feat(research): connect staged goals, guarded requests and execution evidence

apps/presentation/dashboard/src/features/personal-workspace/goal-research-history.tsx

@@ -75,17 +75,28 @@ export function GoalResearchHistory({goalId, active, zh, refreshRevision = 0, re
   const outcomeCopy: Record<string, string> = zh ? {replan: "转向", continue: "继续", stop: "停止", wait: "等待", no_change: "无变化", ask_human: "请人决定"} : {replan: "Replan", continue: "Continue", stop: "Stop", wait: "Wait", no_change: "No change", ask_human: "Ask human"};
   const findingBody = (finding: ExploreHistoryEvent) => {
     const sections = parseExploreFindingSections(finding.summary);
-    if (!sections) return <p>{finding.summary}</p>;
-    const decision = sections.decision;
-    return <dl className="research-finding-sections">
+    const decision = sections?.decision;
+    const execution = finding.execution_observation;
+    const measured = (value: unknown) => value === null ? (zh ? "未测量" : "Not measured") : String(value);
+    return <>{sections ? <dl className="research-finding-sections">
       <dt>{sectionCopy.observation}</dt><dd>{sections.observation}</dd>
       {decision ? <>
         <dt>{zh ? "决策" : "Decision"}</dt><dd><b className={`research-outcome is-${decision.outcome}`}>{outcomeCopy[decision.outcome] ?? decision.outcome}</b></dd>
         {(["changed", "stopped", "retained"] as const).map(key => decision[key].length ? <><dt key={`${key}-t`}>{sectionCopy[key]}</dt><dd key={key}><ul>{decision[key].map(item => <li key={item}>{item}</li>)}</ul></dd></> : null)}
       </> : <><dt>{sectionCopy.interpretation}</dt><dd>{sections.interpretation}</dd></>}
       <dt>{sectionCopy.applicability}</dt><dd className="is-muted">{sections.applicability}</dd>
       <dt>{sectionCopy.inputRevision}</dt><dd><code>{sections.inputRevision}</code></dd>
-    </dl>;
+    </dl> : <p>{finding.summary}</p>}{execution ? <details><summary>{zh ? "实际执行与资源观测" : "Execution and resource observations"}</summary>
+      <dl className="research-finding-sections">
+        <dt>{zh ? "计划方法 → 实际方法" : "Planned → executed method"}</dt><dd>{String(execution.planned_method_id)} → {String(execution.executed_method_id)}</dd>
+        {execution.change_reason ? <><dt>{zh ? "改向原因" : "Reason for change"}</dt><dd>{String(execution.change_reason)}</dd></> : null}
+        <dt>{zh ? "运行秒数 / 上限秒数" : "Elapsed / limit seconds"}</dt><dd>{measured(execution.elapsed_seconds)} / {measured(execution.limit_seconds)}</dd>
+        <dt>{zh ? "搜索次数" : "Search iterations"}</dt><dd>{measured(execution.search_iterations)}</dd>
+        <dt>{zh ? "结束原因" : "Termination reason"}</dt><dd>{measured(execution.stop_reason)}</dd>
+        <dt>{zh ? "执行候选 SHA-256" : "Executed candidate SHA-256"}</dt><dd><code>{String(execution.candidate_sha256)}</code></dd>
+        {execution.result_ref ? <><dt>{zh ? "该候选的结果引用" : "Result for this candidate"}</dt><dd><code>{String(execution.result_ref)}</code></dd></> : null}
+      </dl><p>{zh ? "来源记录的执行观测；不等于方法有效或当前候选已通过验收。" : "Source-recorded execution observations; not proof of method benefit or current-candidate acceptance."}</p>
+    </details> : null}</>;
   };
   const findingElement = (finding: ExploreHistoryEvent) => <div className="research-recorded-finding" key={finding.event_id}><strong>{finding.title}</strong><small>{timeLabel(finding.recorded_at)} · {zh ? "来源标记" : "Source label"} · {stateLabel(finding.status)}{finding.confidence !== null ? ` · ${zh ? "来源自报置信度" : "Source-reported confidence"} ${finding.confidence}` : ""}</small>{findingBody(finding)}{finding.evidence_refs.map(ref => <code key={ref}>{ref}</code>)}</div>;
   const updateRecords = (events: readonly ExploreHistoryEvent[]) => <ol className="research-history-records">{events.map(event => <li key={event.event_id}><time>{timeLabel(event.recorded_at)}</time><span>{stateLabel(event.status)} · {event.title}{event.summary ? ` — ${event.summary}` : ""}</span>{event.parent_id ? <code>{copy.group}: {event.parent_id}</code> : null}{event.evidence_refs.map(ref => <code key={ref}>{ref}</code>)}{event.run_id ? <code>{event.run_id}</code> : null}</li>)}</ol>;

