2.3.0", - "resolved": "https://registry.npmjs.org/tapable/-/tapable-2.3.0.tgz", - "integrity": "sha512-g9ljZiwki/LfxmQADO3dEY1CbpmXT5Hm2fJ+QaGKwSXUylMybePR7/67YW7jOrrvjEgL1Fmz5kzyAjWVWLlucg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/webpack" - } - }, - "node_modules/to-regex-range": { - "version": "5.0.1", - "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz", - "integrity": "sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-number": "^7.0.0" - }, - "engines": { - "node": ">=8.0" - } - }, - "node_modules/toml-eslint-parser": { - "version": "0.9.3", - "resolved": "https://registry.npmjs.org/toml-eslint-parser/-/toml-eslint-parser-0.9.3.tgz", - "integrity": "sha512-moYoCvkNUAPCxSW9jmHmRElhm4tVJpHL8ItC/+uYD0EpPSFXbck7yREz9tNdJVTSpHVod8+HoipcpbQ0oE6gsw==", - "dev": true, - "license": "MIT", - "dependencies": { - "eslint-visitor-keys": "^3.0.0" - }, - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/ota-meshi" - } - }, - "node_modules/toml-eslint-parser/node_modules/eslint-visitor-keys": { - "version": "3.4.3", - "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz", - "integrity": "sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/ts-api-utils": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/ts-api-utils/-/ts-api-utils-2.1.0.tgz", - "integrity": "sha512-CUgTZL1irw8u29bzrOD/nH85jqyc74D6SshFgujOIA7osm2Rz7dYH77agkx7H4FBNxDq7Cjf+IjaX/8zwFW+ZQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=18.12" - }, - "peerDependencies": { - "typescript": ">=4.8.4" - } - }, - "node_modules/tsconfig-paths": { - "version": "3.15.0", - "resolved": "https://registry.npmjs.org/tsconfig-paths/-/tsconfig-paths-3.15.0.tgz", - "integrity": "sha512-2Ac2RgzDe/cn48GvOe3M+o82pEFewD3UPbyoUHHdKasHwJKjds4fLXWf/Ux5kATBKN20oaFGu+jbElp1pos0mg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@types/json5": "^0.0.29", - "json5": "^1.0.2", - "minimist": "^1.2.6", - "strip-bom": "^3.0.0" - } - }, - "node_modules/tslib": { - "version": "2.4.0", - "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.4.0.tgz", - "integrity": "sha512-d6xOpEDfsi2CZVlPQzGeux8XMwLT9hssAsaPYExaQMuYskwb+x1x7J371tWlbBdWHroy99KnVB6qIkUbs5X3UQ==", - "dev": true, - "license": "0BSD" - }, - "node_modules/tunnel-agent": { - "version": "0.6.0", - "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", - "integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "safe-buffer": "^5.0.1" - }, - "engines": { - "node": "*" - } - }, - "node_modules/type-check": { - "version": "0.4.0", - "resolved": "https://registry.npmjs.org/type-check/-/type-check-0.4.0.tgz", - "integrity": "sha512-XleUoc9uwGXqjWwXaUTZAmzMcFZ5858QA2vvx1Ur5xIcixXIP+8LnFDgRplU30us6teqdlskFfu+ae4K79Ooew==", - "dev": true, - "license": "MIT", - "dependencies": { - "prelude-ls": "^1.2.1" - }, - "engines": { - "node": ">= 0.8.0" - } - }, - "node_modules/typed-array-buffer": { - "version": "1.0.3", - "resolved": "https://registry.npmjs.org/typed-array-buffer/-/typed-array-buffer-1.0.3.tgz", - "integrity": "sha512-nAYYwfY3qnzX30IkA6AQZjVbtK6duGontcQm1WSG1MD94YLqK0515GNApXkoxKOWMusVssAHWLh9SeaoefYFGw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "es-errors": "^1.3.0", - "is-typed-array": "^1.1.14" - }, - "engines": { - "node": ">= 0.4" - } - }, - "node_modules/typed-array-byte-length": { - "version": "1.0.3", - "resolved": "https://registry.npmjs.org/typed-array-byte-length/-/typed-array-byte-length-1.0.3.tgz", - "integrity": "sha512-BaXgOuIxz8n8pIq3e7Atg/7s+DpiYrxn4vdot3w9KbnBhcRQq6o3xemQdIfynqSeXeDrF32x+WvfzmOjPiY9lg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.8", - "for-each": "^0.3.3", - "gopd": "^1.2.0", - "has-proto": "^1.2.0", - "is-typed-array": "^1.1.14" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/typed-array-byte-offset": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/typed-array-byte-offset/-/typed-array-byte-offset-1.0.4.tgz", - "integrity": "sha512-bTlAFB/FBYMcuX81gbL4OcpH5PmlFHqlCCpAl8AlEzMz5k53oNDvN8p1PNOWLEmI2x4orp3raOFB51tv9X+MFQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "available-typed-arrays": "^1.0.7", - "call-bind": "^1.0.8", - "for-each": "^0.3.3", - "gopd": "^1.2.0", - "has-proto": "^1.2.0", - "is-typed-array": "^1.1.15", - "reflect.getprototypeof": "^1.0.9" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/typed-array-length": { - "version": "1.0.7", - "resolved": "https://registry.npmjs.org/typed-array-length/-/typed-array-length-1.0.7.tgz", - "integrity": "sha512-3KS2b+kL7fsuk/eJZ7EQdnEmQoaho/r6KUef7hxvltNA5DR8NAUM+8wJMbJyZ4G9/7i3v5zPBIMN5aybAh2/Jg==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bind": "^1.0.7", - "for-each": "^0.3.3", - "gopd": "^1.0.1", - "is-typed-array": "^1.1.13", - "possible-typed-array-names": "^1.0.0", - "reflect.getprototypeof": "^1.0.6" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/typescript": { - "version": "5.8.3", - "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.8.3.tgz", - "integrity": "sha512-p1diW6TqL9L07nNxvRMM7hMMw4c5XOo/1ibL4aAIGmSAt9slTE1Xgw5KWuof2uTOvCg9BY7ZRi+GaF+7sfgPeQ==", - "dev": true, - "license": "Apache-2.0", - "bin": { - "tsc": "bin/tsc", - "tsserver": "bin/tsserver" - }, - "engines": { - "node": ">=14.17" - } - }, - "node_modules/typescript-eslint": { - "version": "8.35.1", - "resolved": "https://registry.npmjs.org/typescript-eslint/-/typescript-eslint-8.35.1.tgz", - "integrity": "sha512-xslJjFzhOmHYQzSB/QTeASAHbjmxOGEP6Coh93TXmUBFQoJ1VU35UHIDmG06Jd6taf3wqqC1ntBnCMeymy5Ovw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@typescript-eslint/eslint-plugin": "8.35.1", - "@typescript-eslint/parser": "8.35.1", - "@typescript-eslint/utils": "8.35.1" - }, - "engines": { - "node": "^18.18.0 || ^20.9.0 || >=21.1.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/typescript-eslint" - }, - "peerDependencies": { - "eslint": "^8.57.0 || ^9.0.0", - "typescript": ">=4.8.4 <5.9.0" - } - }, - "node_modules/unbox-primitive": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/unbox-primitive/-/unbox-primitive-1.1.0.tgz", - "integrity": "sha512-nWJ91DjeOkej/TA8pXQ3myruKpKEYgqvpw9lz4OPHj/NWFNluYrjbz9j01CJ8yKQd2g4jFoOkINCTW2I5LEEyw==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.3", - "has-bigints": "^1.0.2", - "has-symbols": "^1.1.0", - "which-boxed-primitive": "^1.1.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/undici-types": { - "version": "5.26.5", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz", - "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==", - "dev": true, - "license": "MIT" - }, - "node_modules/uri-js": { - "version": "4.4.1", - "resolved": "https://registry.npmjs.org/uri-js/-/uri-js-4.4.1.tgz", - "integrity": "sha512-7rKUyy33Q1yc98pQ1DAmLtwX109F7TIfWlW1Ydo8Wl1ii1SeHieeh0HHfPeL2fMXK6z0s8ecKs9frCuLJvndBg==", - "dev": true, - "license": "BSD-2-Clause", - "dependencies": { - "punycode": "^2.1.0" - } - }, - "node_modules/w3c-keyname": { - "version": "2.2.8", - "resolved": "https://registry.npmjs.org/w3c-keyname/-/w3c-keyname-2.2.8.tgz", - "integrity": "sha512-dpojBhNsCNN7T82Tm7k26A6G9ML3NkhDsnw9n/eoxSRlVBB4CEtIQ/KTCLI2Fwf3ataSXRhYFkQi3SlnFwPvPQ==", - "license": "MIT", - "peer": true - }, - "node_modules/which": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", - "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==", - "dev": true, - "license": "ISC", - "dependencies": { - "isexe": "^2.0.0" - }, - "bin": { - "node-which": "bin/node-which" - }, - "engines": { - "node": ">= 8" - } - }, - "node_modules/which-boxed-primitive": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/which-boxed-primitive/-/which-boxed-primitive-1.1.1.tgz", - "integrity": "sha512-TbX3mj8n0odCBFVlY8AxkqcHASw3L60jIuF8jFP78az3C2YhmGvqbHBpAjTRH2/xqYunrJ9g1jSyjCjpoWzIAA==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-bigint": "^1.1.0", - "is-boolean-object": "^1.2.1", - "is-number-object": "^1.1.1", - "is-string": "^1.1.1", - "is-symbol": "^1.1.1" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/which-builtin-type": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/which-builtin-type/-/which-builtin-type-1.2.1.tgz", - "integrity": "sha512-6iBczoX+kDQ7a3+YJBnh3T+KZRxM/iYNPXicqk66/Qfm1b93iu+yOImkg0zHbj5LNOcNv1TEADiZ0xa34B4q6Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "call-bound": "^1.0.2", - "function.prototype.name": "^1.1.6", - "has-tostringtag": "^1.0.2", - "is-async-function": "^2.0.0", - "is-date-object": "^1.1.0", - "is-finalizationregistry": "^1.1.0", - "is-generator-function": "^1.0.10", - "is-regex": "^1.2.1", - "is-weakref": "^1.0.2", - "isarray": "^2.0.5", - "which-boxed-primitive": "^1.1.0", - "which-collection": "^1.0.2", - "which-typed-array": "^1.1.16" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/which-collection": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/which-collection/-/which-collection-1.0.2.tgz", - "integrity": "sha512-K4jVyjnBdgvc86Y6BkaLZEN933SwYOuBFkdmBu9ZfkcAbdVbpITnDmjvZ/aQjRXQrv5EPkTnD1s39GiiqbngCw==", - "dev": true, - "license": "MIT", - "dependencies": { - "is-map": "^2.0.3", - "is-set": "^2.0.3", - "is-weakmap": "^2.0.2", - "is-weakset": "^2.0.3" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/which-typed-array": { - "version": "1.1.19", - "resolved": "https://registry.npmjs.org/which-typed-array/-/which-typed-array-1.1.19.tgz", - "integrity": "sha512-rEvr90Bck4WZt9HHFC4DJMsjvu7x+r6bImz0/BrbWb7A2djJ8hnZMrWnHo9F8ssv0OMErasDhftrfROTyqSDrw==", - "dev": true, - "license": "MIT", - "dependencies": { - "available-typed-arrays": "^1.0.7", - "call-bind": "^1.0.8", - "call-bound": "^1.0.4", - "for-each": "^0.3.5", - "get-proto": "^1.0.1", - "gopd": "^1.2.0", - "has-tostringtag": "^1.0.2" - }, - "engines": { - "node": ">= 0.4" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" - } - }, - "node_modules/word-wrap": { - "version": "1.2.5", - "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz", - "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, - "node_modules/yaml": { - "version": "2.8.1", - "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.8.1.tgz", - "integrity": "sha512-lcYcMxX2PO9XMGvAJkJ3OsNMw+/7FKes7/hgerGUYWIoWu5j/+YQqcZr5JnPZWzOsEBgMbSbiSTn/dv/69Mkpw==", - "dev": true, - "license": "ISC", - "bin": { - "yaml": "bin.mjs" - }, - "engines": { - "node": ">= 14.6" - } - }, - "node_modules/yaml-eslint-parser": { - "version": "1.3.0", - "resolved": "https://registry.npmjs.org/yaml-eslint-parser/-/yaml-eslint-parser-1.3.0.tgz", - "integrity": "sha512-E/+VitOorXSLiAqtTd7Yqax0/pAS3xaYMP+AUUJGOK1OZG3rhcj9fcJOM5HJ2VrP1FrStVCWr1muTfQCdj4tAA==", - "dev": true, - "license": "MIT", - "dependencies": { - "eslint-visitor-keys": "^3.0.0", - "yaml": "^2.0.0" - }, - "engines": { - "node": "^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/ota-meshi" - } - }, - "node_modules/yaml-eslint-parser/node_modules/eslint-visitor-keys": { - "version": "3.4.3", - "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz", - "integrity": "sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==", - "dev": true, - "license": "Apache-2.0", - "engines": { - "node": "^12.22.0 || ^14.17.0 || >=16.0.0" - }, - "funding": { - "url": "https://opencollective.com/eslint" - } - }, - "node_modules/yocto-queue": { - "version": "0.1.0", - "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", - "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - } - } -} diff --git a/surfsense_obsidian/package.json b/surfsense_obsidian/package.json deleted file mode 100644 index 21504cd5a5..0000000000 --- a/surfsense_obsidian/package.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "name": "surfsense-obsidian", - "version": "0.1.0", - "description": "SurfSense plugin for Obsidian: sync your vault to SurfSense for AI-powered search.", - "main": "main.js", - "type": "module", - "scripts": { - "dev": "node esbuild.config.mjs", - "build": "tsc -noEmit -skipLibCheck && node esbuild.config.mjs production", - "version": "node version-bump.mjs && git add manifest.json versions.json", - "lint": "eslint ." - }, - "keywords": [ - "obsidian", - "surfsense", - "sync", - "search" - ], - "license": "Apache-2.0", - "devDependencies": { - "@eslint/js": "9.30.1", - "@types/node": "^20.19.39", - "esbuild": "0.25.5", - "eslint-plugin-obsidianmd": "0.1.9", - "globals": "14.0.0", - "jiti": "2.6.1", - "tslib": "2.4.0", - "typescript": "^5.8.3", - "typescript-eslint": "8.35.1" - }, - "dependencies": { - "obsidian": "latest" - } -} diff --git a/surfsense_obsidian/pnpm-lock.yaml b/surfsense_obsidian/pnpm-lock.yaml deleted file mode 100644 index 92b2696758..0000000000 --- a/surfsense_obsidian/pnpm-lock.yaml +++ /dev/null @@ -1,3153 +0,0 @@ -lockfileVersion: '9.0' - -settings: - autoInstallPeers: true - excludeLinksFromLockfile: false - -importers: - - .: - dependencies: - obsidian: - specifier: latest - version: 1.13.1(@codemirror/state@6.5.0)(@codemirror/view@6.38.6) - devDependencies: - '@eslint/js': - specifier: 9.30.1 - version: 9.30.1 - '@types/node': - specifier: ^20.19.39 - version: 20.19.43 - esbuild: - specifier: 0.25.5 - version: 0.25.5 - eslint-plugin-obsidianmd: - specifier: 0.1.9 - version: 0.1.9(@eslint/js@9.30.1)(@eslint/json@0.14.0)(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.4(jiti@2.6.1))(obsidian@1.13.1(@codemirror/state@6.5.0)(@codemirror/view@6.38.6))(typescript-eslint@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3)) - globals: - specifier: 14.0.0 - version: 14.0.0 - jiti: - specifier: 2.6.1 - version: 2.6.1 - tslib: - specifier: 2.4.0 - version: 2.4.0 - typescript: - specifier: ^5.8.3 - version: 5.9.3 - typescript-eslint: - specifier: 8.35.1 - version: 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - -packages: - - '@codemirror/state@6.5.0': - resolution: {integrity: sha512-MwBHVK60IiIHDcoMet78lxt6iw5gJOGSbNbOIVBHWVXIH4/Nq1+GQgLLGgI1KlnN86WDXsPudVaqYHKBIx7Eyw==} - - '@codemirror/view@6.38.6': - resolution: {integrity: sha512-qiS0z1bKs5WOvHIAC0Cybmv4AJSkAXgX5aD6Mqd2epSLlVJsQl8NG23jCVouIgkh4All/mrbdsf2UOLFnJw0tw==} - - '@esbuild/aix-ppc64@0.25.5': - resolution: {integrity: sha512-9o3TMmpmftaCMepOdA5k/yDw8SfInyzWWTjYTFCX3kPSDJMROQTb8jg+h9Cnwnmm1vOzvxN7gIfB5V2ewpjtGA==} - engines: {node: '>=18'} - cpu: [ppc64] - os: [aix] - - '@esbuild/android-arm64@0.25.5': - resolution: {integrity: sha512-VGzGhj4lJO+TVGV1v8ntCZWJktV7SGCs3Pn1GRWI1SBFtRALoomm8k5E9Pmwg3HOAal2VDc2F9+PM/rEY6oIDg==} - engines: {node: '>=18'} - cpu: [arm64] - os: [android] - - '@esbuild/android-arm@0.25.5': - resolution: {integrity: sha512-AdJKSPeEHgi7/ZhuIPtcQKr5RQdo6OO2IL87JkianiMYMPbCtot9fxPbrMiBADOWWm3T2si9stAiVsGbTQFkbA==} - engines: {node: '>=18'} - cpu: [arm] - os: [android] - - '@esbuild/android-x64@0.25.5': - resolution: {integrity: sha512-D2GyJT1kjvO//drbRT3Hib9XPwQeWd9vZoBJn+bu/lVsOZ13cqNdDeqIF/xQ5/VmWvMduP6AmXvylO/PIc2isw==} - engines: {node: '>=18'} - cpu: [x64] - os: [android] - - '@esbuild/darwin-arm64@0.25.5': - resolution: {integrity: sha512-GtaBgammVvdF7aPIgH2jxMDdivezgFu6iKpmT+48+F8Hhg5J/sfnDieg0aeG/jfSvkYQU2/pceFPDKlqZzwnfQ==} - engines: {node: '>=18'} - cpu: [arm64] - os: [darwin] - - '@esbuild/darwin-x64@0.25.5': - resolution: {integrity: sha512-1iT4FVL0dJ76/q1wd7XDsXrSW+oLoquptvh4CLR4kITDtqi2e/xwXwdCVH8hVHU43wgJdsq7Gxuzcs6Iq/7bxQ==} - engines: {node: '>=18'} - cpu: [x64] - os: [darwin] - - '@esbuild/freebsd-arm64@0.25.5': - resolution: {integrity: sha512-nk4tGP3JThz4La38Uy/gzyXtpkPW8zSAmoUhK9xKKXdBCzKODMc2adkB2+8om9BDYugz+uGV7sLmpTYzvmz6Sw==} - engines: {node: '>=18'} - cpu: [arm64] - os: [freebsd] - - '@esbuild/freebsd-x64@0.25.5': - resolution: {integrity: sha512-PrikaNjiXdR2laW6OIjlbeuCPrPaAl0IwPIaRv+SMV8CiM8i2LqVUHFC1+8eORgWyY7yhQY+2U2fA55mBzReaw==} - engines: {node: '>=18'} - cpu: [x64] - os: [freebsd] - - '@esbuild/linux-arm64@0.25.5': - resolution: {integrity: sha512-Z9kfb1v6ZlGbWj8EJk9T6czVEjjq2ntSYLY2cw6pAZl4oKtfgQuS4HOq41M/BcoLPzrUbNd+R4BXFyH//nHxVg==} - engines: {node: '>=18'} - cpu: [arm64] - os: [linux] - - '@esbuild/linux-arm@0.25.5': - resolution: {integrity: sha512-cPzojwW2okgh7ZlRpcBEtsX7WBuqbLrNXqLU89GxWbNt6uIg78ET82qifUy3W6OVww6ZWobWub5oqZOVtwolfw==} - engines: {node: '>=18'} - cpu: [arm] - os: [linux] - - '@esbuild/linux-ia32@0.25.5': - resolution: {integrity: sha512-sQ7l00M8bSv36GLV95BVAdhJ2QsIbCuCjh/uYrWiMQSUuV+LpXwIqhgJDcvMTj+VsQmqAHL2yYaasENvJ7CDKA==} - engines: {node: '>=18'} - cpu: [ia32] - os: [linux] - - '@esbuild/linux-loong64@0.25.5': - resolution: {integrity: sha512-0ur7ae16hDUC4OL5iEnDb0tZHDxYmuQyhKhsPBV8f99f6Z9KQM02g33f93rNH5A30agMS46u2HP6qTdEt6Q1kg==} - engines: {node: '>=18'} - cpu: [loong64] - os: [linux] - - '@esbuild/linux-mips64el@0.25.5': - resolution: {integrity: sha512-kB/66P1OsHO5zLz0i6X0RxlQ+3cu0mkxS3TKFvkb5lin6uwZ/ttOkP3Z8lfR9mJOBk14ZwZ9182SIIWFGNmqmg==} - engines: {node: '>=18'} - cpu: [mips64el] - os: [linux] - - '@esbuild/linux-ppc64@0.25.5': - resolution: {integrity: sha512-UZCmJ7r9X2fe2D6jBmkLBMQetXPXIsZjQJCjgwpVDz+YMcS6oFR27alkgGv3Oqkv07bxdvw7fyB71/olceJhkQ==} - engines: {node: '>=18'} - cpu: [ppc64] - os: [linux] - - '@esbuild/linux-riscv64@0.25.5': - resolution: {integrity: sha512-kTxwu4mLyeOlsVIFPfQo+fQJAV9mh24xL+y+Bm6ej067sYANjyEw1dNHmvoqxJUCMnkBdKpvOn0Ahql6+4VyeA==} - engines: {node: '>=18'} - cpu: [riscv64] - os: [linux] - - '@esbuild/linux-s390x@0.25.5': - resolution: {integrity: sha512-K2dSKTKfmdh78uJ3NcWFiqyRrimfdinS5ErLSn3vluHNeHVnBAFWC8a4X5N+7FgVE1EjXS1QDZbpqZBjfrqMTQ==} - engines: {node: '>=18'} - cpu: [s390x] - os: [linux] - - '@esbuild/linux-x64@0.25.5': - resolution: {integrity: sha512-uhj8N2obKTE6pSZ+aMUbqq+1nXxNjZIIjCjGLfsWvVpy7gKCOL6rsY1MhRh9zLtUtAI7vpgLMK6DxjO8Qm9lJw==} - engines: {node: '>=18'} - cpu: [x64] - os: [linux] - - '@esbuild/netbsd-arm64@0.25.5': - resolution: {integrity: sha512-pwHtMP9viAy1oHPvgxtOv+OkduK5ugofNTVDilIzBLpoWAM16r7b/mxBvfpuQDpRQFMfuVr5aLcn4yveGvBZvw==} - engines: {node: '>=18'} - cpu: [arm64] - os: [netbsd] - - '@esbuild/netbsd-x64@0.25.5': - resolution: {integrity: sha512-WOb5fKrvVTRMfWFNCroYWWklbnXH0Q5rZppjq0vQIdlsQKuw6mdSihwSo4RV/YdQ5UCKKvBy7/0ZZYLBZKIbwQ==} - engines: {node: '>=18'} - cpu: [x64] - os: [netbsd] - - '@esbuild/openbsd-arm64@0.25.5': - resolution: {integrity: sha512-7A208+uQKgTxHd0G0uqZO8UjK2R0DDb4fDmERtARjSHWxqMTye4Erz4zZafx7Di9Cv+lNHYuncAkiGFySoD+Mw==} - engines: {node: '>=18'} - cpu: [arm64] - os: [openbsd] - - '@esbuild/openbsd-x64@0.25.5': - resolution: {integrity: sha512-G4hE405ErTWraiZ8UiSoesH8DaCsMm0Cay4fsFWOOUcz8b8rC6uCvnagr+gnioEjWn0wC+o1/TAHt+It+MpIMg==} - engines: {node: '>=18'} - cpu: [x64] - os: [openbsd] - - '@esbuild/sunos-x64@0.25.5': - resolution: {integrity: sha512-l+azKShMy7FxzY0Rj4RCt5VD/q8mG/e+mDivgspo+yL8zW7qEwctQ6YqKX34DTEleFAvCIUviCFX1SDZRSyMQA==} - engines: {node: '>=18'} - cpu: [x64] - os: [sunos] - - '@esbuild/win32-arm64@0.25.5': - resolution: {integrity: sha512-O2S7SNZzdcFG7eFKgvwUEZ2VG9D/sn/eIiz8XRZ1Q/DO5a3s76Xv0mdBzVM5j5R639lXQmPmSo0iRpHqUUrsxw==} - engines: {node: '>=18'} - cpu: [arm64] - os: [win32] - - '@esbuild/win32-ia32@0.25.5': - resolution: {integrity: sha512-onOJ02pqs9h1iMJ1PQphR+VZv8qBMQ77Klcsqv9CNW2w6yLqoURLcgERAIurY6QE63bbLuqgP9ATqajFLK5AMQ==} - engines: {node: '>=18'} - cpu: [ia32] - os: [win32] - - '@esbuild/win32-x64@0.25.5': - resolution: {integrity: sha512-TXv6YnJ8ZMVdX+SXWVBo/0p8LTcrUYngpWjvm91TMjjBQii7Oz11Lw5lbDV5Y0TzuhSJHwiH4hEtC1I42mMS0g==} - engines: {node: '>=18'} - cpu: [x64] - os: [win32] - - '@eslint-community/eslint-utils@4.9.1': - resolution: {integrity: sha512-phrYmNiYppR7znFEdqgfWHXR6NCkZEK7hwWDHZUjit/2/U0r6XvkDl0SYnoM51Hq7FhCGdLDT6zxCCOY1hexsQ==} - engines: {node: ^12.22.0 || ^14.17.0 || >=16.0.0} - peerDependencies: - eslint: ^6.0.0 || ^7.0.0 || >=8.0.0 - - '@eslint-community/regexpp@4.12.2': - resolution: {integrity: sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew==} - engines: {node: ^12.0.0 || ^14.0.0 || >=16.0.0} - - '@eslint/config-array@0.21.2': - resolution: {integrity: sha512-nJl2KGTlrf9GjLimgIru+V/mzgSK0ABCDQRvxw5BjURL7WfH5uoWmizbH7QB6MmnMBd8cIC9uceWnezL1VZWWw==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/config-helpers@0.4.2': - resolution: {integrity: sha512-gBrxN88gOIf3R7ja5K9slwNayVcZgK6SOUORm2uBzTeIEfeVaIhOpCtTox3P6R7o2jLFwLFTLnC7kU/RGcYEgw==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/core@0.17.0': - resolution: {integrity: sha512-yL/sLrpmtDaFEiUj1osRP4TI2MDz1AddJL+jZ7KSqvBuliN4xqYY54IfdN8qD8Toa6g1iloph1fxQNkjOxrrpQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/eslintrc@3.3.5': - resolution: {integrity: sha512-4IlJx0X0qftVsN5E+/vGujTRIFtwuLbNsVUe7TO6zYPDR1O6nFwvwhIKEKSrl6dZchmYBITazxKoUYOjdtjlRg==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/js@9.30.1': - resolution: {integrity: sha512-zXhuECFlyep42KZUhWjfvsmXGX39W8K8LFb8AWXM9gSV9dQB+MrJGLKvW6Zw0Ggnbpw0VHTtrhFXYe3Gym18jg==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/js@9.39.4': - resolution: {integrity: sha512-nE7DEIchvtiFTwBw4Lfbu59PG+kCofhjsKaCWzxTpt4lfRjRMqG6uMBzKXuEcyXhOHoUp9riAm7/aWYGhXZ9cw==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/json@0.14.0': - resolution: {integrity: sha512-rvR/EZtvUG3p9uqrSmcDJPYSH7atmWr0RnFWN6m917MAPx82+zQgPUmDu0whPFG6XTyM0vB/hR6c1Q63OaYtCQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/object-schema@2.1.7': - resolution: {integrity: sha512-VtAOaymWVfZcmZbp6E2mympDIHvyjXs/12LqWYjVw6qjrfF+VK+fyG33kChz3nnK+SU5/NeHOqrTEHS8sXO3OA==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@eslint/plugin-kit@0.4.1': - resolution: {integrity: sha512-43/qtrDUokr7LJqoF2c3+RInu/t4zfrpYdoSDfYyhg52rwLV6TnOvdG4fXm7IkSB3wErkcmJS9iEhjVtOSEjjA==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@humanfs/core@0.19.2': - resolution: {integrity: sha512-UhXNm+CFMWcbChXywFwkmhqjs3PRCmcSa/hfBgLIb7oQ5HNb1wS0icWsGtSAUNgefHeI+eBrA8I1fxmbHsGdvA==} - engines: {node: '>=18.18.0'} - - '@humanfs/node@0.16.8': - resolution: {integrity: sha512-gE1eQNZ3R++kTzFUpdGlpmy8kDZD/MLyHqDwqjkVQI0JMdI1D51sy1H958PNXYkM2rAac7e5/CnIKZrHtPh3BQ==} - engines: {node: '>=18.18.0'} - - '@humanfs/types@0.15.0': - resolution: {integrity: sha512-ZZ1w0aoQkwuUuC7Yf+7sdeaNfqQiiLcSRbfI08oAxqLtpXQr9AIVX7Ay7HLDuiLYAaFPu8oBYNq/QIi9URHJ3Q==} - engines: {node: '>=18.18.0'} - - '@humanwhocodes/module-importer@1.0.1': - resolution: {integrity: sha512-bxveV4V8v5Yb4ncFTT3rPSgZBOpCkjfK0y4oVVVJwIuDVBRMDXrPyXRL988i5ap9m9bnyEEjWfm5WkBmtffLfA==} - engines: {node: '>=12.22'} - - '@humanwhocodes/momoa@3.3.10': - resolution: {integrity: sha512-KWiFQpSAqEIyrTXko3hFNLeQvSK8zXlJQzhhxsyVn58WFRYXST99b3Nqnu+ttOtjds2Pl2grUHGpe2NzhPynuQ==} - engines: {node: '>=18'} - - '@humanwhocodes/retry@0.4.3': - resolution: {integrity: sha512-bV0Tgo9K4hfPCek+aMAn81RppFKv2ySDQeMoSZuvTASywNTnVJCArCZE2FWqpvIatKu7VMRLWlR1EazvVhDyhQ==} - engines: {node: '>=18.18'} - - '@marijn/find-cluster-break@1.0.3': - resolution: {integrity: sha512-FY+MKLBoTsLNJF/eLWaOsXGdz6uh3Iu1axjPf6TUq92IYumcTcXWHoS747JARLkcdlJ/Waiaxc5wQfFO8jC6NA==} - - '@microsoft/eslint-plugin-sdl@1.1.0': - resolution: {integrity: sha512-dxdNHOemLnBhfY3eByrujX9KyLigcNtW8sU+axzWv5nLGcsSBeKW2YYyTpfPo1hV8YPOmIGnfA4fZHyKVtWqBQ==} - engines: {node: '>=18.0.0'} - peerDependencies: - eslint: ^9 - - '@nodelib/fs.scandir@2.1.5': - resolution: {integrity: sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==} - engines: {node: '>= 8'} - - '@nodelib/fs.stat@2.0.5': - resolution: {integrity: sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A==} - engines: {node: '>= 8'} - - '@nodelib/fs.walk@1.2.8': - resolution: {integrity: sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg==} - engines: {node: '>= 8'} - - '@pkgr/core@0.1.2': - resolution: {integrity: sha512-fdDH1LSGfZdTH2sxdpVMw31BanV28K/Gry0cVFxaNP77neJSkd82mM8ErPNYs9e+0O7SdHBLTDzDgwUuy18RnQ==} - engines: {node: ^12.20.0 || ^14.18.0 || >=16.0.0} - - '@rtsao/scc@1.1.0': - resolution: {integrity: sha512-zt6OdqaDoOnJ1ZYsCYGt9YmWzDXl4vQdKTyJev62gFhRGKdx7mcT54V9KIjg+d2wi9EXsPvAPKe7i7WjfVWB8g==} - - '@types/codemirror@5.60.8': - resolution: {integrity: sha512-VjFgDF/eB+Aklcy15TtOTLQeMjTo07k7KAjql8OK5Dirr7a6sJY4T1uVBDuTVG9VEmn1uUsohOpYnVfgC6/jyw==} - - '@types/eslint@8.56.2': - resolution: {integrity: sha512-uQDwm1wFHmbBbCZCqAlq6Do9LYwByNZHWzXppSnay9SuwJ+VRbjkbLABer54kcPnMSlG6Fdiy2yaFXm/z9Z5gw==} - - '@types/estree@1.0.9': - resolution: {integrity: sha512-GhdPgy1el4/ImP05X05Uw4cw2/M93BCUmnEvWZNStlCzEKME4Fkk+YpoA5OiHNQmoS7Cafb8Xa3Pya8m1Qrzeg==} - - '@types/json-schema@7.0.15': - resolution: {integrity: sha512-5+fP8P8MFNC+AyZCDxrB2pkZFPGzqQWUzpSeuuVLvm8VMcorNYavBqoFcxK8bQz4Qsbn4oUEEem4wDLfcysGHA==} - - '@types/json5@0.0.29': - resolution: {integrity: sha512-dRLjCWHYg4oaA77cxO64oO+7JwCwnIzkZPdrrC71jQmQtlhM556pwKo5bUzqvZndkVbeFLIIi+9TC40JNF5hNQ==} - - '@types/node@20.12.12': - resolution: {integrity: sha512-eWLDGF/FOSPtAvEqeRAQ4C8LSA7M1I7i0ky1I8U7kD1J5ITyW3AsRhQrKVoWf5pFKZ2kILsEGJhsI9r93PYnOw==} - - '@types/node@20.19.43': - resolution: {integrity: sha512-6oYBAi5ikg4Pl+kGsoYtawUMBT2zZMCvPNF7pVLnHZfd1zf38DRiWn/gT01RYCdUqkv7Fhr+C9ot4/tb+2sVvA==} - - '@types/tern@0.23.9': - resolution: {integrity: sha512-ypzHFE/wBzh+BlH6rrBgS5I/Z7RD21pGhZ2rltb/+ZrVM1awdZwjx7hE5XfuYgHWk9uvV5HLZN3SloevCAp3Bw==} - - '@typescript-eslint/eslint-plugin@8.35.1': - resolution: {integrity: sha512-9XNTlo7P7RJxbVeICaIIIEipqxLKguyh+3UbXuT2XQuFp6d8VOeDEGuz5IiX0dgZo8CiI6aOFLg4e8cF71SFVg==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - '@typescript-eslint/parser': ^8.35.1 - eslint: ^8.57.0 || ^9.0.0 - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/parser@8.35.1': - resolution: {integrity: sha512-3MyiDfrfLeK06bi/g9DqJxP5pV74LNv4rFTyvGDmT3x2p1yp1lOd+qYZfiRPIOf/oON+WRZR5wxxuF85qOar+w==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - eslint: ^8.57.0 || ^9.0.0 - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/project-service@8.35.1': - resolution: {integrity: sha512-VYxn/5LOpVxADAuP3NrnxxHYfzVtQzLKeldIhDhzC8UHaiQvYlXvKuVho1qLduFbJjjy5U5bkGwa3rUGUb1Q6Q==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/scope-manager@8.35.1': - resolution: {integrity: sha512-s/Bpd4i7ht2934nG+UoSPlYXd08KYz3bmjLEb7Ye1UVob0d1ENiT3lY8bsCmik4RqfSbPw9xJJHbugpPpP5JUg==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@typescript-eslint/tsconfig-utils@8.35.1': - resolution: {integrity: sha512-K5/U9VmT9dTHoNowWZpz+/TObS3xqC5h0xAIjXPw+MNcKV9qg6eSatEnmeAwkjHijhACH0/N7bkhKvbt1+DXWQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/type-utils@8.35.1': - resolution: {integrity: sha512-HOrUBlfVRz5W2LIKpXzZoy6VTZzMu2n8q9C2V/cFngIC5U1nStJgv0tMV4sZPzdf4wQm9/ToWUFPMN9Vq9VJQQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - eslint: ^8.57.0 || ^9.0.0 - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/types@8.35.1': - resolution: {integrity: sha512-q/O04vVnKHfrrhNAscndAn1tuQhIkwqnaW+eu5waD5IPts2eX1dgJxgqcPx5BX109/qAz7IG6VrEPTOYKCNfRQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - '@typescript-eslint/typescript-estree@8.35.1': - resolution: {integrity: sha512-Vvpuvj4tBxIka7cPs6Y1uvM7gJgdF5Uu9F+mBJBPY4MhvjrjWGK4H0lVgLJd/8PWZ23FTqsaJaLEkBCFUk8Y9g==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/utils@8.35.1': - resolution: {integrity: sha512-lhnwatFmOFcazAsUm3ZnZFpXSxiwoa1Lj50HphnDe1Et01NF4+hrdXONSUHIcbVu2eFb1bAf+5yjXkGVkXBKAQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - eslint: ^8.57.0 || ^9.0.0 - typescript: '>=4.8.4 <5.9.0' - - '@typescript-eslint/visitor-keys@8.35.1': - resolution: {integrity: sha512-VRwixir4zBWCSTP/ljEo091lbpypz57PoeAQ9imjG+vbeof9LplljsL1mos4ccG6H9IjfrVGM359RozUnuFhpw==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - acorn-jsx@5.3.2: - resolution: {integrity: sha512-rq9s+JNhf0IChjtDXxllJ7g41oZk5SlXtp0LHwyA5cejwn7vKmKp4pPri6YEePv2PU65sAsegbXtIinmDFDXgQ==} - peerDependencies: - acorn: ^6.0.0 || ^7.0.0 || ^8.0.0 - - acorn@8.17.0: - resolution: {integrity: sha512-xRQbDb9BnwDafYNn6Vwl839DYVjqXYb1XVGtWAZ1kcDc6iwAL4hg3B1dZlRiuENFeO2H53gFG3in621AdERVAg==} - engines: {node: '>=0.4.0'} - hasBin: true - - ajv@6.15.0: - resolution: {integrity: sha512-fgFx7Hfoq60ytK2c7DhnF8jIvzYgOMxfugjLOSMHjLIPgenqa7S7oaagATUq99mV6IYvN2tRmC0wnTYX6iPbMw==} - - ajv@8.20.0: - resolution: {integrity: sha512-Thbli+OlOj+iMPYFBVBfJ3OmCAnaSyNn4M1vz9T6Gka5Jt9ba/HIR56joy65tY6kx/FCF5VXNB819Y7/GUrBGA==} - - ansi-styles@4.3.0: - resolution: {integrity: sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==} - engines: {node: '>=8'} - - argparse@2.0.1: - resolution: {integrity: sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q==} - - array-buffer-byte-length@1.0.2: - resolution: {integrity: sha512-LHE+8BuR7RYGDKvnrmcuSq3tDcKv9OFEXQt/HpbZhY7V6h0zlUXutnAD82GiFx9rdieCMjkvtcsPqBwgUl1Iiw==} - engines: {node: '>= 0.4'} - - array-includes@3.1.9: - resolution: {integrity: sha512-FmeCCAenzH0KH381SPT5FZmiA/TmpndpcaShhfgEN9eCVjnFBqq3l1xrI42y8+PPLI6hypzou4GXw00WHmPBLQ==} - engines: {node: '>= 0.4'} - - array.prototype.findlast@1.2.5: - resolution: {integrity: sha512-CVvd6FHg1Z3POpBLxO6E6zr+rSKEQ9L6rZHAaY7lLfhKsWYUBBOuMs0e9o24oopj6H+geRCX0YJ+TJLBK2eHyQ==} - engines: {node: '>= 0.4'} - - array.prototype.findlastindex@1.2.6: - resolution: {integrity: sha512-F/TKATkzseUExPlfvmwQKGITM3DGTK+vkAsCZoDc5daVygbJBnjEUCbgkAvVFsgfXfX4YIqZ/27G3k3tdXrTxQ==} - engines: {node: '>= 0.4'} - - array.prototype.flat@1.3.3: - resolution: {integrity: sha512-rwG/ja1neyLqCuGZ5YYrznA62D4mZXg0i1cIskIUKSiqF3Cje9/wXAls9B9s1Wa2fomMsIv8czB8jZcPmxCXFg==} - engines: {node: '>= 0.4'} - - array.prototype.flatmap@1.3.3: - resolution: {integrity: sha512-Y7Wt51eKJSyi80hFrJCePGGNo5ktJCslFuboqJsbf57CCPcm5zztluPlc4/aD8sWsKvlwatezpV4U1efk8kpjg==} - engines: {node: '>= 0.4'} - - array.prototype.tosorted@1.1.4: - resolution: {integrity: sha512-p6Fx8B7b7ZhL/gmUsAy0D15WhvDccw3mnGNbZpi3pmeJdxtWsj2jEaI4Y6oo3XiHfzuSgPwKc04MYt6KgvC/wA==} - engines: {node: '>= 0.4'} - - arraybuffer.prototype.slice@1.0.4: - resolution: {integrity: sha512-BNoCY6SXXPQ7gF2opIP4GBE+Xw7U+pHMYKuzjgCN3GwiaIR09UUeKfheyIry77QtrCBlC0KK0q5/TER/tYh3PQ==} - engines: {node: '>= 0.4'} - - async-function@1.0.0: - resolution: {integrity: sha512-hsU18Ae8CDTR6Kgu9DYf0EbCr/a5iGL0rytQDobUcdpYOKokk8LEjVphnXkDkgpi0wYVsqrXuP0bZxJaTqdgoA==} - engines: {node: '>= 0.4'} - - available-typed-arrays@1.0.7: - resolution: {integrity: sha512-wvUjBtSGN7+7SjNpq/9M2Tg350UZD3q62IFZLbRAR1bSMlCo1ZaeW+BJ+D090e4hIIZLBcTDWe4Mh4jvUDajzQ==} - engines: {node: '>= 0.4'} - - balanced-match@1.0.2: - resolution: {integrity: sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==} - - brace-expansion@1.1.15: - resolution: {integrity: sha512-EwOCDEex4quD37XhqM3omwtMoJjr//isUZz1JopUNWms+4Z2ViyM/k1YIRePpoVNnQhENnxtFjLaxNHrT7xIUg==} - - brace-expansion@2.1.1: - resolution: {integrity: sha512-WR1cURNjuvBLMZBMbqM0UoE+WAfdUcEV1ccD8PVBVOI+Z3ND4+SZbN8RsfT2bMuG1qwz5RFvPukSZm5fF2D5eA==} - - braces@3.0.3: - resolution: {integrity: sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==} - engines: {node: '>=8'} - - call-bind-apply-helpers@1.0.2: - resolution: {integrity: sha512-Sp1ablJ0ivDkSzjcaJdxEunN5/XvksFJ2sMBFfq6x0ryhQV/2b/KwFe21cMpmHtPOSij8K99/wSfoEuTObmuMQ==} - engines: {node: '>= 0.4'} - - call-bind@1.0.9: - resolution: {integrity: sha512-a/hy+pNsFUTR+Iz8TCJvXudKVLAnz/DyeSUo10I5yvFDQJBFU2s9uqQpoSrJlroHUKoKqzg+epxyP9lqFdzfBQ==} - engines: {node: '>= 0.4'} - - call-bound@1.0.4: - resolution: {integrity: sha512-+ys997U96po4Kx/ABpBCqhA9EuxJaQWDQg7295H4hBphv3IZg0boBKuwYpt4YXp6MZ5AmZQnU/tyMTlRpaSejg==} - engines: {node: '>= 0.4'} - - callsites@3.1.0: - resolution: {integrity: sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==} - engines: {node: '>=6'} - - chalk@4.1.2: - resolution: {integrity: sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==} - engines: {node: '>=10'} - - color-convert@2.0.1: - resolution: {integrity: sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==} - engines: {node: '>=7.0.0'} - - color-name@1.1.4: - resolution: {integrity: sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==} - - concat-map@0.0.1: - resolution: {integrity: sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==} - - crelt@1.0.7: - resolution: {integrity: sha512-aK6BbWfhf4U/wCcLHKPJl/xa6VkVstRaPywWtMKGwuOLc/wZTyQYuoxgvZnNsBvv7Kg3YTBQYYBCggcviQczuA==} - - cross-spawn@7.0.6: - resolution: {integrity: sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==} - engines: {node: '>= 8'} - - data-view-buffer@1.0.2: - resolution: {integrity: sha512-EmKO5V3OLXh1rtK2wgXRansaK1/mtVdTUEiEI0W8RkvgT05kfxaH29PliLnpLP73yYO6142Q72QNa8Wx/A5CqQ==} - engines: {node: '>= 0.4'} - - data-view-byte-length@1.0.2: - resolution: {integrity: sha512-tuhGbE6CfTM9+5ANGf+oQb72Ky/0+s3xKUpHvShfiz2RxMFgFPjsXuRLBVMtvMs15awe45SRb83D6wH4ew6wlQ==} - engines: {node: '>= 0.4'} - - data-view-byte-offset@1.0.1: - resolution: {integrity: sha512-BS8PfmtDGnrgYdOonGZQdLZslWIeCGFP9tpan0hi1Co2Zr2NKADsvGYA8XxuG/4UWgJ6Cjtv+YJnB6MM69QGlQ==} - engines: {node: '>= 0.4'} - - debug@3.2.7: - resolution: {integrity: sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==} - peerDependencies: - supports-color: '*' - peerDependenciesMeta: - supports-color: - optional: true - - debug@4.4.3: - resolution: {integrity: sha512-RGwwWnwQvkVfavKVt22FGLw+xYSdzARwm0ru6DhTVA3umU5hZc28V3kO4stgYryrTlLpuvgI9GiijltAjNbcqA==} - engines: {node: '>=6.0'} - peerDependencies: - supports-color: '*' - peerDependenciesMeta: - supports-color: - optional: true - - deep-is@0.1.4: - resolution: {integrity: sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==} - - define-data-property@1.1.4: - resolution: {integrity: sha512-rBMvIzlpA8v6E+SJZoo++HAYqsLrkg7MSfIinMPFhmkorw7X+dOXVJQs+QT69zGkzMyfDnIMN2Wid1+NbL3T+A==} - engines: {node: '>= 0.4'} - - define-properties@1.2.1: - resolution: {integrity: sha512-8QmQKqEASLd5nx0U1B1okLElbUuuttJ/AnYmRXbbbGDWh6uS208EjD4Xqq/I9wK7u0v6O08XhTWnt5XtEbR6Dg==} - engines: {node: '>= 0.4'} - - doctrine@2.1.0: - resolution: {integrity: sha512-35mSku4ZXK0vfCuHEDAwt55dg2jNajHZ1odvF+8SSr82EsZY4QmXfuWso8oEd8zRhVObSN18aM0CjSdoBX7zIw==} - engines: {node: '>=0.10.0'} - - dunder-proto@1.0.1: - resolution: {integrity: sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==} - engines: {node: '>= 0.4'} - - empathic@2.0.1: - resolution: {integrity: sha512-YGRs8knHhKHVShLkFET/rWAU8kmHbOV5LwN938RHI0pljAJ1Gf6SzXsSmRaEzcXTtOOmVqJ5+WtQPL5uigY50Q==} - engines: {node: '>=14'} - - enhanced-resolve@5.24.1: - resolution: {integrity: sha512-7DdUaTjmNwMcH2gLr1qycesKII3BK4RLy/mdAb7x10Lq7bR4aNKHt1BR1ZALSv0rPM/hF5wYF0PhGop/rJm8vw==} - engines: {node: '>=10.13.0'} - - es-abstract-get@1.0.0: - resolution: {integrity: sha512-6PMWXpdhshVvFp+FoWYs1EvG1Nj0tvk0dZM+XcK0xMEM1czRVcP6ohqPWHy6qPagSpC8j4+p89WXlT+xXJs/fg==} - engines: {node: '>= 0.4'} - - es-abstract@1.24.2: - resolution: {integrity: sha512-2FpH9Q5i2RRwyEP1AylXe6nYLR5OhaJTZwmlcP0dL/+JCbgg7yyEo/sEK6HeGZRf3dFpWwThaRHVApXSkW3xeg==} - engines: {node: '>= 0.4'} - - es-define-property@1.0.1: - resolution: {integrity: sha512-e3nRfgfUZ4rNGL232gUgX06QNyyez04KdjFrF+LTRoOXmrOgFKDg4BCdsjW8EnT69eqdYGmRpJwiPVYNrCaW3g==} - engines: {node: '>= 0.4'} - - es-errors@1.3.0: - resolution: {integrity: sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==} - engines: {node: '>= 0.4'} - - es-iterator-helpers@1.3.3: - resolution: {integrity: sha512-0PuBxFi+4uPanB97iDxCLWuHeYud2FALrw5HFZGtAF38UpJDbDC8frwp2cnDyae692CQ0dou60UwWfhgsa4U/g==} - engines: {node: '>= 0.4'} - - es-object-atoms@1.1.2: - resolution: {integrity: sha512-HWcBoN6NileqtSydK2FqHbS/LoDd2pqrnQHLyJzBj4kOp/ky2MWMN694xOfkK8/SnUsW2DH7EfyVlydKCsm1Zw==} - engines: {node: '>= 0.4'} - - es-set-tostringtag@2.1.0: - resolution: {integrity: sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==} - engines: {node: '>= 0.4'} - - es-shim-unscopables@1.1.0: - resolution: {integrity: sha512-d9T8ucsEhh8Bi1woXCf+TIKDIROLG5WCkxg8geBCbvk22kzwC5G2OnXVMO6FUsvQlgUUXQ2itephWDLqDzbeCw==} - engines: {node: '>= 0.4'} - - es-to-primitive@1.3.4: - resolution: {integrity: sha512-yPDz7wqpg1/mmHLmS3tcfTfbw5f1eryXvyghYBffGdERwe+mV7ZcWzTR8LR17Kvqt3qfPurjlonmnq3MKXIOXw==} - engines: {node: '>= 0.4'} - - esbuild@0.25.5: - resolution: {integrity: sha512-P8OtKZRv/5J5hhz0cUAdu/cLuPIKXpQl1R9pZtvmHWQvrAUVd0UNIPT4IB4W3rNOqVO0rlqHmCIbSwxh/c9yUQ==} - engines: {node: '>=18'} - hasBin: true - - escape-string-regexp@4.0.0: - resolution: {integrity: sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA==} - engines: {node: '>=10'} - - eslint-compat-utils@0.5.1: - resolution: {integrity: sha512-3z3vFexKIEnjHE3zCMRo6fn/e44U7T1khUjg+Hp0ZQMCigh28rALD0nPFBcGZuiLC5rLZa2ubQHDRln09JfU2Q==} - engines: {node: '>=12'} - peerDependencies: - eslint: '>=6.0.0' - - eslint-import-resolver-node@0.3.10: - resolution: {integrity: sha512-tRrKqFyCaKict5hOd244sL6EQFNycnMQnBe+j8uqGNXYzsImGbGUU4ibtoaBmv5FLwJwcFJNeg1GeVjQfbMrDQ==} - - eslint-module-utils@2.14.0: - resolution: {integrity: sha512-W2WCRZ9Dqntd+2u8jJcVMV2PKulc6RdLgUUoh/yQr3uB6lo/ZOeGx11sv60/8S4QFFKNslAlWhr9u0Ef7ZW6Ig==} - engines: {node: '>=4'} - peerDependencies: - '@typescript-eslint/parser': '*' - eslint: '*' - eslint-import-resolver-node: '*' - eslint-import-resolver-typescript: '*' - eslint-import-resolver-webpack: '*' - peerDependenciesMeta: - '@typescript-eslint/parser': - optional: true - eslint: - optional: true - eslint-import-resolver-node: - optional: true - eslint-import-resolver-typescript: - optional: true - eslint-import-resolver-webpack: - optional: true - - eslint-plugin-depend@1.3.1: - resolution: {integrity: sha512-1uo2rFAr9vzNrCYdp7IBZRB54LiyVxfaIso0R6/QV3t6Dax6DTbW/EV2Hktf0f4UtmGHK8UyzJWI382pwW04jw==} - - eslint-plugin-es-x@7.8.0: - resolution: {integrity: sha512-7Ds8+wAAoV3T+LAKeu39Y5BzXCrGKrcISfgKEqTS4BDN8SFEDQd0S43jiQ8vIa3wUKD07qitZdfzlenSi8/0qQ==} - engines: {node: ^14.18.0 || >=16.0.0} - peerDependencies: - eslint: '>=8' - - eslint-plugin-import@2.32.0: - resolution: {integrity: sha512-whOE1HFo/qJDyX4SnXzP4N6zOWn79WhnCUY/iDR0mPfQZO8wcYE4JClzI2oZrhBnnMUCBCHZhO6VQyoBU95mZA==} - engines: {node: '>=4'} - peerDependencies: - '@typescript-eslint/parser': '*' - eslint: ^2 || ^3 || ^4 || ^5 || ^6 || ^7.2.0 || ^8 || ^9 - peerDependenciesMeta: - '@typescript-eslint/parser': - optional: true - - eslint-plugin-json-schema-validator@5.1.0: - resolution: {integrity: sha512-ZmVyxRIjm58oqe2kTuy90PpmZPrrKvOjRPXKzq8WCgRgAkidCgm5X8domL2KSfadZ3QFAmifMgGTcVNhZ5ez2g==} - engines: {node: ^14.18.0 || >=16.0.0} - peerDependencies: - eslint: '>=6.0.0' - - eslint-plugin-n@17.10.3: - resolution: {integrity: sha512-ySZBfKe49nQZWR1yFaA0v/GsH6Fgp8ah6XV0WDz6CN8WO0ek4McMzb7A2xnf4DCYV43frjCygvb9f/wx7UUxRw==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - eslint: '>=8.23.0' - - eslint-plugin-obsidianmd@0.1.9: - resolution: {integrity: sha512-/gyo5vky3Y7re4BtT/8MQbHU5Wes4o6VRqas3YmXE7aTCnMsdV0kfzV1GDXJN9Hrsc9UQPoeKUMiapKL0aGE4g==} - engines: {node: '>= 18'} - hasBin: true - peerDependencies: - '@eslint/js': ^9.30.1 - '@eslint/json': 0.14.0 - eslint: '>=9.0.0 <10.0.0' - obsidian: 1.8.7 - typescript-eslint: ^8.35.1 - - eslint-plugin-react@7.37.3: - resolution: {integrity: sha512-DomWuTQPFYZwF/7c9W2fkKkStqZmBd3uugfqBYLdkZ3Hii23WzZuOLUskGxB8qkSKqftxEeGL1TB2kMhrce0jA==} - engines: {node: '>=4'} - peerDependencies: - eslint: ^3 || ^4 || ^5 || ^6 || ^7 || ^8 || ^9.7 - - eslint-plugin-security@1.4.0: - resolution: {integrity: sha512-xlS7P2PLMXeqfhyf3NpqbvbnW04kN8M9NtmhpR3XGyOvt/vNKS7XPXT5EDbwKW9vCjWH4PpfQvgD/+JgN0VJKA==} - - eslint-plugin-security@2.1.1: - resolution: {integrity: sha512-7cspIGj7WTfR3EhaILzAPcfCo5R9FbeWvbgsPYWivSurTBKW88VQxtP3c4aWMG9Hz/GfJlJVdXEJ3c8LqS+u2w==} - - eslint-scope@8.4.0: - resolution: {integrity: sha512-sNXOfKCn74rt8RICKMvJS7XKV/Xk9kA7DyJr8mJik3S7Cwgy3qlkkmyS2uQB3jiJg6VNdZd/pDBJu0nvG2NlTg==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - eslint-visitor-keys@3.4.3: - resolution: {integrity: sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==} - engines: {node: ^12.22.0 || ^14.17.0 || >=16.0.0} - - eslint-visitor-keys@4.2.1: - resolution: {integrity: sha512-Uhdk5sfqcee/9H/rCOJikYz67o0a2Tw2hGRPOG2Y1R2dg7brRe1uG0yaNQDHu+TO/uQPF/5eCapvYSmHUjt7JQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - eslint@9.39.4: - resolution: {integrity: sha512-XoMjdBOwe/esVgEvLmNsD3IRHkm7fbKIUGvrleloJXUZgDHig2IPWNniv+GwjyJXzuNqVjlr5+4yVUZjycJwfQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - hasBin: true - peerDependencies: - jiti: '*' - peerDependenciesMeta: - jiti: - optional: true - - espree@10.4.0: - resolution: {integrity: sha512-j6PAQ2uUr79PZhBjP5C5fhl8e39FmRnOjsD5lGnWrFU8i2G776tBK7+nP8KuQUTTyAZUwfQqXAgrVH5MbH9CYQ==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - - espree@9.6.1: - resolution: {integrity: sha512-oruZaFkjorTpF32kDSI5/75ViwGeZginGGy2NoOSg3Q9bnwlnmDm4HLnkl0RE3n+njDXR037aY1+x58Z/zFdwQ==} - engines: {node: ^12.22.0 || ^14.17.0 || >=16.0.0} - - esquery@1.7.0: - resolution: {integrity: sha512-Ap6G0WQwcU/LHsvLwON1fAQX9Zp0A2Y6Y/cJBl9r/JbW90Zyg4/zbG6zzKa2OTALELarYHmKu0GhpM5EO+7T0g==} - engines: {node: '>=0.10'} - - esrecurse@4.3.0: - resolution: {integrity: sha512-KmfKL3b6G+RXvP8N1vr3Tq1kL/oCFgn2NYXEtqP8/L3pKapUA4G8cFVaoF3SU323CD4XypR/ffioHmkti6/Tag==} - engines: {node: '>=4.0'} - - estraverse@5.3.0: - resolution: {integrity: sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==} - engines: {node: '>=4.0'} - - esutils@2.0.3: - resolution: {integrity: sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==} - engines: {node: '>=0.10.0'} - - fast-deep-equal@3.1.3: - resolution: {integrity: sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==} - - fast-glob@3.3.3: - resolution: {integrity: sha512-7MptL8U0cqcFdzIzwOTHoilX9x5BrNqye7Z/LuC7kCMRio1EMSyqRK3BEAUD7sXRq4iT4AzTVuZdhgQ2TCvYLg==} - engines: {node: '>=8.6.0'} - - fast-json-stable-stringify@2.1.0: - resolution: {integrity: sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw==} - - fast-levenshtein@2.0.6: - resolution: {integrity: sha512-DCXu6Ifhqcks7TZKY3Hxp3y6qphY5SJZmrWMDrKcERSOXWQdMhU9Ig/PYrzyw/ul9jOIyh0N4M0tbC5hodg8dw==} - - fast-uri@3.1.3: - resolution: {integrity: sha512-i70LwGWUduXqzicKXWshooq+sWL1K3WUU5rKZNG/0i3a1OSoX3HqhH5WbWwTmqWfor4urUakGPiRQcleRZTwOg==} - - fastq@1.20.1: - resolution: {integrity: sha512-GGToxJ/w1x32s/D2EKND7kTil4n8OVk/9mycTc4VDza13lOvpUZTGX3mFSCtV9ksdGBVzvsyAVLM6mHFThxXxw==} - - file-entry-cache@8.0.0: - resolution: {integrity: sha512-XXTUwCvisa5oacNGRP9SfNtYBNAMi+RPwBFmblZEF7N7swHYQS6/Zfk7SRwx4D5j3CH211YNRco1DEMNVfZCnQ==} - engines: {node: '>=16.0.0'} - - fill-range@7.1.1: - resolution: {integrity: sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==} - engines: {node: '>=8'} - - find-up@5.0.0: - resolution: {integrity: sha512-78/PXT1wlLLDgTzDs7sjq9hzz0vXD+zn+7wypEe4fXQxCmdmqfGsEPQxmiCSQI3ajFV91bVSsvNtrJRiW6nGng==} - engines: {node: '>=10'} - - flat-cache@4.0.1: - resolution: {integrity: sha512-f7ccFPK3SXFHpx15UIGyRJ/FJQctuKZ0zVuN3frBo4HnK3cay9VEW0R6yPYFHC0AgqhukPzKjq22t5DmAyqGyw==} - engines: {node: '>=16'} - - flatted@3.4.2: - resolution: {integrity: sha512-PjDse7RzhcPkIJwy5t7KPWQSZ9cAbzQXcafsetQoD7sOJRQlGikNbx7yZp2OotDnJyrDcbyRq3Ttb18iYOqkxA==} - - for-each@0.3.5: - resolution: {integrity: sha512-dKx12eRCVIzqCxFGplyFKJMPvLEWgmNtUrpTiJIR5u97zEhRG8ySrtboPHZXx7daLxQVrl643cTzbab2tkQjxg==} - engines: {node: '>= 0.4'} - - function-bind@1.1.2: - resolution: {integrity: sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==} - - function.prototype.name@1.2.0: - resolution: {integrity: sha512-jObKIik1P2QjPHP5nz5BaOtUlfgS0fWo8IUByNXkM+o+02sJOi94em77GwJKQSJ3gfPHdgzLNrHc1uokV4P/ew==} - engines: {node: '>= 0.4'} - - functions-have-names@1.2.3: - resolution: {integrity: sha512-xckBUXyTIqT97tq2x2AMb+g163b5JFysYk0x4qxNFwbfQkmNZoiRHb6sPzI9/QV33WeuvVYBUIiD4NzNIyqaRQ==} - - generator-function@2.0.1: - resolution: {integrity: sha512-SFdFmIJi+ybC0vjlHN0ZGVGHc3lgE0DxPAT0djjVg+kjOnSqclqmj0KQ7ykTOLP6YxoqOvuAODGdcHJn+43q3g==} - engines: {node: '>= 0.4'} - - get-intrinsic@1.3.0: - resolution: {integrity: sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==} - engines: {node: '>= 0.4'} - - get-proto@1.0.1: - resolution: {integrity: sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==} - engines: {node: '>= 0.4'} - - get-symbol-description@1.1.0: - resolution: {integrity: sha512-w9UMqWwJxHNOvoNzSJ2oPF5wvYcvP7jUvYzhp67yEhTi17ZDBBC1z9pTdGuzjD+EFIqLSYRweZjqfiPzQ06Ebg==} - engines: {node: '>= 0.4'} - - get-tsconfig@4.14.0: - resolution: {integrity: sha512-yTb+8DXzDREzgvYmh6s9vHsSVCHeC0G3PI5bEXNBHtmshPnO+S5O7qgLEOn0I5QvMy6kpZN8K1NKGyilLb93wA==} - - glob-parent@5.1.2: - resolution: {integrity: sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==} - engines: {node: '>= 6'} - - glob-parent@6.0.2: - resolution: {integrity: sha512-XxwI8EOhVQgWp6iDL+3b0r86f4d6AX6zSU55HfB4ydCEuXLXc5FcYeOu+nnGftS4TEju/11rt4KJPTMgbfmv4A==} - engines: {node: '>=10.13.0'} - - globals@14.0.0: - resolution: {integrity: sha512-oahGvuMGQlPw/ivIYBjVSrWAfWLBeku5tpPE2fOPLi+WHffIWbuh2tCjhyQhTBPMf5E9jDEH4FOmTYgYwbKwtQ==} - engines: {node: '>=18'} - - globals@15.15.0: - resolution: {integrity: sha512-7ACyT3wmyp3I61S4fG682L0VA2RGD9otkqGJIwNUMF1SWUombIIk+af1unuDYgMm082aHYwD+mzJvv9Iu8dsgg==} - engines: {node: '>=18'} - - globalthis@1.0.4: - resolution: {integrity: sha512-DpLKbNU4WylpxJykQujfCcwYWiV/Jhm50Goo0wrVILAv5jOr9d+H+UR3PhSCD2rCCEIg0uc+G+muBTwD54JhDQ==} - engines: {node: '>= 0.4'} - - gopd@1.2.0: - resolution: {integrity: sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==} - engines: {node: '>= 0.4'} - - graceful-fs@4.2.11: - resolution: {integrity: sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==} - - graphemer@1.4.0: - resolution: {integrity: sha512-EtKwoO6kxCL9WO5xipiHTZlSzBm7WLT627TqC/uVRd0HKmq8NXyebnNYxDoBi7wt8eTWrUrKXCOVaFq9x1kgag==} - - has-bigints@1.1.0: - resolution: {integrity: sha512-R3pbpkcIqv2Pm3dUwgjclDRVmWpTJW2DcMzcIhEXEx1oh/CEMObMm3KLmRJOdvhM7o4uQBnwr8pzRK2sJWIqfg==} - engines: {node: '>= 0.4'} - - has-flag@4.0.0: - resolution: {integrity: sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==} - engines: {node: '>=8'} - - has-property-descriptors@1.0.2: - resolution: {integrity: sha512-55JNKuIW+vq4Ke1BjOTjM2YctQIvCT7GFzHwmfZPGo5wnrgkid0YQtnAleFSqumZm4az3n2BS+erby5ipJdgrg==} - - has-proto@1.2.0: - resolution: {integrity: sha512-KIL7eQPfHQRC8+XluaIw7BHUwwqL19bQn4hzNgdr+1wXoU0KKj6rufu47lhY7KbJR2C6T6+PfyN0Ea7wkSS+qQ==} - engines: {node: '>= 0.4'} - - has-symbols@1.1.0: - resolution: {integrity: sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==} - engines: {node: '>= 0.4'} - - has-tostringtag@1.0.2: - resolution: {integrity: sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw==} - engines: {node: '>= 0.4'} - - hasown@2.0.4: - resolution: {integrity: sha512-T2UbfbBEF32wiepXIsMlTW9+dDYC6wMh/t/vYA4tuOMKqWz/n3vr1NFSxQiyP+zk2mXsoMA/i/7qV6LKut1t1A==} - engines: {node: '>= 0.4'} - - ignore@5.3.2: - resolution: {integrity: sha512-hsBTNUqQTDwkWtcdYI2i06Y/nUBEsNEDJKjWdigLvegy8kDuJAS8uRlpkkcQpyEXL0Z/pjDy5HBmMjRCJ2gq+g==} - engines: {node: '>= 4'} - - ignore@7.0.5: - resolution: {integrity: sha512-Hs59xBNfUIunMFgWAbGX5cq6893IbWg4KnrjbYwX3tx0ztorVgTDA6B2sxf8ejHJ4wz8BqGUMYlnzNBer5NvGg==} - engines: {node: '>= 4'} - - import-fresh@3.3.1: - resolution: {integrity: sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ==} - engines: {node: '>=6'} - - imurmurhash@0.1.4: - resolution: {integrity: sha512-JmXMZ6wuvDmLiHEml9ykzqO6lwFbof0GG4IkcGaENdCRDDmMVnny7s5HsIgHCbaq0w2MyPhDqkhTUgS2LU2PHA==} - engines: {node: '>=0.8.19'} - - internal-slot@1.1.0: - resolution: {integrity: sha512-4gd7VpWNQNB4UKKCFFVcp1AVv+FMOgs9NKzjHKusc8jTMhd5eL1NqQqOpE0KzMds804/yHlglp3uxgluOqAPLw==} - engines: {node: '>= 0.4'} - - is-array-buffer@3.0.5: - resolution: {integrity: sha512-DDfANUiiG2wC1qawP66qlTugJeL5HyzMpfr8lLK+jMQirGzNod0B12cFB/9q838Ru27sBwfw78/rdoU7RERz6A==} - engines: {node: '>= 0.4'} - - is-async-function@2.1.1: - resolution: {integrity: sha512-9dgM/cZBnNvjzaMYHVoxxfPj2QXt22Ev7SuuPrs+xav0ukGB0S6d4ydZdEiM48kLx5kDV+QBPrpVnFyefL8kkQ==} - engines: {node: '>= 0.4'} - - is-bigint@1.1.0: - resolution: {integrity: sha512-n4ZT37wG78iz03xPRKJrHTdZbe3IicyucEtdRsV5yglwc3GyUfbAfpSeD0FJ41NbUNSt5wbhqfp1fS+BgnvDFQ==} - engines: {node: '>= 0.4'} - - is-boolean-object@1.2.2: - resolution: {integrity: sha512-wa56o2/ElJMYqjCjGkXri7it5FbebW5usLw/nPmCMs5DeZ7eziSYZhSmPRn0txqeW4LnAmQQU7FgqLpsEFKM4A==} - engines: {node: '>= 0.4'} - - is-callable@1.2.7: - resolution: {integrity: sha512-1BC0BVFhS/p0qtw6enp8e+8OD0UrK0oFLztSjNzhcKA3WDuJxxAPXzPuPtKkjEY9UUoEWlX/8fgKeu2S8i9JTA==} - engines: {node: '>= 0.4'} - - is-core-module@2.16.2: - resolution: {integrity: sha512-evOr8xfXKxE6qSR0hSXL2r3sd7ALj8+7jQEUvPYcm5sgZFdJ+AYzT6yNmJenvIYQBgIGwfwz08sL8zoL7yq2BA==} - engines: {node: '>= 0.4'} - - is-data-view@1.0.2: - resolution: {integrity: sha512-RKtWF8pGmS87i2D6gqQu/l7EYRlVdfzemCJN/P3UOs//x1QE7mfhvzHIApBTRf7axvT6DMGwSwBXYCT0nfB9xw==} - engines: {node: '>= 0.4'} - - is-date-object@1.1.0: - resolution: {integrity: sha512-PwwhEakHVKTdRNVOw+/Gyh0+MzlCl4R6qKvkhuvLtPMggI1WAHt9sOwZxQLSGpUaDnrdyDsomoRgNnCfKNSXXg==} - engines: {node: '>= 0.4'} - - is-document.all@1.0.0: - resolution: {integrity: sha512-+XSoyS05OdBbhFuELhgTCpFNHkpBOJqtsZfUFFpe5QTw+9Sjbh8zitxhQkYAo6wV7e1Vb8cAPvpCk9jGam/82g==} - engines: {node: '>= 0.4'} - - is-extglob@2.1.1: - resolution: {integrity: sha512-SbKbANkN603Vi4jEZv49LeVJMn4yGwsbzZworEoyEiutsN3nJYdbO36zfhGJ6QEDpOZIFkDtnq5JRxmvl3jsoQ==} - engines: {node: '>=0.10.0'} - - is-finalizationregistry@1.1.1: - resolution: {integrity: sha512-1pC6N8qWJbWoPtEjgcL2xyhQOP491EQjeUo3qTKcmV8YSDDJrOepfG8pcC7h/QgnQHYSv0mJ3Z/ZWxmatVrysg==} - engines: {node: '>= 0.4'} - - is-generator-function@1.1.2: - resolution: {integrity: sha512-upqt1SkGkODW9tsGNG5mtXTXtECizwtS2kA161M+gJPc1xdb/Ax629af6YrTwcOeQHbewrPNlE5Dx7kzvXTizA==} - engines: {node: '>= 0.4'} - - is-glob@4.0.3: - resolution: {integrity: sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==} - engines: {node: '>=0.10.0'} - - is-map@2.0.3: - resolution: {integrity: sha512-1Qed0/Hr2m+YqxnM09CjA2d/i6YZNfF6R2oRAOj36eUdS6qIV/huPJNSEpKbupewFs+ZsJlxsjjPbc0/afW6Lw==} - engines: {node: '>= 0.4'} - - is-negative-zero@2.0.3: - resolution: {integrity: sha512-5KoIu2Ngpyek75jXodFvnafB6DJgr3u8uuK0LEZJjrU19DrMD3EVERaR8sjz8CCGgpZvxPl9SuE1GMVPFHx1mw==} - engines: {node: '>= 0.4'} - - is-number-object@1.1.1: - resolution: {integrity: sha512-lZhclumE1G6VYD8VHe35wFaIif+CTy5SJIi5+3y4psDgWu4wPDoBhF8NxUOinEc7pHgiTsT6MaBb92rKhhD+Xw==} - engines: {node: '>= 0.4'} - - is-number@7.0.0: - resolution: {integrity: sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng==} - engines: {node: '>=0.12.0'} - - is-regex@1.2.1: - resolution: {integrity: sha512-MjYsKHO5O7mCsmRGxWcLWheFqN9DJ/2TmngvjKXihe6efViPqc274+Fx/4fYj/r03+ESvBdTXK0V6tA3rgez1g==} - engines: {node: '>= 0.4'} - - is-set@2.0.3: - resolution: {integrity: sha512-iPAjerrse27/ygGLxw+EBR9agv9Y6uLeYVJMu+QNCoouJ1/1ri0mGrcWpfCqFZuzzx3WjtwxG098X+n4OuRkPg==} - engines: {node: '>= 0.4'} - - is-shared-array-buffer@1.0.4: - resolution: {integrity: sha512-ISWac8drv4ZGfwKl5slpHG9OwPNty4jOWPRIhBpxOoD+hqITiwuipOQ2bNthAzwA3B4fIjO4Nln74N0S9byq8A==} - engines: {node: '>= 0.4'} - - is-string@1.1.1: - resolution: {integrity: sha512-BtEeSsoaQjlSPBemMQIrY1MY0uM6vnS1g5fmufYOtnxLGUZM2178PKbhsk7Ffv58IX+ZtcvoGwccYsh0PglkAA==} - engines: {node: '>= 0.4'} - - is-symbol@1.1.1: - resolution: {integrity: sha512-9gGx6GTtCQM73BgmHQXfDmLtfjjTUDSyoxTCbp5WtoixAhfgsDirWIcVQ/IHpvI5Vgd5i/J5F7B9cN/WlVbC/w==} - engines: {node: '>= 0.4'} - - is-typed-array@1.1.15: - resolution: {integrity: sha512-p3EcsicXjit7SaskXHs1hA91QxgTw46Fv6EFKKGS5DRFLD8yKnohjF3hxoju94b/OcMZoQukzpPpBE9uLVKzgQ==} - engines: {node: '>= 0.4'} - - is-weakmap@2.0.2: - resolution: {integrity: sha512-K5pXYOm9wqY1RgjpL3YTkF39tni1XajUIkawTLUo9EZEVUFga5gSQJF8nNS7ZwJQ02y+1YCNYcMh+HIf1ZqE+w==} - engines: {node: '>= 0.4'} - - is-weakref@1.1.1: - resolution: {integrity: sha512-6i9mGWSlqzNMEqpCp93KwRS1uUOodk2OJ6b+sq7ZPDSy2WuI5NFIxp/254TytR8ftefexkWn5xNiHUNpPOfSew==} - engines: {node: '>= 0.4'} - - is-weakset@2.0.4: - resolution: {integrity: sha512-mfcwb6IzQyOKTs84CQMrOwW4gQcaTOAWJ0zzJCl2WSPDrWk/OzDaImWFH3djXhb24g4eudZfLRozAvPGw4d9hQ==} - engines: {node: '>= 0.4'} - - isarray@2.0.5: - resolution: {integrity: sha512-xHjhDr3cNBK0BzdUJSPXZntQUx/mwMS5Rw4A7lPJ90XGAO6ISP/ePDNuo0vhqOZU+UD5JoodwCAAoZQd3FeAKw==} - - isexe@2.0.0: - resolution: {integrity: sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw==} - - iterator.prototype@1.1.5: - resolution: {integrity: sha512-H0dkQoCa3b2VEeKQBOxFph+JAbcrQdE7KC0UkqwpLmv2EC4P41QXP+rqo9wYodACiG5/WM5s9oDApTU8utwj9g==} - engines: {node: '>= 0.4'} - - jiti@2.6.1: - resolution: {integrity: sha512-ekilCSN1jwRvIbgeg/57YFh8qQDNbwDb9xT/qu2DAHbFFZUicIl4ygVaAvzveMhMVr3LnpSKTNnwt8PoOfmKhQ==} - hasBin: true - - js-tokens@4.0.0: - resolution: {integrity: sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==} - - js-yaml@4.3.0: - resolution: {integrity: sha512-1td788aAnnZ5qs7V2QIRl1owjtYpbKt749Y3xauqQgwIIGF/xXWz1wMTEBx5O3LK3lXLVuqXPdPxj2BoFHaW9Q==} - hasBin: true - - json-buffer@3.0.1: - resolution: {integrity: sha512-4bV5BfR2mqfQTJm+V5tPPdf+ZpuhiIvTuAB5g8kcrXOZpTT/QwwVRWBywX1ozr6lEuPdbHxwaJlm9G6mI2sfSQ==} - - json-schema-migrate@2.0.0: - resolution: {integrity: sha512-r38SVTtojDRp4eD6WsCqiE0eNDt4v1WalBXb9cyZYw9ai5cGtBwzRNWjHzJl38w6TxFkXAIA7h+fyX3tnrAFhQ==} - - json-schema-traverse@0.4.1: - resolution: {integrity: sha512-xbbCH5dCYU5T8LcEhhuh7HJ88HXuW3qsI3Y0zOZFKfZEHcpWiHU/Jxzk629Brsab/mMiHQti9wMP+845RPe3Vg==} - - json-schema-traverse@1.0.0: - resolution: {integrity: sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==} - - json-stable-stringify-without-jsonify@1.0.1: - resolution: {integrity: sha512-Bdboy+l7tA3OGW6FjyFHWkP5LuByj1Tk33Ljyq0axyzdk9//JSi2u3fP1QSmd1KNwq6VOKYGlAu87CisVir6Pw==} - - json5@1.0.2: - resolution: {integrity: sha512-g1MWMLBiz8FKi1e4w0UyVL3w+iJceWAFBAaBnnGKOpNa5f8TLktkbre1+s6oICydWAm+HRUGTmI+//xv2hvXYA==} - hasBin: true - - jsonc-eslint-parser@2.4.2: - resolution: {integrity: sha512-1e4qoRgnn448pRuMvKGsFFymUCquZV0mpGgOyIKNgD3JVDTsVJyRBGH/Fm0tBb8WsWGgmB1mDe6/yJMQM37DUA==} - engines: {node: ^12.22.0 || ^14.17.0 || >=16.0.0} - - jsx-ast-utils@3.3.5: - resolution: {integrity: sha512-ZZow9HBI5O6EPgSJLUb8n2NKgmVWTwCvHGwFuJlMjvLFqlGG6pjirPhtdsseaLZjSibD8eegzmYpUZwoIlj2cQ==} - engines: {node: '>=4.0'} - - keyv@4.5.4: - resolution: {integrity: sha512-oxVHkHR/EJf2CNXnWxRLW6mg7JyCCUcG0DtEGmL2ctUo1PNTin1PUil+r/+4r5MpVgC/fn1kjsx7mjSujKqIpw==} - - levn@0.4.1: - resolution: {integrity: sha512-+bT2uH4E5LGE7h/n3evcS/sQlJXCpIp6ym8OWJ5eV6+67Dsql/LaaT7qJBAt2rzfoa/5QBGBhxDix1dMt2kQKQ==} - engines: {node: '>= 0.8.0'} - - locate-path@6.0.0: - resolution: {integrity: sha512-iPZK6eYjbxRu3uB4/WZ3EsEIMJFMqAoopl3R+zuq0UjcAm/MO6KCweDgPfP3elTztoKP3KtnVHxTn2NHBSDVUw==} - engines: {node: '>=10'} - - lodash.merge@4.6.2: - resolution: {integrity: sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ==} - - loose-envify@1.4.0: - resolution: {integrity: sha512-lyuxPGr/Wfhrlem2CL/UcnUc1zcqKAImBDzukY7Y5F/yQiNdko6+fRLevlw1HgMySw7f611UIY408EtxRSoK3Q==} - hasBin: true - - math-intrinsics@1.1.0: - resolution: {integrity: sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==} - engines: {node: '>= 0.4'} - - merge2@1.4.1: - resolution: {integrity: sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==} - engines: {node: '>= 8'} - - micromatch@4.0.8: - resolution: {integrity: sha512-PXwfBhYu0hBCPw8Dn0E+WDYb7af3dSLVWKi3HGv84IdF4TyFoC0ysxFd0Goxw7nSv4T/PzEJQxsYsEiFCKo2BA==} - engines: {node: '>=8.6'} - - minimatch@3.1.5: - resolution: {integrity: sha512-VgjWUsnnT6n+NUk6eZq77zeFdpW2LWDzP6zFGrCbHXiYNul5Dzqk2HHQ5uFH2DNW5Xbp8+jVzaeNt94ssEEl4w==} - - minimatch@8.0.7: - resolution: {integrity: sha512-V+1uQNdzybxa14e/p00HZnQNNcTjnRJjDxg2V8wtkjFctq4M7hXFws4oekyTP0Jebeq7QYtpFyOeBAjc88zvYg==} - engines: {node: '>=16 || 14 >=14.17'} - - minimatch@9.0.9: - resolution: {integrity: sha512-OBwBN9AL4dqmETlpS2zasx+vTeWclWzkblfZk7KTA5j3jeOONz/tRCnZomUyvNg83wL5Zv9Ss6HMJXAgL8R2Yg==} - engines: {node: '>=16 || 14 >=14.17'} - - minimist@1.2.8: - resolution: {integrity: sha512-2yyAR8qBkN3YuheJanUpWC5U3bb5osDywNB8RzDVlDwDHbocAJveqqj1u8+SVD7jkWT4yvsHCpWqqWqAxb0zCA==} - - module-replacements@2.11.0: - resolution: {integrity: sha512-j5sNQm3VCpQQ7nTqGeOZtoJtV3uKERgCBm9QRhmGRiXiqkf7iRFOkfxdJRZWLkqYY8PNf4cDQF/WfXUYLENrRA==} - - moment@2.29.4: - resolution: {integrity: sha512-5LC9SOxjSc2HF6vO2CyuTDNivEdoz2IvyJJGj6X8DJ0eFyfszE0QiEd+iXmBvUP3WHxSjFH/vIsA0EN00cgr8w==} - - ms@2.1.3: - resolution: {integrity: sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==} - - natural-compare@1.4.0: - resolution: {integrity: sha512-OWND8ei3VtNC9h7V60qff3SVobHr996CTwgxubgyQYEpg290h9J0buyECNNJexkFm5sOajh5G116RYA1c8ZMSw==} - - node-exports-info@1.6.2: - resolution: {integrity: sha512-kXs9Go0cah0qHVV2v389IXQLdLCeE1xfFtjOAF+iobu0OIoG1pje8At2vMHyaPMiPMnG/LWP50twML21eMcAag==} - engines: {node: '>= 0.4'} - - object-assign@4.1.1: - resolution: {integrity: sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg==} - engines: {node: '>=0.10.0'} - - object-inspect@1.13.4: - resolution: {integrity: sha512-W67iLl4J2EXEGTbfeHCffrjDfitvLANg0UlX3wFUUSTx92KXRFegMHUVgSqE+wvhAbi4WqjGg9czysTV2Epbew==} - engines: {node: '>= 0.4'} - - object-keys@1.1.1: - resolution: {integrity: sha512-NuAESUOUMrlIXOfHKzD6bpPu3tYt3xvjNdRIQ+FeT0lNb4K8WR70CaDxhuNguS2XG+GjkyMwOzsN5ZktImfhLA==} - engines: {node: '>= 0.4'} - - object.assign@4.1.7: - resolution: {integrity: sha512-nK28WOo+QIjBkDduTINE4JkF/UJJKyf2EJxvJKfblDpyg0Q+pkOHNTL0Qwy6NP6FhE/EnzV73BxxqcJaXY9anw==} - engines: {node: '>= 0.4'} - - object.entries@1.1.9: - resolution: {integrity: sha512-8u/hfXFRBD1O0hPUjioLhoWFHRmt6tKA4/vZPyckBr18l1KE9uHrFaFaUi8MDRTpi4uak2goyPTSNJLXX2k2Hw==} - engines: {node: '>= 0.4'} - - object.fromentries@2.0.8: - resolution: {integrity: sha512-k6E21FzySsSK5a21KRADBd/NGneRegFO5pLHfdQLpRDETUNJueLXs3WCzyQ3tFRDYgbq3KHGXfTbi2bs8WQ6rQ==} - engines: {node: '>= 0.4'} - - object.groupby@1.0.3: - resolution: {integrity: sha512-+Lhy3TQTuzXI5hevh8sBGqbmurHbbIjAi0Z4S63nthVLmLxfbj4T54a4CfZrXIrt9iP4mVAPYMo/v99taj3wjQ==} - engines: {node: '>= 0.4'} - - object.values@1.2.1: - resolution: {integrity: sha512-gXah6aZrcUxjWg2zR2MwouP2eHlCBzdV4pygudehaKXSGW4v2AsRQUK+lwwXhii6KFZcunEnmSUoYp5CXibxtA==} - engines: {node: '>= 0.4'} - - obsidian@1.13.1: - resolution: {integrity: sha512-qtTEA2pmhJzhuhJqzbBFRYhpIOqvW+krDYjtFynv66KbxBbumHBlsJfWw3I4jtnK/6fZwbQhCrmmDdRwXmX56w==} - peerDependencies: - '@codemirror/state': 6.5.0 - '@codemirror/view': 6.38.6 - - optionator@0.9.4: - resolution: {integrity: sha512-6IpQ7mKUxRcZNLIObR0hz7lxsapSSIYNZJwXPGeF0mTVqGKFIXj1DQcMoT22S3ROcLyY/rz0PWaWZ9ayWmad9g==} - engines: {node: '>= 0.8.0'} - - own-keys@1.0.1: - resolution: {integrity: sha512-qFOyK5PjiWZd+QQIh+1jhdb9LpxTF0qs7Pm8o5QHYZ0M3vKqSqzsZaEB6oWlxZ+q2sJBMI/Ktgd2N5ZwQoRHfg==} - engines: {node: '>= 0.4'} - - p-limit@3.1.0: - resolution: {integrity: sha512-TYOanM3wGwNGsZN2cVTYPArw454xnXj5qmWF1bEoAc4+cU/ol7GVh7odevjp1FNHduHc3KZMcFduxU5Xc6uJRQ==} - engines: {node: '>=10'} - - p-locate@5.0.0: - resolution: {integrity: sha512-LaNjtRWUBY++zB5nE/NwcaoMylSPk+S+ZHNB1TzdbMJMny6dynpAGt7X/tl/QYq3TIeE6nxHppbo2LGymrG5Pw==} - engines: {node: '>=10'} - - parent-module@1.0.1: - resolution: {integrity: sha512-GQ2EWRpQV8/o+Aw8YqtfZZPfNRWZYkbidE9k5rpl/hC3vtHHBfGm2Ifi6qWV+coDGkrUKZAxE3Lot5kcsRlh+g==} - engines: {node: '>=6'} - - path-exists@4.0.0: - resolution: {integrity: sha512-ak9Qy5Q7jYb2Wwcey5Fpvg2KoAc/ZIhLSLOSBmRmygPsGwkVVt0fZa0qrtMz+m6tJTAHfZQ8FnmB4MG4LWy7/w==} - engines: {node: '>=8'} - - path-key@3.1.1: - resolution: {integrity: sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q==} - engines: {node: '>=8'} - - path-parse@1.0.7: - resolution: {integrity: sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==} - - picomatch@2.3.2: - resolution: {integrity: sha512-V7+vQEJ06Z+c5tSye8S+nHUfI51xoXIXjHQ99cQtKUkQqqO1kO/KCJUfZXuB47h/YBlDhah2H3hdUGXn8ie0oA==} - engines: {node: '>=8.6'} - - possible-typed-array-names@1.1.0: - resolution: {integrity: sha512-/+5VFTchJDoVj3bhoqi6UeymcD00DAwb1nJwamzPvHEszJ4FpF6SNNbUbOS8yI56qHzdV8eK0qEfOSiodkTdxg==} - engines: {node: '>= 0.4'} - - prelude-ls@1.2.1: - resolution: {integrity: sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g==} - engines: {node: '>= 0.8.0'} - - prop-types@15.8.1: - resolution: {integrity: sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg==} - - punycode@2.3.1: - resolution: {integrity: sha512-vYt7UD1U9Wg6138shLtLOvdAu+8DsC/ilFtEVHcH+wydcSpNE20AfSOduf6MkRFahL5FY7X1oU7nKVZFtfq8Fg==} - engines: {node: '>=6'} - - queue-microtask@1.2.3: - resolution: {integrity: sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==} - - react-is@16.13.1: - resolution: {integrity: sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==} - - reflect.getprototypeof@1.0.10: - resolution: {integrity: sha512-00o4I+DVrefhv+nX0ulyi3biSHCPDe+yLv5o/p6d/UVlirijB8E16FtfwSAi4g3tcqrQ4lRAqQSoFEZJehYEcw==} - engines: {node: '>= 0.4'} - - regexp-tree@0.1.27: - resolution: {integrity: sha512-iETxpjK6YoRWJG5o6hXLwvjYAoW+FEZn9os0PD/b6AP6xQwsa/Y7lCVgIixBbUPMfhu+i2LtdeAqVTgGlQarfA==} - hasBin: true - - regexp.prototype.flags@1.5.4: - resolution: {integrity: sha512-dYqgNSZbDwkaJ2ceRd9ojCGjBq+mOm9LmtXnAnEGyHhN/5R7iDW2TRw3h+o/jCFxus3P2LfWIIiwowAjANm7IA==} - engines: {node: '>= 0.4'} - - require-from-string@2.0.2: - resolution: {integrity: sha512-Xf0nWe6RseziFMu+Ap9biiUbmplq6S9/p+7w7YXP/JBHhrUDDUhwa+vANyubuqfZWTveU//DYVGsDG7RKL/vEw==} - engines: {node: '>=0.10.0'} - - resolve-from@4.0.0: - resolution: {integrity: sha512-pb/MYmXstAkysRFx8piNI1tGFNQIFA3vkE3Gq4EuA1dF6gHp/+vgZqsCGJapvy8N3Q+4o7FwvquPJcnZ7RYy4g==} - engines: {node: '>=4'} - - resolve-pkg-maps@1.0.0: - resolution: {integrity: sha512-seS2Tj26TBVOC2NIc2rOe2y2ZO7efxITtLZcGSOnHHNOQ7CkiUBfw0Iw2ck6xkIhPwLhKNLS8BO+hEpngQlqzw==} - - resolve@2.0.0-next.7: - resolution: {integrity: sha512-tqt+NBWwyaMgw3zDsnygx4CByWjQEJHOPMdslYhppaQSJUtL/D4JO9CcBBlhPoI8lz9oJIDXkwXfhF4aWqP8xQ==} - engines: {node: '>= 0.4'} - hasBin: true - - ret@0.1.15: - resolution: {integrity: sha512-TTlYpa+OL+vMMNG24xSlQGEJ3B/RzEfUlLct7b5G/ytav+wPrplCpVMFuwzXbkecJrb6IYo1iFb0S9v37754mg==} - engines: {node: '>=0.12'} - - reusify@1.1.0: - resolution: {integrity: sha512-g6QUff04oZpHs0eG5p83rFLhHeV00ug/Yf9nZM6fLeUrPguBTkTQOdpAWWspMh55TZfVQDPaN3NQJfbVRAxdIw==} - engines: {iojs: '>=1.0.0', node: '>=0.10.0'} - - run-parallel@1.2.0: - resolution: {integrity: sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA==} - - safe-array-concat@1.1.4: - resolution: {integrity: sha512-wtZlHyOje6OZTGqAoaDKxFkgRtkF9CnHAVnCHKfuj200wAgL+bSJhdsCD2l0Qx/2ekEXjPWcyKkfGb5CPboslg==} - engines: {node: '>=0.4'} - - safe-buffer@5.2.1: - resolution: {integrity: sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==} - - safe-push-apply@1.0.0: - resolution: {integrity: sha512-iKE9w/Z7xCzUMIZqdBsp6pEQvwuEebH4vdpjcDWnyzaI6yl6O9FHvVpmGelvEHNsoY6wGblkxR6Zty/h00WiSA==} - engines: {node: '>= 0.4'} - - safe-regex-test@1.1.0: - resolution: {integrity: sha512-x/+Cz4YrimQxQccJf5mKEbIa1NzeCRNI5Ecl/ekmlYaampdNLPalVyIcCZNNH3MvmqBugV5TMYZXv0ljslUlaw==} - engines: {node: '>= 0.4'} - - safe-regex@1.1.0: - resolution: {integrity: sha512-aJXcif4xnaNUzvUuC5gcb46oTS7zvg4jpMTnuqtrEPlR3vFr4pxtdTwaF1Qs3Enjn9HK+ZlwQui+a7z0SywIzg==} - - safe-regex@2.1.1: - resolution: {integrity: sha512-rx+x8AMzKb5Q5lQ95Zoi6ZbJqwCLkqi3XuJXp5P3rT8OEc6sZCJG5AE5dU3lsgRr/F4Bs31jSlVN+j5KrsGu9A==} - - semver@6.3.1: - resolution: {integrity: sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==} - hasBin: true - - semver@7.8.5: - resolution: {integrity: sha512-Y7/KDsb8LjooZpwaqGyulO6DQlksgCncchHGk+sZIY4SBvUocMBEFH5Ur1fI4dV+Jvl0w6cjvucaIi40puRioA==} - engines: {node: '>=10'} - hasBin: true - - set-function-length@1.2.2: - resolution: {integrity: sha512-pgRc4hJ4/sNjWCSS9AmnS40x3bNMDTknHgL5UaMBTMyJnU90EgWh1Rz+MC9eFu4BuN/UwZjKQuY/1v3rM7HMfg==} - engines: {node: '>= 0.4'} - - set-function-name@2.0.2: - resolution: {integrity: sha512-7PGFlmtwsEADb0WYyvCMa1t+yke6daIG4Wirafur5kcf+MhUnPms1UeR0CKQdTZD81yESwMHbtn+TR+dMviakQ==} - engines: {node: '>= 0.4'} - - set-proto@1.0.0: - resolution: {integrity: sha512-RJRdvCo6IAnPdsvP/7m6bsQqNnn1FCBX5ZNtFL98MmFF/4xAIJTIg1YbHW5DC2W5SKZanrC6i4HsJqlajw/dZw==} - engines: {node: '>= 0.4'} - - shebang-command@2.0.0: - resolution: {integrity: sha512-kHxr2zZpYtdmrN1qDjrrX/Z1rR1kG8Dx+gkpK1G4eXmvXswmcE1hTWBWYUzlraYw1/yZp6YuDY77YtvbN0dmDA==} - engines: {node: '>=8'} - - shebang-regex@3.0.0: - resolution: {integrity: sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==} - engines: {node: '>=8'} - - side-channel-list@1.0.1: - resolution: {integrity: sha512-mjn/0bi/oUURjc5Xl7IaWi/OJJJumuoJFQJfDDyO46+hBWsfaVM65TBHq2eoZBhzl9EchxOijpkbRC8SVBQU0w==} - engines: {node: '>= 0.4'} - - side-channel-map@1.0.1: - resolution: {integrity: sha512-VCjCNfgMsby3tTdo02nbjtM/ewra6jPHmpThenkTYh8pG9ucZ/1P8So4u4FGBek/BjpOVsDCMoLA/iuBKIFXRA==} - engines: {node: '>= 0.4'} - - side-channel-weakmap@1.0.2: - resolution: {integrity: sha512-WPS/HvHQTYnHisLo9McqBHOJk2FkHO/tlpvldyrnem4aeQp4hai3gythswg6p01oSoTl58rcpiFAjF2br2Ak2A==} - engines: {node: '>= 0.4'} - - side-channel@1.1.1: - resolution: {integrity: sha512-6x6dK6zJdpTzF4sQeNYxwtvBzf6Eg4GtlesS94HOvTudUeyK2WXAaIfmDgsyslYrRBeFIlsi54AYsFGUuhmvrQ==} - engines: {node: '>= 0.4'} - - stop-iteration-iterator@1.1.0: - resolution: {integrity: sha512-eLoXW/DHyl62zxY4SCaIgnRhuMr6ri4juEYARS8E6sCEqzKpOiE521Ucofdx+KnDZl5xmvGYaaKCk5FEOxJCoQ==} - engines: {node: '>= 0.4'} - - string.prototype.matchall@4.0.12: - resolution: {integrity: sha512-6CC9uyBL+/48dYizRf7H7VAYCMCNTBeM78x/VTUe9bFEaxBepPJDa1Ow99LqI/1yF7kuy7Q3cQsYMrcjGUcskA==} - engines: {node: '>= 0.4'} - - string.prototype.repeat@1.0.0: - resolution: {integrity: sha512-0u/TldDbKD8bFCQ/4f5+mNRrXwZ8hg2w7ZR8wa16e8z9XpePWl3eGEcUD0OXpEH/VJH/2G3gjUtR3ZOiBe2S/w==} - - string.prototype.trim@1.2.11: - resolution: {integrity: sha512-PwvK7BU+CMTJGYQCTZb5RWXIML92lftJLhQz1tBzgKiqGxJaMlBAa48POXaNAC2s4y8jr3EFqrkF9+44neS46w==} - engines: {node: '>= 0.4'} - - string.prototype.trimend@1.0.10: - resolution: {integrity: sha512-2+3aDAOmPTmuFwjDnmJG2ctEkQKVki7vOSqaxkv42Mowj1V6PnvuwFCRrR5lChUux1TBskPjfkeTOhqczDMxTw==} - engines: {node: '>= 0.4'} - - string.prototype.trimstart@1.0.8: - resolution: {integrity: sha512-UXSH262CSZY1tfu3G3Secr6uGLCFVPMhIqHjlgCUtCCcgihYc/xKs9djMTMUOb2j1mVSeU8EU6NWc/iQKU6Gfg==} - engines: {node: '>= 0.4'} - - strip-bom@3.0.0: - resolution: {integrity: sha512-vavAMRXOgBVNF6nyEEmL3DBK19iRpDcoIwW+swQ+CbGiu7lju6t+JklA1MHweoWtadgt4ISVUsXLyDq34ddcwA==} - engines: {node: '>=4'} - - strip-json-comments@3.1.1: - resolution: {integrity: sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==} - engines: {node: '>=8'} - - style-mod@4.1.3: - resolution: {integrity: sha512-i/n8VsZydrugj3Iuzll8+x/00GH2vnYsk1eomD8QiRrSAeW6ItbCQDtfXCeJHd0iwiNagqjQkvpvREEPtW3IoQ==} - - supports-color@7.2.0: - resolution: {integrity: sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==} - engines: {node: '>=8'} - - supports-preserve-symlinks-flag@1.0.0: - resolution: {integrity: sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==} - engines: {node: '>= 0.4'} - - synckit@0.9.3: - resolution: {integrity: sha512-JJoOEKTfL1urb1mDoEblhD9NhEbWmq9jHEMEnxoC4ujUaZ4itA8vKgwkFAyNClgxplLi9tsUKX+EduK0p/l7sg==} - engines: {node: ^14.18.0 || >=16.0.0} - - tapable@2.3.3: - resolution: {integrity: sha512-uxc/zpqFg6x7C8vOE7lh6Lbda8eEL9zmVm/PLeTPBRhh1xCgdWaQ+J1CUieGpIfm2HdtsUpRv+HshiasBMcc6A==} - engines: {node: '>=6'} - - to-regex-range@5.0.1: - resolution: {integrity: sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==} - engines: {node: '>=8.0'} - - toml-eslint-parser@0.9.3: - resolution: {integrity: sha512-moYoCvkNUAPCxSW9jmHmRElhm4tVJpHL8ItC/+uYD0EpPSFXbck7yREz9tNdJVTSpHVod8+HoipcpbQ0oE6gsw==} - engines: {node: ^12.22.0 || ^14.17.0 || >=16.0.0} - - ts-api-utils@2.5.0: - resolution: {integrity: sha512-OJ/ibxhPlqrMM0UiNHJ/0CKQkoKF243/AEmplt3qpRgkW8VG7IfOS41h7V8TjITqdByHzrjcS/2si+y4lIh8NA==} - engines: {node: '>=18.12'} - peerDependencies: - typescript: '>=4.8.4' - - tsconfig-paths@3.15.0: - resolution: {integrity: sha512-2Ac2RgzDe/cn48GvOe3M+o82pEFewD3UPbyoUHHdKasHwJKjds4fLXWf/Ux5kATBKN20oaFGu+jbElp1pos0mg==} - - tslib@2.4.0: - resolution: {integrity: sha512-d6xOpEDfsi2CZVlPQzGeux8XMwLT9hssAsaPYExaQMuYskwb+x1x7J371tWlbBdWHroy99KnVB6qIkUbs5X3UQ==} - - tslib@2.8.1: - resolution: {integrity: sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==} - - tunnel-agent@0.6.0: - resolution: {integrity: sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==} - - type-check@0.4.0: - resolution: {integrity: sha512-XleUoc9uwGXqjWwXaUTZAmzMcFZ5858QA2vvx1Ur5xIcixXIP+8LnFDgRplU30us6teqdlskFfu+ae4K79Ooew==} - engines: {node: '>= 0.8.0'} - - typed-array-buffer@1.0.3: - resolution: {integrity: sha512-nAYYwfY3qnzX30IkA6AQZjVbtK6duGontcQm1WSG1MD94YLqK0515GNApXkoxKOWMusVssAHWLh9SeaoefYFGw==} - engines: {node: '>= 0.4'} - - typed-array-byte-length@1.0.3: - resolution: {integrity: sha512-BaXgOuIxz8n8pIq3e7Atg/7s+DpiYrxn4vdot3w9KbnBhcRQq6o3xemQdIfynqSeXeDrF32x+WvfzmOjPiY9lg==} - engines: {node: '>= 0.4'} - - typed-array-byte-offset@1.0.4: - resolution: {integrity: sha512-bTlAFB/FBYMcuX81gbL4OcpH5PmlFHqlCCpAl8AlEzMz5k53oNDvN8p1PNOWLEmI2x4orp3raOFB51tv9X+MFQ==} - engines: {node: '>= 0.4'} - - typed-array-length@1.0.8: - resolution: {integrity: sha512-phPGCwqr2+Qo0fwniCE8e4pKnGu/yFb5nD5Y8bf0EEeiI5GklnACYA9GFy/DrAeRrKHXvHn+1SUsOWgJp6RO+g==} - engines: {node: '>= 0.4'} - - typescript-eslint@8.35.1: - resolution: {integrity: sha512-xslJjFzhOmHYQzSB/QTeASAHbjmxOGEP6Coh93TXmUBFQoJ1VU35UHIDmG06Jd6taf3wqqC1ntBnCMeymy5Ovw==} - engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0} - peerDependencies: - eslint: ^8.57.0 || ^9.0.0 - typescript: '>=4.8.4 <5.9.0' - - typescript@5.4.5: - resolution: {integrity: sha512-vcI4UpRgg81oIRUFwR0WSIHKt11nJ7SAVlYNIu+QpqeyXP+gpQJy/Z4+F0aGxSE4MqwjyXvW/TzgkLAx2AGHwQ==} - engines: {node: '>=14.17'} - hasBin: true - - typescript@5.9.3: - resolution: {integrity: sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==} - engines: {node: '>=14.17'} - hasBin: true - - unbox-primitive@1.1.0: - resolution: {integrity: sha512-nWJ91DjeOkej/TA8pXQ3myruKpKEYgqvpw9lz4OPHj/NWFNluYrjbz9j01CJ8yKQd2g4jFoOkINCTW2I5LEEyw==} - engines: {node: '>= 0.4'} - - undici-types@5.26.5: - resolution: {integrity: sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==} - - undici-types@6.21.0: - resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==} - - uri-js@4.4.1: - resolution: {integrity: sha512-7rKUyy33Q1yc98pQ1DAmLtwX109F7TIfWlW1Ydo8Wl1ii1SeHieeh0HHfPeL2fMXK6z0s8ecKs9frCuLJvndBg==} - - w3c-keyname@2.2.8: - resolution: {integrity: sha512-dpojBhNsCNN7T82Tm7k26A6G9ML3NkhDsnw9n/eoxSRlVBB4CEtIQ/KTCLI2Fwf3ataSXRhYFkQi3SlnFwPvPQ==} - - which-boxed-primitive@1.1.1: - resolution: {integrity: sha512-TbX3mj8n0odCBFVlY8AxkqcHASw3L60jIuF8jFP78az3C2YhmGvqbHBpAjTRH2/xqYunrJ9g1jSyjCjpoWzIAA==} - engines: {node: '>= 0.4'} - - which-builtin-type@1.2.1: - resolution: {integrity: sha512-6iBczoX+kDQ7a3+YJBnh3T+KZRxM/iYNPXicqk66/Qfm1b93iu+yOImkg0zHbj5LNOcNv1TEADiZ0xa34B4q6Q==} - engines: {node: '>= 0.4'} - - which-collection@1.0.2: - resolution: {integrity: sha512-K4jVyjnBdgvc86Y6BkaLZEN933SwYOuBFkdmBu9ZfkcAbdVbpITnDmjvZ/aQjRXQrv5EPkTnD1s39GiiqbngCw==} - engines: {node: '>= 0.4'} - - which-typed-array@1.1.22: - resolution: {integrity: sha512-fvO4ExWMFsqyhG3AiPAObMuY1lxaqgYcxbc49CNdWDDECOJNgQyvsOWVwbZc+qf3rzRtxojBK+CMEv0Ld5CYpw==} - engines: {node: '>= 0.4'} - - which@2.0.2: - resolution: {integrity: sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==} - engines: {node: '>= 8'} - hasBin: true - - word-wrap@1.2.5: - resolution: {integrity: sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==} - engines: {node: '>=0.10.0'} - - yaml-eslint-parser@1.3.2: - resolution: {integrity: sha512-odxVsHAkZYYglR30aPYRY4nUGJnoJ2y1ww2HDvZALo0BDETv9kWbi16J52eHs+PWRNmF4ub6nZqfVOeesOvntg==} - engines: {node: ^14.17.0 || >=16.0.0} - - yaml@2.9.0: - resolution: {integrity: sha512-2AvhNX3mb8zd6Zy7INTtSpl1F15HW6Wnqj0srWlkKLcpYl/gMIMJiyuGq2KeI2YFxUPjdlB+3Lc10seMLtL4cA==} - engines: {node: '>= 14.6'} - hasBin: true - - yocto-queue@0.1.0: - resolution: {integrity: sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==} - engines: {node: '>=10'} - -snapshots: - - '@codemirror/state@6.5.0': - dependencies: - '@marijn/find-cluster-break': 1.0.3 - - '@codemirror/view@6.38.6': - dependencies: - '@codemirror/state': 6.5.0 - crelt: 1.0.7 - style-mod: 4.1.3 - w3c-keyname: 2.2.8 - - '@esbuild/aix-ppc64@0.25.5': - optional: true - - '@esbuild/android-arm64@0.25.5': - optional: true - - '@esbuild/android-arm@0.25.5': - optional: true - - '@esbuild/android-x64@0.25.5': - optional: true - - '@esbuild/darwin-arm64@0.25.5': - optional: true - - '@esbuild/darwin-x64@0.25.5': - optional: true - - '@esbuild/freebsd-arm64@0.25.5': - optional: true - - '@esbuild/freebsd-x64@0.25.5': - optional: true - - '@esbuild/linux-arm64@0.25.5': - optional: true - - '@esbuild/linux-arm@0.25.5': - optional: true - - '@esbuild/linux-ia32@0.25.5': - optional: true - - '@esbuild/linux-loong64@0.25.5': - optional: true - - '@esbuild/linux-mips64el@0.25.5': - optional: true - - '@esbuild/linux-ppc64@0.25.5': - optional: true - - '@esbuild/linux-riscv64@0.25.5': - optional: true - - '@esbuild/linux-s390x@0.25.5': - optional: true - - '@esbuild/linux-x64@0.25.5': - optional: true - - '@esbuild/netbsd-arm64@0.25.5': - optional: true - - '@esbuild/netbsd-x64@0.25.5': - optional: true - - '@esbuild/openbsd-arm64@0.25.5': - optional: true - - '@esbuild/openbsd-x64@0.25.5': - optional: true - - '@esbuild/sunos-x64@0.25.5': - optional: true - - '@esbuild/win32-arm64@0.25.5': - optional: true - - '@esbuild/win32-ia32@0.25.5': - optional: true - - '@esbuild/win32-x64@0.25.5': - optional: true - - '@eslint-community/eslint-utils@4.9.1(eslint@9.39.4(jiti@2.6.1))': - dependencies: - eslint: 9.39.4(jiti@2.6.1) - eslint-visitor-keys: 3.4.3 - - '@eslint-community/regexpp@4.12.2': {} - - '@eslint/config-array@0.21.2': - dependencies: - '@eslint/object-schema': 2.1.7 - debug: 4.4.3 - minimatch: 3.1.5 - transitivePeerDependencies: - - supports-color - - '@eslint/config-helpers@0.4.2': - dependencies: - '@eslint/core': 0.17.0 - - '@eslint/core@0.17.0': - dependencies: - '@types/json-schema': 7.0.15 - - '@eslint/eslintrc@3.3.5': - dependencies: - ajv: 6.15.0 - debug: 4.4.3 - espree: 10.4.0 - globals: 14.0.0 - ignore: 5.3.2 - import-fresh: 3.3.1 - js-yaml: 4.3.0 - minimatch: 3.1.5 - strip-json-comments: 3.1.1 - transitivePeerDependencies: - - supports-color - - '@eslint/js@9.30.1': {} - - '@eslint/js@9.39.4': {} - - '@eslint/json@0.14.0': - dependencies: - '@eslint/core': 0.17.0 - '@eslint/plugin-kit': 0.4.1 - '@humanwhocodes/momoa': 3.3.10 - natural-compare: 1.4.0 - - '@eslint/object-schema@2.1.7': {} - - '@eslint/plugin-kit@0.4.1': - dependencies: - '@eslint/core': 0.17.0 - levn: 0.4.1 - - '@humanfs/core@0.19.2': - dependencies: - '@humanfs/types': 0.15.0 - - '@humanfs/node@0.16.8': - dependencies: - '@humanfs/core': 0.19.2 - '@humanfs/types': 0.15.0 - '@humanwhocodes/retry': 0.4.3 - - '@humanfs/types@0.15.0': {} - - '@humanwhocodes/module-importer@1.0.1': {} - - '@humanwhocodes/momoa@3.3.10': {} - - '@humanwhocodes/retry@0.4.3': {} - - '@marijn/find-cluster-break@1.0.3': {} - - '@microsoft/eslint-plugin-sdl@1.1.0(eslint@9.39.4(jiti@2.6.1))': - dependencies: - eslint: 9.39.4(jiti@2.6.1) - eslint-plugin-n: 17.10.3(eslint@9.39.4(jiti@2.6.1)) - eslint-plugin-react: 7.37.3(eslint@9.39.4(jiti@2.6.1)) - eslint-plugin-security: 1.4.0 - - '@nodelib/fs.scandir@2.1.5': - dependencies: - '@nodelib/fs.stat': 2.0.5 - run-parallel: 1.2.0 - - '@nodelib/fs.stat@2.0.5': {} - - '@nodelib/fs.walk@1.2.8': - dependencies: - '@nodelib/fs.scandir': 2.1.5 - fastq: 1.20.1 - - '@pkgr/core@0.1.2': {} - - '@rtsao/scc@1.1.0': {} - - '@types/codemirror@5.60.8': - dependencies: - '@types/tern': 0.23.9 - - '@types/eslint@8.56.2': - dependencies: - '@types/estree': 1.0.9 - '@types/json-schema': 7.0.15 - - '@types/estree@1.0.9': {} - - '@types/json-schema@7.0.15': {} - - '@types/json5@0.0.29': {} - - '@types/node@20.12.12': - dependencies: - undici-types: 5.26.5 - - '@types/node@20.19.43': - dependencies: - undici-types: 6.21.0 - - '@types/tern@0.23.9': - dependencies: - '@types/estree': 1.0.9 - - '@typescript-eslint/eslint-plugin@8.35.1(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3)': - dependencies: - '@eslint-community/regexpp': 4.12.2 - '@typescript-eslint/parser': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - '@typescript-eslint/scope-manager': 8.35.1 - '@typescript-eslint/type-utils': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - '@typescript-eslint/utils': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - '@typescript-eslint/visitor-keys': 8.35.1 - eslint: 9.39.4(jiti@2.6.1) - graphemer: 1.4.0 - ignore: 7.0.5 - natural-compare: 1.4.0 - ts-api-utils: 2.5.0(typescript@5.9.3) - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - '@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3)': - dependencies: - '@typescript-eslint/scope-manager': 8.35.1 - '@typescript-eslint/types': 8.35.1 - '@typescript-eslint/typescript-estree': 8.35.1(typescript@5.9.3) - '@typescript-eslint/visitor-keys': 8.35.1 - debug: 4.4.3 - eslint: 9.39.4(jiti@2.6.1) - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - '@typescript-eslint/project-service@8.35.1(typescript@5.9.3)': - dependencies: - '@typescript-eslint/tsconfig-utils': 8.35.1(typescript@5.9.3) - '@typescript-eslint/types': 8.35.1 - debug: 4.4.3 - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - '@typescript-eslint/scope-manager@8.35.1': - dependencies: - '@typescript-eslint/types': 8.35.1 - '@typescript-eslint/visitor-keys': 8.35.1 - - '@typescript-eslint/tsconfig-utils@8.35.1(typescript@5.9.3)': - dependencies: - typescript: 5.9.3 - - '@typescript-eslint/type-utils@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3)': - dependencies: - '@typescript-eslint/typescript-estree': 8.35.1(typescript@5.9.3) - '@typescript-eslint/utils': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - debug: 4.4.3 - eslint: 9.39.4(jiti@2.6.1) - ts-api-utils: 2.5.0(typescript@5.9.3) - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - '@typescript-eslint/types@8.35.1': {} - - '@typescript-eslint/typescript-estree@8.35.1(typescript@5.9.3)': - dependencies: - '@typescript-eslint/project-service': 8.35.1(typescript@5.9.3) - '@typescript-eslint/tsconfig-utils': 8.35.1(typescript@5.9.3) - '@typescript-eslint/types': 8.35.1 - '@typescript-eslint/visitor-keys': 8.35.1 - debug: 4.4.3 - fast-glob: 3.3.3 - is-glob: 4.0.3 - minimatch: 9.0.9 - semver: 7.8.5 - ts-api-utils: 2.5.0(typescript@5.9.3) - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - '@typescript-eslint/utils@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3)': - dependencies: - '@eslint-community/eslint-utils': 4.9.1(eslint@9.39.4(jiti@2.6.1)) - '@typescript-eslint/scope-manager': 8.35.1 - '@typescript-eslint/types': 8.35.1 - '@typescript-eslint/typescript-estree': 8.35.1(typescript@5.9.3) - eslint: 9.39.4(jiti@2.6.1) - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - '@typescript-eslint/visitor-keys@8.35.1': - dependencies: - '@typescript-eslint/types': 8.35.1 - eslint-visitor-keys: 4.2.1 - - acorn-jsx@5.3.2(acorn@8.17.0): - dependencies: - acorn: 8.17.0 - - acorn@8.17.0: {} - - ajv@6.15.0: - dependencies: - fast-deep-equal: 3.1.3 - fast-json-stable-stringify: 2.1.0 - json-schema-traverse: 0.4.1 - uri-js: 4.4.1 - - ajv@8.20.0: - dependencies: - fast-deep-equal: 3.1.3 - fast-uri: 3.1.3 - json-schema-traverse: 1.0.0 - require-from-string: 2.0.2 - - ansi-styles@4.3.0: - dependencies: - color-convert: 2.0.1 - - argparse@2.0.1: {} - - array-buffer-byte-length@1.0.2: - dependencies: - call-bound: 1.0.4 - is-array-buffer: 3.0.5 - - array-includes@3.1.9: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-object-atoms: 1.1.2 - get-intrinsic: 1.3.0 - is-string: 1.1.1 - math-intrinsics: 1.1.0 - - array.prototype.findlast@1.2.5: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - es-shim-unscopables: 1.1.0 - - array.prototype.findlastindex@1.2.6: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - es-shim-unscopables: 1.1.0 - - array.prototype.flat@1.3.3: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-shim-unscopables: 1.1.0 - - array.prototype.flatmap@1.3.3: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-shim-unscopables: 1.1.0 - - array.prototype.tosorted@1.1.4: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - es-shim-unscopables: 1.1.0 - - arraybuffer.prototype.slice@1.0.4: - dependencies: - array-buffer-byte-length: 1.0.2 - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - get-intrinsic: 1.3.0 - is-array-buffer: 3.0.5 - - async-function@1.0.0: {} - - available-typed-arrays@1.0.7: - dependencies: - possible-typed-array-names: 1.1.0 - - balanced-match@1.0.2: {} - - brace-expansion@1.1.15: - dependencies: - balanced-match: 1.0.2 - concat-map: 0.0.1 - - brace-expansion@2.1.1: - dependencies: - balanced-match: 1.0.2 - - braces@3.0.3: - dependencies: - fill-range: 7.1.1 - - call-bind-apply-helpers@1.0.2: - dependencies: - es-errors: 1.3.0 - function-bind: 1.1.2 - - call-bind@1.0.9: - dependencies: - call-bind-apply-helpers: 1.0.2 - es-define-property: 1.0.1 - get-intrinsic: 1.3.0 - set-function-length: 1.2.2 - - call-bound@1.0.4: - dependencies: - call-bind-apply-helpers: 1.0.2 - get-intrinsic: 1.3.0 - - callsites@3.1.0: {} - - chalk@4.1.2: - dependencies: - ansi-styles: 4.3.0 - supports-color: 7.2.0 - - color-convert@2.0.1: - dependencies: - color-name: 1.1.4 - - color-name@1.1.4: {} - - concat-map@0.0.1: {} - - crelt@1.0.7: {} - - cross-spawn@7.0.6: - dependencies: - path-key: 3.1.1 - shebang-command: 2.0.0 - which: 2.0.2 - - data-view-buffer@1.0.2: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - is-data-view: 1.0.2 - - data-view-byte-length@1.0.2: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - is-data-view: 1.0.2 - - data-view-byte-offset@1.0.1: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - is-data-view: 1.0.2 - - debug@3.2.7: - dependencies: - ms: 2.1.3 - - debug@4.4.3: - dependencies: - ms: 2.1.3 - - deep-is@0.1.4: {} - - define-data-property@1.1.4: - dependencies: - es-define-property: 1.0.1 - es-errors: 1.3.0 - gopd: 1.2.0 - - define-properties@1.2.1: - dependencies: - define-data-property: 1.1.4 - has-property-descriptors: 1.0.2 - object-keys: 1.1.1 - - doctrine@2.1.0: - dependencies: - esutils: 2.0.3 - - dunder-proto@1.0.1: - dependencies: - call-bind-apply-helpers: 1.0.2 - es-errors: 1.3.0 - gopd: 1.2.0 - - empathic@2.0.1: {} - - enhanced-resolve@5.24.1: - dependencies: - graceful-fs: 4.2.11 - tapable: 2.3.3 - - es-abstract-get@1.0.0: - dependencies: - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - is-callable: 1.2.7 - object-inspect: 1.13.4 - - es-abstract@1.24.2: - dependencies: - array-buffer-byte-length: 1.0.2 - arraybuffer.prototype.slice: 1.0.4 - available-typed-arrays: 1.0.7 - call-bind: 1.0.9 - call-bound: 1.0.4 - data-view-buffer: 1.0.2 - data-view-byte-length: 1.0.2 - data-view-byte-offset: 1.0.1 - es-define-property: 1.0.1 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - es-set-tostringtag: 2.1.0 - es-to-primitive: 1.3.4 - function.prototype.name: 1.2.0 - get-intrinsic: 1.3.0 - get-proto: 1.0.1 - get-symbol-description: 1.1.0 - globalthis: 1.0.4 - gopd: 1.2.0 - has-property-descriptors: 1.0.2 - has-proto: 1.2.0 - has-symbols: 1.1.0 - hasown: 2.0.4 - internal-slot: 1.1.0 - is-array-buffer: 3.0.5 - is-callable: 1.2.7 - is-data-view: 1.0.2 - is-negative-zero: 2.0.3 - is-regex: 1.2.1 - is-set: 2.0.3 - is-shared-array-buffer: 1.0.4 - is-string: 1.1.1 - is-typed-array: 1.1.15 - is-weakref: 1.1.1 - math-intrinsics: 1.1.0 - object-inspect: 1.13.4 - object-keys: 1.1.1 - object.assign: 4.1.7 - own-keys: 1.0.1 - regexp.prototype.flags: 1.5.4 - safe-array-concat: 1.1.4 - safe-push-apply: 1.0.0 - safe-regex-test: 1.1.0 - set-proto: 1.0.0 - stop-iteration-iterator: 1.1.0 - string.prototype.trim: 1.2.11 - string.prototype.trimend: 1.0.10 - string.prototype.trimstart: 1.0.8 - typed-array-buffer: 1.0.3 - typed-array-byte-length: 1.0.3 - typed-array-byte-offset: 1.0.4 - typed-array-length: 1.0.8 - unbox-primitive: 1.1.0 - which-typed-array: 1.1.22 - - es-define-property@1.0.1: {} - - es-errors@1.3.0: {} - - es-iterator-helpers@1.3.3: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - es-set-tostringtag: 2.1.0 - function-bind: 1.1.2 - get-intrinsic: 1.3.0 - globalthis: 1.0.4 - gopd: 1.2.0 - has-property-descriptors: 1.0.2 - has-proto: 1.2.0 - has-symbols: 1.1.0 - internal-slot: 1.1.0 - iterator.prototype: 1.1.5 - math-intrinsics: 1.1.0 - - es-object-atoms@1.1.2: - dependencies: - es-errors: 1.3.0 - - es-set-tostringtag@2.1.0: - dependencies: - es-errors: 1.3.0 - get-intrinsic: 1.3.0 - has-tostringtag: 1.0.2 - hasown: 2.0.4 - - es-shim-unscopables@1.1.0: - dependencies: - hasown: 2.0.4 - - es-to-primitive@1.3.4: - dependencies: - es-abstract-get: 1.0.0 - es-define-property: 1.0.1 - es-errors: 1.3.0 - is-callable: 1.2.7 - is-date-object: 1.1.0 - is-symbol: 1.1.1 - - esbuild@0.25.5: - optionalDependencies: - '@esbuild/aix-ppc64': 0.25.5 - '@esbuild/android-arm': 0.25.5 - '@esbuild/android-arm64': 0.25.5 - '@esbuild/android-x64': 0.25.5 - '@esbuild/darwin-arm64': 0.25.5 - '@esbuild/darwin-x64': 0.25.5 - '@esbuild/freebsd-arm64': 0.25.5 - '@esbuild/freebsd-x64': 0.25.5 - '@esbuild/linux-arm': 0.25.5 - '@esbuild/linux-arm64': 0.25.5 - '@esbuild/linux-ia32': 0.25.5 - '@esbuild/linux-loong64': 0.25.5 - '@esbuild/linux-mips64el': 0.25.5 - '@esbuild/linux-ppc64': 0.25.5 - '@esbuild/linux-riscv64': 0.25.5 - '@esbuild/linux-s390x': 0.25.5 - '@esbuild/linux-x64': 0.25.5 - '@esbuild/netbsd-arm64': 0.25.5 - '@esbuild/netbsd-x64': 0.25.5 - '@esbuild/openbsd-arm64': 0.25.5 - '@esbuild/openbsd-x64': 0.25.5 - '@esbuild/sunos-x64': 0.25.5 - '@esbuild/win32-arm64': 0.25.5 - '@esbuild/win32-ia32': 0.25.5 - '@esbuild/win32-x64': 0.25.5 - - escape-string-regexp@4.0.0: {} - - eslint-compat-utils@0.5.1(eslint@9.39.4(jiti@2.6.1)): - dependencies: - eslint: 9.39.4(jiti@2.6.1) - semver: 7.8.5 - - eslint-import-resolver-node@0.3.10: - dependencies: - debug: 3.2.7 - is-core-module: 2.16.2 - resolve: 2.0.0-next.7 - transitivePeerDependencies: - - supports-color - - eslint-module-utils@2.14.0(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-node@0.3.10)(eslint@9.39.4(jiti@2.6.1)): - dependencies: - debug: 3.2.7 - optionalDependencies: - '@typescript-eslint/parser': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - eslint: 9.39.4(jiti@2.6.1) - eslint-import-resolver-node: 0.3.10 - transitivePeerDependencies: - - supports-color - - eslint-plugin-depend@1.3.1: - dependencies: - empathic: 2.0.1 - module-replacements: 2.11.0 - semver: 7.8.5 - - eslint-plugin-es-x@7.8.0(eslint@9.39.4(jiti@2.6.1)): - dependencies: - '@eslint-community/eslint-utils': 4.9.1(eslint@9.39.4(jiti@2.6.1)) - '@eslint-community/regexpp': 4.12.2 - eslint: 9.39.4(jiti@2.6.1) - eslint-compat-utils: 0.5.1(eslint@9.39.4(jiti@2.6.1)) - - eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.4(jiti@2.6.1)): - dependencies: - '@rtsao/scc': 1.1.0 - array-includes: 3.1.9 - array.prototype.findlastindex: 1.2.6 - array.prototype.flat: 1.3.3 - array.prototype.flatmap: 1.3.3 - debug: 3.2.7 - doctrine: 2.1.0 - eslint: 9.39.4(jiti@2.6.1) - eslint-import-resolver-node: 0.3.10 - eslint-module-utils: 2.14.0(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-node@0.3.10)(eslint@9.39.4(jiti@2.6.1)) - hasown: 2.0.4 - is-core-module: 2.16.2 - is-glob: 4.0.3 - minimatch: 3.1.5 - object.fromentries: 2.0.8 - object.groupby: 1.0.3 - object.values: 1.2.1 - semver: 6.3.1 - string.prototype.trimend: 1.0.10 - tsconfig-paths: 3.15.0 - optionalDependencies: - '@typescript-eslint/parser': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - transitivePeerDependencies: - - eslint-import-resolver-typescript - - eslint-import-resolver-webpack - - supports-color - - eslint-plugin-json-schema-validator@5.1.0(eslint@9.39.4(jiti@2.6.1)): - dependencies: - '@eslint-community/eslint-utils': 4.9.1(eslint@9.39.4(jiti@2.6.1)) - ajv: 8.20.0 - debug: 4.4.3 - eslint: 9.39.4(jiti@2.6.1) - eslint-compat-utils: 0.5.1(eslint@9.39.4(jiti@2.6.1)) - json-schema-migrate: 2.0.0 - jsonc-eslint-parser: 2.4.2 - minimatch: 8.0.7 - synckit: 0.9.3 - toml-eslint-parser: 0.9.3 - tunnel-agent: 0.6.0 - yaml-eslint-parser: 1.3.2 - transitivePeerDependencies: - - supports-color - - eslint-plugin-n@17.10.3(eslint@9.39.4(jiti@2.6.1)): - dependencies: - '@eslint-community/eslint-utils': 4.9.1(eslint@9.39.4(jiti@2.6.1)) - enhanced-resolve: 5.24.1 - eslint: 9.39.4(jiti@2.6.1) - eslint-plugin-es-x: 7.8.0(eslint@9.39.4(jiti@2.6.1)) - get-tsconfig: 4.14.0 - globals: 15.15.0 - ignore: 5.3.2 - minimatch: 9.0.9 - semver: 7.8.5 - - eslint-plugin-obsidianmd@0.1.9(@eslint/js@9.30.1)(@eslint/json@0.14.0)(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.4(jiti@2.6.1))(obsidian@1.13.1(@codemirror/state@6.5.0)(@codemirror/view@6.38.6))(typescript-eslint@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3)): - dependencies: - '@eslint/js': 9.30.1 - '@eslint/json': 0.14.0 - '@microsoft/eslint-plugin-sdl': 1.1.0(eslint@9.39.4(jiti@2.6.1)) - '@types/eslint': 8.56.2 - '@types/node': 20.12.12 - eslint: 9.39.4(jiti@2.6.1) - eslint-plugin-depend: 1.3.1 - eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.4(jiti@2.6.1)) - eslint-plugin-json-schema-validator: 5.1.0(eslint@9.39.4(jiti@2.6.1)) - eslint-plugin-security: 2.1.1 - globals: 14.0.0 - obsidian: 1.13.1(@codemirror/state@6.5.0)(@codemirror/view@6.38.6) - typescript: 5.4.5 - typescript-eslint: 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - transitivePeerDependencies: - - '@typescript-eslint/parser' - - eslint-import-resolver-typescript - - eslint-import-resolver-webpack - - supports-color - - eslint-plugin-react@7.37.3(eslint@9.39.4(jiti@2.6.1)): - dependencies: - array-includes: 3.1.9 - array.prototype.findlast: 1.2.5 - array.prototype.flatmap: 1.3.3 - array.prototype.tosorted: 1.1.4 - doctrine: 2.1.0 - es-iterator-helpers: 1.3.3 - eslint: 9.39.4(jiti@2.6.1) - estraverse: 5.3.0 - hasown: 2.0.4 - jsx-ast-utils: 3.3.5 - minimatch: 3.1.5 - object.entries: 1.1.9 - object.fromentries: 2.0.8 - object.values: 1.2.1 - prop-types: 15.8.1 - resolve: 2.0.0-next.7 - semver: 6.3.1 - string.prototype.matchall: 4.0.12 - string.prototype.repeat: 1.0.0 - - eslint-plugin-security@1.4.0: - dependencies: - safe-regex: 1.1.0 - - eslint-plugin-security@2.1.1: - dependencies: - safe-regex: 2.1.1 - - eslint-scope@8.4.0: - dependencies: - esrecurse: 4.3.0 - estraverse: 5.3.0 - - eslint-visitor-keys@3.4.3: {} - - eslint-visitor-keys@4.2.1: {} - - eslint@9.39.4(jiti@2.6.1): - dependencies: - '@eslint-community/eslint-utils': 4.9.1(eslint@9.39.4(jiti@2.6.1)) - '@eslint-community/regexpp': 4.12.2 - '@eslint/config-array': 0.21.2 - '@eslint/config-helpers': 0.4.2 - '@eslint/core': 0.17.0 - '@eslint/eslintrc': 3.3.5 - '@eslint/js': 9.39.4 - '@eslint/plugin-kit': 0.4.1 - '@humanfs/node': 0.16.8 - '@humanwhocodes/module-importer': 1.0.1 - '@humanwhocodes/retry': 0.4.3 - '@types/estree': 1.0.9 - ajv: 6.15.0 - chalk: 4.1.2 - cross-spawn: 7.0.6 - debug: 4.4.3 - escape-string-regexp: 4.0.0 - eslint-scope: 8.4.0 - eslint-visitor-keys: 4.2.1 - espree: 10.4.0 - esquery: 1.7.0 - esutils: 2.0.3 - fast-deep-equal: 3.1.3 - file-entry-cache: 8.0.0 - find-up: 5.0.0 - glob-parent: 6.0.2 - ignore: 5.3.2 - imurmurhash: 0.1.4 - is-glob: 4.0.3 - json-stable-stringify-without-jsonify: 1.0.1 - lodash.merge: 4.6.2 - minimatch: 3.1.5 - natural-compare: 1.4.0 - optionator: 0.9.4 - optionalDependencies: - jiti: 2.6.1 - transitivePeerDependencies: - - supports-color - - espree@10.4.0: - dependencies: - acorn: 8.17.0 - acorn-jsx: 5.3.2(acorn@8.17.0) - eslint-visitor-keys: 4.2.1 - - espree@9.6.1: - dependencies: - acorn: 8.17.0 - acorn-jsx: 5.3.2(acorn@8.17.0) - eslint-visitor-keys: 3.4.3 - - esquery@1.7.0: - dependencies: - estraverse: 5.3.0 - - esrecurse@4.3.0: - dependencies: - estraverse: 5.3.0 - - estraverse@5.3.0: {} - - esutils@2.0.3: {} - - fast-deep-equal@3.1.3: {} - - fast-glob@3.3.3: - dependencies: - '@nodelib/fs.stat': 2.0.5 - '@nodelib/fs.walk': 1.2.8 - glob-parent: 5.1.2 - merge2: 1.4.1 - micromatch: 4.0.8 - - fast-json-stable-stringify@2.1.0: {} - - fast-levenshtein@2.0.6: {} - - fast-uri@3.1.3: {} - - fastq@1.20.1: - dependencies: - reusify: 1.1.0 - - file-entry-cache@8.0.0: - dependencies: - flat-cache: 4.0.1 - - fill-range@7.1.1: - dependencies: - to-regex-range: 5.0.1 - - find-up@5.0.0: - dependencies: - locate-path: 6.0.0 - path-exists: 4.0.0 - - flat-cache@4.0.1: - dependencies: - flatted: 3.4.2 - keyv: 4.5.4 - - flatted@3.4.2: {} - - for-each@0.3.5: - dependencies: - is-callable: 1.2.7 - - function-bind@1.1.2: {} - - function.prototype.name@1.2.0: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - es-define-property: 1.0.1 - es-errors: 1.3.0 - functions-have-names: 1.2.3 - has-property-descriptors: 1.0.2 - hasown: 2.0.4 - is-callable: 1.2.7 - is-document.all: 1.0.0 - - functions-have-names@1.2.3: {} - - generator-function@2.0.1: {} - - get-intrinsic@1.3.0: - dependencies: - call-bind-apply-helpers: 1.0.2 - es-define-property: 1.0.1 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - function-bind: 1.1.2 - get-proto: 1.0.1 - gopd: 1.2.0 - has-symbols: 1.1.0 - hasown: 2.0.4 - math-intrinsics: 1.1.0 - - get-proto@1.0.1: - dependencies: - dunder-proto: 1.0.1 - es-object-atoms: 1.1.2 - - get-symbol-description@1.1.0: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - get-intrinsic: 1.3.0 - - get-tsconfig@4.14.0: - dependencies: - resolve-pkg-maps: 1.0.0 - - glob-parent@5.1.2: - dependencies: - is-glob: 4.0.3 - - glob-parent@6.0.2: - dependencies: - is-glob: 4.0.3 - - globals@14.0.0: {} - - globals@15.15.0: {} - - globalthis@1.0.4: - dependencies: - define-properties: 1.2.1 - gopd: 1.2.0 - - gopd@1.2.0: {} - - graceful-fs@4.2.11: {} - - graphemer@1.4.0: {} - - has-bigints@1.1.0: {} - - has-flag@4.0.0: {} - - has-property-descriptors@1.0.2: - dependencies: - es-define-property: 1.0.1 - - has-proto@1.2.0: - dependencies: - dunder-proto: 1.0.1 - - has-symbols@1.1.0: {} - - has-tostringtag@1.0.2: - dependencies: - has-symbols: 1.1.0 - - hasown@2.0.4: - dependencies: - function-bind: 1.1.2 - - ignore@5.3.2: {} - - ignore@7.0.5: {} - - import-fresh@3.3.1: - dependencies: - parent-module: 1.0.1 - resolve-from: 4.0.0 - - imurmurhash@0.1.4: {} - - internal-slot@1.1.0: - dependencies: - es-errors: 1.3.0 - hasown: 2.0.4 - side-channel: 1.1.1 - - is-array-buffer@3.0.5: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - get-intrinsic: 1.3.0 - - is-async-function@2.1.1: - dependencies: - async-function: 1.0.0 - call-bound: 1.0.4 - get-proto: 1.0.1 - has-tostringtag: 1.0.2 - safe-regex-test: 1.1.0 - - is-bigint@1.1.0: - dependencies: - has-bigints: 1.1.0 - - is-boolean-object@1.2.2: - dependencies: - call-bound: 1.0.4 - has-tostringtag: 1.0.2 - - is-callable@1.2.7: {} - - is-core-module@2.16.2: - dependencies: - hasown: 2.0.4 - - is-data-view@1.0.2: - dependencies: - call-bound: 1.0.4 - get-intrinsic: 1.3.0 - is-typed-array: 1.1.15 - - is-date-object@1.1.0: - dependencies: - call-bound: 1.0.4 - has-tostringtag: 1.0.2 - - is-document.all@1.0.0: - dependencies: - call-bound: 1.0.4 - - is-extglob@2.1.1: {} - - is-finalizationregistry@1.1.1: - dependencies: - call-bound: 1.0.4 - - is-generator-function@1.1.2: - dependencies: - call-bound: 1.0.4 - generator-function: 2.0.1 - get-proto: 1.0.1 - has-tostringtag: 1.0.2 - safe-regex-test: 1.1.0 - - is-glob@4.0.3: - dependencies: - is-extglob: 2.1.1 - - is-map@2.0.3: {} - - is-negative-zero@2.0.3: {} - - is-number-object@1.1.1: - dependencies: - call-bound: 1.0.4 - has-tostringtag: 1.0.2 - - is-number@7.0.0: {} - - is-regex@1.2.1: - dependencies: - call-bound: 1.0.4 - gopd: 1.2.0 - has-tostringtag: 1.0.2 - hasown: 2.0.4 - - is-set@2.0.3: {} - - is-shared-array-buffer@1.0.4: - dependencies: - call-bound: 1.0.4 - - is-string@1.1.1: - dependencies: - call-bound: 1.0.4 - has-tostringtag: 1.0.2 - - is-symbol@1.1.1: - dependencies: - call-bound: 1.0.4 - has-symbols: 1.1.0 - safe-regex-test: 1.1.0 - - is-typed-array@1.1.15: - dependencies: - which-typed-array: 1.1.22 - - is-weakmap@2.0.2: {} - - is-weakref@1.1.1: - dependencies: - call-bound: 1.0.4 - - is-weakset@2.0.4: - dependencies: - call-bound: 1.0.4 - get-intrinsic: 1.3.0 - - isarray@2.0.5: {} - - isexe@2.0.0: {} - - iterator.prototype@1.1.5: - dependencies: - define-data-property: 1.1.4 - es-object-atoms: 1.1.2 - get-intrinsic: 1.3.0 - get-proto: 1.0.1 - has-symbols: 1.1.0 - set-function-name: 2.0.2 - - jiti@2.6.1: {} - - js-tokens@4.0.0: {} - - js-yaml@4.3.0: - dependencies: - argparse: 2.0.1 - - json-buffer@3.0.1: {} - - json-schema-migrate@2.0.0: - dependencies: - ajv: 8.20.0 - - json-schema-traverse@0.4.1: {} - - json-schema-traverse@1.0.0: {} - - json-stable-stringify-without-jsonify@1.0.1: {} - - json5@1.0.2: - dependencies: - minimist: 1.2.8 - - jsonc-eslint-parser@2.4.2: - dependencies: - acorn: 8.17.0 - eslint-visitor-keys: 3.4.3 - espree: 9.6.1 - semver: 7.8.5 - - jsx-ast-utils@3.3.5: - dependencies: - array-includes: 3.1.9 - array.prototype.flat: 1.3.3 - object.assign: 4.1.7 - object.values: 1.2.1 - - keyv@4.5.4: - dependencies: - json-buffer: 3.0.1 - - levn@0.4.1: - dependencies: - prelude-ls: 1.2.1 - type-check: 0.4.0 - - locate-path@6.0.0: - dependencies: - p-locate: 5.0.0 - - lodash.merge@4.6.2: {} - - loose-envify@1.4.0: - dependencies: - js-tokens: 4.0.0 - - math-intrinsics@1.1.0: {} - - merge2@1.4.1: {} - - micromatch@4.0.8: - dependencies: - braces: 3.0.3 - picomatch: 2.3.2 - - minimatch@3.1.5: - dependencies: - brace-expansion: 1.1.15 - - minimatch@8.0.7: - dependencies: - brace-expansion: 2.1.1 - - minimatch@9.0.9: - dependencies: - brace-expansion: 2.1.1 - - minimist@1.2.8: {} - - module-replacements@2.11.0: {} - - moment@2.29.4: {} - - ms@2.1.3: {} - - natural-compare@1.4.0: {} - - node-exports-info@1.6.2: - dependencies: - array.prototype.flatmap: 1.3.3 - es-errors: 1.3.0 - object.entries: 1.1.9 - semver: 6.3.1 - - object-assign@4.1.1: {} - - object-inspect@1.13.4: {} - - object-keys@1.1.1: {} - - object.assign@4.1.7: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-object-atoms: 1.1.2 - has-symbols: 1.1.0 - object-keys: 1.1.1 - - object.entries@1.1.9: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-object-atoms: 1.1.2 - - object.fromentries@2.0.8: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-object-atoms: 1.1.2 - - object.groupby@1.0.3: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - - object.values@1.2.1: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-object-atoms: 1.1.2 - - obsidian@1.13.1(@codemirror/state@6.5.0)(@codemirror/view@6.38.6): - dependencies: - '@codemirror/state': 6.5.0 - '@codemirror/view': 6.38.6 - '@types/codemirror': 5.60.8 - moment: 2.29.4 - - optionator@0.9.4: - dependencies: - deep-is: 0.1.4 - fast-levenshtein: 2.0.6 - levn: 0.4.1 - prelude-ls: 1.2.1 - type-check: 0.4.0 - word-wrap: 1.2.5 - - own-keys@1.0.1: - dependencies: - get-intrinsic: 1.3.0 - object-keys: 1.1.1 - safe-push-apply: 1.0.0 - - p-limit@3.1.0: - dependencies: - yocto-queue: 0.1.0 - - p-locate@5.0.0: - dependencies: - p-limit: 3.1.0 - - parent-module@1.0.1: - dependencies: - callsites: 3.1.0 - - path-exists@4.0.0: {} - - path-key@3.1.1: {} - - path-parse@1.0.7: {} - - picomatch@2.3.2: {} - - possible-typed-array-names@1.1.0: {} - - prelude-ls@1.2.1: {} - - prop-types@15.8.1: - dependencies: - loose-envify: 1.4.0 - object-assign: 4.1.1 - react-is: 16.13.1 - - punycode@2.3.1: {} - - queue-microtask@1.2.3: {} - - react-is@16.13.1: {} - - reflect.getprototypeof@1.0.10: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - get-intrinsic: 1.3.0 - get-proto: 1.0.1 - which-builtin-type: 1.2.1 - - regexp-tree@0.1.27: {} - - regexp.prototype.flags@1.5.4: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-errors: 1.3.0 - get-proto: 1.0.1 - gopd: 1.2.0 - set-function-name: 2.0.2 - - require-from-string@2.0.2: {} - - resolve-from@4.0.0: {} - - resolve-pkg-maps@1.0.0: {} - - resolve@2.0.0-next.7: - dependencies: - es-errors: 1.3.0 - is-core-module: 2.16.2 - node-exports-info: 1.6.2 - object-keys: 1.1.1 - path-parse: 1.0.7 - supports-preserve-symlinks-flag: 1.0.0 - - ret@0.1.15: {} - - reusify@1.1.0: {} - - run-parallel@1.2.0: - dependencies: - queue-microtask: 1.2.3 - - safe-array-concat@1.1.4: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - get-intrinsic: 1.3.0 - has-symbols: 1.1.0 - isarray: 2.0.5 - - safe-buffer@5.2.1: {} - - safe-push-apply@1.0.0: - dependencies: - es-errors: 1.3.0 - isarray: 2.0.5 - - safe-regex-test@1.1.0: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - is-regex: 1.2.1 - - safe-regex@1.1.0: - dependencies: - ret: 0.1.15 - - safe-regex@2.1.1: - dependencies: - regexp-tree: 0.1.27 - - semver@6.3.1: {} - - semver@7.8.5: {} - - set-function-length@1.2.2: - dependencies: - define-data-property: 1.1.4 - es-errors: 1.3.0 - function-bind: 1.1.2 - get-intrinsic: 1.3.0 - gopd: 1.2.0 - has-property-descriptors: 1.0.2 - - set-function-name@2.0.2: - dependencies: - define-data-property: 1.1.4 - es-errors: 1.3.0 - functions-have-names: 1.2.3 - has-property-descriptors: 1.0.2 - - set-proto@1.0.0: - dependencies: - dunder-proto: 1.0.1 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - - shebang-command@2.0.0: - dependencies: - shebang-regex: 3.0.0 - - shebang-regex@3.0.0: {} - - side-channel-list@1.0.1: - dependencies: - es-errors: 1.3.0 - object-inspect: 1.13.4 - - side-channel-map@1.0.1: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - get-intrinsic: 1.3.0 - object-inspect: 1.13.4 - - side-channel-weakmap@1.0.2: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - get-intrinsic: 1.3.0 - object-inspect: 1.13.4 - side-channel-map: 1.0.1 - - side-channel@1.1.1: - dependencies: - es-errors: 1.3.0 - object-inspect: 1.13.4 - side-channel-list: 1.0.1 - side-channel-map: 1.0.1 - side-channel-weakmap: 1.0.2 - - stop-iteration-iterator@1.1.0: - dependencies: - es-errors: 1.3.0 - internal-slot: 1.1.0 - - string.prototype.matchall@4.0.12: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-errors: 1.3.0 - es-object-atoms: 1.1.2 - get-intrinsic: 1.3.0 - gopd: 1.2.0 - has-symbols: 1.1.0 - internal-slot: 1.1.0 - regexp.prototype.flags: 1.5.4 - set-function-name: 2.0.2 - side-channel: 1.1.1 - - string.prototype.repeat@1.0.0: - dependencies: - define-properties: 1.2.1 - es-abstract: 1.24.2 - - string.prototype.trim@1.2.11: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-data-property: 1.1.4 - define-properties: 1.2.1 - es-abstract: 1.24.2 - es-object-atoms: 1.1.2 - has-property-descriptors: 1.0.2 - safe-regex-test: 1.1.0 - - string.prototype.trimend@1.0.10: - dependencies: - call-bind: 1.0.9 - call-bound: 1.0.4 - define-properties: 1.2.1 - es-object-atoms: 1.1.2 - - string.prototype.trimstart@1.0.8: - dependencies: - call-bind: 1.0.9 - define-properties: 1.2.1 - es-object-atoms: 1.1.2 - - strip-bom@3.0.0: {} - - strip-json-comments@3.1.1: {} - - style-mod@4.1.3: {} - - supports-color@7.2.0: - dependencies: - has-flag: 4.0.0 - - supports-preserve-symlinks-flag@1.0.0: {} - - synckit@0.9.3: - dependencies: - '@pkgr/core': 0.1.2 - tslib: 2.8.1 - - tapable@2.3.3: {} - - to-regex-range@5.0.1: - dependencies: - is-number: 7.0.0 - - toml-eslint-parser@0.9.3: - dependencies: - eslint-visitor-keys: 3.4.3 - - ts-api-utils@2.5.0(typescript@5.9.3): - dependencies: - typescript: 5.9.3 - - tsconfig-paths@3.15.0: - dependencies: - '@types/json5': 0.0.29 - json5: 1.0.2 - minimist: 1.2.8 - strip-bom: 3.0.0 - - tslib@2.4.0: {} - - tslib@2.8.1: {} - - tunnel-agent@0.6.0: - dependencies: - safe-buffer: 5.2.1 - - type-check@0.4.0: - dependencies: - prelude-ls: 1.2.1 - - typed-array-buffer@1.0.3: - dependencies: - call-bound: 1.0.4 - es-errors: 1.3.0 - is-typed-array: 1.1.15 - - typed-array-byte-length@1.0.3: - dependencies: - call-bind: 1.0.9 - for-each: 0.3.5 - gopd: 1.2.0 - has-proto: 1.2.0 - is-typed-array: 1.1.15 - - typed-array-byte-offset@1.0.4: - dependencies: - available-typed-arrays: 1.0.7 - call-bind: 1.0.9 - for-each: 0.3.5 - gopd: 1.2.0 - has-proto: 1.2.0 - is-typed-array: 1.1.15 - reflect.getprototypeof: 1.0.10 - - typed-array-length@1.0.8: - dependencies: - call-bind: 1.0.9 - for-each: 0.3.5 - gopd: 1.2.0 - is-typed-array: 1.1.15 - possible-typed-array-names: 1.1.0 - reflect.getprototypeof: 1.0.10 - - typescript-eslint@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3): - dependencies: - '@typescript-eslint/eslint-plugin': 8.35.1(@typescript-eslint/parser@8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - '@typescript-eslint/parser': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - '@typescript-eslint/utils': 8.35.1(eslint@9.39.4(jiti@2.6.1))(typescript@5.9.3) - eslint: 9.39.4(jiti@2.6.1) - typescript: 5.9.3 - transitivePeerDependencies: - - supports-color - - typescript@5.4.5: {} - - typescript@5.9.3: {} - - unbox-primitive@1.1.0: - dependencies: - call-bound: 1.0.4 - has-bigints: 1.1.0 - has-symbols: 1.1.0 - which-boxed-primitive: 1.1.1 - - undici-types@5.26.5: {} - - undici-types@6.21.0: {} - - uri-js@4.4.1: - dependencies: - punycode: 2.3.1 - - w3c-keyname@2.2.8: {} - - which-boxed-primitive@1.1.1: - dependencies: - is-bigint: 1.1.0 - is-boolean-object: 1.2.2 - is-number-object: 1.1.1 - is-string: 1.1.1 - is-symbol: 1.1.1 - - which-builtin-type@1.2.1: - dependencies: - call-bound: 1.0.4 - function.prototype.name: 1.2.0 - has-tostringtag: 1.0.2 - is-async-function: 2.1.1 - is-date-object: 1.1.0 - is-finalizationregistry: 1.1.1 - is-generator-function: 1.1.2 - is-regex: 1.2.1 - is-weakref: 1.1.1 - isarray: 2.0.5 - which-boxed-primitive: 1.1.1 - which-collection: 1.0.2 - which-typed-array: 1.1.22 - - which-collection@1.0.2: - dependencies: - is-map: 2.0.3 - is-set: 2.0.3 - is-weakmap: 2.0.2 - is-weakset: 2.0.4 - - which-typed-array@1.1.22: - dependencies: - available-typed-arrays: 1.0.7 - call-bind: 1.0.9 - call-bound: 1.0.4 - for-each: 0.3.5 - get-proto: 1.0.1 - gopd: 1.2.0 - has-tostringtag: 1.0.2 - - which@2.0.2: - dependencies: - isexe: 2.0.0 - - word-wrap@1.2.5: {} - - yaml-eslint-parser@1.3.2: - dependencies: - eslint-visitor-keys: 3.4.3 - yaml: 2.9.0 - - yaml@2.9.0: {} - - yocto-queue@0.1.0: {} diff --git a/surfsense_obsidian/pnpm-workspace.yaml b/surfsense_obsidian/pnpm-workspace.yaml deleted file mode 100644 index 9af1dfcf1f..0000000000 --- a/surfsense_obsidian/pnpm-workspace.yaml +++ /dev/null @@ -1,5 +0,0 @@ -packages: - - "." - -allowBuilds: - esbuild: true diff --git a/surfsense_obsidian/src/api-client.ts b/surfsense_obsidian/src/api-client.ts deleted file mode 100644 index b66373f025..0000000000 --- a/surfsense_obsidian/src/api-client.ts +++ /dev/null @@ -1,296 +0,0 @@ -import { requestUrl, type RequestUrlParam, type RequestUrlResponse } from "obsidian"; -import type { - ConnectResponse, - DeleteAck, - HealthResponse, - ManifestResponse, - NotePayload, - RenameAck, - RenameItem, - Workspace, - SyncAck, -} from "./types"; - -/** - * SurfSense backend client used by the Obsidian plugin. - * - * Mobile-safety contract (must hold for every transitive import): - * - Use Obsidian `requestUrl` only — no `fetch`, no `axios`, no - * `node:http`, no `node:https`. CORS is bypassed and mobile works. - * - No top-level `node:*` imports anywhere reachable from this file. - * - Hashing happens elsewhere via Web Crypto, not `node:crypto`. - * - * Auth + wire contract: - * - Every request carries `Authorization: Bearer ` only. No - * custom headers — the backend identifies the caller from the PAT - * and feature-detects the API via the `capabilities` array on - * `/health` and `/connect`. - * - 401 surfaces as `AuthError` so the orchestrator can show the - * "token invalid or expired" UX. - * - HealthResponse / ConnectResponse use index signatures so any - * additive backend field (e.g. new capabilities) parses without - * breaking the decoder. This mirrors `ConfigDict(extra='ignore')` - * on the server side. - */ - -export class AuthError extends Error { - constructor(message: string) { - super(message); - this.name = "AuthError"; - } -} - -export class TransientError extends Error { - readonly status: number; - constructor(status: number, message: string) { - super(message); - this.name = "TransientError"; - this.status = status; - } -} - -export class PermanentError extends Error { - readonly status: number; - constructor(status: number, message: string) { - super(message); - this.name = "PermanentError"; - this.status = status; - } -} - -/** 404 `VAULT_NOT_REGISTERED` — `/connect` hasn't committed yet; retry after reconnect. */ -export class VaultNotRegisteredError extends TransientError { - constructor(message: string) { - super(404, message); - this.name = "VaultNotRegisteredError"; - } -} - -export interface ApiClientOptions { - getServerUrl: () => string; - getToken: () => string; - onAuthError?: () => void; -} - -const AUTH_BLOCK_MS = 60_000; - -export class SurfSenseApiClient { - private readonly opts: ApiClientOptions; - private authBlockedUntil = 0; - - constructor(opts: ApiClientOptions) { - this.opts = opts; - } - - updateOptions(partial: Partial): void { - Object.assign(this.opts, partial); - } - - resetAuthBlock(): void { - this.authBlockedUntil = 0; - } - - async health(): Promise { - return await this.request("GET", "/api/v1/obsidian/health"); - } - - async listWorkspaces(): Promise { - const resp = await this.request( - "GET", - "/api/v1/workspaces/" - ); - if (Array.isArray(resp)) return resp; - if (resp && Array.isArray((resp as { items?: Workspace[] }).items)) { - return (resp as { items: Workspace[] }).items; - } - return []; - } - - async verifyToken(): Promise<{ ok: true; health: HealthResponse }> { - // /health is gated by current_active_user, so a successful response - // transitively proves the token works. Cheaper than fetching a list. - const health = await this.health(); - return { ok: true, health }; - } - - async connect(input: { - workspaceId: number; - vaultId: string; - vaultName: string; - vaultFingerprint: string; - }): Promise { - return await this.request( - "POST", - "/api/v1/obsidian/connect", - { - vault_id: input.vaultId, - vault_name: input.vaultName, - workspace_id: input.workspaceId, - vault_fingerprint: input.vaultFingerprint, - } - ); - } - - /** POST /sync — `failed[]` are paths whose `status === "error"` for retry. */ - async syncBatch(input: { - vaultId: string; - notes: NotePayload[]; - }): Promise<{ indexed: number; failed: string[] }> { - const resp = await this.request( - "POST", - "/api/v1/obsidian/sync", - { vault_id: input.vaultId, notes: input.notes } - ); - const failed = resp.items - .filter((it) => it.status === "error") - .map((it) => it.path); - return { indexed: resp.indexed, failed }; - } - - /** POST /rename — `"missing"` counts as success; only `"error"` is retried. */ - async renameBatch(input: { - vaultId: string; - renames: Pick[]; - }): Promise<{ - renamed: number; - failed: Array<{ oldPath: string; newPath: string }>; - }> { - const resp = await this.request( - "POST", - "/api/v1/obsidian/rename", - { - vault_id: input.vaultId, - renames: input.renames.map((r) => ({ - old_path: r.oldPath, - new_path: r.newPath, - })), - } - ); - const failed = resp.items - .filter((it) => it.status === "error") - .map((it) => ({ oldPath: it.old_path, newPath: it.new_path })); - return { renamed: resp.renamed, failed }; - } - - /** DELETE /notes — `"missing"` counts as success; only `"error"` is retried. */ - async deleteBatch(input: { - vaultId: string; - paths: string[]; - }): Promise<{ deleted: number; failed: string[] }> { - const resp = await this.request( - "DELETE", - "/api/v1/obsidian/notes", - { vault_id: input.vaultId, paths: input.paths } - ); - const failed = resp.items - .filter((it) => it.status === "error") - .map((it) => it.path); - return { deleted: resp.deleted, failed }; - } - - async getManifest(vaultId: string): Promise { - return await this.request( - "GET", - `/api/v1/obsidian/manifest?vault_id=${encodeURIComponent(vaultId)}` - ); - } - - private async request( - method: RequestUrlParam["method"], - path: string, - body?: unknown - ): Promise { - const baseUrl = this.opts.getServerUrl().replace(/\/+$/, ""); - const token = this.opts.getToken(); - if (!token) { - throw new AuthError("Missing API token. Open plugin settings to paste one."); - } - if (Date.now() < this.authBlockedUntil) { - throw new AuthError("Token rejected. Paste a fresh one in settings."); - } - const headers: Record = { - Authorization: `Bearer ${token}`, - Accept: "application/json", - }; - if (body !== undefined) headers["Content-Type"] = "application/json"; - - let resp: RequestUrlResponse; - try { - resp = await requestUrl({ - url: `${baseUrl}${path}`, - method, - headers, - body: body === undefined ? undefined : JSON.stringify(body), - throw: false, - }); - } catch (err) { - throw new TransientError(0, `Network error: ${(err as Error).message}`); - } - - if (resp.status >= 200 && resp.status < 300) { - return parseJson(resp); - } - - const detail = extractDetail(resp); - - if (resp.status === 401) { - this.authBlockedUntil = Date.now() + AUTH_BLOCK_MS; - this.opts.onAuthError?.(); - throw new AuthError(detail || "Unauthorized"); - } - - if (resp.status >= 500 || resp.status === 429) { - throw new TransientError(resp.status, detail || `HTTP ${resp.status}`); - } - - if (resp.status === 404 && extractCode(resp) === "VAULT_NOT_REGISTERED") { - throw new VaultNotRegisteredError(detail || "Vault not registered yet"); - } - - throw new PermanentError(resp.status, detail || `HTTP ${resp.status}`); - } -} - -function parseJson(resp: RequestUrlResponse): T { - // Plugin endpoints always return JSON; non-JSON 2xx is usually a - // captive portal or CDN page — surface as transient so we back off. - const text = resp.text ?? ""; - try { - return JSON.parse(text) as T; - } catch { - throw new TransientError( - resp.status, - `Invalid JSON from server (got: ${text.slice(0, 80)})` - ); - } -} - -function safeJson(resp: RequestUrlResponse): Record { - try { - return resp.text ? (JSON.parse(resp.text) as Record) : {}; - } catch { - return {}; - } -} - -function extractDetail(resp: RequestUrlResponse): string { - const json = safeJson(resp); - if (typeof json.detail === "string") return json.detail; - if (typeof json.message === "string") return json.message; - const detailObj = json.detail; - if (detailObj && typeof detailObj === "object") { - const obj = detailObj as Record; - if (typeof obj.message === "string") return obj.message; - } - return resp.text?.slice(0, 200) ?? ""; -} - -function extractCode(resp: RequestUrlResponse): string | undefined { - const json = safeJson(resp); - const detailObj = json.detail; - if (detailObj && typeof detailObj === "object") { - const code = (detailObj as Record).code; - if (typeof code === "string") return code; - } - return undefined; -} diff --git a/surfsense_obsidian/src/attachments-confirm-modal.ts b/surfsense_obsidian/src/attachments-confirm-modal.ts deleted file mode 100644 index 1a79fd2bd8..0000000000 --- a/surfsense_obsidian/src/attachments-confirm-modal.ts +++ /dev/null @@ -1,61 +0,0 @@ -import { type App, Modal, Setting } from "obsidian"; - -/** - * Confirmation modal shown before enabling attachment sync. - * Attachment files can be large and increase sync latency/cost. - */ -export class AttachmentsConfirmModal extends Modal { - private resolver: ((confirmed: boolean) => void) | null = null; - - constructor(app: App) { - super(app); - } - - onOpen(): void { - this.setTitle("Enable attachment sync?"); - this.contentEl.empty(); - - new Setting(this.contentEl).setDesc( - "Syncing attachments (images & PDFs) can make indexing slower, especially on large vaults." - ); - new Setting(this.contentEl).setDesc( - "Syncing attachments can make indexing slower on large vaults. You can disable this anytime.", - ); - - new Setting(this.contentEl) - .addButton((btn) => - btn - .setButtonText("Cancel") - .onClick(() => this.resolveAndClose(false)), - ) - .addButton((btn) => - btn - .setButtonText("Enable") - .setCta() - .onClick(() => this.resolveAndClose(true)), - ); - } - - onClose(): void { - this.contentEl.empty(); - if (this.resolver) { - this.resolver(false); - this.resolver = null; - } - } - - waitForConfirmation(): Promise { - this.open(); - return new Promise((resolve) => { - this.resolver = resolve; - }); - } - - private resolveAndClose(confirmed: boolean): void { - if (this.resolver) { - this.resolver(confirmed); - this.resolver = null; - } - this.close(); - } -} diff --git a/surfsense_obsidian/src/excludes.ts b/surfsense_obsidian/src/excludes.ts deleted file mode 100644 index 1f47170b1c..0000000000 --- a/surfsense_obsidian/src/excludes.ts +++ /dev/null @@ -1,94 +0,0 @@ -/** - * Tiny glob matcher for exclude patterns. - * - * Supports `*` (any chars except `/`), `**` (any chars including `/`), and - * literal segments. Patterns without a slash are matched against any path - * segment (so `templates` excludes `templates/foo.md` and `notes/templates/x.md`). - * - * Intentionally not a full minimatch — Obsidian users overwhelmingly type - * folder names ("templates", ".trash") and the obvious wildcards. Avoiding - * the dependency keeps the bundle small and the mobile attack surface tiny. - */ - -const cache = new Map(); - -function compile(pattern: string): RegExp { - const cached = cache.get(pattern); - if (cached) return cached; - - let body = ""; - let i = 0; - while (i < pattern.length) { - const ch = pattern[i] ?? ""; - if (ch === "*") { - if (pattern[i + 1] === "*") { - body += ".*"; - i += 2; - if (pattern[i] === "/") i += 1; - continue; - } - body += "[^/]*"; - i += 1; - continue; - } - if (".+^${}()|[]\\".includes(ch)) { - body += "\\" + ch; - i += 1; - continue; - } - body += ch; - i += 1; - } - - const anchored = pattern.includes("/") - ? `^${body}(/.*)?$` - : `(^|/)${body}(/.*)?$`; - const re = new RegExp(anchored); - cache.set(pattern, re); - return re; -} - -export function isExcluded(path: string, patterns: string[]): boolean { - if (!patterns.length) return false; - for (const raw of patterns) { - const trimmed = raw.trim(); - if (!trimmed || trimmed.startsWith("#")) continue; - if (compile(trimmed).test(path)) return true; - } - return false; -} - -export function parseExcludePatterns(raw: string): string[] { - return raw - .split(/\r?