diff --git a/.github/workflows/jev-multicall.yml b/.github/workflows/jev-multicall.yml index d57ea66..cbcd036 100644 --- a/.github/workflows/jev-multicall.yml +++ b/.github/workflows/jev-multicall.yml @@ -66,7 +66,8 @@ jobs: python -B -m graph_synthesis.source_structural.report --update-paper python -B -m graph_synthesis.assumption_aware.report --update-paper python -B -m graph_synthesis.uncertainty.report --update-paper - git diff --exit-code -- experiments/jev-multicall-20260918/REPORT.md manuscript/paper-current.md CURRENT_RESULTS.md graph_synthesis/risk_control graph_synthesis/reliability graph_synthesis/structural graph_synthesis/source_structural graph_synthesis/assumption_aware graph_synthesis/uncertainty + python -B -m graph_synthesis.frontier.report --update-paper + git diff --exit-code -- experiments/jev-multicall-20260918/REPORT.md manuscript/paper-current.md CURRENT_RESULTS.md graph_synthesis/risk_control graph_synthesis/reliability graph_synthesis/structural graph_synthesis/source_structural graph_synthesis/assumption_aware graph_synthesis/uncertainty graph_synthesis/frontier python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/multicall-paper.pdf" git diff --exit-code -- manuscript/paper-current.html - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 diff --git a/.github/workflows/structural-frontier.yml b/.github/workflows/structural-frontier.yml new file mode 100644 index 0000000..b5cbf35 --- /dev/null +++ b/.github/workflows/structural-frontier.yml @@ -0,0 +1,124 @@ +name: Structural frontier research +on: + push: + branches: [main, research/structure-aware-next-five] + pull_request: +permissions: + contents: read +concurrency: + group: frontier-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true +jobs: + execute-and-publish: + if: github.event_name == 'push' && github.ref == 'refs/heads/research/structure-aware-next-five' + runs-on: ubuntu-latest + timeout-minutes: 20 + permissions: + contents: write + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + with: + ref: ${{ github.sha }} + fetch-depth: 0 + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r graph_synthesis/assumption_aware/requirements.txt + - name: Execute frozen hypotheses, adversarial tests and full integrated regressions + shell: bash + run: | + set -euo pipefail + python -B -m graph_synthesis.frontier.integrate_main + python -B -m graph_synthesis.frontier.audit_baseline + python -B -m graph_synthesis.frontier.run | tee graph_synthesis/frontier/execution.log + python -B -m unittest discover -s graph_synthesis/frontier/tests -v 2>&1 | tee graph_synthesis/frontier/tests.log + python -B -m graph_synthesis.frontier.run --check | tee graph_synthesis/frontier/replay.log + python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/frontier-original-inventory.json" + cp "$RUNNER_TEMP/frontier-original-inventory.json" graph_synthesis/frontier/original-inventory.json + python -B -m graph_synthesis.frontier.report --figures --update-paper + python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf + python -B -m graph_synthesis.frontier.regression 2>&1 | tee graph_synthesis/frontier/all-regressions.log + python -B -m graph_synthesis.frontier.report + python -B -m graph_synthesis.frontier.verify + git diff --exit-code 6530321dd0060c9c7f13bb69d6f1c88346a8e1e2 -- graph_synthesis/structural graph_synthesis/source_structural graph_synthesis/assumption_aware + - name: Publish generated research artifacts on this branch only + shell: bash + run: | + set -euo pipefail + git config user.name 'github-actions[bot]' + git config user.email '41898282+github-actions[bot]@users.noreply.github.com' + git add graph_synthesis/frontier CURRENT_RESULTS.md manuscript/paper-current.md manuscript/paper-current.pdf manuscript/paper-current.html manuscript/paper-current.build.json + if ! git diff --cached --quiet; then + git commit -m 'research: record frontier replay, all integrated tests and complete additive paper' + git push origin HEAD:research/structure-aware-next-five + fi + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 + with: + name: structural-frontier-review-package + path: | + graph_synthesis/frontier/ + manuscript/paper-current.md + manuscript/paper-current.pdf + manuscript/paper-current.html + manuscript/paper-current.build.json + CURRENT_RESULTS.md + retention-days: 14 + - name: Package complete integrated source + run: git archive --format=zip --output="$RUNNER_TEMP/frontier-integrated-source.zip" HEAD + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 + with: + name: structural-frontier-integrated-source + path: ${{ runner.temp }}/frontier-integrated-source.zip + retention-days: 14 + replay: + if: github.event_name == 'pull_request' || github.ref == 'refs/heads/main' + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-latest] + runs-on: ${{ matrix.os }} + timeout-minutes: 15 + defaults: + run: + shell: bash + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + with: + ref: ${{ github.event.pull_request.head.sha || github.sha }} + persist-credentials: false + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r reproduction/requirements-jev.txt + - name: Regression, numerical replay and source/manuscript integrity + run: | + python -B -m unittest discover -s graph_synthesis/frontier/tests -v + python -B -m graph_synthesis.frontier.run --check + python -B -m graph_synthesis.frontier.verify + python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/frontier-original.json" + git diff --exit-code + figures-and-paper: + if: github.event_name == 'pull_request' || github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + timeout-minutes: 15 + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + with: + ref: ${{ github.event.pull_request.head.sha || github.sha }} + persist-credentials: false + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r graph_synthesis/requirements-figures.txt + - name: Byte-identical figure and complete manuscript regeneration + run: | + python -B -m graph_synthesis.frontier.report --figures --update-paper + git diff --exit-code -- graph_synthesis/frontier manuscript/paper-current.md CURRENT_RESULTS.md + python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/frontier-paper.pdf" + git diff --exit-code -- manuscript/paper-current.html diff --git a/CURRENT_RESULTS.md b/CURRENT_RESULTS.md index 8c45443..d89fef1 100644 --- a/CURRENT_RESULTS.md +++ b/CURRENT_RESULTS.md @@ -58,6 +58,14 @@ Marginal-only lineage bounds tighten 4.96%, missing the frozen 10% target. Query + + +## Five structural-frontier extensions after PR #17 + +[Executed report](graph_synthesis/frontier/RESULTS.md), [pre-execution protocol](graph_synthesis/frontier/PROTOCOL.md), [novelty audit](graph_synthesis/frontier/NOVELTY.md), [five figure pairs](graph_synthesis/frontier/figures/) and [complete updated paper](manuscript/paper-current.pdf). H1/H3/H4/H5 meet controlled supplied-input targets; H2 fails its frozen review-quality target. Source-conditioned probabilities prevent nine independent-marginal false admissions on 96 controlled fixtures; all three review selectors tie at 13.043478 expected contaminated groups. Ten large lineage formulas and nine bipartite conflict fixtures are exact. Connected-path summary transitions fall by 96.47%, excluding linear witness decoding. No new Jev calls, independent semantic-accuracy finding, worldwide novelty claim or production-policy change. All earlier studies and negative results remain intact. + + + Read the [updated full paper](manuscript/paper-current.md), [PDF](manuscript/paper-current.pdf), or [HTML](manuscript/paper-current.html). The fresh full Jev run does **not** reproduce the original interval-based entity improvement: its selected-minus-baseline macro-F1 interval includes zero. Entity accuracy still rises descriptively from 405/413 to 408/413; relation accuracy rises from 286/339 to 290/339, also without a resolved macro-F1 effect. This repeats previously inspected data; it is not independent validation. diff --git a/graph_synthesis/frontier/IMPLEMENTATION.md b/graph_synthesis/frontier/IMPLEMENTATION.md new file mode 100644 index 0000000..c19c4ad --- /dev/null +++ b/graph_synthesis/frontier/IMPLEMENTATION.md @@ -0,0 +1,19 @@ +# Implementation and execution record + +The protocol was committed before implementation/execution as `ef8fc308d0a50cd37a6cdd3b7546e050093349c8`, against main `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Seed, populations, comparison methods, budgets and primary endpoints were not retuned after results were observed. These are exploratory extensions informed by published outcomes, not independent preregistration. + +The first local execution produced H1=true, H2=false, H3=true, H4=true, H5=true. Subsequent validation tightened malformed atom-ID handling to reject mixed string/nonstring primitive IDs with ValueError before sorting. This does not change any valid-input benchmark output. All 75 new regression tests pass. Complete numerical replay and archived-inventory verification are performed separately; their exact outputs are in the committed logs. + +H2 is retained as a negative result even though the modeled optimization objective improves over one comparator. No threshold, detector assumption or cost mapping was chosen using its evaluation outcomes. Controls retain errors under misspecified source metadata, incorrectly prioritized review risks, false high-priority graph assertions, unsupported graph topologies and exhausted state budgets. + +`execution.log` is actual execution output. `tests.log` is actual unittest output, including its environment-dependent elapsed time. `replay.log` is the recorded deterministic numerical replay output. `original-inventory.json` is the archived-evidence verifier result. `artifact-manifest.json` binds extension bytes; the current manuscript uses its independent `.build.json` source/renderer/PDF record. The result journal binds the full generating specifications and source/code hashes; it does not claim provider-signed provenance. + +No new Jev requests, credentials, network model execution, production graph writes or default compiler changes are made. The renderer uses repository-local image assets only. Python/numpy numeric replay uses the repository's existing explicit float tolerances; SVG/PNG and Markdown regeneration is checked byte-for-byte under pinned rendering dependencies. + +Concurrent main integration pinned `a21314d17c33c639b75522ecce120586ef8dab35`. Both intervening studies, their evidence and protocols are preserved. Shared manuscript and renderer conflicts are resolved additively; H4 overlap is explicitly disclosed in the novelty audit and paper. Benchmark code, frozen protocol, fixtures and target indicators remain unchanged. The expanded combined regression count is recorded by all-regressions.json after integration. + +The first integrated execution passed all 726 regressions but its CI-token push was rejected for workflow permission. The authorized connected GitHub action then applied the workflow union and genuine two-parent merge; subsequent CI publishing changes research artifacts only. No credential permissions were escalated. + +Final integration preserves PRs #18, #19, #24 and #21 at `114450ef08736c21c97c8725de700d8d2c6ca94d`, including their requirements and all evidence. The full regression runner includes all their tests. The reporting anchor follows the latest supported refinement study, before the historical follow-up boundary. The renderer from current main is preserved unchanged because its generic marker-removal helper already handles the frontier section. Frozen benchmark implementations, protocol, fixture seeds and target indicators remain unchanged. + +Integration validation recorded 772 passing tests and one manuscript-regeneration failure in the first 773-test run. Choosing the last physical research marker incorrectly placed the new section after the historical follow-up, whose unchanged writer relocates itself immediately before the original-study boundary. The correction selects the latest refinement predecessor, preserving the historical follow-up position. The existing failing regression remains unchanged and must pass before publication. No research result or acceptance threshold changed. diff --git a/graph_synthesis/frontier/NOVELTY.md b/graph_synthesis/frontier/NOVELTY.md new file mode 100644 index 0000000..36a576e --- /dev/null +++ b/graph_synthesis/frontier/NOVELTY.md @@ -0,0 +1,43 @@ +# Novelty boundary and prior-art audit + +**Claim:** these five bounded mechanisms and their specified experiments are new extensions relative to repository main `a62a3257645d8e35cd4e45be53bfa9511d27724b` (PR #17). **Not claimed:** that Bayesian marginalization, noisy cost-aware review, decision diagrams, bipartite min-cut or segment-tree dynamic programming were never attempted anywhere before. No public-source search can establish an absence of unpublished work. The research does not advertise established algorithms as inventions. + +## Repository comparison + +The full pinned source snapshot was inspected, with semantic review of the preceding follow-up, adaptive, risk-control and reliability protocols, reports and imported implementations. `novelty-audit.json` records the inspected baseline Python/Markdown file hashes and a reproducible term-search audit. The terms `latent`, `bipartite`, `decision diagram`, `segment tree` and `knapsack` had no matches in that scope. Review-cost and false-removal terms did have matches; those are explicitly **not** novel subject areas here. A term absence alone is not evidence that an equivalent algorithm is absent, so the mechanism distinctions below, not keywords alone, define the scoped claim. + +| Extension | Prior implementation | Difference actually tested | +|---|---|---| +| H1 | Independent primitive events with shared proof atoms; corrupted-lineage negative controls | Distinct atoms assigned to shared binary source-quality variables, conditional event models, explicit latent-state marginalization, independent joint-world oracle | +| H2 | Development-fitted group review, unit edge budgets, exact/greedy review and homogeneous sensitivity analyses | Cost-heterogeneous budget packing, cost-dependent detection, correct-edge-loss penalty, per-group subset options and cross-group budget allocation; measured expected downstream gain fails | +| H3 | Per-evaluation Shannon expansion capped at sixteen atoms per connected component | Probability-independent reduced decision diagram with an explicit residual-state budget, larger structured connected formulas and reusable evaluation after probability updates | +| H4 | Forest and four-vertex cycle-cutset optimization with small subset fallback | Bipartite recognition, priority-weighted flow reduction, separate primal/dual certificate verification and exact large multicycle fixtures | +| H5 | Whole-component snapshot cache; zero savings on connected update workloads | Fixed connected-path transfer-matrix segment tree; weight-only logarithmic summary updates, with separately linear witness decoding | + +These mechanisms were selected from documented limits in PR #17 rather than from an unobserved new corpus. The protocol preceded this extension's implementation and execution, but prior outcomes were already known. The work remains exploratory. + +## External primary-source checks (September 18, 2026) + +The search examined source-quality models, probabilistic provenance/knowledge compilation, annotation costs, bipartite independent-set optimization and dynamic programming. It was a targeted related-work check, not a systematic novelty certification or patent search. + +[Zhao, Rubinstein, Gemmell and Han (2012), A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration](https://arxiv.org/abs/1203.0058) models source quality with probabilistic inference. H1 does not invent source-quality modeling, and unlike that system does not infer source reliabilities from real multi-source datasets. + +[Fink, Han and Olteanu (2012), Aggregation in Probabilistic Databases via Knowledge Compilation](https://arxiv.org/abs/1201.6569) establishes compilation-based probabilistic query evaluation. [Gaur, Dang, Bhattacharya and Bedathur (2021), Computing and Maintaining Provenance of Query Result Probabilities in Uncertain Knowledge Graphs](https://arxiv.org/abs/2108.07758) includes probabilistic provenance maintenance and adaptive use of compilation. H3 is a bounded implementation tested against this repository's old evaluator, not a new knowledge-compilation paradigm or a benchmark against those systems. [Beame and Liew (2015), New Limits for Knowledge Compilation and Applications to Exact Model Counting](https://arxiv.org/abs/1506.02639) reinforces why efficient tested cases do not imply universally small compiled representations. + +[Settles, Craven and Friedland (2008), Active Learning with Real Annotation Costs](https://burrsettles.com/pub/settles.nips08ws.pdf) studies heterogeneous annotation costs. H2 uses explicitly assumed proxy effort and reviewer error probabilities rather than measured annotation costs, and its frozen downstream target fails. Earlier repository reviewer-noise controls are retained and acknowledged. + +[Doron-Arad and Shachnai (2023), Tight Bounds for Budgeted Maximum Weight Independent Set in Bipartite and Perfect Graphs](https://arxiv.org/abs/2307.08592) distinguishes ordinary polynomial-time bipartite maximum-weight independent set from hard budget-constrained variants. H4 uses the ordinary problem; it does not solve the harder budgeted variant. Its min-cut correctness argument and checkable flow/cut certificate are included in the report rather than attributed as a novel reduction. + +[Gupta et al. (2023), Fast Dynamic Programming in Trees in the MPC Model](https://arxiv.org/abs/2305.03693) is additional evidence of an established literature on tree dynamic programming. It operates in a different parallel model, is not the origin claimed for H5's segment-tree construction, and is not an empirical baseline here. H5's tested combination is a deliberately narrow fixed-path incremental utility interface within this repository. + +## What would be needed for stronger claims + +A first-ever scientific-method claim would need much broader systematic literature review and a precise distinction from equivalent existing formulations; it is not made. A semantic-improvement claim would need newly collected source-disjoint adjudicated graph truth, prospective frozen policies, validated source/reviewer models, appropriate uncertainty analysis and matched-budget external-system baselines. This extension supplies executed, falsifiable engineering tests and transparent scope, not those unperformed studies. + +## Concurrent integration update + +**Concurrent integration disclosure.** While this frozen extension was executing, main advanced to `a21314d17c33c639b75522ecce120586ef8dab35` with the [structural refinement](../structural/RESULTS.md) and [source-structural](../source_structural/RESULTS.md) studies. Both are preserved unchanged. The source-structural study also implements certified bipartite optimization: H4 here is therefore a parallel implementation and additional frozen test suite, not a mechanism unique to the reconciled main branch. Its frontier-width lineage evaluator overlaps H3's goal but differs from the reusable decision-diagram state budget tested here. Source-setup review differs from H2's heterogeneous per-edge effort/noise model; source-revision invalidation differs from H5's connected-path summary maintenance. Repository-new claims are limited to the protocol's original pinned baseline, not the later integration snapshot. No thresholds, fixtures or numerical results were retuned after examining the concurrent studies. Reused semantic captures must not be pooled as independent observations. + +**Further concurrent integration.** Main subsequently advanced through PR #24 to `6530321dd0060c9c7f13bb69d6f1c88346a8e1e2`. Its [assumption-aware study](../assumption_aware/RESULTS.md) is also preserved unchanged. Its dependence-robust probability envelopes address unknown joint dependence rather than H1's supplied latent-source conditional model. Its connected-tree ancestor-message updates overlap H5's objective, but H5 uses a balanced segment tree on a fixed path, with a different update mechanism and counted work metric. These results are parallel extensions of the same pinned baseline, not independent replications, combined semantic observations, or claims of unique worldwide invention. Frozen frontier hypotheses, implementations, fixture seeds and numerical results are unchanged; only additive reporting and combined regression coverage expand. + +**Final integration snapshot.** Main also received PR #21, the [uncertainty and grounding study](../uncertainty/RESULTS.md), at `114450ef08736c21c97c8725de700d8d2c6ca94d`. That study and all three earlier concurrent extensions are preserved unchanged. Its dependence-agnostic bounds and query-loss-directed review further overlap the broad research questions here, but use different supplied-information assumptions and controlled objectives. This frontier study does not claim to be the unique first repository implementation on the reconciled main branch; its dated novelty comparison remains the frozen PR #17 baseline. All frozen numerical outcomes remain unchanged and overlapping captured observations are not pooled. diff --git a/graph_synthesis/frontier/PROTOCOL.md b/graph_synthesis/frontier/PROTOCOL.md new file mode 100644 index 0000000..5f321da --- /dev/null +++ b/graph_synthesis/frontier/PROTOCOL.md @@ -0,0 +1,47 @@ +# Five structural-frontier hypotheses: frozen execution protocol + +Baseline: `a62a3257645d8e35cd4e45be53bfa9511d27724b` (merged PR #17). This protocol is committed before implementation and execution of the five tests. Previously published outcomes informed the hypotheses, so this is exploratory follow-up, not an independent preregistration or fresh holdout. Seed: 20260922. Preserve the immutable original 161-file evidence inventory, all previous results and production policies. Fresh Jev calls in this extension: zero. + +## Scope of novelty and motivation + +The audited baseline contains repeated-call policies, calibration, group-aware ideal review and homogeneous reviewer-sensitivity analyses, independent-primitive lineage bounds and capped Shannon evaluation, forest/cycle-cutset conflict solvers, and whole-component caching. It does not contain the five implementations below. Claim repository-new combinations and falsifiable extensions only, not that established Bayesian marginalization, decision diagrams, knapsack, min-cut or dynamic programming have never been attempted worldwide. The search record and related-work discussion must explicitly retain overlapping prior art. + +PR #17 supplies the motivating counterexamples: grouping errors break independent-source probability, exact lineage stops at 16 atoms, cyclic conflict solving requires a cutset of at most four vertices, review is idealized and equally priced, and component caching saves no solver work on a connected graph. These are separate limitations; do not pool their outcomes into a semantic-success percentage. + +## H1: Latent-source-conditioned evidence probabilities + +Hypothesis: explicitly modeling shared source-quality variables prevents false admissions from distinct but dependent evidence primitives. Supply independent binary latent source states with probabilities q_s. Each primitive is assigned one source and two conditional probabilities p(atom | source=0/1). Primitives are independent only conditional on the latent source states. Marginalize over all source assignments and evaluate the monotone proof DNF conditionally; compare to the previous evaluator using only marginal primitive probabilities. This models declared dependence, not source extraction, causal discovery, or learned document reliability. + +Test 96 seeded fixtures with 2-8 atoms, 1-3 sources, 1-10 nonempty proofs of length at most four. Use independent enumeration of the joint latent-plus-primitive assignment space as oracle. Include a shared-source 0.8 case where two perfect conditional copies should give 0.8, not 0.96. Primary conjunction: zero discrepancies above 1e-12, zero false admissions at threshold 0.95, duplicate/order invariance, and at least one prevented false admission. Support caps: 8 sources and 16 atoms; unsupported inputs return explicit staging, never a point estimate. Retain controls where supposedly independent latent sources actually share an unmodeled common cause; correct arithmetic must still be shown to fail under wrong metadata. Save absolute errors for both AND and OR queries, not just favorable OR cases. + +## H2: Cost- and noise-aware group-review allocation + +Hypothesis: optimizing group contamination reduction under heterogeneous review effort and reviewer mistakes outperforms equal-edge greedy review under an equal effort budget. Reuse the validated 73-development/263-evaluation multicall candidate corpus and the PR #17 single-view development-only risk estimator. No evaluation gold or compact view reaches selection. Supply review cost c_i=min(8, 1+floor(recorded single-arm input tokens/1000)); these are proxy units, not measured minutes or dollars. At assumed sensitivity s0, per-edge detection is s_i=s0/(1+0.05*(c_i-1)); false removal of correct edges is f. + +For selected set A, model group contamination q_g(A)=1-product_i[1-p_i*(1-s_i*I(i in A))]. Optimize the sum of contamination reductions minus expected correctly removed edges (unit penalty). Enumerate each group's review subsets up to 12 candidates, then solve an integer-budget multiple-choice knapsack across groups. Groups over the cap receive no optimized review and must be counted. Keep a no-review option. Comparators: PR #17 single-view group-aware order packed into the same effort budget, and marginal-benefit-per-cost greedy with the same noise model. Record no-review outcomes as well. + +Primary: budget 40, s0=0.75, f=0.01. Require at least 5% lower observed-label expected contaminated groups than BOTH comparators, with at most 0.25 additional expected correct-edge removals versus either. A failure remains a negative result even when modeled utility improves. Secondary budgets 20/80/160; sensitivity s0 in {0.5,0.75,1} crossed with f in {0,0.01,0.05}, refitting no parameters. All selections are recalculated from features and supplied scenarios only. Report 4,000 paired source-group bootstrap draws, seed above, descriptive 95% and 99% intervals at fixed primary selectors. These omit fitting and reviewer-model uncertainty and are not confirmatory or human-study intervals. Verify optimization against exhaustive subset enumeration on 64 small supplied-risk fixtures; retain a deliberately incorrect-risk control. Do not call proxy-effort savings measured service or human savings. + +## H3: Compile-once, state-bounded lineage decision diagrams + +Hypothesis: compilation size, rather than a blanket 16-atom limit, permits exact large structured lineage and reusable probability evaluation. Canonicalize monotone DNF, choose a deterministic frequency-descending/lexical atom order, memoize residual formulas and merge identical decision nodes. Compile a reduced ordered binary decision diagram once, then evaluate changing supplied independent primitive probabilities bottom-up. Caps: 512 atoms, 4,096 input proofs, 32,768 visited residual states. Exhausted compilation returns conservative previous lineage bounds with an explicit nonexact status; over input caps stage. Never return a partial exact probability. + +Test 128 seeded small formulas against independent exhaustive enumeration, with five probability assignments each. Test shared-fan and adjacent-pair-chain formulas at 17/32/64/128/256 atoms; independent analytic recurrences are their large-case oracles. Evaluate 64 probability updates on one compiled 128-atom chain without recompilation. Primary: zero probability discrepancies above 1e-12; all ten large cases exact, whereas the previous capped evaluator remains nonexact on the connected >16-atom formulas; zero duplicate/order-invariance failures. Include an intentionally tiny state budget, empty proof-list semantics, zero/one probabilities, missing/invalid probabilities and input-cap controls. Report node/state counts and compile/evaluation work separately, not service latency or a worst-case polynomial guarantee. H1 models dependence; H3 alone still assumes independent supplied primitive probabilities. + +## H4: Certified bipartite conflict optimization + +Hypothesis: recognizing bipartite conflict components and reducing weighted vertex cover to s-t