benchmark/edgebench/README.md

@@ -1,5 +1,10 @@
 # Native EdgeBench worker trials
 
+The explicit fresh-run [`--research-loop` treatment](../runtime/RESEARCH_LOOP.md)
+connects candidate-stage separation, per-request admission, actual local probe
+diagnostics and source-bound Explore feedback. It is not enabled for existing
+attempts or baselines and does not change scoring or extend budget.
+
 This research adapter calls SForge's native `run_agent`. Task images, prompts,
 submission cooldowns, judge isolation and scoring remain owned by the pinned
 EdgeBench checkout. Best-only adds host capture/admission scheduling; native and

benchmark/edgebench/export_report.py

@@ -29,6 +29,19 @@ def _validate_feedback_payload(runner, profile):
         raise ValueError("Unsupported feedback payload for this mode")
 
 
+def _validate_research_loop(runner, profile):
+    # Compare the original sources before the allowlisted settings projection
+    # can discard a one-sided flag. Historical absence remains distinct from
+    # an explicitly disabled treatment, and bool cannot be supplied as 0/1.
+    if ("research_loop" in runner) != ("research_loop" in profile):
+        raise ValueError("Settings profile disagrees with runtime: research_loop")
+    for source in (runner, profile):
+        if "research_loop" in source and type(source["research_loop"]) is not bool:
+            raise ValueError("research_loop must be a boolean")
+    if runner.get("research_loop") != profile.get("research_loop"):
+        raise ValueError("Settings profile disagrees with runtime: research_loop")
+
+
 def digest(path: Path) -> str:
     return hashlib.sha256(path.read_bytes()).hexdigest()
 
@@ -129,6 +142,7 @@ def _validate_run_data(row, config, summary, points):
             raise ValueError(f"Settings identity mismatch: {key}")
     runner, profile = config["runner"], config["worker_profile"]
     _validate_feedback_payload(runner, profile)
+    _validate_research_loop(runner, profile)
     if (
         runner["runner_commit"] != row["runner_revision"]
         or runner["model"] != row["model_id"]
@@ -254,6 +268,7 @@ def export(runs_root: Path, selection: list, output: Path, *, observed_at: str):
         if row.get("runner_revision") != receipt["runner_commit"]:
             raise ValueError("Board and runner revision disagree")
         _validate_feedback_payload(receipt, profile)
+        _validate_research_loop(receipt, profile)
         if receipt.get("planning_horizon_goal") != profile.get("planning_horizon_goal"):
             raise ValueError("Profile disagrees with runtime: planning_horizon_goal")
         for source in (receipt, profile):
@@ -337,6 +352,9 @@ def export(runs_root: Path, selection: list, output: Path, *, observed_at: str):
         if "planning_horizon_goal" in receipt:
             config["runner"]["planning_horizon_goal"] = receipt["planning_horizon_goal"]
             config["worker_profile"]["planning_horizon_goal"] = profile["planning_horizon_goal"]
+        if "research_loop" in receipt:
+            config["runner"]["research_loop"] = receipt["research_loop"]
+            config["worker_profile"]["research_loop"] = profile["research_loop"]
         if "stride_strategy" in receipt:
             config["runner"]["stride_strategy"] = receipt["stride_strategy"]
             config["worker_profile"]["stride_strategy"] = profile["stride_strategy"]