\n/) - .map((line) => line.trim()) - .filter((line) => line.length > 0 && !line.startsWith("#")); -} - -/** Normalize a folder path: strip leading/trailing slashes; "" or "/" means vault root. */ -export function normalizeFolder(folder: string): string { - return folder.replace(/^\/+|\/+$/g, ""); -} - -/** True if `path` lives inside `folder` (or `folder` is the vault root). */ -export function isInFolder(path: string, folder: string): boolean { - const f = normalizeFolder(folder); - if (f === "") return true; - return path === f || path.startsWith(`${f}/`); -} - -/** Exclude wins over include. Empty includeFolders means "include everything". */ -export function isFolderFiltered( - path: string, - includeFolders: string[], - excludeFolders: string[], -): boolean { - for (const f of excludeFolders) { - if (isInFolder(path, f)) return true; - } - if (includeFolders.length === 0) return false; - for (const f of includeFolders) { - if (isInFolder(path, f)) return false; - } - return true; -} diff --git a/surfsense_obsidian/src/folder-suggest-modal.ts b/surfsense_obsidian/src/folder-suggest-modal.ts deleted file mode 100644 index a037a620f9..0000000000 --- a/surfsense_obsidian/src/folder-suggest-modal.ts +++ /dev/null @@ -1,32 +0,0 @@ -import { type App, FuzzySuggestModal, type TFolder } from "obsidian"; - -/** Folder picker built on Obsidian's stock {@link FuzzySuggestModal}. */ -export class FolderSuggestModal extends FuzzySuggestModal { - private readonly onPick: (path: string) => void; - private readonly excluded: Set; - - constructor(app: App, onPick: (path: string) => void, excluded: string[] = []) { - super(app); - this.onPick = onPick; - this.excluded = new Set(excluded.map((p) => p.replace(/^\/+|\/+$/g, ""))); - this.setPlaceholder("Type to filter folders…"); - } - - getItems(): TFolder[] { - return this.app.vault - .getAllFolders(true) - .filter((f) => !this.excluded.has(this.toPath(f))); - } - - getItemText(folder: TFolder): string { - return this.toPath(folder) || "/"; - } - - onChooseItem(folder: TFolder): void { - this.onPick(this.toPath(folder)); - } - - private toPath(folder: TFolder): string { - return folder.isRoot() ? "" : folder.path; - } -} diff --git a/surfsense_obsidian/src/main.ts b/surfsense_obsidian/src/main.ts deleted file mode 100644 index ca5de7206d..0000000000 --- a/surfsense_obsidian/src/main.ts +++ /dev/null @@ -1,300 +0,0 @@ -import { Notice, Platform, Plugin } from "obsidian"; -import { SurfSenseApiClient } from "./api-client"; -import { PersistentQueue } from "./queue"; -import { SurfSenseSettingTab } from "./settings"; -import { StatusBar } from "./status-bar"; -import { StatusModal } from "./status-modal"; -import { SyncEngine } from "./sync-engine"; -import { - DEFAULT_SETTINGS, - type QueueItem, - type StatusState, - type SurfsensePluginSettings, -} from "./types"; -import { generateVaultUuid } from "./vault-identity"; - -/** SurfSense plugin entry point. */ -export default class SurfSensePlugin extends Plugin { - settings!: SurfsensePluginSettings; - api!: SurfSenseApiClient; - queue!: PersistentQueue; - engine!: SyncEngine; - private statusBar: StatusBar | null = null; - lastStatus: StatusState = { kind: "needs-setup", queueDepth: 0 }; - serverCapabilities: string[] = []; - private settingTab: SurfSenseSettingTab | null = null; - private statusListeners = new Set<() => void>(); - private reconcileTimerId: number | null = null; - private lastAuthToastAt = 0; - - async onload() { - await this.loadSettings(); - this.seedIdentity(); - await this.saveSettings(); - - this.api = new SurfSenseApiClient({ - getServerUrl: () => this.settings.serverUrl, - getToken: () => this.settings.apiToken, - onAuthError: () => this.notifyAuthError(), - }); - - this.queue = new PersistentQueue(this.settings.queue ?? [], { - persist: async (items) => { - this.settings.queue = items; - await this.saveData(this.settings); - }, - }); - - this.engine = new SyncEngine({ - app: this.app, - apiClient: this.api, - queue: this.queue, - getSettings: () => this.settings, - saveSettings: async (mut) => { - mut(this.settings); - await this.saveSettings(); - this.notifyStatusChange(); - }, - setStatus: (s) => { - this.lastStatus = s; - this.statusBar?.update(s); - this.notifyStatusChange(); - }, - onCapabilities: (caps) => { - this.serverCapabilities = [...caps]; - this.notifyStatusChange(); - }, - onReconcileBackoffChanged: () => { - this.restartReconcileTimer(); - }, - }); - - this.queue.setFlushHandler(() => { - if (!this.shouldAutoSync()) return; - void this.engine.flushQueue(); - }); - - this.settingTab = new SurfSenseSettingTab(this.app, this); - this.addSettingTab(this.settingTab); - - const statusHost = this.addStatusBarItem(); - this.statusBar = new StatusBar(statusHost, () => this.openStatusModal()); - this.statusBar.update(this.lastStatus); - - this.registerEvent( - this.app.vault.on("create", (file) => this.engine.onCreate(file)), - ); - this.registerEvent( - this.app.vault.on("modify", (file) => this.engine.onModify(file)), - ); - this.registerEvent( - this.app.vault.on("delete", (file) => this.engine.onDelete(file)), - ); - this.registerEvent( - this.app.vault.on("rename", (file, oldPath) => - this.engine.onRename(file, oldPath), - ), - ); - this.registerEvent( - this.app.metadataCache.on("changed", (file, data, cache) => - this.engine.onMetadataChanged(file, data, cache), - ), - ); - - this.addCommand({ - id: "resync-vault", - name: "Re-sync entire vault", - callback: async () => { - try { - await this.engine.maybeReconcile(true); - new Notice("Surfsense: re-sync started."); - } catch (err) { - new Notice(`Surfsense: re-sync failed — ${(err as Error).message}`); - } - }, - }); - - this.addCommand({ - id: "sync-current-note", - name: "Sync current note", - checkCallback: (checking) => { - const file = this.app.workspace.getActiveFile(); - if (file?.extension.toLowerCase() !== "md") return false; - if (checking) return true; - this.queue.enqueueUpsert(file.path); - void this.engine.flushQueue(); - return true; - }, - }); - - this.addCommand({ - id: "open-status", - name: "Open sync status", - callback: () => this.openStatusModal(), - }); - - this.addCommand({ - id: "open-settings", - name: "Open settings", - callback: () => { - // `app.setting` isn't in the d.ts; fall back silently if it moves. - type SettingHost = { - open?: () => void; - openTabById?: (id: string) => void; - }; - const setting = (this.app as unknown as { setting?: SettingHost }).setting; - if (setting?.open) setting.open(); - if (setting?.openTabById) setting.openTabById(this.manifest.id); - }, - }); - - const onNetChange = () => { - void this.engine.recoverConnectivityStatus(); - if (this.shouldAutoSync()) void this.engine.flushQueue(); - }; - this.registerDomEvent(window, "online", onNetChange); - const conn = (navigator as unknown as { connection?: NetworkConnection }).connection; - if (conn && typeof conn.addEventListener === "function") { - conn.addEventListener("change", onNetChange); - this.register(() => conn.removeEventListener?.("change", onNetChange)); - } - - // Wait for layout so the metadataCache is warm before reconcile. - this.app.workspace.onLayoutReady(() => { - void this.engine.start(); - this.restartReconcileTimer(); - }); - } - - onunload() { - this.queue?.cancelFlush(); - this.queue?.requestStop(); - } - - /** - * Obsidian fires this when another device rewrites our data.json. - * If the synced vault_id differs from ours, adopt it and - * re-handshake so the server routes us to the right row. - */ - async onExternalSettingsChange(): Promise { - const previousVaultId = this.settings.vaultId; - const previousConnectorId = this.settings.connectorId; - await this.loadSettings(); - const changed = - this.settings.vaultId !== previousVaultId || - this.settings.connectorId !== previousConnectorId; - if (!changed) return; - this.engine?.refreshStatus(); - this.notifyStatusChange(); - if (this.settings.workspaceId !== null) { - void this.engine.ensureConnected(); - } - } - - get queueDepth(): number { - return this.queue?.size ?? 0; - } - - openStatusModal(): void { - new StatusModal(this.app, this).open(); - } - - restartReconcileTimer(): void { - if (this.reconcileTimerId !== null) { - window.clearInterval(this.reconcileTimerId); - this.reconcileTimerId = null; - } - const minutes = this.settings.syncIntervalMinutes ?? 10; - if (minutes <= 0) return; - const baseMs = minutes * 60 * 1000; - // Idle vaults back off (×2 → ×4 → ×8); resets on the first edit or non-empty reconcile. - const effectiveMs = this.engine?.getReconcileBackoffMs(baseMs) ?? baseMs; - const id = window.setInterval( - () => { - if (!this.shouldAutoSync()) return; - void this.engine.maybeReconcile(); - }, - effectiveMs, - ); - this.reconcileTimerId = id; - this.registerInterval(id); - } - - /** Gate for background network activity; per-edit flush + periodic reconcile both consult this. */ - shouldAutoSync(): boolean { - if (!this.settings.wifiOnly) return true; - if (!Platform.isMobileApp) return true; - // navigator.connection is supported on Android Capacitor; undefined on iOS. - // When unavailable, behave permissively so iOS users aren't blocked outright. - const conn = (navigator as unknown as { connection?: NetworkConnection }).connection; - if (!conn || typeof conn.type !== "string") return true; - return conn.type === "wifi" || conn.type === "ethernet"; - } - - onStatusChange(listener: () => void): void { - this.statusListeners.add(listener); - } - - offStatusChange(listener: () => void): void { - this.statusListeners.delete(listener); - } - - private notifyStatusChange(): void { - for (const fn of this.statusListeners) fn(); - } - - private notifyAuthError(): void { - this.engine?.reportAuthError(); - const now = Date.now(); - if (now - this.lastAuthToastAt < 10_000) return; - this.lastAuthToastAt = now; - new Notice("Surfsense: API token is invalid or expired. Check your token in settings.", 8000); - } - - async loadSettings() { - // One-time migration: the workspace was previously persisted as `searchSpaceId`. - // Destructure it out so the migrated value moves into `workspaceId` and the - // dead key is not spread back in / re-persisted. - const data = (await this.loadData()) as - | (Partial & { searchSpaceId?: number | null }) - | null; - const { searchSpaceId: legacyWorkspaceId, ...persisted } = data ?? {}; - this.settings = { - ...DEFAULT_SETTINGS, - ...persisted, - workspaceId: - persisted.workspaceId ?? legacyWorkspaceId ?? DEFAULT_SETTINGS.workspaceId, - queue: (persisted.queue ?? []).map((i: QueueItem) => ({ ...i })), - tombstones: { ...(persisted.tombstones ?? {}) }, - includeFolders: [...(persisted.includeFolders ?? [])], - excludeFolders: [...(persisted.excludeFolders ?? [])], - excludePatterns: persisted.excludePatterns?.length - ? [...persisted.excludePatterns] - : [...DEFAULT_SETTINGS.excludePatterns], - }; - } - - async saveSettings() { - await this.saveData(this.settings); - this.engine?.refreshStatus(); - } - - /** - * Mint a tentative vault_id locally on first run. The server's - * fingerprint dedup (see /obsidian/connect) may overwrite it on the - * first /connect when another device of the same vault has already - * registered; we always trust the server's response. - */ - private seedIdentity(): void { - if (!this.settings.vaultId) { - this.settings.vaultId = generateVaultUuid(); - } - } -} - -/** Subset of the Network Information API used to detect WiFi vs cellular on Android. */ -interface NetworkConnection { - type?: string; - addEventListener?: (event: string, handler: () => void) => void; - removeEventListener?: (event: string, handler: () => void) => void; -} diff --git a/surfsense_obsidian/src/payload.ts b/surfsense_obsidian/src/payload.ts deleted file mode 100644 index 3294d62df8..0000000000 --- a/surfsense_obsidian/src/payload.ts +++ /dev/null @@ -1,163 +0,0 @@ -import { - type App, - type CachedMetadata, - type FrontMatterCache, - type HeadingCache, - type ReferenceCache, - type TFile, -} from "obsidian"; -import type { HeadingRef, NotePayload } from "./types"; - -/** - * Build a NotePayload from an Obsidian TFile. - * - * Mobile-safety contract: - * - No top-level `node:fs` / `node:path` / `node:crypto` imports. - * File IO uses `vault.cachedRead` (works on the mobile WASM adapter). - * Hashing uses Web Crypto `subtle.digest`. - * - Caller MUST first wait for `metadataCache.changed` before calling - * this for a `.md` file, otherwise `frontmatter`/`tags`/`headings` - * can lag the actual file contents. - */ -export async function buildNotePayload( - app: App, - file: TFile, - vaultId: string, -): Promise { - const content = await app.vault.cachedRead(file); - const cache: CachedMetadata | null = app.metadataCache.getFileCache(file); - - const frontmatter = normalizeFrontmatter(cache?.frontmatter); - const tags = collectTags(cache); - const headings = collectHeadings(cache?.headings ?? []); - const aliases = collectAliases(frontmatter); - const { embeds, internalLinks } = collectLinks(cache); - const { resolved, unresolved } = resolveLinkTargets( - app, - file.path, - internalLinks, - ); - const contentHash = await computeContentHash(content); - - return { - vault_id: vaultId, - path: file.path, - name: file.basename, - extension: file.extension, - content, - frontmatter, - tags, - headings, - resolved_links: resolved, - unresolved_links: unresolved, - embeds, - aliases, - content_hash: contentHash, - size: file.stat.size, - mtime: file.stat.mtime, - ctime: file.stat.ctime, - }; -} - -export async function computeContentHash(content: string): Promise { - const bytes = new TextEncoder().encode(content); - const digest = await crypto.subtle.digest("SHA-256", bytes); - return bufferToHex(digest); -} - -function bufferToHex(buf: ArrayBuffer): string { - const view = new Uint8Array(buf); - let hex = ""; - for (let i = 0; i < view.length; i++) { - hex += (view[i] ?? 0).toString(16).padStart(2, "0"); - } - return hex; -} - -function normalizeFrontmatter( - fm: FrontMatterCache | undefined, -): Record { - if (!fm) return {}; - // FrontMatterCache extends a plain object; strip the `position` key - // the cache adds so the wire payload stays clean. - const rest: Record = { ...(fm as Record) }; - delete rest.position; - return rest; -} - -function collectTags(cache: CachedMetadata | null): string[] { - const out = new Set(); - for (const t of cache?.tags ?? []) { - const tag = t.tag.startsWith("#") ? t.tag.slice(1) : t.tag; - if (tag) out.add(tag); - } - const fmTags: unknown = - cache?.frontmatter?.tags ?? cache?.frontmatter?.tag; - if (Array.isArray(fmTags)) { - for (const t of fmTags) { - if (typeof t === "string" && t) out.add(t.replace(/^#/, "")); - } - } else if (typeof fmTags === "string" && fmTags) { - for (const t of fmTags.split(/[\s,]+/)) { - if (t) out.add(t.replace(/^#/, "")); - } - } - return [...out]; -} - -function collectHeadings(items: HeadingCache[]): HeadingRef[] { - return items.map((h) => ({ heading: h.heading, level: h.level })); -} - -function collectAliases(frontmatter: Record): string[] { - const raw = frontmatter.aliases ?? frontmatter.alias; - if (Array.isArray(raw)) { - return raw.filter((x): x is string => typeof x === "string" && x.length > 0); - } - if (typeof raw === "string" && raw) return [raw]; - return []; -} - -function collectLinks(cache: CachedMetadata | null): { - embeds: string[]; - internalLinks: ReferenceCache[]; -} { - const linkRefs: ReferenceCache[] = [ - ...((cache?.links) ?? []), - ...((cache?.embeds as ReferenceCache[] | undefined) ?? []), - ]; - const embeds = ((cache?.embeds as ReferenceCache[] | undefined) ?? []).map( - (e) => e.link, - ); - return { embeds, internalLinks: linkRefs }; -} - -function resolveLinkTargets( - app: App, - sourcePath: string, - links: ReferenceCache[], -): { resolved: string[]; unresolved: string[] } { - const resolved = new Set(); - const unresolved = new Set(); - for (const link of links) { - const target = app.metadataCache.getFirstLinkpathDest( - stripSubpath(link.link), - sourcePath, - ); - if (target) { - resolved.add(target.path); - } else { - unresolved.add(link.link); - } - } - return { resolved: [...resolved], unresolved: [...unresolved] }; -} - -function stripSubpath(link: string): string { - const hashIdx = link.indexOf("#"); - const pipeIdx = link.indexOf("|"); - let end = link.length; - if (hashIdx !== -1) end = Math.min(end, hashIdx); - if (pipeIdx !== -1) end = Math.min(end, pipeIdx); - return link.slice(0, end); -} diff --git a/surfsense_obsidian/src/queue.ts b/surfsense_obsidian/src/queue.ts deleted file mode 100644 index 0f7082456b..0000000000 --- a/surfsense_obsidian/src/queue.ts +++ /dev/null @@ -1,228 +0,0 @@ -import { type Debouncer, debounce } from "obsidian"; -import type { QueueItem } from "./types"; - -/** - * Persistent upload queue. - * - * Mobile-safety contract: - * - Persistence is delegated to a save callback (which the plugin wires - * to `plugin.saveData()`); never `node:fs`. Items also live in the - * plugin's settings JSON so a crash mid-flight loses nothing. - * - No top-level `node:*` imports. - * - * Behavioural contract: - * - Per-file debounce: enqueueing the same path coalesces, the latest - * `enqueuedAt` wins so we don't ship a stale snapshot. - * - `delete` for a path drops any pending `upsert` for that path - * (otherwise we'd resurrect a note the user just deleted). - * - `rename` is a first-class op so the backend can update - * `unique_identifier_hash` instead of "delete + create" (which would - * blow away document versions, citations, and the document_id used - * in chat history). - * - Drain takes a worker, returns once the worker either succeeds for - * every batch or hits a stop signal (transient error, mid-drain - * stop request). - */ - -export interface QueueWorker { - processBatch(batch: QueueItem[]): Promise; -} - -export interface BatchResult { - /** Items that succeeded; they will be ack'd off the queue. */ - acked: QueueItem[]; - /** Items that should be retried; their `attempt` is bumped. */ - retry: QueueItem[]; - /** Items that failed permanently (4xx). They get dropped. */ - dropped: QueueItem[]; - /** If true, the drain loop stops (e.g. transient/network error). */ - stop: boolean; - /** Optional retry-after for transient errors (ms). */ - backoffMs?: number; -} - -export interface PersistentQueueOptions { - debounceMs?: number; - batchSize?: number; - maxAttempts?: number; - persist: (items: QueueItem[]) => Promise | void; - now?: () => number; -} - -const DEFAULTS = { - debounceMs: 2000, - batchSize: 15, - maxAttempts: 8, -}; - -export class PersistentQueue { - private items: QueueItem[]; - private readonly opts: Required< - Omit - > & { - persist: PersistentQueueOptions["persist"]; - now: () => number; - }; - private draining = false; - private stopRequested = false; - private debouncedFlush: Debouncer<[], void> | null = null; - - constructor(initial: QueueItem[], opts: PersistentQueueOptions) { - this.items = [...initial]; - this.opts = { - debounceMs: opts.debounceMs ?? DEFAULTS.debounceMs, - batchSize: opts.batchSize ?? DEFAULTS.batchSize, - maxAttempts: opts.maxAttempts ?? DEFAULTS.maxAttempts, - persist: opts.persist, - now: opts.now ?? (() => Date.now()), - }; - } - - get size(): number { - return this.items.length; - } - - snapshot(): QueueItem[] { - return this.items.map((i) => ({ ...i })); - } - - setFlushHandler(handler: () => void): void { - // resetTimer: true → each enqueue postpones the flush. - this.debouncedFlush = debounce(handler, this.opts.debounceMs, true); - } - - enqueueUpsert(path: string): void { - const now = this.opts.now(); - this.items = this.items.filter( - (i) => !(i.op === "upsert" && i.path === path), - ); - this.items.push({ op: "upsert", path, enqueuedAt: now, attempt: 0 }); - void this.persist(); - this.scheduleFlush(); - } - - enqueueDelete(path: string): void { - const now = this.opts.now(); - // A delete supersedes any pending upsert for the same path. - this.items = this.items.filter( - (i) => - !( - (i.op === "upsert" && i.path === path) || - (i.op === "delete" && i.path === path) - ), - ); - this.items.push({ op: "delete", path, enqueuedAt: now, attempt: 0 }); - void this.persist(); - this.scheduleFlush(); - } - - enqueueRename(oldPath: string, newPath: string): void { - const now = this.opts.now(); - this.items = this.items.filter( - (i) => - !( - (i.op === "upsert" && (i.path === oldPath || i.path === newPath)) || - (i.op === "rename" && i.oldPath === oldPath && i.newPath === newPath) - ), - ); - this.items.push({ - op: "rename", - oldPath, - newPath, - enqueuedAt: now, - attempt: 0, - }); - // Pair with an upsert — content may have changed alongside the rename. - this.items.push({ op: "upsert", path: newPath, enqueuedAt: now, attempt: 0 }); - void this.persist(); - this.scheduleFlush(); - } - - requestStop(): void { - this.stopRequested = true; - } - - cancelFlush(): void { - this.debouncedFlush?.cancel(); - } - - private scheduleFlush(): void { - this.debouncedFlush?.(); - } - - async drain(worker: QueueWorker): Promise { - if (this.draining) return { batches: 0, acked: 0, dropped: 0, stopped: false }; - this.draining = true; - this.stopRequested = false; - const summary: DrainSummary = { - batches: 0, - acked: 0, - dropped: 0, - stopped: false, - }; - try { - while (this.items.length > 0 && !this.stopRequested) { - const batch = this.takeBatch(); - summary.batches += 1; - - const result = await worker.processBatch(batch); - summary.acked += result.acked.length; - summary.dropped += result.dropped.length; - - const ackKeys = new Set(result.acked.map(itemKey)); - const dropKeys = new Set(result.dropped.map(itemKey)); - const retryKeys = new Set(result.retry.map(itemKey)); - - // Items the worker didn't classify get retried — never silently dropped. - const unhandled = batch.filter( - (b) => - !ackKeys.has(itemKey(b)) && - !dropKeys.has(itemKey(b)) && - !retryKeys.has(itemKey(b)), - ); - const retry = [...result.retry, ...unhandled].map((i) => ({ - ...i, - attempt: i.attempt + 1, - })); - const survivors = retry.filter((i) => i.attempt <= this.opts.maxAttempts); - summary.dropped += retry.length - survivors.length; - - this.items = [...survivors, ...this.items]; - await this.persist(); - - if (result.stop) { - summary.stopped = true; - if (result.backoffMs) summary.backoffMs = result.backoffMs; - break; - } - } - if (this.stopRequested) summary.stopped = true; - return summary; - } finally { - this.draining = false; - } - } - - private takeBatch(): QueueItem[] { - const head = this.items.slice(0, this.opts.batchSize); - this.items = this.items.slice(this.opts.batchSize); - return head; - } - - private async persist(): Promise { - await this.opts.persist(this.snapshot()); - } -} - -export interface DrainSummary { - batches: number; - acked: number; - dropped: number; - stopped: boolean; - backoffMs?: number; -} - -export function itemKey(i: QueueItem): string { - if (i.op === "rename") return `rename:${i.oldPath}=>${i.newPath}`; - return `${i.op}:${i.path}`; -} diff --git a/surfsense_obsidian/src/settings.ts b/surfsense_obsidian/src/settings.ts deleted file mode 100644 index 79e5762a92..0000000000 --- a/surfsense_obsidian/src/settings.ts +++ /dev/null @@ -1,389 +0,0 @@ -import { - type App, - type ButtonComponent, - Notice, - Platform, - PluginSettingTab, - Setting, - setIcon, -} from "obsidian"; -import { AuthError } from "./api-client"; -import { AttachmentsConfirmModal } from "./attachments-confirm-modal"; -import { normalizeFolder, parseExcludePatterns } from "./excludes"; -import { FolderSuggestModal } from "./folder-suggest-modal"; -import type SurfSensePlugin from "./main"; -import { STATUS_VISUALS } from "./status-visuals"; -import type { Workspace } from "./types"; - -/** Plugin settings tab. */ - -export class SurfSenseSettingTab extends PluginSettingTab { - private readonly plugin: SurfSensePlugin; - private workspaces: Workspace[] = []; - private loadingSpaces = false; - private connectionIndicator: HTMLElement | null = null; - private readonly onStatusChange = (): void => this.updateConnectionIndicator(); - - constructor(app: App, plugin: SurfSensePlugin) { - super(app, plugin); - this.plugin = plugin; - } - - display(): void { - const { containerEl } = this; - containerEl.empty(); - this.plugin.onStatusChange(this.onStatusChange); - - const settings = this.plugin.settings; - - this.renderConnectionHeading(containerEl); - - new Setting(containerEl) - .setName("Server URL") - .setDesc( - "https://surfsense.com for SurfSense Cloud, or your self-hosted URL.", - ) - .addText((text) => - text - .setPlaceholder("https://surfsense.com") - .setValue(settings.serverUrl) - .onChange(async (value) => { - const next = value.trim(); - const previous = this.plugin.settings.serverUrl; - if (previous !== "" && next !== previous) { - this.plugin.settings.workspaceId = null; - this.plugin.settings.connectorId = null; - } - this.plugin.settings.serverUrl = next; - await this.plugin.saveSettings(); - }), - ); - - let verifyButton: ButtonComponent | null = null; - const updateVerifyDisabled = (): void => { - verifyButton?.setDisabled(this.plugin.settings.apiToken.trim().length === 0); - }; - - new Setting(containerEl) - .setName("API token") - .setDesc( - "Paste your Surfsense personal access token from the web app.", - ) - .addText((text) => { - text.inputEl.type = "password"; - text.inputEl.autocomplete = "off"; - text.inputEl.spellcheck = false; - text - .setPlaceholder("Paste token") - .setValue(settings.apiToken) - .onChange(async (value) => { - const next = value.trim(); - const previous = this.plugin.settings.apiToken; - if (previous !== "" && next !