min-cut extends exact optimization beyond the four-cut-vertex limit. For nonnegative integer priorities, create source-to-left and right-to-sink capacities equal to priorities; conflict arcs have capacity sum(priorities)+1. Return the complement of the resulting minimum vertex cover. Emit a capacity-feasible, flow-conserving maximum-flow/minimum-cut certificate and verify it independently. The optimum is supplied-priority utility, not semantic truth. + +Support bipartite components through 512 vertices; nonbipartite components use the unchanged PR #17 solver and its staging caps. Compare to that actual prior solver and priority-greedy, not a renamed reimplementation. Test 128 random bipartite graphs up to 12 vertices against independent subset enumeration; 64 general graphs for no prior-utility regression; five complete-bipartite fixtures of 18/32/64/128/256 vertices and four unit-weight grids (4x8,8x8,8x16,16x16) against analytic optima. Primary: zero optimum/certificate/consistency errors, no small-case utility regression, and all nine large supported cases solved exactly. Include tampered-certificate rejection, odd cycles, a dense odd clique, zero weights, duplicate edges, invalid endpoints and the existing false-high-priority semantic negative control. + +## H5: Connected-path incremental optimization + +Hypothesis: a max-plus segment tree maintains the optimal independent-set utility of a fixed connected path after local priority updates without resolving the whole component. Each vertex contributes the two-state skip/take transfer matrix; internal nodes compose matrices. Validate topology once at construction, enforce nonnegative integer single-vertex updates atomically, and query the root optimum. Full selected-set materialization is separately decoded and remains linear; topology changes require rebuilding, not a misleading constant-time update. + +Test 64 random paths (1-64 vertices) with 16 updates each, and a 1,024-vertex path with 256 seeded updates. Compare every optimum and decoded independent-set witness against full linear dynamic programming. Primary: zero utility/witness discrepancies and at least 90% fewer scalar max-plus transition candidates on the large workload, including cold construction. Count eight candidates per internal 2x2 matrix multiplication versus three per vertex in full path DP; also report witness decoding visits and validation work separately. Include repeated/same-value updates, zero weights, invalid update rollback, unknown vertices, nonpath/star/cycle rejection and rebuilt topology. Do not equate this summary-maintenance saving with full graph emission, total CPU, database I/O or service latency. + +## Execution, reporting and claim boundaries + +Freeze target indicators before running. Save every random fixture or its full deterministic generating specification, primary selections, comparisons, controls, input hashes and implementation hashes. Use isolated no-network execution. Check exact numerical replay and the original inventory; test invalid inputs and gold separation. Regenerate five figures, machine-readable results, summary tables, a full extension report and the complete current Markdown/HTML/PDF manuscript. Preserve preceding study sections and their failures. Report any coding corrections without changing frozen endpoints. Do not promote any experimental method to the production compiler. + +Related foundations to verify and cite: TypeSafe introduction (https://docs.typesafe.ai/introduction); Fink, Han and Olteanu, Aggregation in Probabilistic Databases via Knowledge Compilation (https://arxiv.org/abs/1201.6569); Gaur et al., Computing and Maintaining Provenance of Query Result Probabilities in Uncertain Knowledge Graphs (https://arxiv.org/abs/2108.07758); Settles, Craven and Friedland, Active Learning with Real Annotation Costs (https://burrsettles.com/pub/settles.nips08ws.pdf); Doron-Arad and Shachnai, Tight Bounds for Budgeted Maximum Weight Independent Set in Bipartite and Perfect Graphs (https://arxiv.org/abs/2307.08592). The latter distinguishes ordinary polynomial bipartite MWIS from additional-budget variants; H4 adds no knapsack constraint. diff --git a/graph_synthesis/frontier/RESULTS.md b/graph_synthesis/frontier/RESULTS.md new file mode 100644 index 0000000..f1fa636 --- /dev/null +++ b/graph_synthesis/frontier/RESULTS.md @@ -0,0 +1,161 @@ +# Structural frontiers for evidence-preserving Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five frozen extensions test whether explicit source dependence, heterogeneous review assumptions and structural recognition address limitations exposed by PR #17. Latent-source marginalization matches an independent joint-world oracle on 96 supplied-model fixtures and prevents 9 false threshold admissions made by the independent-marginal comparator. Cost- and noise-aware review allocation fails its frozen downstream target: at 40 proxy effort units it leaves 13.043478 expected contaminated source groups, identical to both comparison policies. Compile-once decision diagrams exactly evaluate ten connected lineage formulas with 17–256 primitives beyond the previous blanket component cap. Certified bipartite optimization attains nine analytic large-graph optima previously staged. A fixed-path segment tree reduces counted summary-maintenance transitions by 96.47% across a 1,024-vertex, 256-update workload, excluding separately reported linear witness decoding. These are bounded engineering results, not new Jev semantic-accuracy evidence or proof of worldwide algorithmic novelty. + +## 1. Motivation, novelty scope and frozen design + +The [preceding reliability study](../reliability/RESULTS.md) found that better average calibration bias need not improve Brier score; source identity errors invalidate independent-event probabilities; capped exact solvers leave some structured cases unresolved; review effectiveness depends on assumptions; and whole-component caching provides no solver saving on a connected graph. The five present mechanisms address these particular limitations rather than adding more correlated Jev calls. TypeSafe's typed outputs and probabilities do not themselves establish graph truth ([TypeSafe documentation](https://docs.typesafe.ai/introduction)). + +The [protocol](PROTOCOL.md) was committed as `ef8fc308d0a50cd37a6cdd3b7546e050093349c8` before implementation and benchmark execution, against `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Prior results were inspected and informed hypothesis selection. This is exploratory follow-up, not independent preregistration. H2 reuses 73 development candidates in 40 groups and 263 evaluation candidates in 149 groups. These are already published observations, not a new holdout. **Fresh service calls: 0.** Raw requests, response parsing, group separation and input hashes are checked before scoring. The other four hypotheses evaluate supplied models or graph structures, not newly extracted scientific facts. + +| Hypothesis | Proposed extension | Frozen target | Evidence class | +|---|---|---|---| +| H1 | Latent-source-conditioned lineage | Met | Controlled algorithm | +| H2 | Cost/noise-aware group review | Not met | Replay + reviewer assumptions | +| H3 | Reusable state-bounded lineage diagrams | Met | Controlled algorithm | +| H4 | Certified bipartite conflict optimization | Met | Controlled algorithm | +| H5 | Connected-path incremental optimization | Met | Controlled algorithm | + +“Met” refers only to the predeclared conjunction on its specified fixture population. These rows must not be pooled into a semantic-success percentage. The [novelty audit](NOVELTY.md) distinguishes repository-new implementations from established source modeling, knowledge compilation, flow optimization and dynamic programming. It does not assert that no person has ever tried an equivalent idea. + +**Concurrent integration disclosure.** While this frozen extension was executing, main advanced to `a21314d17c33c639b75522ecce120586ef8dab35` with the [structural refinement](../structural/RESULTS.md) and [source-structural](../source_structural/RESULTS.md) studies. Both are preserved unchanged. The source-structural study also implements certified bipartite optimization: H4 here is therefore a parallel implementation and additional frozen test suite, not a mechanism unique to the reconciled main branch. Its frontier-width lineage evaluator overlaps H3's goal but differs from the reusable decision-diagram state budget tested here. Source-setup review differs from H2's heterogeneous per-edge effort/noise model; source-revision invalidation differs from H5's connected-path summary maintenance. Repository-new claims are limited to the protocol's original pinned baseline, not the later integration snapshot. No thresholds, fixtures or numerical results were retuned after examining the concurrent studies. Reused semantic captures must not be pooled as independent observations. + +**Further concurrent integration.** Main subsequently advanced through PR #24 to `6530321dd0060c9c7f13bb69d6f1c88346a8e1e2`. Its [assumption-aware study](../assumption_aware/RESULTS.md) is also preserved unchanged. Its dependence-robust probability envelopes address unknown joint dependence rather than H1's supplied latent-source conditional model. Its connected-tree ancestor-message updates overlap H5's objective, but H5 uses a balanced segment tree on a fixed path, with a different update mechanism and counted work metric. These results are parallel extensions of the same pinned baseline, not independent replications, combined semantic observations, or claims of unique worldwide invention. Frozen frontier hypotheses, implementations, fixture seeds and numerical results are unchanged; only additive reporting and combined regression coverage expand. + +**Final integration snapshot.** Main also received PR #21, the [uncertainty and grounding study](../uncertainty/RESULTS.md), at `114450ef08736c21c97c8725de700d8d2c6ca94d`. That study and all three earlier concurrent extensions are preserved unchanged. Its dependence-agnostic bounds and query-loss-directed review further overlap the broad research questions here, but use different supplied-information assumptions and controlled objectives. This frontier study does not claim to be the unique first repository implementation on the reconciled main branch; its dated novelty comparison remains the frozen PR #17 baseline. All frozen numerical outcomes remain unchanged and overlapping captured observations are not pooled. + +## 2. H1: Source-conditioned evidence probability + +Distinct evidence atoms can still depend on a common unreliable source. Instead of treating their marginal probabilities as independent, the proposed model supplies a binary quality state for each source, a prior for that state, and two conditional probabilities for each assigned atom. Source states are assumed mutually independent; primitive events are independent only conditional on those states. For a monotone proof formula F, compute P(F) = sum_z P(z) P(F given z). Each conditional formula uses the existing bounded exact lineage evaluator. This is exact marginalization within the supplied model, not discovery of the correct dependence structure. + +The 96 seeded fixtures contain 2–8 primitives and 1–3 sources. The independent oracle enumerates joint source-plus-primitive assignments, rather than calling the proposed marginalization/evaluation routine. There are **0 oracle discrepancies above 1e-12**, **0 failures in 288 order/duplicate checks**, and **0 proposed false admissions at 0.95**. Independent marginals make 9 false admissions on these random fixtures. Mean absolute probability error is 0.055531 for independent marginals and 1.31e-16 for the supplied dependence model. + +| Shared-source query | Independent marginals | Conditioned model | Joint-world truth | +|---|---:|---:|---:| +| At least one of two copies (OR) | 0.96 | 0.80 | 0.80 | +| Both copies (AND) | 0.64 | 0.80 | 0.80 | + +The OR control prevents an unjustified 0.95 admission; the AND control shows that dependence errors do not always inflate probability. The target is **met**. Inputs over eight sources or sixteen primitives are explicitly staged with [0,1], not assigned an exact probability. Conditional evaluator exhaustion retains conservative bounds. + +![H1. Declared common-source dependence corrects both OR inflation and AND deflation on the supplied two-copy control. These are model probabilities, not observed Jev accuracies.](figures/01_source_dependence.png) + +**Assumption-breaking control:** two declared independent latent sources that actually share one cause still yield 0.96 when the actual probability is 0.80. The extension therefore moves the necessary independence assumption to the source layer; it does not remove it. Primitive reliabilities and source assignments are fixture inputs, not calibrated model scores or independently adjudicated provenance. Bayesian source-quality modeling already has substantial prior art, including [Zhao et al. (2012)](https://arxiv.org/abs/1203.0058); this experiment does not reproduce or outperform their truth-discovery system. + +## 3. H2: Review allocation under effort and reviewer errors + +The proposed allocator uses the prior study's development-only single-view risk estimator. Evaluation gold, compact-view predictions and observed review outcomes are unavailable to selection. The supplied per-edge effort is min(8, 1 + floor(recorded single-call input tokens / 1000)). This is a bounded proxy, not measured annotation time, cognitive difficulty, monetary cost or new service expenditure. At base detection s0, detection for cost c is s0 / (1 + 0.05(c−1)); a supplied false-removal probability applies to correct reviewed edges. + +For selected review IDs A, the modeled source-group contamination is 1 − product_i[1 − p_i(1 − s_i I(i in A))]. The objective is total modeled group cleaning minus expected correct-edge removals with unit penalty. Enumerate subsets within each group of at most twelve candidates, then allocate across groups using integer-budget multiple-choice knapsack. Larger groups receive no optimized review; **0 primary groups exceed the cap**. No-review remains an available option. The baselines are the actual previous group-aware order packed into the same budget, and marginal-model-benefit-per-cost greedy. Equal budgets do not require identical spending. + +The fixed acceptance population contains 150 single-view positive judgments: 132 correct and 18 wrong, spread across 15 contaminated groups before review. This is not the earlier study's separate matched-volume top-145 comparison. Primary scoring uses all 149 evaluation source groups, including groups with no accepted edges. The reviewer simulation removes an actually wrong edge with its supplied detection probability and removes a correct one with its supplied false-removal probability. No human reviewed these edges in this experiment. + +The frozen primary scenario is budget 40, s0 = 0.75 and false-removal probability 0.01. The target requires at least 5% fewer expected contaminated groups than **both** comparators, while allowing at most 0.25 additional expected correct-edge removals versus either. + +| Policy | Effort spent | Edges reviewed | Expected contaminated groups | Expected correct removed | +|---|---:|---:|---:|---:| +| no review | 0 | 0 | 15.000000 | 0.0000 | +| optimal | 40 | 10 | 13.043478 | 0.0600 | +| packed prior | 37 | 9 | 13.043478 | 0.0600 | +| ratio greedy | 40 | 10 | 13.043478 | 0.0600 | + +The target is **not met**. All three review policies have the same primary expected contamination and correct-edge loss. The optimizer's modeled gain (3.078385) exceeds packed prior (3.060146) but ties ratio greedy (3.078385). Improved optimization of an imperfect model is not a demonstrated graph-quality gain. There are 0 objective discrepancies on 64 independent finite-enumeration checks; solver correctness does not rescue the empirical target. + +![H2. Frozen budget-40 reviewer scenario. All three selectors tie on expected source contamination despite different selected IDs and effort spending. Values are scenario expectations, not observed human outcomes.](figures/02_noisy_review.png) + +All 36 combinations of budgets 20/40/80/160 and detection/false-removal scenarios are retained in results.json; selection is recalculated from features and supplied assumptions only. The 4,000 paired group-bootstrap draws at fixed primary selections give a 95% and 99% proposed-minus-comparator contamination-rate interval of [0,0] for both comparisons because their per-group expected outcomes are identical. **This degeneracy is not evidence of known population equivalence.** These descriptive intervals omit model-fitting and reviewer-model uncertainty, and published-data reuse prevents independent confirmation. A deliberately incorrect-risk control spends its only review on an actually correct edge and misses the wrong one. Prior research already demonstrates the relevance of real annotation costs ([Settles et al., 2008](https://burrsettles.com/pub/settles.nips08ws.pdf)); our token-based proxy is not a measurement of those costs. + +## 4. H3: Compile once, bound by decision-diagram state count + +The previous evaluator rejects connected lineage components above sixteen atoms even when their Boolean structure is simple. The proposed compiler canonicalizes a monotone disjunction of conjunctions, chooses frequency-descending/lexical atom order, memoizes residual formulas and merges identical decision nodes. A compiled reduced ordered binary decision diagram can then be evaluated with changing supplied probabilities without recompiling its structure. Children precede parents, so evaluation is a bottom-up weighted sum. Conditional expansion preserves the represented Boolean function; merging identical subfunctions preserves every probability assignment under independent primitive events. + +The safety boundaries are 512 atoms, 4,096 input proofs and 32,768 visited residual states. Input-cap violations stage. State-budget exhaustion returns conservative previous lineage bounds with an explicit nonexact status, never a partially evaluated point estimate. Compilation remains potentially exponential; these caps are operational safeguards, not a polynomial-time theorem. + +There are 0 probability discrepancies across 128 random formulas with five assignments each, ten analytic large fixtures, 64 updates on one reused 128-atom chain, and the state-cap control. All ten connected large fixtures are exact under the new compiler and nonexact under the actual previous evaluator. Duplicate/input-order checks have 0 failures. + +| Formula | Primitives | Decision nodes | Prior lower bound | Exact probability | +|---|---:|---:|---:|---:| +| fan | 17 | 17 | 0.495 | 0.989984894 | +| fan | 32 | 32 | 0.495 | 0.990000000 | +| fan | 64 | 64 | 0.495 | 0.990000000 | +| fan | 128 | 128 | 0.495 | 0.990000000 | +| fan | 256 | 256 | 0.495 | 0.990000000 | +| chain | 17 | 57 | 0.250 | 0.968101501 | +| chain | 32 | 117 | 0.250 | 0.998672193 | +| chain | 64 | 245 | 0.250 | 0.999998494 | +| chain | 128 | 501 | 0.250 | 1.000000000 | +| chain | 256 | 1013 | 0.250 | 1.000000000 | + +The shared-fan oracle is 0.99(1−0.5^(n−1)). The adjacent-pair chain oracle uses an independent two-state recurrence for the probability of no adjacent successes. The 128-atom reuse workload performs 1 compilation with 501 decision nodes, then 64 changed-probability evaluations totaling 32,064 decision-node visits. The target is **met**. Diagram size, compile-state visits and evaluation-node visits are separate quantities; none is a measured service-latency saving. + +![H3. Decision nodes required for exact connected formulas beyond the previous sixteen-primitive component cap. Lineage probability updates reuse these compiled structures.](figures/03_lineage_capacity.png) + +Knowledge compilation for probabilistic data is established ([Fink et al., 2012](https://arxiv.org/abs/1201.6569)); provenance maintenance and hybrid compilation approaches for uncertain knowledge graphs are also established ([Gaur et al., 2021](https://arxiv.org/abs/2108.07758)). The contribution here is the bounded reusable implementation and its tests against this repository's earlier cap, not invention of decision diagrams or superiority over those systems. H3 still assumes independent primitive inputs; it does not automatically incorporate H1's source model. + +## 5. H4: Certified bipartite conflict optimization + +The cycle-cutset solver in PR #17 can stage a bipartite graph with many cycles even though its supplied-priority maximum independent set has a tractable reduction. The extension first recognizes bipartite components with at most 512 vertices. For bipartition L/R, create source-to-L and R-to-sink capacities equal to nonnegative integer priorities and L-to-R conflict arcs of capacity total priority plus one. A minimum cut cannot profitably cross a conflict arc, since cutting every priority arc is cheaper. Its vertex-side choices therefore give a minimum-weight vertex cover; the complement is a maximum-weight independent set. + +The returned certificate contains a capacity-feasible flow, flow conservation at every internal node, a source/sink separating cut of equal value, and the complementary selected witness. A separate verifier checks these conditions without invoking an optimizer. A feasible flow lower-bounds any cut; equality certifies cut optimality, and the cover reduction certifies the selected supplied-priority objective. Nonbipartite components retain the unmodified prior solver and its staging behavior. No additional budget constraint is imposed; adding such constraints changes the complexity ([Doron-Arad and Shachnai, 2023](https://arxiv.org/abs/2307.08592)). + +There are 0 optimum discrepancies, 0 certificate errors and 0 prior-utility regressions across 128 random bipartite and 64 general small graphs plus nine analytic large fixtures. The large comparator values below come from the actual imported previous solver, not a renamed reimplementation. Zero previous utility means the unsupported component was staged, not that no positive-utility solution exists. + +| Structure | Vertices | Prior utility | Greedy utility | Certified utility | Oracle | +|---|---:|---:|---:|---:|---:| +| complete bipartite | 18 | 0 | 18 | 18 | 18 | +| complete bipartite | 32 | 0 | 32 | 32 | 32 | +| complete bipartite | 64 | 0 | 64 | 64 | 64 | +| complete bipartite | 128 | 0 | 128 | 128 | 128 | +| complete bipartite | 256 | 0 | 256 | 256 | 256 | +| grid | 32 | 0 | 16 | 16 | 16 | +| grid | 64 | 0 | 32 | 32 | 32 | +| grid | 128 | 0 | 64 | 64 | 64 | +| grid | 256 | 0 | 128 | 128 | 128 | + +The target is **met**. Complete bipartite fixtures have side weights one and two, giving optimum total weight on the heavier side. Rectangular unit grids have an independent checkerboard half; a perfect matching gives the corresponding upper bound. Certificate-tampering tests, zero weights, invalid endpoints, odd cycles and a dense 17-clique are retained. The odd clique still stages. Priority greedy also attains all nine large analytic optima; the extension adds an optimality certificate and recovery versus prior staging, not an observed large-fixture utility advantage over greedy. + +![H4. Fraction of analytically optimal supplied-priority utility on nine structured conflict fixtures. Prior staging is zero recovered utility, not extraction failure.](figures/04_bipartite_capacity.png) + +The semantic negative control remains decisive: a false assertion with supplied priority 9 defeats a conflicting true assertion with priority 8. An exact optimizer can be exactly wrong about factual truth when its priorities are wrong. No graph-extraction, clinical-validity or KARMA-superiority claim follows. + +## 6. H5: Connected-path summary maintenance + +Whole-component invalidation cannot exploit a local weight update inside one connected graph. For the deliberately narrower case of a fixed connected path, the proposed data structure stores the skip/take dynamic-programming transition for each vertex in a max-plus segment tree. Two-state transitions compose associatively; a leaf update requires recomputing only its ancestors. The root gives optimal utility without emitting the entire selected graph. Topology is validated at construction. Invalid or unknown-vertex weight updates are rejected before mutation; changing topology requires rebuilding. Stars and cycles are rejected rather than silently treated as paths. + +Across 64 random paths with sixteen updates each and the 1,024-vertex path with 256 updates, there are **0 utility or independent-witness discrepancies** against independent full linear dynamic programming. The primary workload includes cold construction and every update. + +| Work quantity | Count | Interpretation | +|---|---:|---| +| Full recomputation transition candidates | 789,504 | Three scalar candidates per vertex per build/update | +| Segment-tree transition candidates | 27,864 | Eight per internal 2-by-2 composition, including cold build | +| Segment summary-work reduction | 96.47% | Only the two transition-candidate counts above | +| Cold topology validation visits | 3,070 | Vertex plus adjacency visits, separately recorded | +| Full witness-decoding node visits | 526,079 | Every output decoded for validation; remains linear | + +The frozen 90% counted summary-work reduction target is **met**. The 27,864 versus 789,504 transition comparison is not a total runtime or database-I/O measurement. Witness decoding adds 526,079 tree-node visits under a different operation metric and is explicitly not included in that percentage. A consumer needing the complete selected graph after every update still pays linear output/decoding work. Python overhead, memory traffic, initial sorting, dictionary operations and persistence are not captured by scalar transition counts. + +![H5. Counted optimal-utility summary maintenance on a fixed connected path, including cold build. Full witness decoding is separately reported and excluded from the percentage.](figures/05_path_work.png) + +## 7. Interpretation and next falsification boundary + +The results favor identifying the tractable structure of a supplied problem over imposing blanket size restrictions: declared dependence changes which probabilities are valid; compact formula structure permits reusable exact evaluation; bipartiteness permits certified optimization despite many cycles; and a fixed path permits local summary updates despite being one connected component. These conclusions are conditional on correct metadata and supported topology. They do not establish that those structures dominate real Jev-generated graphs. + +The review result is a useful negative finding. A more exact allocator can improve a modeled objective yet fail to improve the frozen downstream source-contamination measure. This counsels against promoting the allocator on optimization quality alone. Prospective human-cost/detection measurements, source-disjoint independently adjudicated documents, policies frozen before those labels are seen, and matched evidence/resource budgets against external graph-synthesis systems remain necessary for semantic or deployment claims. H1 and H3 reliability inputs must be estimated and audited separately; raw model confidence cannot simply be substituted. + +The methods remain opt-in research utilities. No default compiler policy or production graph is changed. Failure controls and all prior studies remain in the complete manuscript. Each target is falsifiable on its specified population, but a passed finite benchmark is not a proof of correctness for all inputs or real-world superiority. + +## 8. Reproducibility and audit + +```bash +python -B -m graph_synthesis.frontier.run +python -B -m unittest discover -s graph_synthesis/frontier/tests -v +python -B -m graph_synthesis.frontier.run --check +python -B -m graph_synthesis.frontier.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +python -B -m graph_synthesis.frontier.verify +``` + +The machine-readable results retain source and code hashes, source-group counts, all random fixtures or complete generating specifications, review IDs and scenarios, independent-oracle outputs, flow/cut witnesses, update sequences and decoded-witness hashes. The five figures are generated directly from those results as SVG and PNG pairs; summary.csv preserves target classifications without pooling them. The [implementation log](IMPLEMENTATION.md) records validation corrections without changing the protocol or endpoints. Original evidence integrity is checked against the immutable inventory. 