benchmark/edgebench/research_cost.py

@@ -0,0 +1,188 @@
+"""Passive whole-run cost readback over existing private research artifacts.
+
+The TypeScript trajectory owner interprets Codex counters. This adapter only
+selects non-overlapping complete captures and reports observed counts; it does
+not price tokens, divide costs between stages, or infer missing consumption.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+from pathlib import Path
+import re
+
+
+def _json(path):
+    try:
+        return json.loads(path.read_bytes())
+    except (OSError, ValueError):
+        return None
+
+
+def _coverage(known, missing):
+    return "partial" if known and missing else "complete" if known else "unknown"
+
+
+def _collection(trial):
+    rows = _json(trial / "collected" / "artifact-collection.json")
+    return {row["artifact"] for row in rows if isinstance(row, dict)
+            and isinstance(row.get("artifact"), str) and row.get("collected") is True} \
+        if isinstance(rows, list) else set()
+
+
+def _requests(trial, collected):
+    paths = sorted((trial / "collected" / "runtime").rglob("provider-requests.jsonl"))
+    grouped, malformed, readable = {}, 0, 0
+    for path in paths:
+        try:
+            if path.resolve() != path.absolute():
+                raise ValueError("nonphysical capture")
+            lines = path.read_text(encoding="utf-8").splitlines()
+            readable += 1
+            for line in lines:
+                try:
+                    row = json.loads(line)
+                    if (not isinstance(row, dict) or not isinstance(row.get("request_id"), str)
+                            or not row["request_id"] or row.get("event") not in {"dispatch", "finished"}):
+                        raise ValueError("invalid request record")
+                    grouped.setdefault(row["request_id"], []).append(row)
+                except (ValueError, TypeError):
+                    malformed += 1
+        except (OSError, UnicodeError, ValueError):
+            malformed += 1
+    counts = {"forwarded": 0, "not_forwarded": 0, "send_unknown": 0}
+    for rows in grouped.values():
+        finished = {json.dumps(row, sort_keys=True, separators=(",", ":")):
+                    row for row in rows if row["event"] == "finished"}
+        value = next(iter(finished.values())) if len(finished) == 1 else {}
+        forwarded = value.get("forwarded")
+        if forwarded is True:
+            counts["forwarded"] += 1
+        elif forwarded is False and not any(row["event"] == "dispatch" for row in rows):
+            counts["not_forwarded"] += 1
+        else:
+            counts["send_unknown"] += 1
+    return {"capture_coverage": _coverage(readable, malformed or "runtime" not in collected),
+            "captured_distinct_requests": len(grouped) if readable else None,
+            **(counts if readable else {key: None for key in counts}), "invalid_records": malformed,
+            "scope": "observed_gateway_requests_only"}
+
+
+def _prefix(shorter, longer):
+    with shorter.open("rb") as left, longer.open("rb") as right:
+        while chunk := left.read(1024 * 1024):
+            if chunk != right.read(len(chunk)):
+                return False
+    return True
+
+
+def _tokens(trial, collected):
+    from loopx.capabilities.benchmark_toolkit.trajectory_assessment_store import _usage_capture
+
+    root = trial / "collected"
+    paths = {path for name in ("native-sessions", "worker-sessions") for path in (root / name).rglob("*.jsonl")}
+    paths.update(path for path in (root / "runtime").rglob("*.jsonl")
+                 if "sessions" in path.relative_to(root / "runtime").parts)
+    grouped, invalid = {}, 0
+    for path in sorted(paths):
+        try:
+            if path.resolve() != path.absolute():
+                raise ValueError("nonphysical session capture")
+            with path.open("rb") as stream:
+                header = json.loads(stream.readline())
+                payload = header.get("payload") or {}
+                session = payload.get("id")
+                if header.get("type") != "session_meta" or not isinstance(session, str) or not session:
+                    raise ValueError("missing native session identity")
+                stream.seek(0)
+                digest = hashlib.file_digest(stream, "sha256").hexdigest()
+            grouped.setdefault(session, []).append((path, path.stat().st_size, digest))
+        except (OSError, ValueError, AttributeError):
+            invalid += 1
+    fields = ("input_tokens", "cached_input_tokens", "uncached_input_tokens", "output_tokens")
+    totals, qualified, unknown, duplicates = dict.fromkeys(fields, 0), 0, invalid, 0
+    for session, captures in grouped.items():
+        longest, _, digest = max(captures, key=lambda row: row[1])
+        try:
+            # Same identity alone does not make divergent/forked snapshots one