== previous) { - this.plugin.settings.workspaceId = null; - this.plugin.settings.connectorId = null; - } - this.plugin.settings.apiToken = next; - updateVerifyDisabled(); - await this.plugin.saveSettings(); - this.plugin.api.resetAuthBlock(); - }); - }) - .addButton((btn) => { - verifyButton = btn; - updateVerifyDisabled(); - btn.setButtonText("Verify").setCta().onClick(async () => { - if (this.plugin.settings.apiToken.trim().length === 0) { - new Notice("Surfsense: paste an API token before verifying."); - return; - } - btn.setDisabled(true); - try { - await this.plugin.api.verifyToken(); - new Notice("Surfsense: token verified."); - this.plugin.engine.refreshStatus({ force: true }); - await this.refreshWorkspaces(); - this.display(); - } catch (err) { - this.handleApiError(err); - } finally { - updateVerifyDisabled(); - } - }); - }); - - new Setting(containerEl) - .setName("Search space") - .setDesc( - "Which Surfsense workspace this vault syncs into. Reload after changing your token.", - ) - .addDropdown((drop) => { - drop.addOption("", this.loadingSpaces ? "Loading…" : "Select a workspace"); - for (const space of this.workspaces) { - drop.addOption(String(space.id), space.name); - } - if (settings.workspaceId !== null) { - drop.setValue(String(settings.workspaceId)); - } - drop.onChange(async (value) => { - this.plugin.settings.workspaceId = value ? Number(value) : null; - this.plugin.settings.connectorId = null; - await this.plugin.saveSettings(); - if (this.plugin.settings.workspaceId !== null) { - try { - await this.plugin.engine.ensureConnected(); - await this.plugin.engine.maybeReconcile(true); - new Notice("Surfsense: vault connected."); - this.display(); - } catch (err) { - this.handleApiError(err); - } - } - }); - }) - .addExtraButton((btn) => - btn - .setIcon("refresh-ccw") - .setTooltip("Reload workspaces") - .onClick(async () => { - await this.refreshWorkspaces(); - this.display(); - }), - ); - - new Setting(containerEl).setName("Vault").setHeading(); - - new Setting(containerEl) - .setName("Sync interval") - .setDesc( - "How often to check for changes made outside Obsidian.", - ) - .addDropdown((drop) => { - const options: Array<[number, string]> = [ - [0, "Off"], - [5, "5 minutes"], - [10, "10 minutes"], - [15, "15 minutes"], - [30, "30 minutes"], - [60, "60 minutes"], - [120, "2 hours"], - [360, "6 hours"], - [720, "12 hours"], - [1440, "24 hours"], - ]; - for (const [value, label] of options) { - drop.addOption(String(value), label); - } - drop.setValue(String(settings.syncIntervalMinutes)); - drop.onChange(async (value) => { - this.plugin.settings.syncIntervalMinutes = Number(value); - await this.plugin.saveSettings(); - this.plugin.restartReconcileTimer(); - }); - }); - - this.renderFolderList( - containerEl, - "Include folders", - "Folders to sync (leave empty to sync entire vault).", - settings.includeFolders, - (next) => { - this.plugin.settings.includeFolders = next; - }, - ); - - this.renderFolderList( - containerEl, - "Exclude folders", - "Folders to exclude from sync (takes precedence over includes).", - settings.excludeFolders, - (next) => { - this.plugin.settings.excludeFolders = next; - }, - ); - - new Setting(containerEl) - .setName("Advanced exclude patterns") - .setDesc( - "Glob fallback for power users. One pattern per line, supports * and **. Lines starting with # are comments. Applied on top of the folder lists above.", - ) - .addTextArea((area) => { - area.inputEl.rows = 4; - area - .setPlaceholder(".trash\n_attachments\ntemplates/**") - .setValue(settings.excludePatterns.join("\n")) - .onChange(async (value) => { - this.plugin.settings.excludePatterns = parseExcludePatterns(value); - await this.plugin.saveSettings(); - }); - }); - - new Setting(containerEl) - .setName("Include attachments") - .setDesc( - "Also sync non-Markdown files such as images and PDFs. Other file types are skipped.", - ) - .addToggle((toggle) => - toggle - .setValue(settings.includeAttachments) - .onChange(async (value) => { - const isEnabling = - value && !this.plugin.settings.includeAttachments; - if (isEnabling) { - const confirmed = await new AttachmentsConfirmModal( - this.app, - ).waitForConfirmation(); - if (!confirmed) { - this.display(); - return; - } - } - this.plugin.settings.includeAttachments = value; - await this.plugin.saveSettings(); - }), - ); - - if (Platform.isAndroidApp) { - new Setting(containerEl) - .setName("Sync only on WiFi") - .setDesc("Pause automatic syncing on cellular.") - .addToggle((toggle) => - toggle - .setValue(settings.wifiOnly) - .onChange(async (value) => { - this.plugin.settings.wifiOnly = value; - await this.plugin.saveSettings(); - }), - ); - } - - new Setting(containerEl) - .setName("Force sync") - .setDesc("Manually re-index the entire vault now.") - .addButton((btn) => - btn.setButtonText("Update").onClick(async () => { - btn.setDisabled(true); - try { - await this.plugin.engine.maybeReconcile(true); - new Notice("Surfsense: re-sync requested."); - } catch (err) { - this.handleApiError(err); - } finally { - btn.setDisabled(false); - } - }), - ); - - new Setting(containerEl) - .addButton((btn) => - btn - .setButtonText("View sync status") - .setCta() - .onClick(() => this.plugin.openStatusModal()), - ) - .addButton((btn) => - btn.setButtonText("Open releases").onClick(() => { - window.open( - "https://github.com/MODSetter/SurfSense/releases?q=obsidian", - "_blank", - ); - }), - ); - } - - hide(): void { - this.plugin.offStatusChange(this.onStatusChange); - this.connectionIndicator = null; - } - - private renderConnectionHeading(containerEl: HTMLElement): void { - const heading = new Setting(containerEl).setName("Connection").setHeading(); - heading.nameEl.addClass("surfsense-connection-heading"); - this.connectionIndicator = heading.nameEl.createSpan({ - cls: "surfsense-connection-indicator", - }); - this.updateConnectionIndicator(); - } - - private updateConnectionIndicator(): void { - const indicator = this.connectionIndicator; - if (!indicator) return; - const visual = STATUS_VISUALS[this.plugin.lastStatus.kind]; - indicator.empty(); - indicator.removeClass("surfsense-connection-indicator--err"); - if (visual.isError) { - indicator.addClass("surfsense-connection-indicator--err"); - } - setIcon(indicator, visual.icon); - indicator.setAttr("aria-label", visual.label); - indicator.setAttr("title", visual.label); - } - - private async refreshWorkspaces(): Promise { - this.loadingSpaces = true; - try { - this.workspaces = await this.plugin.api.listWorkspaces(); - } catch (err) { - this.handleApiError(err); - this.workspaces = []; - } finally { - this.loadingSpaces = false; - } - } - - private renderFolderList( - containerEl: HTMLElement, - title: string, - desc: string, - current: string[], - write: (next: string[]) => void, - ): void { - const setting = new Setting(containerEl).setName(title).setDesc(desc); - - const persist = async (next: string[]): Promise => { - const dedup = Array.from(new Set(next.map(normalizeFolder))); - write(dedup); - await this.plugin.saveSettings(); - this.display(); - }; - - setting.addButton((btn) => - btn - .setButtonText("Add folder") - .setCta() - .onClick(() => { - new FolderSuggestModal( - this.app, - (picked) => { - void persist([...current, picked]); - }, - current, - ).open(); - }), - ); - - for (const folder of current) { - new Setting(containerEl).setName(folder || "/").addExtraButton((btn) => - btn - .setIcon("cross") - .setTooltip("Remove") - .onClick(() => { - void persist(current.filter((f) => f !== folder)); - }), - ); - } - } - - private handleApiError(err: unknown): void { - if (err instanceof AuthError) { - if (err.message.startsWith("Missing API token")) { - new Notice("Surfsense: paste an API token before verifying."); - } - return; - } - this.plugin.engine.reportError(err); - new Notice( - `SurfSense: request failed — ${(err as Error).message ?? "unknown error"}`, - ); - } -} diff --git a/surfsense_obsidian/src/status-bar.ts b/surfsense_obsidian/src/status-bar.ts deleted file mode 100644 index 30abea50ce..0000000000 --- a/surfsense_obsidian/src/status-bar.ts +++ /dev/null @@ -1,46 +0,0 @@ -import { setIcon } from "obsidian"; -import { STATUS_VISUALS } from "./status-visuals"; -import type { StatusState } from "./types"; - -/** - * Tiny status-bar adornment. - * - * Plain DOM (no HTML strings, no CSS-in-JS) so it stays cheap on mobile - * and Obsidian's lint doesn't complain about innerHTML. - */ - -export class StatusBar { - private readonly el: HTMLElement; - private readonly icon: HTMLElement; - private readonly text: HTMLElement; - - constructor(host: HTMLElement, onClick?: () => void) { - this.el = host; - this.el.addClass("surfsense-status"); - this.icon = this.el.createSpan({ cls: "surfsense-status__icon" }); - this.text = this.el.createSpan({ cls: "surfsense-status__text" }); - if (onClick) { - this.el.addClass("surfsense-status--clickable"); - this.el.addEventListener("click", onClick); - } - this.update({ kind: "idle", queueDepth: 0 }); - } - - update(state: StatusState): void { - const visual = STATUS_VISUALS[state.kind]; - this.el.removeClass("surfsense-status--err"); - if (visual.isError) this.el.addClass("surfsense-status--err"); - setIcon(this.icon, visual.icon); - - let label = `SurfSense: ${visual.label}`; - if (state.queueDepth > 0 && state.kind !== "idle") { - label += ` (${state.queueDepth})`; - } - this.text.setText(label); - this.el.setAttr( - "aria-label", - state.detail ? `${label} — ${state.detail}` : label, - ); - this.el.setAttr("title", state.detail ?? label); - } -} diff --git a/surfsense_obsidian/src/status-modal.ts b/surfsense_obsidian/src/status-modal.ts deleted file mode 100644 index e05b3a5bc8..0000000000 --- a/surfsense_obsidian/src/status-modal.ts +++ /dev/null @@ -1,77 +0,0 @@ -import { type App, Modal, Notice, Setting } from "obsidian"; -import type SurfSensePlugin from "./main"; -import { STATUS_VISUALS } from "./status-visuals"; - -/** Live status panel reachable from the status bar / command palette. */ -export class StatusModal extends Modal { - private readonly plugin: SurfSensePlugin; - private readonly onChange = (): void => this.render(); - - constructor(app: App, plugin: SurfSensePlugin) { - super(app); - this.plugin = plugin; - } - - onOpen(): void { - this.setTitle("Surfsense status"); - this.plugin.onStatusChange(this.onChange); - this.render(); - } - - onClose(): void { - this.plugin.offStatusChange(this.onChange); - this.contentEl.empty(); - } - - private render(): void { - const { contentEl, plugin } = this; - contentEl.empty(); - const s = plugin.settings; - - const rows: Array<[string, string]> = [ - ["Status", STATUS_VISUALS[plugin.lastStatus.kind].label], - [ - "Last sync", - s.lastSyncAt ? new Date(s.lastSyncAt).toLocaleString() : "—", - ], - [ - "Last reconcile", - s.lastReconcileAt - ? new Date(s.lastReconcileAt).toLocaleString() - : "—", - ], - ["Files synced", String(s.filesSynced ?? 0)], - ["Queue depth", String(plugin.queueDepth)], - [ - "Capabilities", - plugin.serverCapabilities.length - ? plugin.serverCapabilities.join(", ") - : "(not yet handshaken)", - ], - ]; - for (const [label, value] of rows) { - new Setting(contentEl).setName(label).setDesc(value); - } - - new Setting(contentEl) - .addButton((btn) => - btn - .setButtonText("Re-sync entire vault") - .setCta() - .onClick(async () => { - btn.setDisabled(true); - try { - await plugin.engine.maybeReconcile(true); - new Notice("Surfsense: re-sync requested."); - } catch (err) { - new Notice( - `Surfsense: re-sync failed — ${(err as Error).message}`, - ); - } finally { - btn.setDisabled(false); - } - }), - ) - .addButton((btn) => btn.setButtonText("Close").onClick(() => this.close())); - } -} diff --git a/surfsense_obsidian/src/status-visuals.ts b/surfsense_obsidian/src/status-visuals.ts deleted file mode 100644 index 96a3c8f341..0000000000 --- a/surfsense_obsidian/src/status-visuals.ts +++ /dev/null @@ -1,18 +0,0 @@ -import type { StatusKind } from "./types"; - -/** Shared by the status bar and the settings "Connection" heading. */ -export interface StatusVisual { - icon: string; - label: string; - isError: boolean; -} - -export const STATUS_VISUALS: Record = { - idle: { icon: "check-circle", label: "Synced", isError: false }, - syncing: { icon: "refresh-ccw", label: "Syncing", isError: false }, - queued: { icon: "clock", label: "Queued", isError: false }, - "needs-setup": { icon: "cloud-off", label: "Setup required", isError: false }, - offline: { icon: "wifi-off", label: "Offline", isError: false }, - "auth-error": { icon: "alert-circle", label: "Reauthenticate", isError: true }, - error: { icon: "alert-circle", label: "Error", isError: true }, -}; diff --git a/surfsense_obsidian/src/sync-engine.ts b/surfsense_obsidian/src/sync-engine.ts deleted file mode 100644 index 921067a37d..0000000000 --- a/surfsense_obsidian/src/sync-engine.ts +++ /dev/null @@ -1,751 +0,0 @@ -import { - type App, - type CachedMetadata, - type Debouncer, - Notice, - type TAbstractFile, - TFile, - debounce, -} from "obsidian"; -import { - AuthError, - PermanentError, - type SurfSenseApiClient, - TransientError, - VaultNotRegisteredError, -} from "./api-client"; -import { isExcluded, isFolderFiltered } from "./excludes"; -import { buildNotePayload } from "./payload"; -import { type BatchResult, PersistentQueue } from "./queue"; -import type { - HealthResponse, - ManifestEntry, - NotePayload, - QueueItem, - StatusKind, - StatusState, -} from "./types"; -import { computeVaultFingerprint } from "./vault-identity"; - -/** - * Reconciles vault state with the server. - * Start order: connect (or /health) → drain queue → reconcile → subscribe events. - */ - -export interface SyncEngineDeps { - app: App; - apiClient: SurfSenseApiClient; - queue: PersistentQueue; - getSettings: () => SyncEngineSettings; - saveSettings: (mut: (s: SyncEngineSettings) => void) => Promise; - setStatus: (s: StatusState) => void; - onCapabilities: (caps: string[]) => void; - /** Fired when the adaptive backoff multiplier may have changed; main.ts uses it to reschedule. */ - onReconcileBackoffChanged?: () => void; -} - -export interface SyncEngineSettings { - vaultId: string; - apiToken: string; - connectorId: number | null; - workspaceId: number | null; - includeFolders: string[]; - excludeFolders: string[]; - excludePatterns: string[]; - includeAttachments: boolean; - lastReconcileAt: number | null; - lastSyncAt: number | null; - filesSynced: number; - tombstones: Record; -} - -export const RECONCILE_MIN_INTERVAL_MS = 5 * 60 * 1000; -const TOMBSTONE_TTL_MS = 24 * 60 * 60 * 1000; // 1 day -const PENDING_DEBOUNCE_MS = 1500; - -export class SyncEngine { - private readonly deps: SyncEngineDeps; - private capabilities: string[] = []; - private pendingMdEdits = new Map>(); - /** Consecutive reconciles that found no work; powers the adaptive interval. */ - private idleReconcileStreak = 0; - /** 2^streak is capped at this value (e.g. 8 → max ×8 backoff). */ - private readonly maxBackoffMultiplier = 8; - private lastAppliedKind: StatusKind = "needs-setup"; - - constructor(deps: SyncEngineDeps) { - this.deps = deps; - } - - /** Returns the next-tick interval given the user's base, scaled by the idle streak. */ - getReconcileBackoffMs(baseMs: number): number { - const multiplier = Math.min(2 ** this.idleReconcileStreak, this.maxBackoffMultiplier); - return baseMs * multiplier; - } - - getCapabilities(): readonly string[] { - return this.capabilities; - } - - supports(capability: string): boolean { - return this.capabilities.includes(capability); - } - - /** Run the onload sequence described in this file's docstring. */ - async start(): Promise { - this.setStatus("syncing", "Connecting to SurfSense…"); - - const settings = this.deps.getSettings(); - if (!settings.workspaceId) { - // No target yet — /health still surfaces auth/network errors. - try { - const health = await this.deps.apiClient.health(); - this.applyHealth(health); - } catch (err) { - this.handleStartupError(err); - return; - } - this.setStatus("idle"); - return; - } - - // Re-announce so the backend sees the latest vault_name + last_connect_at. - // flushQueue gates on connectorId, so a failed connect leaves the queue intact. - await this.ensureConnected(); - - await this.flushQueue(); - await this.maybeReconcile(); - this.setStatus(this.queueStatusKind(), undefined); - } - - /** - * (Re)register the vault. Adopts server's `vault_id` in case fingerprint - * dedup routed us to an existing row from another device. - */ - async ensureConnected(): Promise { - const settings = this.deps.getSettings(); - if (!settings.workspaceId) { - this.setStatus("idle"); - return false; - } - this.setStatus("syncing", "Connecting to SurfSense"); - try { - const fingerprint = await computeVaultFingerprint(this.deps.app); - const resp = await this.deps.apiClient.connect({ - workspaceId: settings.workspaceId, - vaultId: settings.vaultId, - vaultName: this.deps.app.vault.getName(), - vaultFingerprint: fingerprint, - }); - this.applyHealth(resp); - await this.deps.saveSettings((s) => { - s.vaultId = resp.vault_id; - s.connectorId = resp.connector_id; - }); - this.setStatus(this.queueStatusKind(), this.statusDetail()); - return true; - } catch (err) { - this.handleStartupError(err); - return false; - } - } - - applyHealth(h: HealthResponse): void { - this.capabilities = Array.isArray(h.capabilities) ? [...h.capabilities] : []; - this.deps.onCapabilities(this.capabilities); - } - - // ---- vault event handlers -------------------------------------------- - - onCreate(file: TAbstractFile): void { - if (!this.shouldTrack(file)) return; - const settings = this.deps.getSettings(); - if (this.isExcluded(file.path, settings)) return; - this.resetIdleStreak(); - if (this.isMarkdown(file)) { - this.scheduleMdUpsert(file.path); - return; - } - this.deps.queue.enqueueUpsert(file.path); - } - - onModify(file: TAbstractFile): void { - if (!this.shouldTrack(file)) return; - const settings = this.deps.getSettings(); - if (this.isExcluded(file.path, settings)) return; - this.resetIdleStreak(); - if (this.isMarkdown(file)) { - // Wait for metadataCache.changed so the payload sees fresh metadata. - this.scheduleMdUpsert(file.path); - return; - } - this.deps.queue.enqueueUpsert(file.path); - } - - onDelete(file: TAbstractFile): void { - if (!this.shouldTrack(file)) return; - this.resetIdleStreak(); - this.deps.queue.enqueueDelete(file.path); - void this.deps.saveSettings((s) => { - s.tombstones[file.path] = Date.now(); - }); - } - - onRename(file: TAbstractFile, oldPath: string): void { - if (!this.shouldTrack(file)) return; - this.resetIdleStreak(); - const settings = this.deps.getSettings(); - if (this.isExcluded(file.path, settings)) { - this.deps.queue.enqueueDelete(oldPath); - void this.deps.saveSettings((s) => { - s.tombstones[oldPath] = Date.now(); - }); - return; - } - this.deps.queue.enqueueRename(oldPath, file.path); - } - - onMetadataChanged(file: TFile, _data: string, _cache: CachedMetadata): void { - if (!this.shouldTrack(file)) return; - const settings = this.deps.getSettings(); - if (this.isExcluded(file.path, settings)) return; - if (!this.isMarkdown(file)) return; - // Metadata is fresh now — cancel the deferred upsert and enqueue immediately. - const pending = this.pendingMdEdits.get(file.path); - if (pending) { - pending.cancel(); - this.pendingMdEdits.delete(file.path); - } - this.deps.queue.enqueueUpsert(file.path); - } - - private scheduleMdUpsert(path: string): void { - let pending = this.pendingMdEdits.get(path); - if (!pending) { - // resetTimer: true → each edit pushes the upsert out by another PENDING_DEBOUNCE_MS. - pending = debounce( - () => { - this.pendingMdEdits.delete(path); - this.deps.queue.enqueueUpsert(path); - }, - PENDING_DEBOUNCE_MS, - true, - ); - this.pendingMdEdits.set(path, pending); - } - pending(); - } - - // ---- queue draining --------------------------------------------------- - - async flushQueue(): Promise { - if (this.deps.queue.size === 0) { - await this.recoverStatusIfNeeded(); - return; - } - // Shared gate for every flush trigger so the first /sync can't race /connect. - if (!this.deps.getSettings().connectorId) { - const connected = await this.ensureConnected(); - if (!connected) return; - if (!this.deps.getSettings().connectorId) return; - } - this.setStatus("syncing", `Syncing ${this.deps.queue.size} item(s)…`); - const summary = await this.deps.queue.drain({ - processBatch: (batch) => this.processBatch(batch), - }); - if (summary.acked > 0) { - await this.deps.saveSettings((s) => { - s.lastSyncAt = Date.now(); - s.filesSynced = (s.filesSynced ?? 0) + summary.acked; - }); - } - this.setStatus(this.queueStatusKind(), this.statusDetail()); - } - - /** - * Lightweight status recovery path used after network-change signals. - * Clears stale offline/auth/error only when connectivity/auth is explicitly re-validated. - */ - async recoverConnectivityStatus(): Promise { - const settings = this.deps.getSettings(); - if (!settings.apiToken) { - this.refreshStatus({ force: true }); - return; - } - if (!settings.workspaceId) { - try { - const health = await this.deps.apiClient.health(); - this.applyHealth(health); - this.refreshStatus({ force: true }); - } catch (err) { - this.handleStartupError(err); - } - return; - } - const connected = await this.ensureConnected(); - if (!connected) return; - this.refreshStatus({ force: true }); - } - - private async processBatch(batch: QueueItem[]): Promise { - const settings = this.deps.getSettings(); - const upserts = batch.filter((b): b is QueueItem & { op: "upsert" } => b.op === "upsert"); - const renames = batch.filter((b): b is QueueItem & { op: "rename" } => b.op === "rename"); - const deletes = batch.filter((b): b is QueueItem & { op: "delete" } => b.op === "delete"); - - const acked: QueueItem[] = []; - const retry: QueueItem[] = []; - const dropped: QueueItem[] = []; - - // Renames first so paths line up before content upserts. - if (renames.length > 0) { - try { - const resp = await this.deps.apiClient.renameBatch({ - vaultId: settings.vaultId, - renames: renames.map((r) => ({ oldPath: r.oldPath, newPath: r.newPath })), - }); - const failed = new Set( - resp.failed.map((f) => `${f.oldPath}\u0000${f.newPath}`), - ); - for (const r of renames) { - if (failed.has(`${r.oldPath}\u0000${r.newPath}`)) retry.push(r); - else acked.push(r); - } - } catch (err) { - if (await this.handleVaultNotRegistered(err)) { - retry.push(...renames); - } else { - const verdict = this.classify(err); - if (verdict === "stop") return { acked, retry: [...retry, ...renames], dropped, stop: true }; - if (verdict === "retry") retry.push(...renames); - else dropped.push(...renames); - } - } - } - - if (deletes.length > 0) { - try { - const resp = await this.deps.apiClient.deleteBatch({ - vaultId: settings.vaultId, - paths: deletes.map((d) => d.path), - }); - const failed = new Set(resp.failed); - for (const d of deletes) { - if (failed.has(d.path)) retry.push(d); - else acked.push(d); - } - } catch (err) { - if (await this.handleVaultNotRegistered(err)) { - retry.push(...deletes); - } else { - const verdict = this.classify(err); - if (verdict === "stop") return { acked, retry: [...retry, ...deletes], dropped, stop: true }; - if (verdict === "retry") retry.push(...deletes); - else dropped.push(...deletes); - } - } - } - - if (upserts.length > 0) { - const payloads: NotePayload[] = []; - for (const item of upserts) { - const file = this.deps.app.vault.getFileByPath(item.path); - if (!file) { - // Vanished — ack now; the delete event will follow if needed. - acked.push(item); - continue; - } - try { - const payload = this.isMarkdown(file) - ? await buildNotePayload(this.deps.app, file, settings.vaultId) - : await this.buildBinaryPayload(file, settings.vaultId); - payloads.push(payload); - } catch (err) { - console.error("SurfSense: failed to build payload", item.path, err); - retry.push(item); - } - } - - if (payloads.length > 0) { - try { - const resp = await this.deps.apiClient.syncBatch({ - vaultId: settings.vaultId, - notes: payloads, - }); - // Per-note failures retry; queue maxAttempts drops poison pills. - const failed = new Set(resp.failed); - for (const item of upserts) { - if (retry.find((r) => r === item)) continue; - if (failed.has(item.path)) retry.push(item); - else acked.push(item); - } - } catch (err) { - if (await this.handleVaultNotRegistered(err)) { - for (const item of upserts) { - if (retry.find((r) => r === item)) continue; - retry.push(item); - } - } else { - const verdict = this.classify(err); - if (verdict === "stop") - return { acked, retry: [...retry, ...upserts], dropped, stop: true }; - if (verdict === "retry") retry.push(...upserts); - else dropped.push(...upserts); - } - } - } - } - - return { acked, retry, dropped, stop: false }; - } - - private async buildBinaryPayload(file: TFile, vaultId: string): Promise { - // Attachments skip buildNotePayload (no markdown metadata) but still - // need raw bytes + hash + stat so the backend can ETL-extract text - // and manifest diff still works. - const buf = await this.deps.app.vault.readBinary(file); - const digest = await crypto.subtle.digest("SHA-256", buf); - const hash = bufferToHex(digest); - const binaryBase64 = arrayBufferToBase64(buf); - return { - vault_id: vaultId, - path: file.path, - name: file.basename, - extension: file.extension, - content: "", - frontmatter: {}, - tags: [], - headings: [], - resolved_links: [], - unresolved_links: [], - embeds: [], - aliases: [], - content_hash: hash, - size: file.stat.size, - mtime: file.stat.mtime, - ctime: file.stat.ctime, - is_binary: true, - binary_base64: binaryBase64, - mime_type: mimeTypeFor(file.extension), - }; - } - - // ---- reconcile -------------------------------------------------------- - - async maybeReconcile(force = false): Promise { - const settings = this.deps.getSettings(); - if (!settings.connectorId) return; - if (!force && settings.lastReconcileAt) { - if (Date.now() - settings.lastReconcileAt < RECONCILE_MIN_INTERVAL_MS) return; - } - - // Re-handshake first: if the vault grew enough to match another - // device's fingerprint, the server merges and routes us to the - // survivor row, which the /manifest call below then uses. - const connected = await this.ensureConnected(); - if (!connected) return; - const refreshed = this.deps.getSettings(); - if (!refreshed.connectorId) return; - - this.setStatus("syncing", "Reconciling vault with server…"); - try { - const manifest = await this.deps.apiClient.getManifest(refreshed.vaultId); - const remote = manifest.items ?? {}; - const enqueued = this.diffAndQueue(refreshed, remote); - await this.deps.saveSettings((s) => { - s.lastReconcileAt = Date.now(); - s.tombstones = pruneTombstones(s.tombstones); - }); - this.updateIdleStreak(enqueued); - await this.flushQueue(); - this.refreshStatus({ force: true }); - } catch (err) { - this.classifyAndStatus(err, "Reconcile failed"); - } - } - - /** - * Diff local vault vs server manifest and enqueue work. Skips disk reads - * on idle reconciles by short-circuiting on `mtime + size`; false positives - * collapse to a no-op upsert via the server's `content_hash` check. - * Returns the enqueued count to drive adaptive backoff. - */ - private diffAndQueue( - settings: SyncEngineSettings, - remote: Record, - ): number { - const localFiles = this.deps.app.vault.getFiles().filter((f) => { - if (!this.shouldTrack(f)) return false; - if (this.isExcluded(f.path, settings)) return false; - return true; - }); - const localPaths = new Set(localFiles.map((f) => f.path)); - let enqueued = 0; - - for (const file of localFiles) { - const remoteEntry = remote[file.path]; - if (!remoteEntry) { - this.deps.queue.enqueueUpsert(file.path); - enqueued++; - continue; - } - const remoteMtimeMs = toMillis(remoteEntry.mtime); - const mtimeMatches = file.stat.mtime <= remoteMtimeMs + 1000; - // Older server rows lack `size` — treat as unknown and re-upsert. - const sizeMatches = - typeof remoteEntry.size === "number" && file.stat.size === remoteEntry.size; - if (mtimeMatches && sizeMatches) continue; - this.deps.queue.enqueueUpsert(file.path); - enqueued++; - } - - // Remote-only → delete, unless a fresh tombstone is already in the queue. - for (const path of Object.keys(remote)) { - if (localPaths.has(path)) continue; - const tombstone = settings.tombstones[path]; - if (tombstone && Date.now() - tombstone < TOMBSTONE_TTL_MS) continue; - this.deps.queue.enqueueDelete(path); - enqueued++; - } - - return enqueued; - } - - /** Bump (idle) or reset (active) the streak; notify only when the capped multiplier changes. */ - private updateIdleStreak(enqueued: number): void { - const previousStreak = this.idleReconcileStreak; - if (enqueued === 0) this.idleReconcileStreak++; - else this.idleReconcileStreak = 0; - const cap = Math.log2(this.maxBackoffMultiplier); - const cappedPrev = Math.min(previousStreak, cap); - const cappedNow = Math.min(this.idleReconcileStreak, cap); - if (cappedPrev !== cappedNow) this.deps.onReconcileBackoffChanged?.(); - } - - /** Vault edit — drop back to base interval immediately. */ - private resetIdleStreak(): void { - if (this.idleReconcileStreak === 0) return; - this.idleReconcileStreak = 0; - this.deps.onReconcileBackoffChanged?.(); - } - - // ---- status helpers --------------------------------------------------- - - /** - * Conservative by default: real errors are preserved while setup is - * complete, so unrelated edits don't optimistically clear the indicator. - * Pass `force: true` after an explicit verify/reconcile confirmation. - */ - refreshStatus(opts: { force?: boolean } = {}): void { - if (!opts.force) { - const last = this.lastAppliedKind; - if (last === "syncing") return; - const isError = - last === "auth-error" || last === "offline" || last === "error"; - const s = this.deps.getSettings(); - const setupComplete = !!(s.apiToken && s.workspaceId && s.connectorId); - if (isError && setupComplete) return; - } - this.setStatus(this.queueStatusKind(), this.statusDetail()); - } - - reportAuthError(message?: string): void { - this.setStatus("auth-error", message ?? "API token expired or invalid"); - } - - reportError(err: unknown): void { - if (err instanceof AuthError) { - this.reportAuthError(err.message); - return; - } - if (err instanceof TransientError) { - this.setStatus("offline", err.message); - return; - } - this.setStatus("error", (err as Error).message ?? "Unknown error"); - } - - private setStatus(kind: StatusKind, detail?: string): void { - const s = this.deps.getSettings(); - if (!s.apiToken) { - kind = "needs-setup"; - detail = this.setupHint(s); - } else if (kind !== "auth-error" && kind !== "offline" && kind !== "error") { - if (!s.workspaceId || !s.connectorId) { - kind = "needs-setup"; - detail = this.setupHint(s); - } - } - this.lastAppliedKind = kind; - this.deps.setStatus({ kind, detail, queueDepth: this.deps.queue.size }); - } - - private setupHint(s: SyncEngineSettings): string { - if (!s.apiToken) return "Paste your API token in settings."; - if (!s.workspaceId) return "Pick a workspace in settings."; - return "Connecting…"; - } - - private queueStatusKind(): StatusKind { - if (this.deps.queue.size > 0) return "queued"; - return "idle"; - } - - private statusDetail(): string | undefined { - const settings = this.deps.getSettings(); - if (settings.lastSyncAt) { - return `Last sync ${formatRelative(settings.lastSyncAt)}`; - } - return undefined; - } - - private handleStartupError(err: unknown): void { - if (err instanceof AuthError) { - this.setStatus("auth-error", err.message); - return; - } - if (err instanceof TransientError) { - this.setStatus("offline", err.message); - return; - } - this.setStatus("error", (err as Error).message ?? "Unknown error"); - } - - /** Re-connect on VAULT_NOT_REGISTERED so the next drain sees the new row. */ - private async handleVaultNotRegistered(err: unknown): Promise { - if (!(err instanceof VaultNotRegisteredError)) return false; - console.warn("SurfSense: vault not registered, re-connecting before retry", err); - await this.ensureConnected(); - return true; - } - - private classify(err: unknown): "ack" | "retry" | "drop" | "stop" { - if (err instanceof AuthError) { - this.setStatus("auth-error", err.message); - return "stop"; - } - if (err instanceof TransientError) { - this.setStatus("offline", err.message); - return "stop"; - } - if (err instanceof PermanentError) { - console.warn("SurfSense: permanent error, dropping batch", err); - new Notice(`Surfsense: ${err.message}`); - return "drop"; - } - console.error("SurfSense: unknown error", err); - return "retry"; - } - - private classifyAndStatus(err: unknown, prefix: string): void { - const verdict = this.classify(err); - if (verdict === "stop") return; - this.setStatus(this.queueStatusKind(), `${prefix}: ${(err as Error).message}`); - } - - private async recoverStatusIfNeeded(): Promise { - if (!this.isRecoverableErrorState()) return; - await this.recoverConnectivityStatus(); - } - - private isRecoverableErrorState(): boolean { - return ( - this.lastAppliedKind === "offline" || - this.lastAppliedKind === "auth-error" || - this.lastAppliedKind === "error" - ); - } - - // ---- predicates ------------------------------------------------------- - - private shouldTrack(file: TAbstractFile): boolean { - if (!isTFile(file)) return false; - if (this.isMarkdown(file)) return true; - const settings = this.deps.getSettings(); - if (!settings.includeAttachments) return false; - return ALLOWED_ATTACHMENT_EXTENSIONS.has(file.extension.toLowerCase()); - } - - private isExcluded(path: string, settings: SyncEngineSettings): boolean { - if (isFolderFiltered(path, settings.includeFolders, settings.excludeFolders)) { - return true; - } - return isExcluded(path, settings.excludePatterns); - } - - private isMarkdown(file: TAbstractFile): boolean { - return isTFile(file) && file.extension.toLowerCase() === "md"; - } -} - -function isTFile(f: TAbstractFile): f is TFile { - return f instanceof TFile; -} - -function bufferToHex(buf: ArrayBuffer): string { - const view = new Uint8Array(buf); - let hex = ""; - for (let i = 0; i < view.length; i++) hex += (view[i] ?? 0).toString(16).padStart(2, "0"); - return hex; -} - -function arrayBufferToBase64(buf: ArrayBuffer): string { - const bytes = new Uint8Array(buf); - const chunkSize = 0x8000; - let binary = ""; - for (let i = 0; i < bytes.length; i += chunkSize) { - const chunk = bytes.subarray(i, i + chunkSize); - binary += String.fromCharCode(...Array.from(chunk)); - } - return btoa(binary); -} - -/** Source of truth for the attachment whitelist. Mirrors ATTACHMENT_MIME_TYPES on the backend. */ -export const MIME_BY_EXTENSION = { - pdf: "application/pdf", - png: "image/png", - jpg: "image/jpeg", - jpeg: "image/jpeg", - gif: "image/gif", - webp: "image/webp", - svg: "image/svg+xml", - txt: "text/plain", -} as const satisfies Record; - -export const ALLOWED_ATTACHMENT_EXTENSIONS: ReadonlySet = new Set( - Object.keys(MIME_BY_EXTENSION), -); - -function mimeTypeFor(extension: string): string { - const ext = extension.toLowerCase() as keyof typeof MIME_BY_EXTENSION; - const mime = MIME_BY_EXTENSION[ext]; - if (!mime) { - throw new Error(`Unsupported attachment extension: .${extension}`); - } - return mime; -} - -function formatRelative(ts: number): string { - const diff = Date.now() - ts; - if (diff < 60_000) return "just now"; - if (diff < 3600_000) return `${Math.round(diff / 60_000)}m ago`; - if (diff < 86_400_000) return `${Math.round(diff / 3600_000)}h ago`; - return `${Math.round(diff / 86_400_000)}d ago`; -} - -/** Manifest mtimes arrive as ISO strings, vault stats as epoch ms — normalise. */ -function toMillis(value: number | string | Date): number { - if (typeof value === "number") return value; - if (value instanceof Date) return value.getTime(); - const parsed = Date.parse(value); - return Number.isFinite(parsed) ? parsed : 0; -} - -function pruneTombstones(tombstones: Record): Record { - const out: Record = {}; - const cutoff = Date.now() - TOMBSTONE_TTL_MS; - for (const [k, v] of Object.entries(tombstones)) { - if (v >= cutoff) out[k] = v; - } - return out; -} diff --git a/surfsense_obsidian/src/types.ts b/surfsense_obsidian/src/types.ts deleted file mode 100644 index cfa9fbb74c..0000000000 --- a/surfsense_obsidian/src/types.ts +++ /dev/null @@ -1,202 +0,0 @@ -/** Shared types for the SurfSense Obsidian plugin. Leaf module — no src/ imports. */ - -export interface SurfsensePluginSettings { - serverUrl: string; - apiToken: string; - workspaceId: number | null; - connectorId: number | null; - /** UUID for the vault — lives here so Obsidian Sync replicates it across devices. */ - vaultId: string; - /** 0 disables periodic reconcile (Force sync still works). */ - syncIntervalMinutes: number; - /** Mobile-only: pause auto-sync when on cellular. iOS can't detect network type, so the toggle is a no-op there. */ - wifiOnly: boolean; - includeFolders: string[]; - excludeFolders: string[]; - excludePatterns: string[]; - includeAttachments: boolean; - lastSyncAt: number | null; - lastReconcileAt: number | null; - filesSynced: number; - queue: QueueItem[]; - tombstones: Record; -} - -export const DEFAULT_SETTINGS: SurfsensePluginSettings = { - serverUrl: "https://surfsense.com", - apiToken: "", - workspaceId: null, - connectorId: null, - vaultId: "", - syncIntervalMinutes: 10, - wifiOnly: false, - includeFolders: [], - excludeFolders: [], - excludePatterns: [".trash", "_attachments", "templates"], - includeAttachments: false, - lastSyncAt: null, - lastReconcileAt: null, - filesSynced: 0, - queue: [], - tombstones: {}, -}; - -export type QueueOp = "upsert" | "delete" | "rename"; - -export interface UpsertItem { - op: "upsert"; - path: string; - enqueuedAt: number; - attempt: number; -} - -export interface DeleteItem { - op: "delete"; - path: string; - enqueuedAt: number; - attempt: number; -} - -export interface RenameItem { - op: "rename"; - oldPath: string; - newPath: string; - enqueuedAt: number; - attempt: number; -} - -export type QueueItem = UpsertItem | DeleteItem | RenameItem; - -interface NotePayloadBase { - vault_id: string; - path: string; - name: string; - extension: string; - content: string; - frontmatter: Record; - tags: string[]; - headings: HeadingRef[]; - resolved_links: string[]; - unresolved_links: string[]; - embeds: string[]; - aliases: string[]; - content_hash: string; - /** Byte size of the local file; pairs with mtime for the reconcile short-circuit. */ - size: number; - mtime: number; - ctime: number; -} - -export interface MarkdownNotePayload extends NotePayloadBase { - is_binary?: false; -} - -export interface BinaryNotePayload extends NotePayloadBase { - /** Non-markdown attachment marker; enables backend ETL path. */ - is_binary: true; - /** Base64-encoded file bytes for binary attachments. */ - binary_base64: string; - /** Canonical MIME type for the extension; required by the backend. */ - mime_type: string; -} - -export type NotePayload = MarkdownNotePayload | BinaryNotePayload; - -export interface HeadingRef { - heading: string; - level: number; -} - -export interface Workspace { - id: number; - name: string; - description?: string; - [key: string]: unknown; -} - -export interface ConnectResponse { - connector_id: number; - vault_id: string; - workspace_id: number; - capabilities: string[]; - server_time_utc: string; - [key: string]: unknown; -} - -export interface HealthResponse { - capabilities: string[]; - server_time_utc: string; - [key: string]: unknown; -} - -export interface ManifestEntry { - hash: string; - mtime: number; - /** Optional: byte size of stored content. Enables mtime+size short-circuit; falls back to upsert when missing. */ - size?: number; - [key: string]: unknown; -} - -export interface ManifestResponse { - vault_id: string; - items: Record; - [key: string]: unknown; -} - -/** Per-item ack shapes — mirror `app/schemas/obsidian_plugin.py` 1:1. */ -export interface SyncAckItem { - path: string; - status: "ok" | "queued" | "error"; - document_id?: number; - error?: string; -} - -export interface SyncAck { - vault_id: string; - indexed: number; - failed: number; - items: SyncAckItem[]; -} - -export interface RenameAckItem { - old_path: string; - new_path: string; - status: "ok" | "error" | "missing"; - document_id?: number; - error?: string; -} - -export interface RenameAck { - vault_id: string; - renamed: number; - missing: number; - items: RenameAckItem[]; -} - -export interface DeleteAckItem { - path: string; - status: "ok" | "error" | "missing"; - error?: string; -} - -export interface DeleteAck { - vault_id: string; - deleted: number; - missing: number; - items: DeleteAckItem[]; -} - -export type StatusKind = - | "idle" - | "syncing" - | "queued" - | "needs-setup" - | "offline" - | "auth-error" - | "error"; - -export interface StatusState { - kind: StatusKind; - detail?: string; - queueDepth: number; -} diff --git a/surfsense_obsidian/src/vault-identity.ts b/surfsense_obsidian/src/vault-identity.ts deleted file mode 100644 index 86ae8b3b58..0000000000 --- a/surfsense_obsidian/src/vault-identity.ts +++ /dev/null @@ -1,43 +0,0 @@ -import type { App } from "obsidian"; - -/** - * Deterministic SHA-256 over the vault name + sorted markdown paths. - * - * Two devices observing the same vault content compute the same value, - * regardless of how it was synced (iCloud, Syncthing, Obsidian Sync, …). - * The server uses this as the cross-device dedup key on /connect. - */ -export async function computeVaultFingerprint(app: App): Promise { - const vaultName = app.vault.getName(); - const paths = app.vault - .getMarkdownFiles() - .map((f) => f.path) - .sort(); - const payload = `${vaultName}\n${paths.join("\n")}`; - const bytes = new TextEncoder().encode(payload); - const digest = await crypto.subtle.digest("SHA-256", bytes); - return bufferToHex(digest); -} - -function bufferToHex(buf: ArrayBuffer): string { - const view = new Uint8Array(buf); - let hex = ""; - for (let i = 0; i < view.length; i++) { - hex += (view[i] ?? 0).toString(16).padStart(2, "0"); - } - return hex; -} - -export function generateVaultUuid(): string { - const c = globalThis.crypto; - if (c?.randomUUID) return c.randomUUID(); - const buf = new Uint8Array(16); - c.getRandomValues(buf); - buf[6] = ((buf[6] ?? 0) & 0x0f) | 0x40; - buf[8] = ((buf[8] ?? 0) & 0x3f) | 0x80; - const hex = Array.from(buf, (b) => b.toString(16).padStart(2, "0")).join(""); - return `${hex.slice(0, 8)}-${hex.slice(8, 12)}-${hex.slice(12, 16)}-${hex.slice( - 16, - 20, - )}-${hex.slice(20)}`; -} diff --git a/surfsense_obsidian/styles.css b/surfsense_obsidian/styles.css deleted file mode 100644 index 4aa831e6c6..0000000000 --- a/surfsense_obsidian/styles.css +++ /dev/null @@ -1,48 +0,0 @@ -/* - * SurfSense Obsidian plugin styles. Status-bar widget only — the settings - * tab uses Obsidian's stock Setting rows, no custom CSS needed. - */ - -.surfsense-status { - gap: 6px; -} - -.surfsense-status--clickable { - cursor: pointer; -} - -.surfsense-status__icon { - display: inline-flex; - width: 14px; - height: 14px; -} - -.surfsense-status__icon svg { - width: 14px; - height: 14px; -} - -.surfsense-status--err .surfsense-status__icon { - color: var(--color-red); -} - -.surfsense-connection-indicator { - display: inline-flex; - width: 14px; - height: 14px; -} - -.surfsense-connection-heading { - display: inline-flex; - align-items: center; - gap: 8px; -} - -.surfsense-connection-indicator svg { - width: 14px; - height: 14px; -} - -.surfsense-connection-indicator--err { - color: var(--color-red); -} diff --git a/surfsense_obsidian/tsconfig.json b/surfsense_obsidian/tsconfig.json deleted file mode 100644 index 222535dee2..0000000000 --- a/surfsense_obsidian/tsconfig.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "compilerOptions": { - "baseUrl": "src", - "inlineSourceMap": true, - "inlineSources": true, - "module": "ESNext", - "target": "ES6", - "allowJs": true, - "noImplicitAny": true, - "noImplicitThis": true, - "noImplicitReturns": true, - "moduleResolution": "node", - "importHelpers": true, - "noUncheckedIndexedAccess": true, - "isolatedModules": true, - "strictNullChecks": true, - "strictBindCallApply": true, - "allowSyntheticDefaultImports": true, - "useUnknownInCatchVariables": true, - "lib": [ - "DOM", - "ES5", - "ES6", - "ES7" - ] - }, - "include": [ - "src/**/*.ts" - ] -} diff --git a/surfsense_obsidian/version-bump.mjs b/surfsense_obsidian/version-bump.mjs deleted file mode 100644 index 55d631fb6c..0000000000 --- a/surfsense_obsidian/version-bump.mjs +++ /dev/null @@ -1,17 +0,0 @@ -import { readFileSync, writeFileSync } from "fs"; - -const targetVersion = process.env.npm_package_version; - -// read minAppVersion from manifest.json and bump version to target version -const manifest = JSON.parse(readFileSync("manifest.json", "utf8")); -const { minAppVersion } = manifest; -manifest.version = targetVersion; -writeFileSync("manifest.json", JSON.stringify(manifest, null, "\t")); - -// update versions.json with target version and minAppVersion from manifest.json -// but only if the target version is not already in versions.json -const versions = JSON.parse(readFileSync('versions.json', 'utf8')); -if (!Object.values(versions).includes(minAppVersion)) { - versions[targetVersion] = minAppVersion; - writeFileSync('versions.json', JSON.stringify(versions, null, '\t')); -} diff --git a/surfsense_obsidian/versions.json b/surfsense_obsidian/versions.json deleted file mode 100644 index 9a3c3429d1..0000000000 --- a/surfsense_obsidian/versions.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "0.1.0": "1.5.4" -} diff --git a/surfsense_web/.cursorrules b/surfsense_web/.cursorrules deleted file mode 100644 index 0320360a1e..0000000000 --- a/surfsense_web/.cursorrules +++ /dev/null @@ -1 +0,0 @@ -use pnpm as default package manager \ No newline at end of file diff --git a/surfsense_web/.cursorrules b/surfsense_web/.cursorrules new file mode 120000 index 0000000000..47dc3e3d86 --- /dev/null +++ b/surfsense_web/.cursorrules @@ -0,0 +1 @@ +AGENTS.md \ No newline at end of file diff --git a/surfsense_web/AGENTS.md b/surfsense_web/AGENTS.md new file mode 100644 index 0000000000..a79f28b279 --- /dev/null +++ b/surfsense_web/AGENTS.md @@ -0,0 +1,29 @@ +# surfsense_web + +Next.js App Router, `pnpm`, Biome. Root `AGENTS.md` owns organization and TDD. + +Package manager is **pnpm**, not npm or yarn. + +Next.js docs for this installed version: `node_modules/next/dist/docs/`. + +## Commands + +```bash +pnpm install +pnpm dev +pnpm format +pnpm test:unit +pnpm test:e2e +``` + +## Do + +- New UI: vertical slice, one responsibility per file. See root `AGENTS.md`. +- React/Next: `vercel-react-best-practices` skill. +- shadcn / `components.json`: `shadcn` skill. +- New behavior: `tdd` skill. + +## Do not + +- Do not add npm lockfiles. +- Do not rewrite existing routes to the new layout unless that is the task. diff --git a/surfsense_web/README.md b/surfsense_web/README.md new file mode 100644 index 0000000000..5793e39565 --- /dev/null +++ b/surfsense_web/README.md @@ -0,0 +1,48 @@ +# SurfSense web + +Next.js UI for the self-host stack (App Router, `pnpm`). + +This is not the desktop UI. Desktop frontend lives in [`surfsense_local/frontend/`](../surfsense_local/frontend/). + +## You need + +- Node.js 20+ and [pnpm](https://pnpm.io/installation) (not npm) +- The API in [`surfsense_backend/`](../surfsense_backend/README.md) on http://localhost:8000 + +## Run it + +```bash +cp .env.example .env +``` + +In `.env`, point at a backend on your machine (the example file uses Docker hostnames): + +```bash +NEXT_PUBLIC_FASTAPI_BACKEND_URL=http://localhost:8000 +SURFSENSE_BACKEND_INTERNAL_URL=http://localhost:8000 +AUTH_TYPE=LOCAL +ETL_SERVICE=DOCLING +DEPLOYMENT_MODE=self-hosted +``` + +Then: + +```bash +pnpm install +pnpm dev +``` + +App: http://localhost:3000 + +Match `AUTH_TYPE` with the backend. Leave `SUNSET_MODE` unset. + +Live document/chat updates need [zero-cache](content/docs/manual-installation.mdx). Most UI PRs can run without it; the page just will not refresh itself. + +## Tests + +```bash +pnpm test:unit +pnpm format +``` + +Playwright needs Postgres, Redis, the API, and a worker. See [tests/README.md](tests/README.md). diff --git a/versions.json b/versions.json deleted file mode 100644 index 9a3c3429d1..0000000000 --- a/versions.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "0.1.0": "1.5.4" -}