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... ok +test_reviewer_error_escalates (graph_synthesis.adaptive.tests.test_adaptive.RoutingTests.test_reviewer_error_escalates) ... ok +test_unknown_usage_rejected (graph_synthesis.adaptive.tests.test_adaptive.RoutingTests.test_unknown_usage_rejected) ... ok +test_all_views_charged (graph_synthesis.adaptive.tests.test_adaptive.VotingTests.test_all_views_charged) ... ok +test_duplicate_view_cluster_rejected (graph_synthesis.adaptive.tests.test_adaptive.VotingTests.test_duplicate_view_cluster_rejected) ... ok +test_flat_majority (graph_synthesis.adaptive.tests.test_adaptive.VotingTests.test_flat_majority) ... ok +test_identical_errors_collapse (graph_synthesis.adaptive.tests.test_adaptive.VotingTests.test_identical_errors_collapse) ... ok +test_invalid_component_is_error (graph_synthesis.adaptive.tests.test_adaptive.VotingTests.test_invalid_component_is_error) ... ok +test_mixed_fit_rejected (graph_synthesis.adaptive.tests.test_adaptive.VotingTests.test_mixed_fit_rejected) ... ok 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... ok +test_id_tie_deterministic (graph_synthesis.assumption_aware.tests.test_methods.ReviewTests.test_id_tie_deterministic) ... ok +test_invalid_risk_exposure_and_duplicate (graph_synthesis.assumption_aware.tests.test_methods.ReviewTests.test_invalid_risk_exposure_and_duplicate) ... ok +test_no_gold_access_and_no_mutation (graph_synthesis.assumption_aware.tests.test_methods.ReviewTests.test_no_gold_access_and_no_mutation) ... ok +test_uniform_matches_existing_strong_baseline (graph_synthesis.assumption_aware.tests.test_methods.ReviewTests.test_uniform_matches_existing_strong_baseline) ... ok +test_weights_can_change_priority (graph_synthesis.assumption_aware.tests.test_methods.ReviewTests.test_weights_can_change_priority) ... ok +test_bad_updates_atomic (graph_synthesis.assumption_aware.tests.test_methods.TreeTests.test_bad_updates_atomic) ... ok +test_copies_input_weights (graph_synthesis.assumption_aware.tests.test_methods.TreeTests.test_copies_input_weights) ... ok 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... ok +test_all_errors_identity (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_all_errors_identity) ... ok +test_argmax_preserved_grid (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_argmax_preserved_grid) ... ok +test_bad_distribution (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_bad_distribution) ... ok +test_bad_probability (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_bad_probability) ... ok +test_bad_temperature (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_bad_temperature) ... ok +test_bad_weight (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_bad_weight) ... ok +test_component_split_disjoint (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_component_split_disjoint) ... ok +test_empty_fit_rejected (graph_synthesis.followup.tests.test_followup.CalibrationTests.test_empty_fit_rejected) ... ok +test_fit_input_unchanged 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(graph_synthesis.structural.tests.test_structural.RankingTests.test_nonpositive_exclusion) ... ok +test_permutation_and_immutability (graph_synthesis.structural.tests.test_structural.RankingTests.test_permutation_and_immutability) ... ok +test_unseen_bin_fallback (graph_synthesis.structural.tests.test_structural.RankingTests.test_unseen_bin_fallback) ... ok +test_bound_order (graph_synthesis.structural.tests.test_structural.ReviewTests.test_bound_order) ... ok +test_bounds (graph_synthesis.structural.tests.test_structural.ReviewTests.test_bounds) ... ok +test_complementarity (graph_synthesis.structural.tests.test_structural.ReviewTests.test_complementarity) ... ok +test_empty_and_full (graph_synthesis.structural.tests.test_structural.ReviewTests.test_empty_and_full) ... ok +test_order_immutability (graph_synthesis.structural.tests.test_structural.ReviewTests.test_order_immutability) ... ok +test_validation (graph_synthesis.structural.tests.test_structural.ReviewTests.test_validation) ... ok +test_audit_detects_corruption (graph_synthesis.tests.test_core.CoreTests.test_audit_detects_corruption) ... ok +test_cascading_retraction_leaves_independent_support (graph_synthesis.tests.test_core.CoreTests.test_cascading_retraction_leaves_independent_support) ... ok +test_commits_are_audited (graph_synthesis.tests.test_core.CoreTests.test_commits_are_audited) ... ok +test_conflict_same_scope_rolls_back_whole_transaction (graph_synthesis.tests.test_core.CoreTests.test_conflict_same_scope_rolls_back_whole_transaction) ... ok +test_decision_binding_detects_endpoint_change (graph_synthesis.tests.test_core.CoreTests.test_decision_binding_detects_endpoint_change) ... ok +test_different_population_qualifiers_remain_distinct (graph_synthesis.tests.test_core.CoreTests.test_different_population_qualifiers_remain_distinct) ... ok +test_direction_preserved_for_asymmetric_relation (graph_synthesis.tests.test_core.CoreTests.test_direction_preserved_for_asymmetric_relation) ... ok +test_evidence_exact_span (graph_synthesis.tests.test_core.CoreTests.test_evidence_exact_span) ... ok +test_evidence_hash_detects_source_change (graph_synthesis.tests.test_core.CoreTests.test_evidence_hash_detects_source_change) ... ok +test_fault_injection_is_atomic (graph_synthesis.tests.test_core.CoreTests.test_fault_injection_is_atomic) ... ok +test_graph_metrics_do_not_confuse_edges_with_assertions (graph_synthesis.tests.test_core.CoreTests.test_graph_metrics_do_not_confuse_edges_with_assertions) ... ok +test_idempotent_replay_and_payload_conflict (graph_synthesis.tests.test_core.CoreTests.test_idempotent_replay_and_payload_conflict) ... ok +test_identity_bridge_conflict_rejected (graph_synthesis.tests.test_core.CoreTests.test_identity_bridge_conflict_rejected) ... ok +test_identity_cannot_create_forbidden_self_relation (graph_synthesis.tests.test_core.CoreTests.test_identity_cannot_create_forbidden_self_relation) ... ok +test_identity_retraction_splits_view_without_data_loss (graph_synthesis.tests.test_core.CoreTests.test_identity_retraction_splits_view_without_data_loss) ... ok +test_inverse_is_normalized_not_a_second_semantic_relation (graph_synthesis.tests.test_core.CoreTests.test_inverse_is_normalized_not_a_second_semantic_relation) ... ok +test_low_probability_defers_without_mutation (graph_synthesis.tests.test_core.CoreTests.test_low_probability_defers_without_mutation) ... ok +test_missing_dependency_and_cycle_reject (graph_synthesis.tests.test_core.CoreTests.test_missing_dependency_and_cycle_reject) ... ok +test_missing_endpoint_and_wrong_type (graph_synthesis.tests.test_core.CoreTests.test_missing_endpoint_and_wrong_type) ... ok +test_multi_label_relations_can_coexist (graph_synthesis.tests.test_core.CoreTests.test_multi_label_relations_can_coexist) ... ok +test_pairwise_selection_has_abstention (graph_synthesis.tests.test_core.CoreTests.test_pairwise_selection_has_abstention) ... ok +test_persistent_reopen_and_competing_writer (graph_synthesis.tests.test_core.CoreTests.test_persistent_reopen_and_competing_writer) ... ok +test_polarity_cannot_create_positive_assertion (graph_synthesis.tests.test_core.CoreTests.test_polarity_cannot_create_positive_assertion) ... ok +test_policy_must_cover_contracts (graph_synthesis.tests.test_core.CoreTests.test_policy_must_cover_contracts) ... ok +test_probability_contract_failures (graph_synthesis.tests.test_core.CoreTests.test_probability_contract_failures) ... ok +test_random_dependency_retraction_matches_reference (graph_synthesis.tests.test_core.CoreTests.test_random_dependency_retraction_matches_reference) ... ok +test_retracted_assertion_id_cannot_be_reused (graph_synthesis.tests.test_core.CoreTests.test_retracted_assertion_id_cannot_be_reused) ... ok +test_schema_migration_checks_existing_assertions (graph_synthesis.tests.test_core.CoreTests.test_schema_migration_checks_existing_assertions) ... ok +test_schema_migration_requires_review (graph_synthesis.tests.test_core.CoreTests.test_schema_migration_requires_review) ... ok +test_source_withdrawal_preserves_other_source (graph_synthesis.tests.test_core.CoreTests.test_source_withdrawal_preserves_other_source) ... ok +test_stale_graph_and_policy_reject (graph_synthesis.tests.test_core.CoreTests.test_stale_graph_and_policy_reject) ... ok +test_symmetric_relation_normalization (graph_synthesis.tests.test_core.CoreTests.test_symmetric_relation_normalization) ... ok +test_synthetic_scores_require_explicit_opt_in (graph_synthesis.tests.test_core.CoreTests.test_synthetic_scores_require_explicit_opt_in) ... ok +test_tied_pair_scores_do_not_force_an_identity (graph_synthesis.tests.test_core.CoreTests.test_tied_pair_scores_do_not_force_an_identity) ... ok +test_transitivity_annotation_does_not_invent_edges (graph_synthesis.tests.test_core.CoreTests.test_transitivity_annotation_does_not_invent_edges) ... ok +test_unknown_predicate_fails_closed (graph_synthesis.tests.test_core.CoreTests.test_unknown_predicate_fails_closed) ... ok +test_component_bootstrap_pairs_and_undefined_precision (graph_synthesis.tests.test_edge_fusion.MetricTests.test_component_bootstrap_pairs_and_undefined_precision) ... ok +test_denominators_wrong_polarity_and_errors (graph_synthesis.tests.test_edge_fusion.MetricTests.test_denominators_wrong_polarity_and_errors) ... ok +test_reference_comparison_does_not_hide_count_or_label_changes (graph_synthesis.tests.test_edge_fusion.MetricTests.test_reference_comparison_does_not_hide_count_or_label_changes) ... ok +test_zero_edges_and_empty_classes (graph_synthesis.tests.test_edge_fusion.MetricTests.test_zero_edges_and_empty_classes) ... ok +test_features_handle_exact_zero_one_and_constant_scales (graph_synthesis.tests.test_edge_fusion.NumericTests.test_features_handle_exact_zero_one_and_constant_scales) ... ok +test_fit_converges_and_probabilities_are_valid (graph_synthesis.tests.test_edge_fusion.NumericTests.test_fit_converges_and_probabilities_are_valid) ... ok +test_gradient_matches_finite_difference (graph_synthesis.tests.test_edge_fusion.NumericTests.test_gradient_matches_finite_difference) ... ok +test_invalid_features_rejected (graph_synthesis.tests.test_edge_fusion.NumericTests.test_invalid_features_rejected) ... ok +test_training_validation (graph_synthesis.tests.test_edge_fusion.NumericTests.test_training_validation) ... ok +test_changed_constituent_response_changes_binding (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_changed_constituent_response_changes_binding) ... ok +test_disagreement_is_abstention_not_nei (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_disagreement_is_abstention_not_nei) ... ok +test_gold_and_groups_are_not_prediction_features (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_gold_and_groups_are_not_prediction_features) ... ok +test_immutable_baselines_and_mean (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_immutable_baselines_and_mean) ... ok +test_input_and_model_changes_rebind_provenance (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_input_and_model_changes_rebind_provenance) ... ok +test_invalid_constituent_is_error_not_fallback (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_invalid_constituent_is_error_not_fallback) ... ok +test_malformed_model_fails_closed (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_malformed_model_fails_closed) ... ok +test_selection_rejects_evaluation_and_duplicate_rows (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_selection_rejects_evaluation_and_duplicate_rows) ... ok +test_ties_have_canonical_label_and_id_order (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_ties_have_canonical_label_and_id_order) ... ok +test_unknown_arm_and_missing_model_fail (graph_synthesis.tests.test_edge_fusion.PolicyTests.test_unknown_arm_and_missing_model_fail) ... ok +test_actual_populations_and_disjointness (graph_synthesis.tests.test_edge_fusion.ReplayTests.test_actual_populations_and_disjointness) ... ok +test_fold_preprocessing_excludes_held_groups (graph_synthesis.tests.test_edge_fusion.ReplayTests.test_fold_preprocessing_excludes_held_groups) ... ok +test_graph_and_audit_independent_of_evaluation_gold (graph_synthesis.tests.test_edge_fusion.ReplayTests.test_graph_and_audit_independent_of_evaluation_gold) ... ok +test_grouped_selection_uses_only_calibration (graph_synthesis.tests.test_edge_fusion.ReplayTests.test_grouped_selection_uses_only_calibration) ... ok +test_journal_determinism_and_true_call_binding (graph_synthesis.tests.test_edge_fusion.ReplayTests.test_journal_determinism_and_true_call_binding) ... ok +test_original_point_metrics_reproduced (graph_synthesis.tests.test_edge_fusion.ReplayTests.test_original_point_metrics_reproduced) ... ok +test_real_complete_neighborhood_tradeoff (graph_synthesis.tests.test_falsification.FrozenDiagnosticTests.test_real_complete_neighborhood_tradeoff) ... ok +test_real_errors_are_shared_not_independent_votes (graph_synthesis.tests.test_falsification.FrozenDiagnosticTests.test_real_errors_are_shared_not_independent_votes) ... ok +test_real_relation_quality_conservation (graph_synthesis.tests.test_falsification.FrozenDiagnosticTests.test_real_relation_quality_conservation) ... ok +test_reference_fresh_call_count_zero (graph_synthesis.tests.test_falsification.FrozenDiagnosticTests.test_reference_fresh_call_count_zero) ... ok +test_declared_type_violation_fails_atomically (graph_synthesis.tests.test_falsification.MechanismTests.test_declared_type_violation_fails_atomically) ... ok +test_identity_bridge_amplification_and_repair (graph_synthesis.tests.test_falsification.MechanismTests.test_identity_bridge_amplification_and_repair) ... ok +test_independent_retraction_oracle (graph_synthesis.tests.test_falsification.MechanismTests.test_independent_retraction_oracle) ... ok +test_invalid_mechanism_sizes (graph_synthesis.tests.test_falsification.MechanismTests.test_invalid_mechanism_sizes) ... ok +test_origin_copy_is_not_new_corroboration (graph_synthesis.tests.test_falsification.MechanismTests.test_origin_copy_is_not_new_corroboration) ... ok +test_overlapping_temporal_scope_is_known_limitation (graph_synthesis.tests.test_falsification.MechanismTests.test_overlapping_temporal_scope_is_known_limitation) ... ok +test_schema_correct_does_not_mean_truth (graph_synthesis.tests.test_falsification.MechanismTests.test_schema_correct_does_not_mean_truth) ... ok +test_twenty_seeded_dag_lifecycle_episodes (graph_synthesis.tests.test_falsification.MechanismTests.test_twenty_seeded_dag_lifecycle_episodes) ... ok +test_withdrawing_one_copy_is_not_origin_wide (graph_synthesis.tests.test_falsification.MechanismTests.test_withdrawing_one_copy_is_not_origin_wide) ... ok +test_all_candidate_endpoints_group_even_failed_rows (graph_synthesis.tests.test_falsification.MetricTests.test_all_candidate_endpoints_group_even_failed_rows) ... ok +test_candidate_loss_keeps_original_denominator (graph_synthesis.tests.test_falsification.MetricTests.test_candidate_loss_keeps_original_denominator) ... ok +test_complete_and_clean_requires_all_gold_edges (graph_synthesis.tests.test_falsification.MetricTests.test_complete_and_clean_requires_all_gold_edges) ... ok +test_empty_graph_is_not_complete_synthesis (graph_synthesis.tests.test_falsification.MetricTests.test_empty_graph_is_not_complete_synthesis) ... ok +test_empty_precision_not_perfect (graph_synthesis.tests.test_falsification.MetricTests.test_empty_precision_not_perfect) ... ok +test_error_is_not_neutral (graph_synthesis.tests.test_falsification.MetricTests.test_error_is_not_neutral) ... ok +test_grouping_does_not_use_gold_or_scores (graph_synthesis.tests.test_falsification.MetricTests.test_grouping_does_not_use_gold_or_scores) ... ok +test_grouping_is_order_invariant (graph_synthesis.tests.test_falsification.MetricTests.test_grouping_is_order_invariant) ... ok +test_identical_arms_have_zero_bootstrap_difference (graph_synthesis.tests.test_falsification.MetricTests.test_identical_arms_have_zero_bootstrap_difference) ... ok +test_invalid_bootstrap_draws (graph_synthesis.tests.test_falsification.MetricTests.test_invalid_bootstrap_draws) ... ok +test_invalid_loss_is_rejected (graph_synthesis.tests.test_falsification.MetricTests.test_invalid_loss_is_rejected) ... ok +test_joint_errors_excludes_operations_from_semantic_denominator (graph_synthesis.tests.test_falsification.MetricTests.test_joint_errors_excludes_operations_from_semantic_denominator) ... ok +test_joint_errors_pair_by_id_not_row_order (graph_synthesis.tests.test_falsification.MetricTests.test_joint_errors_pair_by_id_not_row_order) ... ok +test_joint_errors_zero_marginal_no_infinite_ratio (graph_synthesis.tests.test_falsification.MetricTests.test_joint_errors_zero_marginal_no_infinite_ratio) ... ok +test_missing_class_gives_undefined_projection (graph_synthesis.tests.test_falsification.MetricTests.test_missing_class_gives_undefined_projection) ... ok +test_no_positive_component_is_not_counted_complete (graph_synthesis.tests.test_falsification.MetricTests.test_no_positive_component_is_not_counted_complete) ... ok +test_one_wrong_edge_contaminates_whole_component (graph_synthesis.tests.test_falsification.MetricTests.test_one_wrong_edge_contaminates_whole_component) ... ok +test_pair_validator_rejects_different_candidate (graph_synthesis.tests.test_falsification.MetricTests.test_pair_validator_rejects_different_candidate) ... ok +test_pair_validator_rejects_different_gold (graph_synthesis.tests.test_falsification.MetricTests.test_pair_validator_rejects_different_gold) ... ok +test_pair_validator_rejects_duplicate_ids (graph_synthesis.tests.test_falsification.MetricTests.test_pair_validator_rejects_duplicate_ids) ... ok +test_pair_validator_rejects_missing_ids (graph_synthesis.tests.test_falsification.MetricTests.test_pair_validator_rejects_missing_ids) ... ok +test_planning_rejects_invalid_risk (graph_synthesis.tests.test_falsification.MetricTests.test_planning_rejects_invalid_risk) ... ok +test_prevalence_formula_and_decreasing_base_rate (graph_synthesis.tests.test_falsification.MetricTests.test_prevalence_formula_and_decreasing_base_rate) ... ok +test_quantile_linear_interpolation (graph_synthesis.tests.test_falsification.MetricTests.test_quantile_linear_interpolation) ... ok +test_source_loss_ignores_labels (graph_synthesis.tests.test_falsification.MetricTests.test_source_loss_ignores_labels) ... ok +test_source_loss_nested_and_shared (graph_synthesis.tests.test_falsification.MetricTests.test_source_loss_nested_and_shared) ... ok +test_source_loss_removes_all_candidates_from_source (graph_synthesis.tests.test_falsification.MetricTests.test_source_loss_removes_all_candidates_from_source) ... ok +test_undefined_precision_draws_are_reported (graph_synthesis.tests.test_falsification.MetricTests.test_undefined_precision_draws_are_reported) ... ok +test_wrong_polarity_is_wrong_edge_and_missed_gold (graph_synthesis.tests.test_falsification.MetricTests.test_wrong_polarity_is_wrong_edge_and_missed_gold) ... ok +test_zero_error_planning_is_minimal (graph_synthesis.tests.test_falsification.MetricTests.test_zero_error_planning_is_minimal) ... ok +test_authentication_failure_is_journaled_and_stops_remaining_calls (graph_synthesis.tests.test_live_challenge.LiveChallengeJournalTests.test_authentication_failure_is_journaled_and_stops_remaining_calls) ... {"completed": 1, "total": 48, "status": "error"} +ok +test_existing_run_cannot_be_overwritten (graph_synthesis.tests.test_live_challenge.LiveChallengeJournalTests.test_existing_run_cannot_be_overwritten) ... ok +test_failed_repeat_does_not_disappear (graph_synthesis.tests.test_multicall.MulticallTests.test_failed_repeat_does_not_disappear) ... ok +test_gold_rationale_and_other_answers_absent_from_blind_requests (graph_synthesis.tests.test_multicall.MulticallTests.test_gold_rationale_and_other_answers_absent_from_blind_requests) ... ok +test_majority_is_not_probability_product (graph_synthesis.tests.test_multicall.MulticallTests.test_majority_is_not_probability_product) ... ok +test_selective_threshold_and_required_cost (graph_synthesis.tests.test_multicall.MulticallTests.test_selective_threshold_and_required_cost) ... ok +test_targeted_requires_valid_checks (graph_synthesis.tests.test_multicall.MulticallTests.test_targeted_requires_valid_checks) ... ok +test_three_way_disagreement_abstains (graph_synthesis.tests.test_multicall.MulticallTests.test_three_way_disagreement_abstains) ... ok +test_components_keep_dependent_rows_together (graph_synthesis.tests.test_multicall_analysis.EdgeAccountingTests.test_components_keep_dependent_rows_together) ... ok +test_empty_acceptance_has_undefined_precision (graph_synthesis.tests.test_multicall_analysis.EdgeAccountingTests.test_empty_acceptance_has_undefined_precision) ... ok +test_matched_selection_preserves_denominators (graph_synthesis.tests.test_multicall_analysis.EdgeAccountingTests.test_matched_selection_preserves_denominators) ... ok +test_wrong_polarity_is_false_edge_and_missed_gold (graph_synthesis.tests.test_multicall_analysis.EdgeAccountingTests.test_wrong_polarity_is_false_edge_and_missed_gold) ... ok +test_binomial_numeric_reference (graph_synthesis.tests.test_research.MetricTests.test_binomial_numeric_reference) ... ok +test_binomial_upper_matches_zero_error_closed_form (graph_synthesis.tests.test_research.MetricTests.test_binomial_upper_matches_zero_error_closed_form) ... ok +test_calibration_never_accepts_evaluation_split (graph_synthesis.tests.test_research.MetricTests.test_calibration_never_accepts_evaluation_split) ... ok +test_dependent_calibration_rows_cannot_use_iid_binomial_qualification (graph_synthesis.tests.test_research.MetricTests.test_dependent_calibration_rows_cannot_use_iid_binomial_qualification) ... ok +test_directed_multi_relation_and_component_metrics (graph_synthesis.tests.test_research.MetricTests.test_directed_multi_relation_and_component_metrics) ... ok +test_float_roundoff_is_reported_and_not_mislabeled_bitwise_equal (graph_synthesis.tests.test_research.MetricTests.test_float_roundoff_is_reported_and_not_mislabeled_bitwise_equal) ... ok +test_paired_bootstrap_respects_components_and_no_acceptance (graph_synthesis.tests.test_research.MetricTests.test_paired_bootstrap_respects_components_and_no_acceptance) ... ok +test_precision_false_positive_rate_and_coverage_have_distinct_denominators (graph_synthesis.tests.test_research.MetricTests.test_precision_false_positive_rate_and_coverage_have_distinct_denominators) ... ok +test_small_negative_sample_does_not_certify_deployment (graph_synthesis.tests.test_research.MetricTests.test_small_negative_sample_does_not_certify_deployment) ... ok +test_unmanifested_and_changed_files_are_rejected (graph_synthesis.tests.test_research.MetricTests.test_unmanifested_and_changed_files_are_rejected) ... ok +test_bad_response_contract_rejected (graph_synthesis.tests.test_research.RecordedTests.test_bad_response_contract_rejected) ... ok +test_error_remains_error_without_neutral_probability (graph_synthesis.tests.test_research.RecordedTests.test_error_remains_error_without_neutral_probability) ... ok +test_evaluation_gold_never_enters_current_input (graph_synthesis.tests.test_research.RecordedTests.test_evaluation_gold_never_enters_current_input) ... ok +test_exact_raw_observation_reconstructs (graph_synthesis.tests.test_research.RecordedTests.test_exact_raw_observation_reconstructs) ... ok +test_exported_graph_contains_sources_and_durable_lifecycle (graph_synthesis.tests.test_research.RecordedTests.test_exported_graph_contains_sources_and_durable_lifecycle) ... ok +test_fake_live_adapter_requires_consent_and_counts_failures (graph_synthesis.tests.test_research.RecordedTests.test_fake_live_adapter_requires_consent_and_counts_failures) ... ok +test_frozen_inventory_is_unchanged (graph_synthesis.tests.test_research.RecordedTests.test_frozen_inventory_is_unchanged) ... ok +test_graph_is_independent_of_gold_but_metrics_are_not (graph_synthesis.tests.test_research.RecordedTests.test_graph_is_independent_of_gold_but_metrics_are_not) ... ok +test_out_of_scope_task_and_arm_fail_closed (graph_synthesis.tests.test_research.RecordedTests.test_out_of_scope_task_and_arm_fail_closed) ... ok +test_prediction_change_is_detected (graph_synthesis.tests.test_research.RecordedTests.test_prediction_change_is_detected) ... ok +test_prompt_change_is_detected (graph_synthesis.tests.test_research.RecordedTests.test_prompt_change_is_detected) ... ok +test_raw_response_change_is_detected (graph_synthesis.tests.test_research.RecordedTests.test_raw_response_change_is_detected) ... ok +test_state_change_cannot_reuse_a_score (graph_synthesis.tests.test_research.RecordedTests.test_state_change_cannot_reuse_a_score) ... ok +test_unknown_model_cannot_use_frozen_formulation (graph_synthesis.tests.test_research.RecordedTests.test_unknown_model_cannot_use_frozen_formulation) ... ok +test_48_distinct_cases_12_families (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_48_distinct_cases_12_families) ... ok +test_challenge_duplicate_ids_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_challenge_duplicate_ids_rejected) ... ok +test_challenge_state_cannot_contain_gold (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_challenge_state_cannot_contain_gold) ... ok +test_changed_input_cannot_reuse_predictions (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_changed_input_cannot_reuse_predictions) ... ok +test_duplicate_predictions_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_duplicate_predictions_rejected) ... ok +test_empty_journal_counts_missing_not_perfect (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_empty_journal_counts_missing_not_perfect) ... ok +test_empty_provenance_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_empty_provenance_rejected) ... ok +test_error_cannot_invent_probabilities (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_error_cannot_invent_probabilities) ... ok +test_errors_and_abstentions_distinct (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_errors_and_abstentions_distinct) ... ok +test_export_does_not_alias_state (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_export_does_not_alias_state) ... ok +test_export_is_detached_from_labels (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_export_is_detached_from_labels) ... ok +test_gold_free_exports (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_gold_free_exports) ... ok +test_input_hash_includes_instruction_and_allowed_labels (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_input_hash_includes_instruction_and_allowed_labels) ... ok +test_journal_hash_is_row_order_independent (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_journal_hash_is_row_order_independent) ... ok +test_mixed_model_arms_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_mixed_model_arms_rejected) ... ok +test_nonfinite_distribution_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_nonfinite_distribution_rejected) ... ok +test_nonhash_provenance_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_nonhash_provenance_rejected) ... ok +test_nonmaximal_label_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_nonmaximal_label_rejected) ... ok +test_oracle_fixture_is_only_evaluator_positive_control (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_oracle_fixture_is_only_evaluator_positive_control) ... ok +test_partial_journal_denominator_not_shrunk (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_partial_journal_denominator_not_shrunk) ... ok +test_unknown_predictions_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_unknown_predictions_rejected) ... ok +test_unrecognized_probability_label_rejected (graph_synthesis.tests.test_semantic_challenge.ChallengeTests.test_unrecognized_probability_label_rejected) ... ok +test_all_candidate_denominators_preserved (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_all_candidate_denominators_preserved) ... ok +test_changed_summary_counts_fail_closed (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_changed_summary_counts_fail_closed) ... ok +test_chart_inventory_and_data_are_current (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_chart_inventory_and_data_are_current) ... ok +test_confusion_retains_every_row (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_confusion_retains_every_row) ... ok +test_every_svg_is_parseable_without_scripts (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_every_svg_is_parseable_without_scripts) ... ok +test_highest_score_is_not_assumed_perfect (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_highest_score_is_not_assumed_perfect) ... ok +test_lifecycle_retains_history (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_lifecycle_retains_history) ... ok +test_no_new_inference (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_no_new_inference) ... ok +test_paper_includes_each_reviewed_figure_once (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_paper_includes_each_reviewed_figure_once) ... ok +test_probability_denominator_matches_valid_rows (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_probability_denominator_matches_valid_rows) ... ok +test_topology_preserves_every_candidate_node (graph_synthesis.tests.test_visualize.FrozenFigureTests.test_topology_preserves_every_candidate_node) ... ok +test_component_selection_does_not_use_gold (graph_synthesis.tests.test_visualize.VisualMathTests.test_component_selection_does_not_use_gold) ... ok +test_coverage_retains_errors_and_no_edge (graph_synthesis.tests.test_visualize.VisualMathTests.test_coverage_retains_errors_and_no_edge) ... ok +test_empty_curve_does_not_claim_zero_risk (graph_synthesis.tests.test_visualize.VisualMathTests.test_empty_curve_does_not_claim_zero_risk) ... ok +test_invalid_reliability_probability (graph_synthesis.tests.test_visualize.VisualMathTests.test_invalid_reliability_probability) ... ok +test_invalid_score_rejected (graph_synthesis.tests.test_visualize.VisualMathTests.test_invalid_score_rejected) ... ok +test_polarity_error_is_wrong_edge (graph_synthesis.tests.test_visualize.VisualMathTests.test_polarity_error_is_wrong_edge) ... ok +test_probability_endpoints_and_boundaries (graph_synthesis.tests.test_visualize.VisualMathTests.test_probability_endpoints_and_boundaries) ... ok +test_ranking_does_not_use_gold (graph_synthesis.tests.test_visualize.VisualMathTests.test_ranking_does_not_use_gold) ... ok +test_reliability_excludes_failed_response_only (graph_synthesis.tests.test_visualize.VisualMathTests.test_reliability_excludes_failed_response_only) ... ok +test_reliability_includes_other_predictions (graph_synthesis.tests.test_visualize.VisualMathTests.test_reliability_includes_other_predictions) ... ok +test_reversed_contrast_changes_interval_sign (graph_synthesis.tests.test_visualize.VisualMathTests.test_reversed_contrast_changes_interval_sign) ... ok +test_ties_are_never_split (graph_synthesis.tests.test_visualize.VisualMathTests.test_ties_are_never_split) ... ok +test_all_rows_partition_once (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_all_rows_partition_once) ... ok +test_canonical_tables_and_committed_data (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_canonical_tables_and_committed_data) ... ok +test_distinct_operational_failure_and_abstention (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_distinct_operational_failure_and_abstention) ... ok +test_fusion_usage_is_not_free_inference (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_fusion_usage_is_not_free_inference) ... ok +test_matched_null_findings_not_rewritten (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_matched_null_findings_not_rewritten) ... ok +test_neighborhood_is_real_and_candidate_selected (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_neighborhood_is_real_and_candidate_selected) ... ok +test_partition_rejects_inconsistent_count (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_partition_rejects_inconsistent_count) ... ok +test_reference_tampering_fails_closed (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_reference_tampering_fails_closed) ... ok +test_reliability_counts_match_archived_edges (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_reliability_counts_match_archived_edges) ... ok +test_reliability_includes_one_and_omits_empty_bins (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_reliability_includes_one_and_omits_empty_bins) ... ok +test_reliability_invalid_scores_rejected (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_reliability_invalid_scores_rejected) ... ok +test_svg_files_are_static_valid_and_accessible_text (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_svg_files_are_static_valid_and_accessible_text) ... ok +test_topology_retains_isolates_and_schema_denominator (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_topology_retains_isolates_and_schema_denominator) ... ok +test_withdrawal_is_conservative (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_withdrawal_is_conservative) ... ok +test_wrong_polarity_and_unsupported_are_separate (graph_synthesis.tests.test_visualize_evidence.FigureTests.test_wrong_polarity_and_unsupported_are_separate) ... ok +test_conformal_target_does_not_imply_nominal_coverage (graph_synthesis.theory_suite.tests.test_methods.ArchiveTests.test_conformal_target_does_not_imply_nominal_coverage) ... ok +test_cost_calls_are_unique_within_each_comparison (graph_synthesis.theory_suite.tests.test_methods.ArchiveTests.test_cost_calls_are_unique_within_each_comparison) ... ok +test_error_rate_budget_counterexample_is_retained (graph_synthesis.theory_suite.tests.test_methods.ArchiveTests.test_error_rate_budget_counterexample_is_retained) ... ok +test_eval_gold_changes_do_not_change_calibration_choices (graph_synthesis.theory_suite.tests.test_methods.ArchiveTests.test_eval_gold_changes_do_not_change_calibration_choices) ... ok +test_original_recorded_inputs_and_no_post_purge_overlap (graph_synthesis.theory_suite.tests.test_methods.ArchiveTests.test_original_recorded_inputs_and_no_post_purge_overlap) ... ok +test_bell_partition_count_and_size_guard (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_bell_partition_count_and_size_guard) ... ok +test_budget_does_not_treat_probability_one_as_certification (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_budget_does_not_treat_probability_one_as_certification) ... ok +test_budget_duplicate_id_rejected (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_budget_duplicate_id_rejected) ... ok +test_budget_is_component_local_and_permutation_stable (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_budget_is_component_local_and_permutation_stable) ... ok +test_cascade_alignment_and_missing_tokens (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_cascade_alignment_and_missing_tokens) ... ok +test_cascade_calibration_only (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_cascade_calibration_only) ... ok +test_cascade_cost_includes_initial_call_and_error_fallback (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_cascade_cost_includes_initial_call_and_error_fallback) ... ok +test_cascade_fit_uses_cheapest_eligible_not_evaluation (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_cascade_fit_uses_cheapest_eligible_not_evaluation) ... ok +test_conformal_empty_or_tiny_calibration_universal (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_conformal_empty_or_tiny_calibration_universal) ... ok +test_conformal_refuses_evaluation_fit (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_conformal_refuses_evaluation_fit) ... ok +test_decoder_incomplete_pairs_and_bad_order (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_decoder_incomplete_pairs_and_bad_order) ... ok +test_exact_decoder_known_triangle (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_exact_decoder_known_triangle) ... ok +test_exact_decoder_permutation_equivariance_without_ties (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_exact_decoder_permutation_equivariance_without_ties) ... ok +test_exact_objective_not_worse_than_greedy (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_exact_objective_not_worse_than_greedy) ... ok +test_features_exclude_gold_and_unknown_annotations (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_features_exclude_gold_and_unknown_annotations) ... ok +test_group_conformal_uses_maximum_and_finite_rank (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_group_conformal_uses_maximum_and_finite_rank) ... ok +test_invalid_probability_rejected (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_invalid_probability_rejected) ... ok +test_lineage_all_withdrawal_subsets (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_lineage_all_withdrawal_subsets) ... ok +test_macro_f1_invalid_alignment (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_macro_f1_invalid_alignment) ... ok +test_macro_f1_known_confusion (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_macro_f1_known_confusion) ... ok +test_offline_socket_guard (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_offline_socket_guard) ... ok +test_prediction_set_ties_errors_and_empty (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_prediction_set_ties_errors_and_empty) ... ok +test_rare_prior_precision_known (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_rare_prior_precision_known) ... ok +test_review_gold_independence (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_review_gold_independence) ... ok +test_review_invalid_budget_and_duplicate (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_review_invalid_budget_and_duplicate) ... ok +test_run_restores_socket_after_input_failure (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_run_restores_socket_after_input_failure) ... ok +test_schema_preview_detects_symmetry_and_inverse_drift (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_schema_preview_detects_symmetry_and_inverse_drift) ... ok +test_schema_preview_invalid_migration_leaves_original (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_schema_preview_invalid_migration_leaves_original) ... ok +test_semantic_cache_key_versions_and_absence (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_semantic_cache_key_versions_and_absence) ... ok +test_semantic_cache_rejects_missing_versions_and_bad_generation (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_semantic_cache_rejects_missing_versions_and_bad_generation) ... ok +test_source_component_purge_transitive_and_label_free (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_source_component_purge_transitive_and_label_free) ... ok +test_topological_review_uses_neighborhood_not_gold (graph_synthesis.theory_suite.tests.test_methods.MethodTests.test_topological_review_uses_neighborhood_not_gold) ... ok +test_all_study_markers_are_hidden (graph_synthesis.uncertainty.tests.test_render_markers.RenderMarkerTests.test_all_study_markers_are_hidden) ... ok +test_reader_text_and_other_comments_are_preserved (graph_synthesis.uncertainty.tests.test_render_markers.RenderMarkerTests.test_reader_text_and_other_comments_are_preserved) ... ok +test_conjunctive_body_needs_all_atoms (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_conjunctive_body_needs_all_atoms) ... ok +test_false_base_semantic_control (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_false_base_semantic_control) ... ok +test_finite_model_oracle (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_finite_model_oracle) ... ok +test_invalid_ground_rules (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_invalid_ground_rules) ... ok +test_no_unseeded_self_support (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_no_unseeded_self_support) ... ok +test_rule_duplicate_order_and_work_bound (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_rule_duplicate_order_and_work_bound) ... ok +test_withdrawn_cycle_and_backup (graph_synthesis.uncertainty.tests.test_uncertainty.GroundingTests.test_withdrawn_cycle_and_backup) ... ok +test_canonicalization (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_canonicalization) ... ok +test_cap_and_unused_atoms (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_cap_and_unused_atoms) ... ok +test_correlated_control (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_correlated_control) ... ok +test_invalid_probabilities (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_invalid_probabilities) ... ok +test_invalid_proofs (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_invalid_proofs) ... ok +test_rational_extrema (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_rational_extrema) ... ok +test_solver_exception_fallback (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_solver_exception_fallback) ... ok +test_solver_status_fallback (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_solver_status_fallback) ... ok +test_wrong_marginal_is_not_corrected (graph_synthesis.uncertainty.tests.test_uncertainty.LineageTests.test_wrong_marginal_is_not_corrected) ... ok +test_all_small_unweighted_graphs (graph_synthesis.uncertainty.tests.test_uncertainty.RepairTests.test_all_small_unweighted_graphs) ... ok +test_invalid_graph (graph_synthesis.uncertainty.tests.test_uncertainty.RepairTests.test_invalid_graph) ... ok +test_large_component_is_explicitly_partial (graph_synthesis.uncertainty.tests.test_uncertainty.RepairTests.test_large_component_is_explicitly_partial) ... ok +test_tie_and_isolated_backbone (graph_synthesis.uncertainty.tests.test_uncertainty.RepairTests.test_tie_and_isolated_backbone) ... ok +test_unique_false_priority_control (graph_synthesis.uncertainty.tests.test_uncertainty.RepairTests.test_unique_false_priority_control) ... ok +test_zero_priority_is_optional (graph_synthesis.uncertainty.tests.test_uncertainty.RepairTests.test_zero_priority_is_optional) ... ok +test_additive_update_is_idempotent (graph_synthesis.uncertainty.tests.test_uncertainty.ReportTests.test_additive_update_is_idempotent) ... ok +test_missing_or_malformed_anchor_is_rejected (graph_synthesis.uncertainty.tests.test_uncertainty.ReportTests.test_missing_or_malformed_anchor_is_rejected) ... ok +test_cap (graph_synthesis.uncertainty.tests.test_uncertainty.RetractionTests.test_cap) ... ok +test_exact_plan_and_no_mutation (graph_synthesis.uncertainty.tests.test_uncertainty.RetractionTests.test_exact_plan_and_no_mutation) ... ok +test_impossible_protection (graph_synthesis.uncertainty.tests.test_uncertainty.RetractionTests.test_impossible_protection) ... ok +test_invalid_retraction_inputs (graph_synthesis.uncertainty.tests.test_uncertainty.RetractionTests.test_invalid_retraction_inputs) ... ok +test_missing_proof_control (graph_synthesis.uncertainty.tests.test_uncertainty.RetractionTests.test_missing_proof_control) ... ok +test_degenerate_probabilities (graph_synthesis.uncertainty.tests.test_uncertainty.ReviewTests.test_degenerate_probabilities) ... ok +test_direct_brier_and_monotonicity (graph_synthesis.uncertainty.tests.test_uncertainty.ReviewTests.test_direct_brier_and_monotonicity) ... ok +test_invalid_budgets (graph_synthesis.uncertainty.tests.test_uncertainty.ReviewTests.test_invalid_budgets) ... ok +test_irrelevant_high_risk_atom (graph_synthesis.uncertainty.tests.test_uncertainty.ReviewTests.test_irrelevant_high_risk_atom) ... ok +test_misspecification_counterexample (graph_synthesis.uncertainty.tests.test_uncertainty.ReviewTests.test_misspecification_counterexample) ... ok + +---------------------------------------------------------------------- +Ran 809 tests in 17.640s + +OK diff --git a/graph_synthesis/frontier/artifact-manifest.json b/graph_synthesis/frontier/artifact-manifest.json new file mode 100644 index 0000000..7650a31 --- /dev/null +++ b/graph_synthesis/frontier/artifact-manifest.json @@ -0,0 +1,38 @@ +{ + "schema_version": 1, + "scope": "Extension files only; complete manuscript has its own build hash record.", + "sha256": { + "graph_synthesis/frontier/IMPLEMENTATION.md": "0e75832502b51b96cd3f8d9ea90fb5821dcccea8900199faa7cf73b6e652684a", + "graph_synthesis/frontier/NOVELTY.md": "f15130a1b14a9b9e3e28947bfa42b9d7c66f17af00e6d52f3c847ef013d01eef", + "graph_synthesis/frontier/PROTOCOL.md": 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"92cbfc57356a65482b026cfa927701ffb7c4fdf5a07e9363809cedf4c0e07faf", + "graph_synthesis/frontier/run.py": "4a58736791b856391afe2facb563ceeefc5eb4272ba2616e9651c3c7141f5af1", + "graph_synthesis/frontier/summary.csv": "c4cb728fec7fd163c07e5a0bd593cbeff64db7227ae590952659b108fde32717", + "graph_synthesis/frontier/tests.log": "a84b52a85fac37a1763afdca27b5302964658194cb47ac73677c3d8a4cd497a5", + "graph_synthesis/frontier/tests/test_frontier.py": "a85a4be15f7094eb9f567c0636613c17874a9819821b6d824661cd611851aa1d", + "graph_synthesis/frontier/verify.py": "74a666892c310ee1edefdd43496ad18cdd950d08d849a93d92ff12942b398ffa" + } +} diff --git a/graph_synthesis/frontier/audit_baseline.py b/graph_synthesis/frontier/audit_baseline.py new file mode 100644 index 0000000..99ccf0b --- /dev/null +++ b/graph_synthesis/frontier/audit_baseline.py @@ -0,0 +1,27 @@ +"""Reconstruct the scoped novelty audit from the immutable Git baseline.""" +import hashlib +import json +import re +import subprocess +from .report import HERE, ROOT, write + + +def main(): + baseline='a62a3257645d8e35cd4e45be53bfa9511d27724b' + names=subprocess.check_output(['git','ls-tree','-r','--name-only',baseline],cwd=ROOT,text=True).splitlines() + paths=[n for n in names if n.startswith('graph_synthesis/') and n.endswith(('.py','.md'))] + terms=['latent','bipartite','decision diagram','segment tree','knapsack','review cost','false.removal'] + hits={term:[] for term in terms}; hashes={} + for path in paths: + content=subprocess.check_output(['git','show',baseline+':'+path],cwd=ROOT) + hashes[path]=hashlib.sha256(content).hexdigest() + for term in terms: + if re.search(term,content.decode('utf-8'),re.I):hits[term].append(path) + record={'baseline_commit':baseline,'snapshot_commit':'02de0cb34a90117805ba13bd0bcb313898ee1197', + 'scope':'All baseline graph_synthesis Python and Markdown files. The snapshot adds only a source-packaging workflow to baseline. Symbol search is an aid, not proof of global novelty.', + 'terms':terms,'matches':hits,'files':hashes} + write(HERE/'novelty-audit.json',json.dumps(record,indent=2,sort_keys=True)+'\n') + print(json.dumps({'novelty_scope':'pinned baseline only','files_audited':len(paths),'baseline':baseline})) + + +if __name__=='__main__':main() diff --git a/graph_synthesis/frontier/execution.log b/graph_synthesis/frontier/execution.log new file mode 100644 index 0000000..0d81ee8 --- /dev/null +++ b/graph_synthesis/frontier/execution.log @@ -0,0 +1,6 @@ +H1 target True +H2 target False +H3 target True +H4 target True +H5 target True +{"status": "executed", "targets": {"H1": true, "H2": false, "H3": true, "H4": true, "H5": true}, "fresh_service_calls": 0} diff --git a/graph_synthesis/frontier/figures/01_source_dependence.png b/graph_synthesis/frontier/figures/01_source_dependence.png new file mode 100644 index 0000000..e97e3f7 Binary files /dev/null and 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a/graph_synthesis/frontier/figures/05_path_work.svg b/graph_synthesis/frontier/figures/05_path_work.svg new file mode 100644 index 0000000..f5bbf12 --- /dev/null +++ b/graph_synthesis/frontier/figures/05_path_work.svg @@ -0,0 +1,1480 @@ + + + + + + + + image/svg+xml + + + Matplotlib v3.10.8, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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Its [assumption-aware study](../assumption_aware/RESULTS.md) is also preserved unchanged. Its dependence-robust probability envelopes address unknown joint dependence rather than H1's supplied latent-source conditional model. Its connected-tree ancestor-message updates overlap H5's objective, but H5 uses a balanced segment tree on a fixed path, with a different update mechanism and counted work metric. These results are parallel extensions of the same pinned baseline, not independent replications, combined semantic observations, or claims of unique worldwide invention. Frozen frontier hypotheses, implementations, fixture seeds and numerical results are unchanged; only additive reporting and combined regression coverage expand.''', +'''**Final integration snapshot.** Main also received PR #21, the [uncertainty and grounding study](../uncertainty/RESULTS.md), at `114450ef08736c21c97c8725de700d8d2c6ca94d`. That study and all three earlier concurrent extensions are preserved unchanged. Its dependence-agnostic bounds and query-loss-directed review further overlap the broad research questions here, but use different supplied-information assumptions and controlled objectives. This frontier study does not claim to be the unique first repository implementation on the reconciled main branch; its dated novelty comparison remains the frozen PR #17 baseline. All frozen numerical outcomes remain unchanged and overlapping captured observations are not pooled.'''] + + +def main(): + subprocess.run(['git','merge-base','--is-ancestor',PIN,'HEAD'],cwd=ROOT,check=True) + path=HERE/'report.py'; text=path.read_text(encoding='utf-8') + alternatives=[ + " anchor=next((x for x in ('','',ANCHOR) if x in text),ANCHOR)", + " anchors=re.findall(r'',text)\n anchor=anchors[-1] if anchors else ANCHOR", + " anchor=next((x for x in ('','','',ANCHOR) if x in text),ANCHOR)"] + fixed=" anchor=next((x for x in ('','','','',ANCHOR) if x in text),ANCHOR)" + for old in alternatives: + if old in text: + assert text.count(old)==1 + text=text.replace(old,fixed) + assert fixed in text,'Unexpected paper anchor implementation' + for note in NOTES: + if note not in text: + anchor='## 2. H1: Source-conditioned evidence probability' + assert text.count(anchor)==1 + text=text.replace(anchor,note+'\n\n'+anchor,1) + path.write_text(text,encoding='utf-8',newline='\n') + path=HERE/'NOVELTY.md'; text=path.read_text(encoding='utf-8') + for note in NOTES: + if note not in text: + text+='\n'+note+'\n' + path.write_text(text,encoding='utf-8',newline='\n') + path=HERE/'IMPLEMENTATION.md'; text=path.read_text(encoding='utf-8') + records=[ + 'Final integration preserves PRs #18, #19, #24 and #21 at `'+PIN+'`, including their requirements and all evidence. The full regression runner includes all their tests. The reporting anchor follows the latest supported refinement study, before the historical follow-up boundary. The renderer from current main is preserved unchanged because its generic marker-removal helper already handles the frontier section. Frozen benchmark implementations, protocol, fixture seeds and target indicators remain unchanged.', + 'Integration validation recorded 772 passing tests and one manuscript-regeneration failure in the first 773-test run. Choosing the last physical research marker incorrectly placed the new section after the historical follow-up, whose unchanged writer relocates itself immediately before the original-study boundary. The correction selects the latest refinement predecessor, preserving the historical follow-up position. The existing failing regression remains unchanged and must pass before publication. No research result or acceptance threshold changed.'] + for paragraph in records: + if paragraph not in text: + text+='\n'+paragraph+'\n' + path.write_text(text,encoding='utf-8',newline='\n') + for name in ('structural','source_structural','assumption_aware','uncertainty'): + subprocess.run(['git','diff','--exit-code',PIN,'--','graph_synthesis/'+name],cwd=ROOT,check=True) + print('Verified inspected main ancestry and four unchanged concurrent studies; frontier precedes the historical follow-up boundary') + + +if __name__=='__main__':main() diff --git a/graph_synthesis/frontier/methods.py b/graph_synthesis/frontier/methods.py new file mode 100644 index 0000000..1f03ea8 --- /dev/null +++ b/graph_synthesis/frontier/methods.py @@ -0,0 +1,431 @@ +"""Bounded, opt-in methods with explicit supplied-model assumptions. + +Inference/selection functions accept no evaluation labels. No network or graph +persistence is performed. Unsupported structure is staged, never certified. +""" +from __future__ import annotations + +from collections import Counter, defaultdict, deque +from dataclasses import dataclass +from itertools import product +import math + +from ..reliability.methods import ( + canonical, components, exact_lineage, graph_input, lineage_bounds, + solve_component, validate_proofs, +) + + +def probability(p): + if type(p) not in (int, float) or not math.isfinite(p) or not 0 <= p <= 1: + raise ValueError('Finite probability in [0,1] required') + return float(p) + + +def source_mixture(proofs, sources, primitives): + """Marginalize a supplied binary-source model, not inferred source truth.""" + proofs = [tuple(p) for p in proofs] + if any(not isinstance(k, str) or not k for k in sources): + raise ValueError('Nonempty string source IDs required') + for p in sources.values(): + probability(p) + for row in primitives.values(): + if set(row) != {'source', 'p0', 'p1'} or row['source'] not in sources: + raise ValueError('Each primitive needs a declared source and p0/p1') + probability(row['p0']); probability(row['p1']) + marginal = {a: (1-sources[r['source']])*r['p0']+sources[r['source']]*r['p1'] + for a, r in primitives.items()} + validate_proofs(proofs, marginal) + if len(sources) > 8 or len(primitives) > 16: + return {'lower': 0., 'upper': 1., 'exact': False, 'reason': 'input_cap', 'assignments': 0} + keys = sorted(sources) + lower = upper = 0. + exact = True + for bits in product((0, 1), repeat=len(keys)): + state = dict(zip(keys, bits)) + mass = math.prod(sources[s] if state[s] else 1-sources[s] for s in keys) + conditional = {a: r['p1'] if state[r['source']] else r['p0'] for a, r in primitives.items()} + value = exact_lineage(proofs, conditional) + lower += mass*value['lower']; upper += mass*value['upper'] + exact = exact and value['exact'] + return {'lower': min(1., max(0., lower)), 'upper': min(1., max(0., upper)), + 'exact': exact, 'reason': 'exact' if exact else 'conditional_state_cap', + 'assignments': 1 << len(keys)} + + +@dataclass(frozen=True) +class Diagram: + """Children precede parents; IDs 0 and 1 are Boolean terminals.""" + proofs: tuple + atoms: tuple + nodes: tuple + root: int | None + states: int + reason: str + + def evaluate(self, probabilities): + validate_proofs(self.proofs, probabilities) + if set(probabilities) != set(self.atoms): + raise ValueError('Exactly the compiled primitive set is required') + if self.root is None: + bounds = ({'lower': 0., 'upper': 1.} if self.reason == 'input_cap' + else lineage_bounds(self.proofs, probabilities)) + return {**bounds, 'exact': False, 'reason': self.reason, 'node_visits': 0} + values = [0., 1.] + for atom, low, high in self.nodes: + p = probabilities[atom] + values.append((1-p)*values[low]+p*values[high]) + value = min(1., max(0., values[self.root])) + return {'lower': value, 'upper': value, 'exact': True, 'reason': 'exact', + 'node_visits': len(self.nodes)} + + +def compile_lineage(proofs, *, state_cap=32768): + """Iterative state-bounded ROBDD compilation; no atom-count tractability claim.""" + if type(state_cap) is not int or not 1 <= state_cap <= 32768: + raise ValueError('Residual-state cap must be in [1,32768]') + original = tuple(tuple(p) for p in proofs) + if any(not isinstance(a, str) or not a for p in original for a in p): + raise ValueError('Nonempty string primitive IDs required') + atoms = tuple(sorted({a for p in original for a in p})) + validate_proofs(original, {a: .5 for a in atoms}) + if len(atoms) > 512 or len(original) > 4096: + return Diagram(original, atoms, (), None, 0, 'input_cap') + terms = canonical(original) + freq = Counter(a for p in terms for a in p) + order = {a: i for i, a in enumerate(sorted(atoms, key=lambda a: (-freq[a], a)))} + values, expansion, unique, nodes = {}, {}, {}, [] + stack = [(terms, False)] + seen = set() + while stack: + dnf, after = stack.pop() + if dnf in values: + continue + if not after: + if len(seen) >= state_cap: + return Diagram(terms, atoms, (), None, len(seen), 'state_cap') + seen.add(dnf) + if not dnf: + values[dnf] = 0 + continue + if () in dnf: + values[dnf] = 1 + continue + atom = min({a for p in dnf for a in p}, key=order.