+            # charge. Only byte-identical or strict-prefix captures are deduped.
+            if not all(row[2] == digest or _prefix(row[0], longest) for row in captures):
+                raise ValueError("divergent session captures")
+            result = _usage_capture(root, longest.relative_to(root).as_posix(), session)
+            if result["artifact_sha256"] != digest:
+                raise ValueError("session moved during readback")
+            projection = result["projection"]
+            for field in fields:
+                totals[field] += projection[field]
+            qualified += 1
+            duplicates += len(captures) - 1
+        except (OSError, ValueError, RuntimeError):
+            # Missing prefixes, forks, gaps and unfinished Turns are rejected
+            # by the existing typed owner. Never substitute the maximum count.
+            unknown += 1
+    complete_collection = {"runtime", "native-sessions", "worker-sessions"}.issubset(collected)
+    return {"capture_coverage": _coverage(qualified, unknown or not complete_collection),
+            "whole_run_coverage": "unknown", "qualified_sessions": qualified,
+            "unknown_sessions_or_files": unknown, "duplicate_or_prefix_captures": duplicates,
+            "known_captured_totals": ({**totals, "input_plus_output_tokens":
+                totals["input_tokens"] + totals["output_tokens"]} if qualified else None),
+            "method": "codex_complete_session_counters_v1",
+            "limitations": "Known complete captured sessions only. Input includes cached input; output includes reasoning. Missing/provider-side consumption and billing remain unknown."}
+
+
+def _submissions(trial):
+    from .research_feedback import validate_packet
+
+    packet = _json(trial / "native-research-feedback" / "native.json")
+    final = _json(trial / "final_result.json")
+    native_total = final.get("total_rounds") if isinstance(final, dict) else None
+    if type(native_total) is not int or native_total < 0:
+        native_total = None
+    try:
+        validate_packet(packet)
+    except (ValueError, TypeError):
+        return {"capture_coverage": "unknown", "manual_accepted_count": None,
+                "first_accepted": None, "native_total_rounds": native_total}
+    grouped, unknown = {}, 0
+    for row in packet["records"]:
+        if (not isinstance(row, dict) or row.get("accepted_record") is not True
+                or not isinstance(row.get("submission_id"), str) or not row["submission_id"]
+                or not isinstance(row.get("round_id"), str)
+                or not re.fullmatch(r"agent-[1-9][0-9]*", row["round_id"])):
+            unknown += 1
+            continue
+        grouped.setdefault(row["submission_id"], []).append(row)
+    accepted = []
+    for rows in grouped.values():
+        if len({json.dumps(row, sort_keys=True) for row in rows}) != 1:
+            unknown += 1
+        else:
+            accepted.append(rows[0])
+    rounds = [row["round_id"] for row in accepted]
+    unique = [row for row in accepted if rounds.count(row["round_id"]) == 1]
+    unknown += len(accepted) - len(unique)
+    ordered = sorted(unique, key=lambda row: int(row["round_id"].split("-")[1]))
+    first = ordered[0] if ordered and ordered[0]["round_id"] == "agent-1" else None
+    released = first and first.get("attribution_status") == "verified" and first.get("result_ref")
+    summary = first.get("result_summary") if released else None
+    safe_summary = {key: summary[key] for key in ("score", "pass_rate", "valid", "passed", "failed", "total_tests")
+                    if isinstance(summary, dict) and key in summary and (summary[key] is None
+                        or type(summary[key]) is bool or type(summary[key]) in (int, float) and math.isfinite(summary[key]))}
+    return {"capture_coverage": "partial" if unknown else "complete", "manual_accepted_count": len(unique),
+            "first_accepted": ({"acceptance_ordinal": 1, "released": bool(released),
+                "result_summary": safe_summary if released else None}
+                if first else None), "native_total_rounds": native_total}
+
+
+def summarize_research_cost(trial: Path, runner_elapsed_seconds):
+    """No new I/O to models/judges; report only already captured local evidence."""
+    trial = Path(trial)
+    elapsed = runner_elapsed_seconds if type(runner_elapsed_seconds) in (int, float) \
+        and math.isfinite(runner_elapsed_seconds) and runner_elapsed_seconds >= 0 else None
+    collected = _collection(trial)
+    return {"schema_version": "edgebench_research_cost_v0", "runner_wall_seconds": elapsed,
+            "wall_scope": "measured_runner_entry_through_native_cleanup_and_final_result",
+            "stage_seconds": None, "stage_allocation_coverage": "unknown", "billing_usd": None,
+            "provider_requests": _requests(trial, collected), "tokens": _tokens(trial, collected),
+            "evaluations": _submissions(trial)}