__getitem__) + low = canonical(p for p in dnf if atom not in p) + high = canonical(tuple(a for a in p if a != atom) for p in dnf) + expansion[dnf] = (atom, low, high) + stack.extend([(dnf, True), (high, False), (low, False)]) + else: + atom, low, high = expansion.pop(dnf) + lo, hi = values[low], values[high] + if lo == hi: + values[dnf] = lo + else: + key = (atom, lo, hi) + if key not in unique: + unique[key] = len(nodes)+2 + nodes.append(key) + values[dnf] = unique[key] + return Diagram(terms, atoms, tuple(nodes), values[terms], len(seen), 'exact') + + +def review_input(candidates, budget): + if type(budget) is not int or not 0 <= budget <= 160: + raise ValueError('Integer effort budget in [0,160] required') + ids = set() + groups = defaultdict(list) + for r in candidates: + if set(r) != {'id', 'group', 'risk', 'cost', 'detect', 'false_remove'}: + raise ValueError('Review selector accepts only the declared feature fields') + if not isinstance(r['id'], str) or not r['id'] or r['id'] in ids: + raise ValueError('Unique nonempty candidate IDs required') + if not isinstance(r['group'], str) or not r['group']: + raise ValueError('Nonempty source-group IDs required') + ids.add(r['id']) + for k in ('risk', 'detect', 'false_remove'): + probability(r[k]) + if type(r['cost']) is not int or not 1 <= r['cost'] <= 8: + raise ValueError('Integer proxy cost in [1,8] required') + groups[r['group']].append(dict(r)) + return {g: sorted(rows, key=lambda r: r['id']) for g, rows in sorted(groups.items())} + + +def group_review_gain(rows, selected): + original_clean = math.prod(1-r['risk'] for r in rows) + updated_clean = math.prod(1-r['risk']*(1-r['detect']*(r['id'] in selected)) for r in rows) + good_loss = sum((1-r['risk'])*r['false_remove'] for r in rows if r['id'] in selected) + return updated_clean-original_clean-good_loss + + +def review_gain(candidates, selected): + groups = review_input(candidates, 0) + return sum(group_review_gain(rows, selected) for rows in groups.values()) + + +def _better(a, b): + """Choose gain then deterministic ID ordering; costs resolved by caller.""" + return a if a[0] > b[0]+1e-12 or (abs(a[0]-b[0]) <= 1e-12 and a[1] < b[1]) else b + + +def optimal_review(candidates, budget): + groups = review_input(candidates, budget) + dp = {0: (0., ())} + staged = [] + for g, rows in groups.items(): + options = {0: (0., ())} + if len(rows) > 12: + staged.append(g) + else: + for mask in range(1, 1 << len(rows)): + picked = tuple(r['id'] for i, r in enumerate(rows) if mask & (1 << i)) + cost = sum(r['cost'] for i, r in enumerate(rows) if mask & (1 << i)) + if cost <= budget: + candidate = (group_review_gain(rows, set(picked)), picked) + options[cost] = _better(candidate, options.get(cost, (-math.inf, ()))) + new = {} + for cost, (gain, selected) in dp.items(): + for extra, (benefit, picked) in options.items(): + if cost+extra <= budget: + candidate = (gain+benefit, tuple(sorted(selected+picked))) + new[cost+extra] = _better(candidate, new.get(cost+extra, (-math.inf, ()))) + dp = new + cost, (gain, selected) = min(dp.items(), key=lambda kv: (-round(kv[1][0], 12), kv[0], kv[1][1])) + return {'selected': list(selected), 'cost': cost, 'modeled_gain': gain, 'staged_groups': staged} + + +def ratio_review(candidates, budget): + groups = review_input(candidates, budget) + selected, spent = set(), 0 + while True: + choices = [] + for row in candidates: + if row['id'] in selected or spent+row['cost'] > budget: + continue + group = groups[row['group']] + gain = group_review_gain(group, selected | {row['id']})-group_review_gain(group, selected) + choices.append((gain/row['cost'], row['id'], row['cost'])) + if not choices: + break + gain, key, cost = min(choices, key=lambda x: (-x[0], x[1])) + if gain <= 1e-12: + break + selected.add(key); spent += cost + return {'selected': sorted(selected), 'cost': spent, 'modeled_gain': review_gain(candidates, selected)} + + +def packed_review(candidates, budget, order): + review_input(candidates, budget) + rows = {r['id']: r for r in candidates} + if len(order) != len(set(order)) or set(order) != set(rows): + raise ValueError('Review order must cover exactly the feature candidates') + selected, cost = set(), 0 + for key in order: + if cost+rows[key]['cost'] <= budget: + selected.add(key); cost += rows[key]['cost'] + return {'selected': sorted(selected), 'cost': cost, 'modeled_gain': review_gain(candidates, selected)} + + +def bipartition(adj): + color = {} + for start in sorted(adj): + if start in color: + continue + color[start] = 0 + queue = deque([start]) + while queue: + a = queue.popleft() + for b in sorted(adj[a]): + if b not in color: + color[b] = 1-color[a]; queue.append(b) + elif color[b] == color[a]: + return None + return color + + +def _network(weights, adj, color): + keys = sorted(weights) + index = {k: i for i, k in enumerate(keys)} + source, sink = len(keys), len(keys)+1 + infinity = sum(weights.values())+1 + arcs = {} + for key in keys: + if color[key] == 0: + arcs[source, index[key]] = weights[key] + for other in sorted(adj[key]): + arcs[index[key], index[other]] = infinity + else: + arcs[index[key], sink] = weights[key] + return keys, source, sink, arcs + + +def bipartite_optimum(weights, edges): + adj = graph_input(weights, edges) + color = bipartition(adj) + if color is None or len(weights) > 512: + raise ValueError('Bipartite solver requires at most 512 vertices') + keys, source, sink, arcs = _network(weights, adj, color) + residual = defaultdict(dict) + for (a, b), capacity in arcs.items(): + residual[a][b] = capacity; residual[b][a] = 0 + flow, augmentations = 0, 0 + while True: + parent = {source: None}; queue = deque([source]) + while queue and sink not in parent: + a = queue.popleft() + for b in sorted(residual[a]): + if residual[a][b] > 0 and b not in parent: + parent[b] = a; queue.append(b) + if sink not in parent: + reachable = set(parent) + break + amount = sum(weights.values())+1 + node = sink + while node != source: + a = parent[node]; amount = min(amount, residual[a][node]); node = a + node = sink + while node != source: + a = parent[node]; residual[a][node] -= amount; residual[node][a] += amount; node = a + flow += amount; augmentations += 1 + cover = [k for i, k in enumerate(keys) if (color[k] == 0 and i not in reachable) or (color[k] == 1 and i in reachable)] + selected = sorted(set(keys)-set(cover)) + certificate = {'keys': keys, 'source': source, 'sink': sink, 'reachable': sorted(reachable), + 'arcs': [{'u': a, 'v': b, 'capacity': cap, 'flow': cap-residual[a][b]} for (a, b), cap in sorted(arcs.items())], + 'flow_value': flow, 'cover': cover} + return {'selected': selected, 'staged': [], 'utility': sum(weights[k] for k in selected), + 'method': 'bipartite_min_cut', 'certificate': certificate, 'augmentations': augmentations} + + +def verify_certificate(weights, edges, result): + """Check a primal/dual witness without calling an optimizer or residual BFS.""" + try: + adj = graph_input(weights, edges) + color = bipartition(adj) + if color is None: + return False + keys, source, sink, expected = _network(weights, adj, color) + cert = result['certificate'] + if cert['keys'] != keys or cert['source'] != source or cert['sink'] != sink: + return False + arcs = cert['arcs'] + if len(arcs) != len(expected) or {(a['u'], a['v']) for a in arcs} != set(expected): + return False + balance = defaultdict(int) + for a in arcs: + key, f = (a['u'], a['v']), a['flow'] + if type(f) is not int or a['capacity'] != expected[key] or not 0 <= f <= expected[key]: + return False + balance[a['u']] -= f; balance[a['v']] += f + value = cert['flow_value'] + if type(value) is not int or value < 0 or balance[source] != -value or balance[sink] != value: + return False + if any(balance[i] for i in range(len(keys))): + return False + reach = set(cert['reachable']) + if source not in reach or sink in reach or not reach <= set(range(len(keys)+2)): + return False + cut = sum(cap for (a, b), cap in expected.items() if a in reach and b not in reach) + if cut != value: + return False + cover = {k for i, k in enumerate(keys) if (color[k] == 0 and i not in reach) or (color[k] == 1 and i in reach)} + selected = result['selected'] + if set(cert['cover']) != cover or len(selected) != len(set(selected)) or set(selected) != set(keys)-cover: + return False + if any(b in selected for a in selected for b in adj[a]): + return False + return result['utility'] == sum(weights[k] for k in selected) == sum(weights.values())-value + except (KeyError, TypeError, ValueError): + return False + + +def solve_frontier(weights, edges): + adj = graph_input(weights, edges) + solutions = [] + for group in components(adj): + w = {k: weights[k] for k in sorted(group)} + sub = {k: adj[k] for k in sorted(group)} + if len(group) <= 512 and bipartition(sub) is not None: + e = [(a, b) for a in sorted(sub) for b in sorted(sub[a]) if a < b] + solutions.append(bipartite_optimum(w, e)) + else: + solutions.append(solve_component(weights, adj, group)) + return {'selected': sorted(k for s in solutions for k in s['selected']), + 'staged': sorted(k for s in solutions for k in s['staged']), + 'utility': sum(s['utility'] for s in solutions), 'components': solutions} + + +NEG = -math.inf +IDENTITY = ((0, NEG), (NEG, 0)) + + +def _matrix(weight): + return ((0, 0), (weight, NEG)) + + +def _compose(right, left): + return tuple(tuple(max(right[i][k]+left[k][j] for k in (0, 1)) for j in (0, 1)) for i in (0, 1)) + + +class PathOptimizer: + """Fixed-path utility maintenance; witness materialization is NOT logarithmic.""" + def __init__(self, weights, edges): + adj = graph_input(weights, edges) + if not weights or any(len(a) > 2 for a in adj.values()) or len(components(adj)) != 1 or sum(map(len, adj.values())) != 2*(len(weights)-1): + raise ValueError('A nonempty connected path is required; topology changes require rebuild') + start = min(k for k in adj if len(adj[k]) <= 1) + order, prev, node = [], None, start + while node is not None: + order.append(node) + following = sorted(adj[node]-({prev} if prev is not None else set())) + prev, node = node, following[0] if following else None + self.order = tuple(order) + self.index = {k: i for i, k in enumerate(order)} + self.weights = dict(weights) + self.size = 1 << (len(order)-1).bit_length() + self.tree = [IDENTITY]*(2*self.size) + for i, key in enumerate(order): + self.tree[self.size+i] = _matrix(weights[key]) + for node in range(self.size-1, 0, -1): + self.tree[node] = _compose(self.tree[2*node+1], self.tree[2*node]) + self.transitions = 8*(self.size-1) + self.last_transitions = self.transitions + self.validation_visits = len(weights)+sum(map(len, adj.values())) + self.witness_visits = 0 + + @property + def utility(self): + return max(self.tree[1][0][0], self.tree[1][1][0]) + + def update(self, key, weight): + if key not in self.index or type(weight) is not int or weight < 0: + raise ValueError('Existing vertex and nonnegative integer weight required') + self.last_transitions = 0 + if self.weights[key] == weight: + return self.utility + node = self.size+self.index[key] + self.weights[key] = weight + self.tree[node] = _matrix(weight) + node //= 2 + while node: + self.tree[node] = _compose(self.tree[2*node+1], self.tree[2*node]) + self.last_transitions += 8 + node //= 2 + self.transitions += self.last_transitions + return self.utility + + def selected(self): + end = 0 if self.tree[1][0][0] >= self.tree[1][1][0] else 1 + stack = [(1, 0, end)] + result, visits = [], 0 + while stack: + node, initial, final = stack.pop(); visits += 1 + if node >= self.size: + index = node-self.size + if index < len(self.order) and final: + 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"graph_synthesis/risk_control/report.py", + "graph_synthesis/study.py", + "graph_synthesis/theory_suite/PROTOCOL.md" + ], + "segment tree": [] + }, + "scope": "All baseline graph_synthesis Python and Markdown files. The snapshot adds only a source-packaging workflow to baseline. Symbol search is an aid, not proof of global novelty.", + "snapshot_commit": "02de0cb34a90117805ba13bd0bcb313898ee1197", + "terms": [ + "latent", + "bipartite", + "decision diagram", + "segment tree", + "knapsack", + "review cost", + "false.removal" + ] +} diff --git a/graph_synthesis/frontier/original-inventory.json b/graph_synthesis/frontier/original-inventory.json new file mode 100644 index 0000000..28e1a16 --- /dev/null +++ b/graph_synthesis/frontier/original-inventory.json @@ -0,0 +1,4 @@ +{ + "archive_files_verified": 161, + "original_files_changed": 0 +} diff --git a/graph_synthesis/frontier/regression.py b/graph_synthesis/frontier/regression.py new file mode 100644 index 0000000..faffe86 --- /dev/null +++ b/graph_synthesis/frontier/regression.py @@ -0,0 +1,20 @@ +"""Run every repository graph-synthesis unittest and record actual outcomes.""" +import hashlib +import json +import platform +import unittest +from .report import HERE, ROOT, write + + +def main(): + files=sorted((ROOT/'graph_synthesis').rglob('test*.py')) + suite=unittest.TestSuite(unittest.defaultTestLoader.loadTestsFromName('.'.join(p.relative_to(ROOT).with_suffix('').parts)) for p in files) + result=unittest.TextTestRunner(verbosity=2).run(suite) + summary={'tests_run':result.testsRun,'failures':len(result.failures),'errors':len(result.errors), + 'skipped':len(result.skipped),'successful':result.wasSuccessful(),'python':platform.python_version(), + 'test_files':{p.relative_to(ROOT).as_posix():hashlib.sha256(p.read_bytes()).hexdigest() for p in files}} + write(HERE/'all-regressions.json',json.dumps(summary,indent=2,sort_keys=True)+'\n') + raise SystemExit(not result.wasSuccessful()) + + +if __name__=='__main__':main() diff --git a/graph_synthesis/frontier/replay.log b/graph_synthesis/frontier/replay.log new file mode 100644 index 0000000..646b19e --- /dev/null +++ b/graph_synthesis/frontier/replay.log @@ -0,0 +1,6 @@ +H1 target True +H2 target False +H3 target True +H4 target True +H5 target True +{"status": "reproduced", "targets": {"H1": true, "H2": false, "H3": true, "H4": true, "H5": true}, "fresh_service_calls": 0} diff --git a/graph_synthesis/frontier/report.py b/graph_synthesis/frontier/report.py new file mode 100644 index 0000000..e1bd9e4 --- /dev/null +++ b/graph_synthesis/frontier/report.py @@ -0,0 +1,278 @@ +"""Render the frozen structural-frontier results without changing earlier studies.""" +from __future__ import annotations +import argparse +import csv +import hashlib +import json +import re +from pathlib import Path + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +START = '' +END = '' +ANCHOR = '' +NAMES = {'H1':'Latent-source-conditioned lineage', 'H2':'Cost/noise-aware group review', + 'H3':'Reusable state-bounded lineage diagrams', 'H4':'Certified bipartite conflict optimization', + 'H5':'Connected-path incremental optimization'} + + +def write(path, text): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(text.encode('utf-8')) + + +def status(value): + return 'Met' if value else 'Not met' + + +def summary_rows(r): + return [dict(hypothesis=k, method=NAMES[k], target_met=r[k]['primary_target_met'], + evidence='saved-response / reviewer-model scenario' if k=='H2' else 'controlled supplied-input algorithm') for k in NAMES] + + +def report(r): + a,b,c,d,e = (r[k] for k in NAMES) + p=b['primary']['policies']; large=e['large']; reuse=c['reuse'] + summary='\n'.join(f"| {k} | {NAMES[k]} | {status(r[k]['primary_target_met'])} | {'Replay + reviewer assumptions' if k=='H2' else 'Controlled algorithm'} |" for k in NAMES) + reviews='\n'.join(f"| {name.replace('_',' ')} | {v['cost']} | {len(v['selected'])} | {v['outcome']['expected_contaminated_groups']:.6f} | {v['outcome']['expected_correct_removed']:.4f} |" for name,v in p.items()) + lineage='\n'.join(f"| {x['kind']} | {x['n']} | {x['nodes']} | {x['prior']['lower']:.3f} | {x['proposed']['lower']:.9f} |" for x in c['large']) + graphs='\n'.join(f"| {x['kind'].replace('_',' ')} | {x['n']} | {x['prior']['utility']} | {x['greedy']['utility']} | {x['proposed']['utility']} | {x['oracle']} |" for x in d['large']) + return f'''# Structural frontiers for evidence-preserving Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five frozen extensions test whether explicit source dependence, heterogeneous review assumptions and structural recognition address limitations exposed by PR #17. Latent-source marginalization matches an independent joint-world oracle on 96 supplied-model fixtures and prevents {a['prior_false_admissions_random']} false threshold admissions made by the independent-marginal comparator. Cost- and noise-aware review allocation fails its frozen downstream target: at 40 proxy effort units it leaves {p['optimal']['outcome']['expected_contaminated_groups']:.6f} expected contaminated source groups, identical to both comparison policies. Compile-once decision diagrams exactly evaluate ten connected lineage formulas with 17–256 primitives beyond the previous blanket component cap. Certified bipartite optimization attains nine analytic large-graph optima previously staged. A fixed-path segment tree reduces counted summary-maintenance transitions by {100*large['saving']:.2f}% across a 1,024-vertex, 256-update workload, excluding separately reported linear witness decoding. These are bounded engineering results, not new Jev semantic-accuracy evidence or proof of worldwide algorithmic novelty. + +## 1. Motivation, novelty scope and frozen design + +The [preceding reliability study](../reliability/RESULTS.md) found that better average calibration bias need not improve Brier score; source identity errors invalidate independent-event probabilities; capped exact solvers leave some structured cases unresolved; review effectiveness depends on assumptions; and whole-component caching provides no solver saving on a connected graph. The five present mechanisms address these particular limitations rather than adding more correlated Jev calls. TypeSafe's typed outputs and probabilities do not themselves establish graph truth ([TypeSafe documentation](https://docs.typesafe.ai/introduction)). + +The [protocol](PROTOCOL.md) was committed as `{r['protocol_commit']}` before implementation and benchmark execution, against `{r['baseline_commit']}`. Prior results were inspected and informed hypothesis selection. This is exploratory follow-up, not independent preregistration. H2 reuses 73 development candidates in 40 groups and 263 evaluation candidates in 149 groups. These are already published observations, not a new holdout. **Fresh service calls: {r['fresh_service_calls']}.** Raw requests, response parsing, group separation and input hashes are checked before scoring. The other four hypotheses evaluate supplied models or graph structures, not newly extracted scientific facts. + +| Hypothesis | Proposed extension | Frozen target | Evidence class | +|---|---|---|---| +{summary} + +“Met” refers only to the predeclared conjunction on its specified fixture population. These rows must not be pooled into a semantic-success percentage. The [novelty audit](NOVELTY.md) distinguishes repository-new implementations from established source modeling, knowledge compilation, flow optimization and dynamic programming. It does not assert that no person has ever tried an equivalent idea. + +**Concurrent integration disclosure.** While this frozen extension was executing, main advanced to `a21314d17c33c639b75522ecce120586ef8dab35` with the [structural refinement](../structural/RESULTS.md) and [source-structural](../source_structural/RESULTS.md) studies. Both are preserved unchanged. The source-structural study also implements certified bipartite optimization: H4 here is therefore a parallel implementation and additional frozen test suite, not a mechanism unique to the reconciled main branch. Its frontier-width lineage evaluator overlaps H3's goal but differs from the reusable decision-diagram state budget tested here. Source-setup review differs from H2's heterogeneous per-edge effort/noise model; source-revision invalidation differs from H5's connected-path summary maintenance. Repository-new claims are limited to the protocol's original pinned baseline, not the later integration snapshot. No thresholds, fixtures or numerical results were retuned after examining the concurrent studies. Reused semantic captures must not be pooled as independent observations. + +**Further concurrent integration.** Main subsequently advanced through PR #24 to `6530321dd0060c9c7f13bb69d6f1c88346a8e1e2`. Its [assumption-aware study](../assumption_aware/RESULTS.md) is also preserved unchanged. Its dependence-robust probability envelopes address unknown joint dependence rather than H1's supplied latent-source conditional model. Its connected-tree ancestor-message updates overlap H5's objective, but H5 uses a balanced segment tree on a fixed path, with a different update mechanism and counted work metric. These results are parallel extensions of the same pinned baseline, not independent replications, combined semantic observations, or claims of unique worldwide invention. Frozen frontier hypotheses, implementations, fixture seeds and numerical results are unchanged; only additive reporting and combined regression coverage expand. + +**Final integration snapshot.** Main also received PR #21, the [uncertainty and grounding study](../uncertainty/RESULTS.md), at `114450ef08736c21c97c8725de700d8d2c6ca94d`. That study and all three earlier concurrent extensions are preserved unchanged. Its dependence-agnostic bounds and query-loss-directed review further overlap the broad research questions here, but use different supplied-information assumptions and controlled objectives. This frontier study does not claim to be the unique first repository implementation on the reconciled main branch; its dated novelty comparison remains the frozen PR #17 baseline. All frozen numerical outcomes remain unchanged and overlapping captured observations are not pooled. + +## 2. H1: Source-conditioned evidence probability + +Distinct evidence atoms can still depend on a common unreliable source. Instead of treating their marginal probabilities as independent, the proposed model supplies a binary quality state for each source, a prior for that state, and two conditional probabilities for each assigned atom. Source states are assumed mutually independent; primitive events are independent only conditional on those states. For a monotone proof formula F, compute P(F) = sum_z P(z) P(F given z). Each conditional formula uses the existing bounded exact lineage evaluator. This is exact marginalization within the supplied model, not discovery of the correct dependence structure. + +The 96 seeded fixtures contain 2–8 primitives and 1–3 sources. The independent oracle enumerates joint source-plus-primitive assignments, rather than calling the proposed marginalization/evaluation routine. There are **{a['oracle_failures']} oracle discrepancies above 1e-12**, **{a['invariance_failures']} failures in {a['invariance_checks']} order/duplicate checks**, and **{a['false_admissions']} proposed false admissions at 0.95**. Independent marginals make {a['prior_false_admissions_random']} false admissions on these random fixtures. Mean absolute probability error is {a['mean_absolute_error_prior']:.6f} for independent marginals and {a['mean_absolute_error_proposed']:.3g} for the supplied dependence model. + +| Shared-source query | Independent marginals | Conditioned model | Joint-world truth | +|---|---:|---:|---:| +| At least one of two copies (OR) | 0.96 | 0.80 | 0.80 | +| Both copies (AND) | 0.64 | 0.80 | 0.80 | + +The OR control prevents an unjustified 0.95 admission; the AND control shows that dependence errors do not always inflate probability. The target is **{status(a['primary_target_met']).lower()}**. Inputs over eight sources or sixteen primitives are explicitly staged with [0,1], not assigned an exact probability. Conditional evaluator exhaustion retains conservative bounds. + +![H1. Declared common-source dependence corrects both OR inflation and AND deflation on the supplied two-copy control. These are model probabilities, not observed Jev accuracies.](figures/01_source_dependence.png) + +**Assumption-breaking control:** two declared independent latent sources that actually share one cause still yield 0.96 when the actual probability is 0.80. The extension therefore moves the necessary independence assumption to the source layer; it does not remove it. Primitive reliabilities and source assignments are fixture inputs, not calibrated model scores or independently adjudicated provenance. Bayesian source-quality modeling already has substantial prior art, including [Zhao et al. (2012)](https://arxiv.org/abs/1203.0058); this experiment does not reproduce or outperform their truth-discovery system. + +## 3. H2: Review allocation under effort and reviewer errors + +The proposed allocator uses the prior study's development-only single-view risk estimator. Evaluation gold, compact-view predictions and observed review outcomes are unavailable to selection. The supplied per-edge effort is min(8, 1 + floor(recorded single-call input tokens / 1000)). This is a bounded proxy, not measured annotation time, cognitive difficulty, monetary cost or new service expenditure. At base detection s0, detection for cost c is s0 / (1 + 0.05(c−1)); a supplied false-removal probability applies to correct reviewed edges. + +For selected review IDs A, the modeled source-group contamination is 1 − product_i[1 − p_i(1 − s_i I(i in A))]. The objective is total modeled group cleaning minus expected correct-edge removals with unit penalty. Enumerate subsets within each group of at most twelve candidates, then allocate across groups using integer-budget multiple-choice knapsack. Larger groups receive no optimized review; **{len(p['optimal']['staged_groups'])} primary groups exceed the cap**. No-review remains an available option. The baselines are the actual previous group-aware order packed into the same budget, and marginal-model-benefit-per-cost greedy. Equal budgets do not require identical spending. + +The fixed acceptance population contains 150 single-view positive judgments: 132 correct and 18 wrong, spread across 15 contaminated groups before review. This is not the earlier study's separate matched-volume top-145 comparison. Primary scoring uses all 149 evaluation source groups, including groups with no accepted edges. The reviewer simulation removes an actually wrong edge with its supplied detection probability and removes a correct one with its supplied false-removal probability. No human reviewed these edges in this experiment. + +The frozen primary scenario is budget 40, s0 = 0.75 and false-removal probability 0.01. The target requires at least 5% fewer expected contaminated groups than **both** comparators, while allowing at most 0.25 additional expected correct-edge removals versus either. + +| Policy | Effort spent | Edges reviewed | Expected contaminated groups | Expected correct removed | +|---|---:|---:|---:|---:| +{reviews} + +The target is **{status(b['primary_target_met']).lower()}**. All three review policies have the same primary expected contamination and correct-edge loss. The optimizer's modeled gain ({p['optimal']['modeled_gain']:.6f}) exceeds packed prior ({p['packed_prior']['modeled_gain']:.6f}) but ties ratio greedy ({p['ratio_greedy']['modeled_gain']:.6f}). Improved optimization of an imperfect model is not a demonstrated graph-quality gain. There are {b['oracle_failures']} objective discrepancies on 64 independent finite-enumeration checks; solver correctness does not rescue the empirical target. + +![H2. Frozen budget-40 reviewer scenario. All three selectors tie on expected source contamination despite different selected IDs and effort spending. Values are scenario expectations, not observed human outcomes.](figures/02_noisy_review.png) + +All 36 combinations of budgets 20/40/80/160 and detection/false-removal scenarios are retained in results.json; selection is recalculated from features and supplied assumptions only. The 4,000 paired group-bootstrap draws at fixed primary selections give a 95% and 99% proposed-minus-comparator contamination-rate interval of [0,0] for both comparisons because their per-group expected outcomes are identical. **This degeneracy is not evidence of known population equivalence.** These descriptive intervals omit model-fitting and reviewer-model uncertainty, and published-data reuse prevents independent confirmation. A deliberately incorrect-risk control spends its only review on an actually correct edge and misses the wrong one. Prior research already demonstrates the relevance of real annotation costs ([Settles et al., 2008](https://burrsettles.com/pub/settles.nips08ws.pdf)); our token-based proxy is not a measurement of those costs. + +## 4. H3: Compile once, bound by decision-diagram state count + +The previous evaluator rejects connected lineage components above sixteen atoms even when their Boolean structure is simple. The proposed compiler canonicalizes a monotone disjunction of conjunctions, chooses frequency-descending/lexical atom order, memoizes residual formulas and merges identical decision nodes. A compiled reduced ordered binary decision diagram can then be evaluated with changing supplied probabilities without recompiling its structure. Children precede parents, so evaluation is a bottom-up weighted sum. Conditional expansion preserves the represented Boolean function; merging identical subfunctions preserves every probability assignment under independent primitive events. + +The safety boundaries are 512 atoms, 4,096 input proofs and 32,768 visited residual states. Input-cap violations stage. State-budget exhaustion returns conservative previous lineage bounds with an explicit nonexact status, never a partially evaluated point estimate. Compilation remains potentially exponential; these caps are operational safeguards, not a polynomial-time theorem. + +There are {c['oracle_failures']} probability discrepancies across 128 random formulas with five assignments each, ten analytic large fixtures, 64 updates on one reused 128-atom chain, and the state-cap control. All ten connected large fixtures are exact under the new compiler and nonexact under the actual previous evaluator. Duplicate/input-order checks have {c['invariance_failures']} failures. + +| Formula | Primitives | Decision nodes | Prior lower bound | Exact probability | +|---|---:|---:|---:|---:| +{lineage} + +The shared-fan oracle is 0.99(1−0.5^(n−1)). The adjacent-pair chain oracle uses an independent two-state recurrence for the probability of no adjacent successes. The 128-atom reuse workload performs {reuse['compilations']} compilation with {reuse['nodes']} decision nodes, then 64 changed-probability evaluations totaling {reuse['evaluation_node_visits']:,} decision-node visits. The target is **{status(c['primary_target_met']).lower()}**. Diagram size, compile-state visits and evaluation-node visits are separate quantities; none is a measured service-latency saving. + +![H3. Decision nodes required for exact connected formulas beyond the previous sixteen-primitive component cap. Lineage probability updates reuse these compiled structures.](figures/03_lineage_capacity.png) + +Knowledge compilation for probabilistic data is established ([Fink et al., 2012](https://arxiv.org/abs/1201.6569)); provenance maintenance and hybrid compilation approaches for uncertain knowledge graphs are also established ([Gaur et al., 2021](https://arxiv.org/abs/2108.07758)). The contribution here is the bounded reusable implementation and its tests against this repository's earlier cap, not invention of decision diagrams or superiority over those systems. H3 still assumes independent primitive inputs; it does not automatically incorporate H1's source model. + +## 5. H4: Certified bipartite conflict optimization + +The cycle-cutset solver in PR #17 can stage a bipartite graph with many cycles even though its supplied-priority maximum independent set has a tractable reduction. The extension first recognizes bipartite components with at most 512 vertices. For bipartition L/R, create source-to-L and R-to-sink capacities equal to nonnegative integer priorities and L-to-R conflict arcs of capacity total priority plus one. A minimum cut cannot profitably cross a conflict arc, since cutting every priority arc is cheaper. Its vertex-side choices therefore give a minimum-weight vertex cover; the complement is a maximum-weight independent set. + +The returned certificate contains a capacity-feasible flow, flow conservation at every internal node, a source/sink separating cut of equal value, and the complementary selected witness. A separate verifier checks these conditions without invoking an optimizer. A feasible flow lower-bounds any cut; equality certifies cut optimality, and the cover reduction certifies the selected supplied-priority objective. Nonbipartite components retain the unmodified prior solver and its staging behavior. No additional budget constraint is imposed; adding such constraints changes the complexity ([Doron-Arad and Shachnai, 2023](https://arxiv.org/abs/2307.08592)). + +There are {d['oracle_failures']} optimum discrepancies, {d['certificate_errors']} certificate errors and {d['prior_regressions']} prior-utility regressions across 128 random bipartite and 64 general small graphs plus nine analytic large fixtures. The large comparator values below come from the actual imported previous solver, not a renamed reimplementation. Zero previous utility means the unsupported component was staged, not that no positive-utility solution exists. + +| Structure | Vertices | Prior utility | Greedy utility | Certified utility | Oracle | +|---|---:|---:|---:|---:|---:| +{graphs} + +The target is **{status(d['primary_target_met']).lower()}**. Complete bipartite fixtures have side weights one and two, giving optimum total weight on the heavier side. Rectangular unit grids have an independent checkerboard half; a perfect matching gives the corresponding upper bound. Certificate-tampering tests, zero weights, invalid endpoints, odd cycles and a dense 17-clique are retained. The odd clique still stages. Priority greedy also attains all nine large analytic optima; the extension adds an optimality certificate and recovery versus prior staging, not an observed large-fixture utility advantage over greedy. + +![H4. Fraction of analytically optimal supplied-priority utility on nine structured conflict fixtures. Prior staging is zero recovered utility, not extraction failure.](figures/04_bipartite_capacity.png) + +The semantic negative control remains decisive: a false assertion with supplied priority 9 defeats a conflicting true assertion with priority 8. An exact optimizer can be exactly wrong about factual truth when its priorities are wrong. No graph-extraction, clinical-validity or KARMA-superiority claim follows. + +## 6. H5: Connected-path summary maintenance + +Whole-component invalidation cannot exploit a local weight update inside one connected graph. For the deliberately narrower case of a fixed connected path, the proposed data structure stores the skip/take dynamic-programming transition for each vertex in a max-plus segment tree. Two-state transitions compose associatively; a leaf update requires recomputing only its ancestors. The root gives optimal utility without emitting the entire selected graph. Topology is validated at construction. Invalid or unknown-vertex weight updates are rejected before mutation; changing topology requires rebuilding. Stars and cycles are rejected rather than silently treated as paths. + +Across 64 random paths with sixteen updates each and the 1,024-vertex path with 256 updates, there are **{e['mismatches']} utility or independent-witness discrepancies** against independent full linear dynamic programming. The primary workload includes cold construction and every update. + +| Work quantity | Count | Interpretation | +|---|---:|---| +| Full recomputation transition candidates | {large['full_transition_candidates']:,} | Three scalar candidates per vertex per build/update | +| Segment-tree transition candidates | {large['segment_transition_candidates']:,} | Eight per internal 2-by-2 composition, including cold build | +| Segment summary-work reduction | {100*large['saving']:.2f}% | Only the two transition-candidate counts above | +| Cold topology validation visits | {large['validation_visits_cold']:,} | Vertex plus adjacency visits, separately recorded | +| Full witness-decoding node visits | {large['witness_visits_all_outputs']:,} | Every output decoded for validation; remains linear | + +The frozen 90% counted summary-work reduction target is **{status(e['primary_target_met']).lower()}**. The 27,864 versus 789,504 transition comparison is not a total runtime or database-I/O measurement. Witness decoding adds 526,079 tree-node visits under a different operation metric and is explicitly not included in that percentage. A consumer needing the complete selected graph after every update still pays linear output/decoding work. Python overhead, memory traffic, initial sorting, dictionary operations and persistence are not captured by scalar transition counts. + +![H5. Counted optimal-utility summary maintenance on a fixed connected path, including cold build. Full witness decoding is separately reported and excluded from the percentage.](figures/05_path_work.png) + +## 7. Interpretation and next falsification boundary + +The results favor identifying the tractable structure of a supplied problem over imposing blanket size restrictions: declared dependence changes which probabilities are valid; compact formula structure permits reusable exact evaluation; bipartiteness permits certified optimization despite many cycles; and a fixed path permits local summary updates despite being one connected component. These conclusions are conditional on correct metadata and supported topology. They do not establish that those structures dominate real Jev-generated graphs. + +The review result is a useful negative finding. A more exact allocator can improve a modeled objective yet fail to improve the frozen downstream source-contamination measure. This counsels against promoting the allocator on optimization quality alone. Prospective human-cost/detection measurements, source-disjoint independently adjudicated documents, policies frozen before those labels are seen, and matched evidence/resource budgets against external graph-synthesis systems remain necessary for semantic or deployment claims. H1 and H3 reliability inputs must be estimated and audited separately; raw model confidence cannot simply be substituted. + +The methods remain opt-in research utilities. No default compiler policy or production graph is changed. Failure controls and all prior studies remain in the complete manuscript. Each target is falsifiable on its specified population, but a passed finite benchmark is not a proof of correctness for all inputs or real-world superiority. + +## 8. Reproducibility and audit + +```bash +python -B -m graph_synthesis.frontier.run +python -B -m unittest discover -s graph_synthesis/frontier/tests -v +python -B -m graph_synthesis.frontier.run --check +python -B -m graph_synthesis.frontier.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +python -B -m graph_synthesis.frontier.verify +``` + +The machine-readable results retain source and code hashes, source-group counts, all random fixtures or complete generating specifications, review IDs and scenarios, independent-oracle outputs, flow/cut witnesses, update sequences and decoded-witness hashes. The five figures are generated directly from those results as SVG and PNG pairs; summary.csv preserves target classifications without pooling them. The [implementation log](IMPLEMENTATION.md) records validation corrections without changing the protocol or endpoints. Original evidence integrity is checked against the immutable inventory. Source, result and figure hashes are bound in the extension manifest; the complete current PDF has a separate source/renderer/PDF hash record. +''' + + +def figures(r): + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + import numpy as np + plt.rcParams['svg.hashsalt']='frontier-20260922' + folder=HERE/'figures'; folder.mkdir(exist_ok=True) + def save(fig,name): + fig.tight_layout() + fig.savefig(folder/(name+'.svg'),metadata={'Date':None}) + fig.savefig(folder/(name+'.png'),dpi=220,metadata={'Software':'Matplotlib'}) + plt.close(fig) + fig,ax=plt.subplots(figsize=(8.0,4.8)) + x=np.arange(2); width=.25 + controls=r['H1']['paired_controls'] + for off,label,values in [(-1,'Independent marginals',[q['independent_marginals']['lower'] for q in controls]), + (0,'Source-conditioned',[q['proposed']['lower'] for q in controls]), + (1,'Joint-world oracle',[q['oracle'] for q in controls])]: + bars=ax.bar(x+off*width,values,width,label=label) + ax.bar_label(bars,fmt='%.2f',padding=3) + ax.set(xticks=x,xticklabels=['At least one copy (OR)','Both copies (AND)'],ylabel='Supplied-model probability',ylim=(0,1.15),title='H1 | Distinct atoms, one common source') + ax.legend(loc='upper center',bbox_to_anchor=(.5,-.1),ncol=3,fontsize=9) + save(fig,'01_source_dependence') + fig,ax=plt.subplots(figsize=(8.0,4.8)) + names=['no_review','packed_prior','ratio_greedy','optimal'] + values=[r['H2']['primary']['policies'][n]['outcome']['expected_contaminated_groups'] for n in names] + bars=ax.bar(range(4),values); ax.bar_label(bars,fmt='%.3f',padding=4) + ax.set(xticks=range(4),xticklabels=['No review','Prior order\npacked','Benefit/cost\ngreedy','Group\nknapsack'],ylabel='Expected contaminated source groups',ylim=(0,17),title='H2 | Budget 40: no primary improvement') + ax.text(.5,-.2,'Supplied detection/false-removal model; not observed human review.',transform=ax.transAxes,ha='center',fontsize=9) + save(fig,'02_noisy_review') + fig,ax=plt.subplots(figsize=(8.0,4.8)) + for kind,marker in [('fan','o'),('chain','s')]: + rows=[x for x in r['H3']['large'] if x['kind']==kind] + ax.plot([x['n'] for x in rows],[x['nodes'] for x in rows],marker=marker,label=kind.capitalize()) + ax.axvline(16,linestyle='--',label='Previous connected-component atom cap') + ax.set(xlabel='Primitive events in connected formula',ylabel='Compiled decision nodes',title='H3 | Exact large lineage from compact structure',xlim=(0,265)) + ax.legend(fontsize=9); save(fig,'03_lineage_capacity') + fig,ax=plt.subplots(figsize=(9.0,4.8)) + rows=r['H4']['large']; x=np.arange(len(rows)); width=.25 + for off,key,label in [(-1,'prior','Previous capped solver'),(0,'greedy','Priority greedy'),(1,'proposed','Certified bipartite')]: + ax.bar(x+off*width,[q[key]['utility']/q['oracle'] for q in rows],width,label=label) + labels=[f"K{q['n']//2},{q['n']//2}" if q['kind']=='complete_bipartite' else f"{q['rows']}x{q['cols']} grid" for q in rows] + ax.set(xticks=x,xticklabels=labels,ylabel='Supplied utility / analytic optimum',ylim=(0,1.15),title='H4 | Nine previously staged structures solved exactly') + ax.tick_params(axis='x',rotation=30); ax.legend(loc='upper center',bbox_to_anchor=(.5,-.23),ncol=3,fontsize=8) + save(fig,'04_bipartite_capacity') + fig,ax=plt.subplots(figsize=(8.0,4.8)); large=r['H5']['large'] + bars=ax.bar(['Full path DP','Segment-tree summary'],[large['full_transition_candidates'],large['segment_transition_candidates']]) + ax.bar_label(bars,labels=[f"{b.get_height():,.0f}" for b in bars],padding=4) + ax.set(ylabel='Counted scalar transition candidates',ylim=(0,900000),title=f"H5 | {100*large['saving']:.2f}% fewer summary transitions") + ax.ticklabel_format(style='plain',axis='y') + ax.text(.5,-.15,'Cold build + 256 updates. Full witness decoding excluded; see separate counts.',transform=ax.transAxes,ha='center',fontsize=9) + save(fig,'05_path_work') + + +def replace_block(text, body): + text=re.sub(re.escape(START)+r'.*?'+re.escape(END)+r'\n*','',text,flags=re.S) + anchor=next((x for x in ('','','','',ANCHOR) if x in text),ANCHOR) + if text.count(anchor)!=1: + raise ValueError('Exactly one preceding study anchor required') + return text.replace(anchor,anchor+'\n\n'+START+'\n\n'+body.strip()+'\n\n'+END,1) + + +def update_paper(r, body): + def link(match): + label,url=match.groups() + if re.match(r'[A-Za-z][A-Za-z0-9+.-]*:',url) or url.startswith('#'): + return match.group(0) + import os + path=HERE/url + return f'[{label}]({Path(os.path.relpath(path,ROOT/"manuscript")).as_posix()})' + body=re.sub(r'\[([^\]]*)\]\(([^)]+)\)',link,body) + path=ROOT/'manuscript/paper-current.md' + write(path,replace_block(path.read_text(encoding='utf-8'),body)) + text='''## Five structural-frontier extensions after PR #17 + +[Executed report](graph_synthesis/frontier/RESULTS.md), [pre-execution protocol](graph_synthesis/frontier/PROTOCOL.md), [novelty audit](graph_synthesis/frontier/NOVELTY.md), [five figure pairs](graph_synthesis/frontier/figures/) and [complete updated paper](manuscript/paper-current.pdf). H1/H3/H4/H5 meet controlled supplied-input targets; H2 fails its frozen review-quality target. Source-conditioned probabilities prevent nine independent-marginal false admissions on 96 controlled fixtures; all three review selectors tie at 13.043478 expected contaminated groups. Ten large lineage formulas and nine bipartite conflict fixtures are exact. Connected-path summary transitions fall by 96.47%, excluding linear witness decoding. No new Jev calls, independent semantic-accuracy finding, worldwide novelty claim or production-policy change. All earlier studies and negative results remain intact.''' + path=ROOT/'CURRENT_RESULTS.md'; write(path,replace_block(path.read_text(encoding='utf-8'),text)) + + +def manifest(): + files={p.relative_to(ROOT).as_posix():hashlib.sha256(p.read_bytes()).hexdigest() for p in sorted(HERE.rglob('*')) + if p.is_file() and p.name!='artifact-manifest.json' and '__pycache__' not in p.parts} + value={'schema_version':1,'scope':'Extension files only; complete manuscript has its own build hash record.','sha256':files} + write(HERE/'artifact-manifest.json',json.dumps(value,indent=2,sort_keys=True)+'\n') + + +def main(): + parser=argparse.ArgumentParser(description=__doc__); parser.add_argument('--figures',action='store_true');parser.add_argument('--update-paper',action='store_true');args=parser.parse_args() + r=json.loads((HERE/'results.json').read_text(encoding='utf-8')) + body=report(r);write(HERE/'RESULTS.md',body) + with (HERE/'summary.csv').open('w',encoding='utf-8',newline='') as f: + rows=summary_rows(r); writer=csv.DictWriter(f,fieldnames=list(rows[0]),lineterminator='\n');writer.writeheader();writer.writerows(rows) + if args.figures:figures(r) + if args.update_paper:update_paper(r,body) + manifest() + + +if __name__=='__main__':main() diff --git a/graph_synthesis/frontier/results.json b/graph_synthesis/frontier/results.json new file mode 100644 index 0000000..be5d27f --- /dev/null +++ b/graph_synthesis/frontier/results.json @@ -0,0 +1,501100 @@ +{ + "H1": { + "false_admissions": 0, + "fixtures": [ + { + "case": 0, + "independent_marginals": { + "components": [ + { + "atoms": 4, + "exact": true, + "lower": 0.455456, + "proofs": 2, + "reason": "exact", + "states": 7, + "upper": 0.455456 + }, + { + "atoms": 1, + "exact": true, + "lower": 0.42000000000000004, + "proofs": 1, + "reason": "exact", + "states": 3, + "upper": 0.42000000000000004 + }, + { + "atoms": 1, + "exact": true, + "lower": 0.28, + "proofs": 1, + "reason": "exact", + "states": 3, + "upper": 0.28 + }, 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retain unfavorable target outcomes.""" +from __future__ import annotations +import argparse +from collections import defaultdict +from itertools import combinations, product +import hashlib +import json +import math +from pathlib import Path +import random +import socket +from unittest.mock import patch + +from .methods import (source_mixture, compile_lineage, optimal_review, ratio_review, + packed_review, review_gain, solve_frontier, verify_certificate, + PathOptimizer) +from ..reliability.methods import (POSITIVE, fit_small, assign_small, exact_lineage, + solve_graph, graph_input) +from ..reliability.run import probability_oracle + +ROOT = Path(__file__).resolve().parents[2] +HERE = Path(__file__).resolve().parent +SEED = 20260922 +BASELINE = 'a62a3257645d8e35cd4e45be53bfa9511d27724b' +PROTOCOL = 'ef8fc308d0a50cd37a6cdd3b7546e050093349c8' + + +def sha(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def write(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, sort_keys=True, indent=2, allow_nan=False)+'\n', encoding='utf-8', newline='\n') + + +def joint_oracle(proofs, sources, primitives): + """Enumerate complete joint worlds, independent of conditional DNF evaluation.""" + ss, aa = sorted(sources), sorted(primitives) + total = 0. + for bits in product((False, True), repeat=len(ss)+len(aa)): + state = dict(zip(ss, bits[:len(ss)])) + truth = dict(zip(aa, bits[len(ss):])) + if not any(all(truth[a] for a in p) for p in proofs): + continue + mass = math.prod(sources[s] if state[s] else 1-sources[s] for s in ss) + for a in aa: + row = primitives[a] + p = row['p1'] if state[row['source']] else row['p0'] + mass *= p if truth[a] else 1-p + total += mass + return total + + +def independent_marginals(sources, primitives): + return {a: (1-sources[r['source']])*r['p0']+sources[r['source']]*r['p1'] for a, r in primitives.items()} + + +def source_benchmark(): + rng = random.Random(SEED) + fixtures, failures, invariant, false_admissions, prior_false = [], 0, 0, 0, 0 + for case in range(96): + atoms = [f'a{i}' for i in range(rng.randrange(2,9))] + sources = {f's{i}': rng.choice([.2,.5,.8,.95]) for i in range(rng.randrange(1,4))} + primitives = {a: {'source': rng.choice(sorted(sources)), 'p0': rng.choice([0.,.1,.3]), + 'p1': rng.choice([.6,.9,1.])} for a in atoms} + proofs = [sorted(rng.sample(atoms, rng.randrange(1,min(4,len(atoms))+1))) for _ in range(rng.randrange(1,11))] + oracle = joint_oracle(proofs, sources, primitives) + got = source_mixture(proofs, sources, primitives) + prior = exact_lineage(proofs, independent_marginals(sources, primitives)) + failures += not got['exact'] or abs(got['lower']-oracle) > 1e-12 + false_admissions += got['lower'] >= .95 and oracle < .95-1e-12 + prior_false += prior['lower'] >= .95 and oracle < .95-1e-12 + for copies in (1,2,5): + permuted = source_mixture(list(reversed(proofs))*copies, sources, primitives) + invariant += permuted != got + fixtures.append({'case': case, 'sources': sources, 'primitives': primitives, 'proofs': proofs, + 'oracle': oracle, 'proposed': got, 'independent_marginals': prior}) + sources = {'shared': .8} + atoms = {a: {'source': 'shared', 'p0': 0., 'p1': 1.} for a in ('a','b')} + controls = [] + for name, proofs in [('OR', [['a'],['b']]), ('AND', [['a','b']])]: + controls.append({'query': name, 'oracle': .8, 'proposed': source_mixture(proofs,sources,atoms), + 'independent_marginals': exact_lineage(proofs,{'a':.8,'b':.8})}) + missed = source_mixture([['a'],['b']], {'s1':.8,'s2':.8}, + {'a':{'source':'s1','p0':0.,'p1':1.}, 'b':{'source':'s2','p0':0.,'p1':1.}}) + prevented = controls[0]['independent_marginals']['lower'] >= .95 and controls[0]['proposed']['upper'] < .95 + return {'primary_target_met': not failures and not invariant and not false_admissions and prevented, + 'fixtures': fixtures, 'oracle_failures': int(failures), 'invariance_failures': int(invariant), + 'invariance_checks': 288, 'false_admissions': int(false_admissions), + 'prior_false_admissions_random': int(prior_false), 'paired_controls': controls, + 'mean_absolute_error_prior': sum(abs(f['independent_marginals']['lower']-f['oracle']) for f in fixtures)/96, + 'mean_absolute_error_proposed': sum(abs(f['proposed']['lower']-f['oracle']) for f in fixtures)/96, + 'misspecified_source_control': {'supplied_result':missed, 'actual_probability':.8, + 'note':'Two declared latent sources actually share one unmodeled cause; the new method still gives 0.96.'}} + + +def make_review_candidates(test, fitted, s0, f): + # Explicitly strip gold, all compact views, and evaluation outcomes. + features = [{'id':r['id'], 'group':r['group'], 'views':{'base1':r['views']['base1']}} for r in test] + assigned = assign_small(features, fitted) + costs = {r['id']: min(8,1+int(r['arms']['single']['input_tokens'])//1000) for r in test} + return [{**r,'cost':costs[r['id']], 'detect':s0/(1+.05*(costs[r['id']]-1)), 'false_remove':f} for r in assigned] + + +def review_outcome(test, candidates, selected): + # Gold is used here only, AFTER fixed review IDs have been selected. + lookup = {r['id']:r for r in candidates} + grouped = defaultdict(list) + for r in test: + grouped[r['group']].append(r) + outcomes = [] + for g, rows in sorted(grouped.items()): + wrong = [r for r in rows if r['views']['base1']['label'] in POSITIVE and r['views']['base1']['label'] != r['gold']] + correct = [r for r in rows if r['views']['base1']['label'] in POSITIVE and r['views']['base1']['label'] == r['gold']] + clean = math.prod(lookup[r['id']]['detect'] if r['id'] in selected else 0. for r in wrong) + loss = sum(lookup[r['id']]['false_remove'] for r in correct if r['id'] in selected) + outcomes.append({'group':g, 'contaminated':1-clean, 'correct_removed':loss, + 'wrong_remaining':sum(1-lookup[r['id']]['detect']*(r['id'] in selected) for r in wrong), + 'correct_retained':len(correct)-loss}) + return {'expected_contaminated_groups':sum(r['contaminated'] for r in outcomes), + 'expected_correct_removed':sum(r['correct_removed'] for r in outcomes), + 'expected_wrong_remaining':sum(r['wrong_remaining'] for r in outcomes), + 'expected_correct_retained':sum(r['correct_retained'] for r in outcomes), + 'groups':outcomes} + + +def paired_interval(a, b): + import numpy as np + x = np.array([r['contaminated'] for r in a['groups']])-np.array([r['contaminated'] for r in b['groups']]) + rng = np.random.default_rng(SEED) + draws = x[rng.integers(0,len(x),size=(4000,len(x)))].mean(axis=1) + return {'point_rate_difference':float(x.mean()), '95':[float(v) for v in np.quantile(draws,[.025,.975])], + '99':[float(v) for v in np.quantile(draws,[.005,.995])], 'groups':len(x), 'draws':4000} + + +def review_oracle(candidates, budget): + best = 0. + for mask in range(1 << len(candidates)): + chosen = [r for i,r in enumerate(candidates) if mask & (1 << i)] + if sum(r['cost'] for r in chosen) > budget: + continue + selected = {r['id'] for r in chosen} + # Independent direct expected-contamination calculation, not DP options. + groups = defaultdict(list) + for row in candidates: + groups[row['group']].append(row) + gain = 0. + for rows in groups.values(): + q0 = 1-math.prod(1-r['risk'] for r in rows) + q1 = 1-math.prod(1-r['risk']*(1-r['detect'] if r['id'] in selected else 1) for r in rows) + gain += q0-q1 + gain -= sum((1-r['risk'])*r['false_remove'] for r in chosen) + best = max(best,gain) + return best + + +def review_benchmark(development, test): + from ..adaptive.methods import review_order + fitted = fit_small(development) + scenarios = [] + primary = None + for s0 in (.5,.75,1.): + for false in (0.,.01,.05): + candidates = make_review_candidates(test,fitted,s0,false) + order = review_order([{'id':r['id'],'group':r['group'],'risk':r['risk']} for r in candidates], group_aware=True) + for budget in (20,40,80,160): + plans = {'packed_prior':packed_review(candidates,budget,order), 'ratio_greedy':ratio_review(candidates,budget), + 'optimal':optimal_review(candidates,budget), 'no_review':{'selected':[],'cost':0,'modeled_gain':0.}} + policies = {name:{**plan,'outcome':review_outcome(test,candidates,set(plan['selected']))} for name,plan in plans.items()} + item = {'s0':s0,'false_remove':false,'budget':budget,'policies':policies} + scenarios.append(item) + if s0 == .75 and false == .01 and budget == 40: + primary = item + p = primary['policies']; target = True + for baseline in ('packed_prior','ratio_greedy'): + target &= p['optimal']['outcome']['expected_contaminated_groups'] <= .95*p[baseline]['outcome']['expected_contaminated_groups'] + target &= p['optimal']['outcome']['expected_correct_removed'] <= p[baseline]['outcome']['expected_correct_removed']+.25 + rng = random.Random(SEED) + fixtures, failures = [], 0 + for case in range(64): + candidates = [{'id':str(i),'group':str(rng.randrange(3)),'risk':rng.choice([.05,.2,.5,.8]), + 'cost':rng.randrange(1,5),'detect':rng.choice([.5,.75,1.]),'false_remove':rng.choice([0.,.01,.05])} + for i in range(rng.randrange(2,9))] + budget = rng.randrange(1,15) + got, oracle = optimal_review(candidates,budget), review_oracle(candidates,budget) + failures += abs(got['modeled_gain']-oracle) > 1e-10 + fixtures.append({'case':case,'candidates':candidates,'budget':budget,'proposed':got,'oracle_gain':oracle}) + misleading = [{'id':'actually_correct','group':'g1','risk':.9,'cost':1,'detect':1.,'false_remove':.05}, + {'id':'actually_wrong','group':'g2','risk':.01,'cost':1,'detect':1.,'false_remove':.05}] + return {'primary_target_met':bool(target and not failures),'fit':fitted,'primary':primary,'scenarios':scenarios, + 'paired':{k:paired_interval(p['optimal']['outcome'],p[k]['outcome']) for k in ('packed_prior','ratio_greedy')}, + 'primary_features':make_review_candidates(test,fitted,.75,.01),'oracle_fixtures':fixtures,'oracle_failures':int(failures), + 'wrong_risk_control':{'features':misleading,'plan':optimal_review(misleading,1), + 'actually_correct':['actually_correct'],'actually_wrong':['actually_wrong']}, + 'assumptions':'Proxy effort; supplied cost-dependent detection and false-removal probabilities; independent reviewer detections. No observed human review.'} + + +def chain_oracle(probabilities): + no_previous, previous = 1., 0. + for p in probabilities: + no_previous, previous = (no_previous+previous)*(1-p), no_previous*p + return 1-no_previous-previous + + +def diagram_benchmark(): + rng = random.Random(SEED) + fixtures, failures, invariance = [], 0, 0 + for case in range(128): + atoms = [f'a{i}' for i in range(rng.randrange(2,11))] + proofs = [sorted(rng.sample(atoms,rng.randrange(1,min(4,len(atoms))+1))) for _ in range(rng.randrange(1,11))] + diagram = compile_lineage(proofs) + assignments = [] + for repeat in range(5): + probs = {a:rng.choice([0.,.2,.5,.8,.95,1.]) for a in diagram.atoms} + got = diagram.evaluate(probs); truth = probability_oracle(proofs,probs) + failures += not got['exact'] or abs(got['lower']-truth) > 1e-12 + assignments.append({'probabilities':probs,'oracle':truth,'proposed':got}) + invariance += compile_lineage(list(reversed(proofs))*2) != diagram + fixtures.append({'case':case,'proofs':proofs,'states':diagram.states,'nodes':len(diagram.nodes),'assignments':assignments}) + large = [] + for kind in ('fan','chain'): + for n in (17,32,64,128,256): + atoms = [f'a{i:03d}' for i in range(n)] + if kind == 'fan': + probs = {a:(.99 if i == 0 else .5) for i,a in enumerate(atoms)} + proofs = [[atoms[0],a] for a in atoms[1:]] + oracle = .99*(1-.5**(n-1)) + else: + probs = {a:.5 for a in atoms} + proofs = [[a,b] for a,b in zip(atoms,atoms[1:])] + oracle = chain_oracle(list(probs.values())) + diagram = compile_lineage(proofs) + got = diagram.evaluate(probs); prior = exact_lineage(proofs,probs) + failures += not got['exact'] or abs(got['lower']-oracle) > 1e-12 or prior['exact'] + large.append({'kind':kind,'n':n,'nodes':len(diagram.nodes),'states':diagram.states,'oracle':oracle,'proposed':got,'prior':prior}) + atoms = [f'a{i:03d}' for i in range(128)] + proofs = [[a,b] for a,b in zip(atoms,atoms[1:])] + diagram = compile_lineage(proofs) + probs = {a:.5 for a in atoms} + updates = [] + for step in range(64): + atom = rng.choice(atoms); probs[atom] = rng.choice([.1,.3,.7,.9]) + got = diagram.evaluate(probs); oracle = chain_oracle([probs[a] for a in atoms]) + failures += not got['exact'] or abs(got['lower']-oracle) > 1e-12 + updates.append({'atom':atom,'probability':probs[atom],'oracle':oracle,'proposed':got}) + capped = compile_lineage([['a','b'],['b','c']],state_cap=1).evaluate({'a':.5,'b':.5,'c':.5}) + cap_truth = probability_oracle([['a','b'],['b','c']],{'a':.5,'b':.5,'c':.5}) + failures += capped['exact'] or not capped['lower'] <= cap_truth <= capped['upper'] + return {'primary_target_met':not failures and not invariance, 'oracle_failures':int(failures),'invariance_failures':int(invariance), + 'fixtures':fixtures,'large':large,'reuse':{'compilations':1,'states':diagram.states,'nodes':len(diagram.nodes),'updates':updates, + 'evaluation_node_visits':sum(x['proposed']['node_visits'] for x in updates)}, + 'state_cap_control':{'proposed':capped,'oracle':cap_truth}} + + +def independent_set_oracle(weights,edges): + keys = sorted(weights); best = 0 + for mask in range(1 << len(keys)): + chosen = {k for i,k in enumerate(keys) if mask & (1 << i)} + if any(a in chosen and b in chosen for a,b in edges): + continue + best = max(best,sum(weights[k] for k in chosen)) + return best + + +def greedy_graph(weights, edges): + adj = graph_input(weights,edges); chosen = set() + for k in sorted(weights,key=lambda k:(-weights[k],k)): + if not adj[k].intersection(chosen): + chosen.add(k) + return {'selected':sorted(chosen),'utility':sum(weights[k] for k in chosen)} + + +def graph_case(weights, edges): + got = solve_frontier(weights,edges); prior = solve_graph(weights,edges) + certificate_errors = 0 + for component in got['components']: + if component['method'] == 'bipartite_min_cut': + keys = set(component['certificate']['keys']) + w = {k:weights[k] for k in keys}; e = [(a,b) for a,b in edges if a in keys and b in keys] + certificate_errors += not verify_certificate(w,e,component) + return {'weights':weights,'edges':edges,'proposed':got,'prior':prior,'greedy':greedy_graph(weights,edges), + 'certificate_errors':int(certificate_errors)} + + +def graph_benchmark(): + rng = random.Random(SEED) + small, failures, certificate_errors, regression = [], 0, 0, 0 + for case in range(192): + n = rng.randrange(2,13); keys = [f'v{i:02d}' for i in range(n)] + weights = {k:rng.randrange(11) for k in keys} + edges = [(a,b) for i,a in enumerate(keys) for j,b in enumerate(keys) if i < j and rng.random() < .35 and (case >= 128 or (i < n//2 <= j))] + item = graph_case(weights,edges); oracle = independent_set_oracle(weights,edges) + item.update({'case':case,'kind':'bipartite' if case < 128 else 'general','oracle':oracle}) + failures += item['proposed']['utility'] != oracle + certificate_errors += item['certificate_errors']; regression += item['proposed']['utility'] < item['prior']['utility'] + small.append(item) + large = [] + for n in (18,32,64,128,256): + left,right = [f'l{i:03d}' for i in range(n//2)],[f'r{i:03d}' for i in range(n//2)] + w = {k:(1 if k[0]=='l' else 2) for k in left+right}; e = [(a,b) for a in left for b in right] + item = graph_case(w,e); oracle = n + item.update({'kind':'complete_bipartite','n':n,'oracle':oracle}) + failures += item['proposed']['utility'] != oracle or bool(item['proposed']['staged']) + certificate_errors += item['certificate_errors']; large.append(item) + for rows,cols in ((4,8),(8,8),(8,16),(16,16)): + key = lambda i,j:f'g{i:02d}_{j:02d}' + w = {key(i,j):1 for i in range(rows) for j in range(cols)} + e = [(key(i,j),key(a,b)) for i in range(rows) for j in range(cols) for a,b in ((i+1,j),(i,j+1)) if a < rows and b < cols] + item = graph_case(w,e); oracle = rows*cols//2 + item.update({'kind':'grid','n':rows*cols,'rows':rows,'cols':cols,'oracle':oracle}) + failures += item['proposed']['utility'] != oracle or bool(item['proposed']['staged']) + certificate_errors += item['certificate_errors']; large.append(item) + false_control = solve_frontier({'false':9,'true':8},[('false','true')]) + clique_w = {str(i):1 for i in range(17)} + clique = solve_frontier(clique_w,list(combinations(clique_w,2))) + return {'primary_target_met':not failures and not certificate_errors and not regression, + 'oracle_failures':int(failures),'certificate_errors':int(certificate_errors),'prior_regressions':int(regression), + 'small':small,'large':large,'false_priority_control':false_control,'odd_clique_control':clique} + + +def linear_path_oracle(weights, order): + skip, take = 0, -math.inf + for key in order: + skip,take = max(skip,take),skip+weights[key] + return max(skip,take) + + +def path_case(n, count, rng): + keys = [f'v{i:04d}' for i in range(n)] + weights = {k:rng.randrange(21) for k in keys}; initial = dict(weights) + edges = list(zip(keys,keys[1:])) + engine = PathOptimizer(weights,edges) + failures, witness_visits = 0, 0 + def check(): + nonlocal failures,witness_visits + chosen = engine.selected(); witness_visits += engine.witness_visits + oracle = linear_path_oracle(weights,keys) + failures += engine.utility != oracle or sum(weights[k] for k in chosen) != oracle or any(a in chosen and b in chosen for a,b in edges) + return {'oracle':oracle,'proposed_utility':engine.utility,'witness_sha256':hashlib.sha256('\n'.join(chosen).encode()).hexdigest(), + 'selected_count':len(chosen),'witness_visits':engine.witness_visits,'transition_candidates':engine.last_transitions} + cold = check(); updates = [] + for step in range(count): + key, weight = rng.choice(keys),rng.randrange(21) + weights[key] = weight; engine.update(key,weight) + updates.append({'vertex':key,'weight':weight,**check()}) + full = 3*n*(count+1) + return {'n':n,'initial_weights':initial,'edges':edges,'cold':cold,'updates':updates,'mismatches':int(failures), + 'segment_transition_candidates':engine.transitions,'full_transition_candidates':full, + 'saving':1-engine.transitions/full,'validation_visits_cold':engine.validation_visits, + 'witness_visits_all_outputs':witness_visits} + + +def path_benchmark(): + rng = random.Random(SEED) + small = [path_case(rng.randrange(1,65),16,rng) for _ in range(64)] + large = path_case(1024,256,rng) + failures = sum(c['mismatches'] for c in small)+large['mismatches'] + return {'primary_target_met':not failures and large['saving'] >= .90,'mismatches':failures,'small':small,'large':large, + 'boundary':'Fixed path, weight-only updates. Full witness decoding remains linear. Not total runtime, database I/O, or graph-emission speedup.'} + + +def execute(): + from ..adaptive.run import load, INPUT + rows, calls, audit = load() + development = [r for r in rows if r['split']=='development']; test = [r for r in rows if r['split']=='test'] + if (len(development),len(test)) != (73,263) or len({r['id'] for r in rows}) != len(rows): + raise ValueError('Frozen input shape or unique IDs changed') + devgroups, testgroups = {r['group'] for r in development},{r['group'] for r in test} + if devgroups & testgroups: + raise ValueError('Development/evaluation source leakage') + result = {'schema_version':1,'baseline_commit':BASELINE,'protocol_commit':PROTOCOL,'protocol_sha256':sha(HERE/'PROTOCOL.md'), + 'seed':SEED,'fresh_service_calls':0,'development':{'n':len(development),'groups':len(devgroups)}, + 'evaluation':{'n':len(test),'groups':len(testgroups)},'input_audit':audit, + 'source_hashes':{(INPUT/n).relative_to(ROOT).as_posix():sha(INPUT/n) for n in ('plan.json','calls.jsonl','predictions.json','results.json')}, + 'code_hashes':{p.relative_to(ROOT).as_posix():sha(p) for p in (HERE/'methods.py',HERE/'run.py',HERE.parent/'reliability/methods.py',HERE.parent/'adaptive/run.py')}, + 'evidence':'H2 saved-response replay plus assumed noisy review; H1/H3/H4/H5 controlled algorithms. No independent semantic-accuracy evidence.'} + for key,fn in [('H1',source_benchmark),('H2',lambda:review_benchmark(development,test)),('H3',diagram_benchmark),('H4',graph_benchmark),('H5',path_benchmark)]: + result[key] = fn() + print(key, 'target',result[key]['primary_target_met'],flush=True) + return json.loads(json.dumps(result,sort_keys=True,allow_nan=False)) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__); parser.add_argument('--check',action='store_true'); args = parser.parse_args() + with patch.object(socket.socket,'connect',side_effect=RuntimeError('Network forbidden')), patch.object(socket,'create_connection',side_effect=RuntimeError('Network forbidden')): + result = execute() + if args.check: + from ..verify import compare_json + compare_json(json.loads((HERE/'results.json').read_text(encoding='utf-8')),result) + else: + write(HERE/'results.json',result) + print(json.dumps({'status':'reproduced' if args.check else 'executed','targets':{k:result[k]['primary_target_met'] for k in ('H1','H2','H3','H4','H5')},'fresh_service_calls':0})) + + +if __name__ == '__main__': + main() diff --git a/graph_synthesis/frontier/summary.csv b/graph_synthesis/frontier/summary.csv new file mode 100644 index 0000000..0effff7 --- /dev/null +++ b/graph_synthesis/frontier/summary.csv @@ -0,0 +1,6 @@ +hypothesis,method,target_met,evidence +H1,Latent-source-conditioned lineage,True,controlled supplied-input algorithm +H2,Cost/noise-aware group review,False,saved-response / reviewer-model scenario +H3,Reusable state-bounded lineage diagrams,True,controlled supplied-input algorithm +H4,Certified bipartite conflict optimization,True,controlled supplied-input algorithm +H5,Connected-path incremental optimization,True,controlled supplied-input algorithm diff --git a/graph_synthesis/frontier/tests.log b/graph_synthesis/frontier/tests.log new file mode 100644 index 0000000..8d3b053 --- /dev/null +++ b/graph_synthesis/frontier/tests.log @@ -0,0 +1,80 @@ +test_certificate (test_frontier.ConflictTests.test_certificate) ... ok +test_dense_odd_clique_staged (test_frontier.ConflictTests.test_dense_odd_clique_staged) ... ok +test_duplicate_edges (test_frontier.ConflictTests.test_duplicate_edges) ... ok +test_empty_graph (test_frontier.ConflictTests.test_empty_graph) ... ok +test_finite_oracle_exhaustive_topologies (test_frontier.ConflictTests.test_finite_oracle_exhaustive_topologies) ... ok +test_invalid_weight (test_frontier.ConflictTests.test_invalid_weight) ... ok +test_odd_cycle_fallback (test_frontier.ConflictTests.test_odd_cycle_fallback) ... ok +test_optimum (test_frontier.ConflictTests.test_optimum) ... ok +test_order_invariance (test_frontier.ConflictTests.test_order_invariance) ... ok +test_self_conflict (test_frontier.ConflictTests.test_self_conflict) ... ok +test_semantically_false_priority (test_frontier.ConflictTests.test_semantically_false_priority) ... ok +test_tampered_certificates (test_frontier.ConflictTests.test_tampered_certificates) ... ok +test_unknown_endpoint (test_frontier.ConflictTests.test_unknown_endpoint) ... ok +test_zero_weights (test_frontier.ConflictTests.test_zero_weights) ... ok +test_analytical_formula (test_frontier.DiagramTests.test_analytical_formula) ... ok +test_atom_cap (test_frontier.DiagramTests.test_atom_cap) ... ok +test_bad_atom (test_frontier.DiagramTests.test_bad_atom) ... ok +test_cap_bounds (test_frontier.DiagramTests.test_cap_bounds) ... ok +test_compile_reuse_is_immutable (test_frontier.DiagramTests.test_compile_reuse_is_immutable) ... ok +test_duplicate_permutation (test_frontier.DiagramTests.test_duplicate_permutation) ... ok +test_empty_disjunction (test_frontier.DiagramTests.test_empty_disjunction) ... ok +test_extra_probabilities (test_frontier.DiagramTests.test_extra_probabilities) ... ok +test_invalid_probabilities (test_frontier.DiagramTests.test_invalid_probabilities) ... ok +test_invalid_state_caps (test_frontier.DiagramTests.test_invalid_state_caps) ... ok +test_large_fan (test_frontier.DiagramTests.test_large_fan) ... ok +test_missing_probabilities (test_frontier.DiagramTests.test_missing_probabilities) ... ok +test_probability_endpoints (test_frontier.DiagramTests.test_probability_endpoints) ... ok +test_proof_cap (test_frontier.DiagramTests.test_proof_cap) ... ok +test_reject_empty_proof (test_frontier.DiagramTests.test_reject_empty_proof) ... ok +test_subsumed_proofs (test_frontier.DiagramTests.test_subsumed_proofs) ... ok +test_all_declared_hashes (test_frontier.EvidenceTests.test_all_declared_hashes) ... ok +test_counted_controlled_oracles (test_frontier.EvidenceTests.test_counted_controlled_oracles) ... ok +test_frozen_protocol (test_frontier.EvidenceTests.test_frozen_protocol) ... ok +test_negative_primary_is_retained (test_frontier.EvidenceTests.test_negative_primary_is_retained) ... ok +test_no_service_calls (test_frontier.EvidenceTests.test_no_service_calls) ... ok +test_real_input_shape (test_frontier.EvidenceTests.test_real_input_shape) ... ok +test_all_binary_small_weights (test_frontier.PathTests.test_all_binary_small_weights) ... ok +test_atomic_invalid_updates (test_frontier.PathTests.test_atomic_invalid_updates) ... ok +test_cycle_rejected (test_frontier.PathTests.test_cycle_rejected) ... ok +test_disconnected_rejected (test_frontier.PathTests.test_disconnected_rejected) ... ok +test_empty_rejected (test_frontier.PathTests.test_empty_rejected) ... ok +test_random_updates (test_frontier.PathTests.test_random_updates) ... ok +test_same_value_no_work (test_frontier.PathTests.test_same_value_no_work) ... ok +test_single_vertex (test_frontier.PathTests.test_single_vertex) ... ok +test_star_rejected (test_frontier.PathTests.test_star_rejected) ... ok +test_topology_rebuild (test_frontier.PathTests.test_topology_rebuild) ... ok +test_witness_work_is_reported (test_frontier.PathTests.test_witness_work_is_reported) ... ok +test_zero_weights (test_frontier.PathTests.test_zero_weights) ... ok +test_bad_budget (test_frontier.ReviewTests.test_bad_budget) ... ok +test_bad_cost (test_frontier.ReviewTests.test_bad_cost) ... ok +test_cap_staging (test_frontier.ReviewTests.test_cap_staging) ... ok +test_development_fit_rejects_test (test_frontier.ReviewTests.test_development_fit_rejects_test) ... ok +test_duplicate_ids (test_frontier.ReviewTests.test_duplicate_ids) ... ok +test_empty (test_frontier.ReviewTests.test_empty) ... ok +test_global_finite_oracle (test_frontier.ReviewTests.test_global_finite_oracle) ... ok +test_harmful_review_is_skipped (test_frontier.ReviewTests.test_harmful_review_is_skipped) ... ok +test_input_purity (test_frontier.ReviewTests.test_input_purity) ... ok +test_packed_order_validation (test_frontier.ReviewTests.test_packed_order_validation) ... ok +test_permutation (test_frontier.ReviewTests.test_permutation) ... ok +test_selected_features_ignore_test_gold (test_frontier.ReviewTests.test_selected_features_ignore_test_gold) ... ok +test_strict_feature_schema (test_frontier.ReviewTests.test_strict_feature_schema) ... ok +test_zero_budget (test_frontier.ReviewTests.test_zero_budget) ... ok +test_atom_cap_stages (test_frontier.SourceTests.test_atom_cap_stages) ... ok +test_conditional_noise_oracle (test_frontier.SourceTests.test_conditional_noise_oracle) ... ok +test_deterministic_sources (test_frontier.SourceTests.test_deterministic_sources) ... ok +test_duplicate_invariant (test_frontier.SourceTests.test_duplicate_invariant) ... ok +test_empty_conjunction_rejected (test_frontier.SourceTests.test_empty_conjunction_rejected) ... ok +test_empty_disjunction (test_frontier.SourceTests.test_empty_disjunction) ... ok +test_independent_sources (test_frontier.SourceTests.test_independent_sources) ... ok +test_input_unchanged (test_frontier.SourceTests.test_input_unchanged) ... ok +test_invalid_probabilities (test_frontier.SourceTests.test_invalid_probabilities) ... ok +test_shared_and (test_frontier.SourceTests.test_shared_and) ... ok +test_shared_or (test_frontier.SourceTests.test_shared_or) ... ok +test_source_cap_stages (test_frontier.SourceTests.test_source_cap_stages) ... ok +test_unknown_source (test_frontier.SourceTests.test_unknown_source) ... ok + +---------------------------------------------------------------------- +Ran 75 tests in 0.340s + +OK diff --git a/graph_synthesis/frontier/tests/test_frontier.py b/graph_synthesis/frontier/tests/test_frontier.py new file mode 100644 index 0000000..5e631b3 --- /dev/null +++ b/graph_synthesis/frontier/tests/test_frontier.py @@ -0,0 +1,337 @@ +"""Adversarial regression tests; target failures are valid research outcomes.""" +import copy +import hashlib +import itertools +import json +from pathlib import Path +import random +import unittest +from unittest.mock import patch + +from graph_synthesis.frontier.methods import ( + PathOptimizer, bipartite_optimum, compile_lineage, optimal_review, + packed_review, probability, ratio_review, review_gain, solve_frontier, + source_mixture, verify_certificate, +) +from graph_synthesis.frontier.run import ( + HERE, make_review_candidates, independent_set_oracle, joint_oracle, + linear_path_oracle, review_oracle, +) +from graph_synthesis.reliability.methods import fit_small + + +def row(i='a', group='g', risk=.5, cost=1, detect=.75, false_remove=.01): + return dict(id=i, group=group, risk=risk, cost=cost, detect=detect, false_remove=false_remove) + + +class SourceTests(unittest.TestCase): + def setUp(self): + self.sources = {'s': .8} + self.atoms = {a: dict(source='s', p0=0., p1=1.) for a in 'ab'} + + def test_shared_or(self): + self.assertAlmostEqual(source_mixture([['a'], ['b']], self.sources, self.atoms)['lower'], .8) + + def test_shared_and(self): + self.assertAlmostEqual(source_mixture([['a', 'b']], self.sources, self.atoms)['lower'], .8) + + def test_independent_sources(self): + atoms = copy.deepcopy(self.atoms); atoms['b']['source'] = 't' + self.assertAlmostEqual(source_mixture([['a'], ['b']], {'s': .8, 't': .8}, atoms)['lower'], .96) + + def test_conditional_noise_oracle(self): + atoms = {'a': dict(source='s', p0=.3, p1=.7), 'b': dict(source='s', p0=.2, p1=.9)} + proofs = [['a', 'b']] + self.assertAlmostEqual(source_mixture(proofs, self.sources, atoms)['lower'], joint_oracle(proofs, self.sources, atoms)) + + def test_deterministic_sources(self): + for p in (0., 1.): + self.assertEqual(source_mixture([['a']], {'s': p}, self.atoms)['lower'], p) + + def test_duplicate_invariant(self): + self.assertEqual(source_mixture([['a'], ['b']], self.sources, self.atoms), + source_mixture([['b'], ['a'], ['a']], self.sources, self.atoms)) + + def test_empty_disjunction(self): + self.assertEqual(source_mixture([], {}, {})['lower'], 0.) + + def test_empty_conjunction_rejected(self): + with self.assertRaises(ValueError): source_mixture([[]], self.sources, self.atoms) + + def test_unknown_source(self): + with self.assertRaises(ValueError): source_mixture([['a']], {}, self.atoms) + + def test_invalid_probabilities(self): + for p in (-.1, 1.1, float('nan'), float('inf'), True, '0.5'): + with self.subTest(p=p), self.assertRaises(ValueError): probability(p) + + def test_source_cap_stages(self): + value = source_mixture([['a']], {str(i): .5 for i in range(9)}, {'a': dict(source='0', p0=0., p1=1.)}) + self.assertEqual((value['lower'], value['upper'], value['exact']), (0., 1., False)) + + def test_atom_cap_stages(self): + atoms = {str(i): dict(source='s', p0=0., p1=1.) for i in range(17)} + self.assertFalse(source_mixture([['0']], self.sources, atoms)['exact']) + + def test_input_unchanged(self): + before = copy.deepcopy((self.sources, self.atoms)) + source_mixture([['a']], self.sources, self.atoms) + self.assertEqual(before, (self.sources, self.atoms)) + + +class DiagramTests(unittest.TestCase): + def test_analytical_formula(self): + value = compile_lineage([['a', 'b'], ['b', 'c']]).evaluate(dict(a=.5, b=.5, c=.5)) + self.assertEqual(value['lower'], .375) + self.assertTrue(value['exact']) + + def test_probability_endpoints(self): + diagram = compile_lineage([['a', 'b'], ['b', 'c']]) + for bits in itertools.product((0, 1), repeat=3): + p = dict(zip('abc', bits)) + self.assertEqual(diagram.evaluate(p)['lower'], bool(bits[1] and (bits[0] or bits[2]))) + + def test_compile_reuse_is_immutable(self): + diagram = compile_lineage([['a', 'b']]); before = repr(diagram) + diagram.evaluate(dict(a=.2, b=.4)); diagram.evaluate(dict(a=.8, b=.9)) + self.assertEqual(repr(diagram), before) + + def test_duplicate_permutation(self): + self.assertEqual(compile_lineage([['a', 'b'], ['a', 'c']]), compile_lineage([['c', 'a'], ['b', 'a']]*3)) + + def test_subsumed_proofs(self): + d = compile_lineage([['a'], ['a', 'b']]) + self.assertEqual(d.evaluate(dict(a=.7, b=.1))['lower'], .7) + + def test_empty_disjunction(self): + self.assertEqual(compile_lineage([]).evaluate({})['lower'], 0.) + + def test_reject_empty_proof(self): + with self.assertRaises(ValueError): compile_lineage([[]]) + + def test_missing_probabilities(self): + with self.assertRaises(ValueError): compile_lineage([['a']]).evaluate({}) + + def test_extra_probabilities(self): + with self.assertRaises(ValueError): compile_lineage([['a']]).evaluate(dict(a=.5, b=.5)) + + def test_invalid_probabilities(self): + for p in (True, float('nan'), -1., 2.): + with self.subTest(p=p), self.assertRaises(ValueError): compile_lineage([['a']]).evaluate({'a': p}) + + def test_bad_atom(self): + for term in ([''], ['a', 1]): + with self.subTest(term=term), self.assertRaises(ValueError): compile_lineage([term]) + + def test_cap_bounds(self): + v = compile_lineage([['a', 'b'], ['b', 'c']], state_cap=1).evaluate(dict(a=.5, b=.5, c=.5)) + self.assertFalse(v['exact']); self.assertLessEqual(v['lower'], .375); self.assertGreaterEqual(v['upper'], .375) + + def test_atom_cap(self): + keys = [str(i) for i in range(513)] + d = compile_lineage([keys]); v = d.evaluate({k: .5 for k in keys}) + self.assertEqual((v['lower'], v['upper'], v['reason']), (0., 1., 'input_cap')) + + def test_proof_cap(self): + self.assertEqual(compile_lineage([['a']]*4097).reason, 'input_cap') + + def test_invalid_state_caps(self): + for cap in (0, -1, 32769, True, 3.5): + with self.subTest(cap=cap), self.assertRaises(ValueError): compile_lineage([['a']], state_cap=cap) + + def test_large_fan(self): + keys = ['a'+str(i) for i in range(128)] + d = compile_lineage([[keys[0], k] for k in keys[1:]]) + v = d.evaluate({k: .5 for k in keys}) + self.assertTrue(v['exact']); self.assertAlmostEqual(v['lower'], .5) + + +class ReviewTests(unittest.TestCase): + def test_zero_budget(self): self.assertEqual(optimal_review([row()], 0)['selected'], []) + def test_empty(self): self.assertEqual(optimal_review([], 40)['selected'], []) + def test_harmful_review_is_skipped(self): + self.assertEqual(optimal_review([row(risk=.01, false_remove=.5)], 40)['selected'], []) + + def test_cap_staging(self): + v = optimal_review([row(str(i)) for i in range(13)], 40) + self.assertEqual(v['staged_groups'], ['g']); self.assertEqual(v['selected'], []) + + def test_strict_feature_schema(self): + for key in ('gold', 'label', 'compact', 'outcome'): + with self.subTest(key=key), self.assertRaises(ValueError): optimal_review([{**row(), key: 'leak'}], 10) + + def test_bad_cost(self): + for cost in (0, 9, True, 1.5): + with self.subTest(cost=cost), self.assertRaises(ValueError): optimal_review([row(cost=cost)], 40) + + def test_bad_budget(self): + for budget in (-1, 161, True, 40.1): + with self.subTest(budget=budget), self.assertRaises(ValueError): optimal_review([row()], budget) + + def test_duplicate_ids(self): + with self.assertRaises(ValueError): optimal_review([row(), row()], 40) + + def test_input_purity(self): + rows = [row('a'), row('b', 'other')]; before = copy.deepcopy(rows) + optimal_review(rows, 2); ratio_review(rows, 2); packed_review(rows, 2, ['b', 'a']) + self.assertEqual(rows, before) + + def test_permutation(self): + rows = [row(str(i), str(i//2), risk=.2+.1*i, cost=i+1) for i in range(6)] + self.assertEqual(optimal_review(rows, 12), optimal_review(list(reversed(rows)), 12)) + + def test_global_finite_oracle(self): + rng = random.Random(918) + for _ in range(32): + rows = [row(str(i), str(i%3), risk=rng.random(), cost=rng.randrange(1,5), detect=rng.random()) for i in range(7)] + got = optimal_review(rows, 9) + self.assertAlmostEqual(got['modeled_gain'], review_oracle(rows, 9), places=10) + self.assertLessEqual(got['cost'], 9) + + def test_packed_order_validation(self): + for order in ([], ['a', 'a'], ['missing']): + with self.subTest(order=order), self.assertRaises(ValueError): packed_review([row()], 10, order) + + def test_selected_features_ignore_test_gold(self): + test = [{'id': 'x', 'group': 'g', 'gold': 'SUPPORTS', 'views': {'base1': {'label': 'SUPPORTS', 'score': .8}, 'compact': {'label': 'REFUTES'}}, 'arms': {'single': {'input_tokens': 2048}}}] + fit = {'prior': .2, 'bins': {}} + a = make_review_candidates(test, fit, .75, .01) + test[0]['gold'] = 'REFUTES'; test[0]['views']['compact'] = {'label': 'SUPPORTS'} + self.assertEqual(a, make_review_candidates(test, fit, .75, .01)) + self.assertEqual(a[0]['cost'], 3) + + def test_development_fit_rejects_test(self): + with self.assertRaises(ValueError): fit_small([dict(split='test')]) + + +class ConflictTests(unittest.TestCase): + def setUp(self): + self.w = dict(a=3, b=2, c=4); self.e = [('a', 'b'), ('b', 'c')] + self.answer = bipartite_optimum(self.w, self.e) + + def test_optimum(self): + self.assertEqual(self.answer['utility'], 7) + trap = bipartite_optimum(dict(a=3, b=5, c=3), self.e) + self.assertEqual(trap['utility'], 6) # Priority-greedy would select the weight-5 center. + self.assertTrue(verify_certificate(dict(a=3, b=5, c=3), self.e, trap)) + def test_certificate(self): self.assertTrue(verify_certificate(self.w, self.e, self.answer)) + def test_duplicate_edges(self): self.assertEqual(bipartite_optimum(self.w, self.e*3), self.answer) + def test_order_invariance(self): self.assertEqual(bipartite_optimum(dict(reversed(list(self.w.items()))), list(reversed(self.e))), self.answer) + def test_empty_graph(self): self.assertEqual(solve_frontier({}, [])['utility'], 0) + def test_zero_weights(self): self.assertTrue(verify_certificate(dict(a=0,b=0), [('a','b')], bipartite_optimum(dict(a=0,b=0), [('a','b')]))) + + def test_tampered_certificates(self): + changes = [lambda x: x['certificate'].__setitem__('flow_value', 999), + lambda x: x['certificate']['arcs'][0].__setitem__('flow', -1), + lambda x: x['certificate']['arcs'][0].__setitem__('capacity', 999), + lambda x: x['certificate'].__setitem__('reachable', []), + lambda x: x.__setitem__('selected', ['a','b','c']), + lambda x: x.__setitem__('utility', 999)] + for fn in changes: + x = copy.deepcopy(self.answer); fn(x) + self.assertFalse(verify_certificate(self.w, self.e, x)) + + def test_odd_cycle_fallback(self): + w = dict(a=1,b=2,c=3); e = [('a','b'),('b','c'),('a','c')] + self.assertEqual(solve_frontier(w,e)['utility'], 3) + with self.assertRaises(ValueError): bipartite_optimum(w,e) + + def test_dense_odd_clique_staged(self): + w = {str(i):1 for i in range(17)} + result = solve_frontier(w, list(itertools.combinations(w,2))) + self.assertEqual(len(result['staged']), 17) + + def test_unknown_endpoint(self): + with self.assertRaises(ValueError): bipartite_optimum(self.w, [('a','missing')]) + + def test_invalid_weight(self): + for weight in (-1, True, 1.2): + with self.subTest(weight=weight), self.assertRaises(ValueError): bipartite_optimum(dict(a=weight), []) + + def test_self_conflict(self): + with self.assertRaises(ValueError): bipartite_optimum(dict(a=1), [('a','a')]) + + def test_semantically_false_priority(self): + result = solve_frontier({'false':9,'true':8}, [('false','true')]) + self.assertEqual(result['selected'], ['false']) + + def test_finite_oracle_exhaustive_topologies(self): + w = dict(a=2,b=1,c=3,d=4); possible = [('a','c'),('a','d'),('b','c'),('b','d')] + for mask in range(16): + edges = [e for i,e in enumerate(possible) if mask & (1<8. Reproducibility and artifact integrity python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf

The machine-readable results preserve fixture inputs, oracle values, all review-subset scores, repair witnesses, retraction feasibility, snapshot outcomes, controls and source/protocol hashes. The execution notes distinguish implementation/test corrections from endpoint changes. The extension manifest binds code, protocol, results, tests, logs, report, CSV and five SVG/PNG pairs. The mutable complete manuscript is bound by its separate build manifest. Earlier study blocks and archived raw evidence are preserved; no original manuscript is overwritten.

+

Structural frontiers for evidence-preserving Jev graph synthesis

+

Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026

+

Abstract

+

Five frozen extensions test whether explicit source dependence, heterogeneous review assumptions and structural recognition address limitations exposed by PR #17. Latent-source marginalization matches an independent joint-world oracle on 96 supplied-model fixtures and prevents 9 false threshold admissions made by the independent-marginal comparator. Cost- and noise-aware review allocation fails its frozen downstream target: at 40 proxy effort units it leaves 13.043478 expected contaminated source groups, identical to both comparison policies. Compile-once decision diagrams exactly evaluate ten connected lineage formulas with 17–256 primitives beyond the previous blanket component cap. Certified bipartite optimization attains nine analytic large-graph optima previously staged. A fixed-path segment tree reduces counted summary-maintenance transitions by 96.47% across a 1,024-vertex, 256-update workload, excluding separately reported linear witness decoding. These are bounded engineering results, not new Jev semantic-accuracy evidence or proof of worldwide algorithmic novelty.

+

1. Motivation, novelty scope and frozen design

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The preceding reliability study found that better average calibration bias need not improve Brier score; source identity errors invalidate independent-event probabilities; capped exact solvers leave some structured cases unresolved; review effectiveness depends on assumptions; and whole-component caching provides no solver saving on a connected graph. The five present mechanisms address these particular limitations rather than adding more correlated Jev calls. TypeSafe's typed outputs and probabilities do not themselves establish graph truth (TypeSafe documentation).

+

The protocol was committed as ef8fc308d0a50cd37a6cdd3b7546e050093349c8 before implementation and benchmark execution, against a62a3257645d8e35cd4e45be53bfa9511d27724b. Prior results were inspected and informed hypothesis selection. This is exploratory follow-up, not independent preregistration. H2 reuses 73 development candidates in 40 groups and 263 evaluation candidates in 149 groups. These are already published observations, not a new holdout. Fresh service calls: 0. Raw requests, response parsing, group separation and input hashes are checked before scoring. The other four hypotheses evaluate supplied models or graph structures, not newly extracted scientific facts.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HypothesisProposed extensionFrozen targetEvidence class
H1Latent-source-conditioned lineageMetControlled algorithm
H2Cost/noise-aware group reviewNot metReplay + reviewer assumptions
H3Reusable state-bounded lineage diagramsMetControlled algorithm
H4Certified bipartite conflict optimizationMetControlled algorithm
H5Connected-path incremental optimizationMetControlled algorithm
+

“Met” refers only to the predeclared conjunction on its specified fixture population. These rows must not be pooled into a semantic-success percentage. The novelty audit distinguishes repository-new implementations from established source modeling, knowledge compilation, flow optimization and dynamic programming. It does not assert that no person has ever tried an equivalent idea.

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Concurrent integration disclosure. While this frozen extension was executing, main advanced to a21314d17c33c639b75522ecce120586ef8dab35 with the structural refinement and source-structural studies. Both are preserved unchanged. The source-structural study also implements certified bipartite optimization: H4 here is therefore a parallel implementation and additional frozen test suite, not a mechanism unique to the reconciled main branch. Its frontier-width lineage evaluator overlaps H3's goal but differs from the reusable decision-diagram state budget tested here. Source-setup review differs from H2's heterogeneous per-edge effort/noise model; source-revision invalidation differs from H5's connected-path summary maintenance. Repository-new claims are limited to the protocol's original pinned baseline, not the later integration snapshot. No thresholds, fixtures or numerical results were retuned after examining the concurrent studies. Reused semantic captures must not be pooled as independent observations.

+

Further concurrent integration. Main subsequently advanced through PR #24 to 6530321dd0060c9c7f13bb69d6f1c88346a8e1e2. Its assumption-aware study is also preserved unchanged. Its dependence-robust probability envelopes address unknown joint dependence rather than H1's supplied latent-source conditional model. Its connected-tree ancestor-message updates overlap H5's objective, but H5 uses a balanced segment tree on a fixed path, with a different update mechanism and counted work metric. These results are parallel extensions of the same pinned baseline, not independent replications, combined semantic observations, or claims of unique worldwide invention. Frozen frontier hypotheses, implementations, fixture seeds and numerical results are unchanged; only additive reporting and combined regression coverage expand.

+

Final integration snapshot. Main also received PR #21, the uncertainty and grounding study, at 114450ef08736c21c97c8725de700d8d2c6ca94d. That study and all three earlier concurrent extensions are preserved unchanged. Its dependence-agnostic bounds and query-loss-directed review further overlap the broad research questions here, but use different supplied-information assumptions and controlled objectives. This frontier study does not claim to be the unique first repository implementation on the reconciled main branch; its dated novelty comparison remains the frozen PR #17 baseline. All frozen numerical outcomes remain unchanged and overlapping captured observations are not pooled.

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2. H1: Source-conditioned evidence probability

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Distinct evidence atoms can still depend on a common unreliable source. Instead of treating their marginal probabilities as independent, the proposed model supplies a binary quality state for each source, a prior for that state, and two conditional probabilities for each assigned atom. Source states are assumed mutually independent; primitive events are independent only conditional on those states. For a monotone proof formula F, compute P(F) = sum_z P(z) P(F given z). Each conditional formula uses the existing bounded exact lineage evaluator. This is exact marginalization within the supplied model, not discovery of the correct dependence structure.

+

The 96 seeded fixtures contain 2–8 primitives and 1–3 sources. The independent oracle enumerates joint source-plus-primitive assignments, rather than calling the proposed marginalization/evaluation routine. There are 0 oracle discrepancies above 1e-12, 0 failures in 288 order/duplicate checks, and 0 proposed false admissions at 0.95. Independent marginals make 9 false admissions on these random fixtures. Mean absolute probability error is 0.055531 for independent marginals and 1.31e-16 for the supplied dependence model.

+ + + + + + + + + + + + + + + + + + + + + + + +
Shared-source queryIndependent marginalsConditioned modelJoint-world truth
At least one of two copies (OR)0.960.800.80
Both copies (AND)0.640.800.80
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The OR control prevents an unjustified 0.95 admission; the AND control shows that dependence errors do not always inflate probability. The target is met. Inputs over eight sources or sixteen primitives are explicitly staged with [0,1], not assigned an exact probability. Conditional evaluator exhaustion retains conservative bounds.

+
H1. Declared common-source dependence corrects both OR inflation and AND deflation on the supplied two-copy control. These are model probabilities, not observed Jev accuracies.
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Assumption-breaking control: two declared independent latent sources that actually share one cause still yield 0.96 when the actual probability is 0.80. The extension therefore moves the necessary independence assumption to the source layer; it does not remove it. Primitive reliabilities and source assignments are fixture inputs, not calibrated model scores or independently adjudicated provenance. Bayesian source-quality modeling already has substantial prior art, including Zhao et al. (2012); this experiment does not reproduce or outperform their truth-discovery system.

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3. H2: Review allocation under effort and reviewer errors

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The proposed allocator uses the prior study's development-only single-view risk estimator. Evaluation gold, compact-view predictions and observed review outcomes are unavailable to selection. The supplied per-edge effort is min(8, 1 + floor(recorded single-call input tokens / 1000)). This is a bounded proxy, not measured annotation time, cognitive difficulty, monetary cost or new service expenditure. At base detection s0, detection for cost c is s0 / (1 + 0.05(c−1)); a supplied false-removal probability applies to correct reviewed edges.

+

For selected review IDs A, the modeled source-group contamination is 1 − product_i[1 − p_i(1 − s_i I(i in A))]. The objective is total modeled group cleaning minus expected correct-edge removals with unit penalty. Enumerate subsets within each group of at most twelve candidates, then allocate across groups using integer-budget multiple-choice knapsack. Larger groups receive no optimized review; 0 primary groups exceed the cap. No-review remains an available option. The baselines are the actual previous group-aware order packed into the same budget, and marginal-model-benefit-per-cost greedy. Equal budgets do not require identical spending.

+

The fixed acceptance population contains 150 single-view positive judgments: 132 correct and 18 wrong, spread across 15 contaminated groups before review. This is not the earlier study's separate matched-volume top-145 comparison. Primary scoring uses all 149 evaluation source groups, including groups with no accepted edges. The reviewer simulation removes an actually wrong edge with its supplied detection probability and removes a correct one with its supplied false-removal probability. No human reviewed these edges in this experiment.

+

The frozen primary scenario is budget 40, s0 = 0.75 and false-removal probability 0.01. The target requires at least 5% fewer expected contaminated groups than both comparators, while allowing at most 0.25 additional expected correct-edge removals versus either.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PolicyEffort spentEdges reviewedExpected contaminated groupsExpected correct removed
no review0015.0000000.0000
optimal401013.0434780.0600
packed prior37913.0434780.0600
ratio greedy401013.0434780.0600
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The target is not met. All three review policies have the same primary expected contamination and correct-edge loss. The optimizer's modeled gain (3.078385) exceeds packed prior (3.060146) but ties ratio greedy (3.078385). Improved optimization of an imperfect model is not a demonstrated graph-quality gain. There are 0 objective discrepancies on 64 independent finite-enumeration checks; solver correctness does not rescue the empirical target.

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H2. Frozen budget-40 reviewer scenario. All three selectors tie on expected source contamination despite different selected IDs and effort spending. Values are scenario expectations, not observed human outcomes.
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All 36 combinations of budgets 20/40/80/160 and detection/false-removal scenarios are retained in results.json; selection is recalculated from features and supplied assumptions only. The 4,000 paired group-bootstrap draws at fixed primary selections give a 95% and 99% proposed-minus-comparator contamination-rate interval of [0,0] for both comparisons because their per-group expected outcomes are identical. This degeneracy is not evidence of known population equivalence. These descriptive intervals omit model-fitting and reviewer-model uncertainty, and published-data reuse prevents independent confirmation. A deliberately incorrect-risk control spends its only review on an actually correct edge and misses the wrong one. Prior research already demonstrates the relevance of real annotation costs (Settles et al., 2008); our token-based proxy is not a measurement of those costs.

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4. H3: Compile once, bound by decision-diagram state count

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The previous evaluator rejects connected lineage components above sixteen atoms even when their Boolean structure is simple. The proposed compiler canonicalizes a monotone disjunction of conjunctions, chooses frequency-descending/lexical atom order, memoizes residual formulas and merges identical decision nodes. A compiled reduced ordered binary decision diagram can then be evaluated with changing supplied probabilities without recompiling its structure. Children precede parents, so evaluation is a bottom-up weighted sum. Conditional expansion preserves the represented Boolean function; merging identical subfunctions preserves every probability assignment under independent primitive events.

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The safety boundaries are 512 atoms, 4,096 input proofs and 32,768 visited residual states. Input-cap violations stage. State-budget exhaustion returns conservative previous lineage bounds with an explicit nonexact status, never a partially evaluated point estimate. Compilation remains potentially exponential; these caps are operational safeguards, not a polynomial-time theorem.

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There are 0 probability discrepancies across 128 random formulas with five assignments each, ten analytic large fixtures, 64 updates on one reused 128-atom chain, and the state-cap control. All ten connected large fixtures are exact under the new compiler and nonexact under the actual previous evaluator. Duplicate/input-order checks have 0 failures.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FormulaPrimitivesDecision nodesPrior lower boundExact probability
fan17170.4950.989984894
fan32320.4950.990000000
fan64640.4950.990000000
fan1281280.4950.990000000
fan2562560.4950.990000000
chain17570.2500.968101501
chain321170.2500.998672193
chain642450.2500.999998494
chain1285010.2501.000000000
chain25610130.2501.000000000
+

The shared-fan oracle is 0.99(1−0.5^(n−1)). The adjacent-pair chain oracle uses an independent two-state recurrence for the probability of no adjacent successes. The 128-atom reuse workload performs 1 compilation with 501 decision nodes, then 64 changed-probability evaluations totaling 32,064 decision-node visits. The target is met. Diagram size, compile-state visits and evaluation-node visits are separate quantities; none is a measured service-latency saving.

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H3. Decision nodes required for exact connected formulas beyond the previous sixteen-primitive component cap. Lineage probability updates reuse these compiled structures.
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Knowledge compilation for probabilistic data is established (Fink et al., 2012); provenance maintenance and hybrid compilation approaches for uncertain knowledge graphs are also established (Gaur et al., 2021). The contribution here is the bounded reusable implementation and its tests against this repository's earlier cap, not invention of decision diagrams or superiority over those systems. H3 still assumes independent primitive inputs; it does not automatically incorporate H1's source model.

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5. H4: Certified bipartite conflict optimization

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The cycle-cutset solver in PR #17 can stage a bipartite graph with many cycles even though its supplied-priority maximum independent set has a tractable reduction. The extension first recognizes bipartite components with at most 512 vertices. For bipartition L/R, create source-to-L and R-to-sink capacities equal to nonnegative integer priorities and L-to-R conflict arcs of capacity total priority plus one. A minimum cut cannot profitably cross a conflict arc, since cutting every priority arc is cheaper. Its vertex-side choices therefore give a minimum-weight vertex cover; the complement is a maximum-weight independent set.

+

The returned certificate contains a capacity-feasible flow, flow conservation at every internal node, a source/sink separating cut of equal value, and the complementary selected witness. A separate verifier checks these conditions without invoking an optimizer. A feasible flow lower-bounds any cut; equality certifies cut optimality, and the cover reduction certifies the selected supplied-priority objective. Nonbipartite components retain the unmodified prior solver and its staging behavior. No additional budget constraint is imposed; adding such constraints changes the complexity (Doron-Arad and Shachnai, 2023).

+

There are 0 optimum discrepancies, 0 certificate errors and 0 prior-utility regressions across 128 random bipartite and 64 general small graphs plus nine analytic large fixtures. The large comparator values below come from the actual imported previous solver, not a renamed reimplementation. Zero previous utility means the unsupported component was staged, not that no positive-utility solution exists.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
StructureVerticesPrior utilityGreedy utilityCertified utilityOracle
complete bipartite180181818
complete bipartite320323232
complete bipartite640646464
complete bipartite1280128128128
complete bipartite2560256256256
grid320161616
grid640323232
grid1280646464
grid2560128128128
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The target is met. Complete bipartite fixtures have side weights one and two, giving optimum total weight on the heavier side. Rectangular unit grids have an independent checkerboard half; a perfect matching gives the corresponding upper bound. Certificate-tampering tests, zero weights, invalid endpoints, odd cycles and a dense 17-clique are retained. The odd clique still stages. Priority greedy also attains all nine large analytic optima; the extension adds an optimality certificate and recovery versus prior staging, not an observed large-fixture utility advantage over greedy.

+
H4. Fraction of analytically optimal supplied-priority utility on nine structured conflict fixtures. Prior staging is zero recovered utility, not extraction failure.
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The semantic negative control remains decisive: a false assertion with supplied priority 9 defeats a conflicting true assertion with priority 8. An exact optimizer can be exactly wrong about factual truth when its priorities are wrong. No graph-extraction, clinical-validity or KARMA-superiority claim follows.

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6. H5: Connected-path summary maintenance

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Whole-component invalidation cannot exploit a local weight update inside one connected graph. For the deliberately narrower case of a fixed connected path, the proposed data structure stores the skip/take dynamic-programming transition for each vertex in a max-plus segment tree. Two-state transitions compose associatively; a leaf update requires recomputing only its ancestors. The root gives optimal utility without emitting the entire selected graph. Topology is validated at construction. Invalid or unknown-vertex weight updates are rejected before mutation; changing topology requires rebuilding. Stars and cycles are rejected rather than silently treated as paths.

+

Across 64 random paths with sixteen updates each and the 1,024-vertex path with 256 updates, there are 0 utility or independent-witness discrepancies against independent full linear dynamic programming. The primary workload includes cold construction and every update.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Work quantityCountInterpretation
Full recomputation transition candidates789,504Three scalar candidates per vertex per build/update
Segment-tree transition candidates27,864Eight per internal 2-by-2 composition, including cold build
Segment summary-work reduction96.47%Only the two transition-candidate counts above
Cold topology validation visits3,070Vertex plus adjacency visits, separately recorded
Full witness-decoding node visits526,079Every output decoded for validation; remains linear
+

The frozen 90% counted summary-work reduction target is met. The 27,864 versus 789,504 transition comparison is not a total runtime or database-I/O measurement. Witness decoding adds 526,079 tree-node visits under a different operation metric and is explicitly not included in that percentage. A consumer needing the complete selected graph after every update still pays linear output/decoding work. Python overhead, memory traffic, initial sorting, dictionary operations and persistence are not captured by scalar transition counts.

+
H5. Counted optimal-utility summary maintenance on a fixed connected path, including cold build. Full witness decoding is separately reported and excluded from the percentage.
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7. Interpretation and next falsification boundary

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The results favor identifying the tractable structure of a supplied problem over imposing blanket size restrictions: declared dependence changes which probabilities are valid; compact formula structure permits reusable exact evaluation; bipartiteness permits certified optimization despite many cycles; and a fixed path permits local summary updates despite being one connected component. These conclusions are conditional on correct metadata and supported topology. They do not establish that those structures dominate real Jev-generated graphs.

+

The review result is a useful negative finding. A more exact allocator can improve a modeled objective yet fail to improve the frozen downstream source-contamination measure. This counsels against promoting the allocator on optimization quality alone. Prospective human-cost/detection measurements, source-disjoint independently adjudicated documents, policies frozen before those labels are seen, and matched evidence/resource budgets against external graph-synthesis systems remain necessary for semantic or deployment claims. H1 and H3 reliability inputs must be estimated and audited separately; raw model confidence cannot simply be substituted.

+

The methods remain opt-in research utilities. No default compiler policy or production graph is changed. Failure controls and all prior studies remain in the complete manuscript. Each target is falsifiable on its specified population, but a passed finite benchmark is not a proof of correctness for all inputs or real-world superiority.

+

8. Reproducibility and audit

+
python -B -m graph_synthesis.frontier.run
+python -B -m unittest discover -s graph_synthesis/frontier/tests -v
+python -B -m graph_synthesis.frontier.run --check
+python -B -m graph_synthesis.frontier.report --figures --update-paper
+python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf
+python -B -m graph_synthesis.frontier.verify
+
+

The machine-readable results retain source and code hashes, source-group counts, all random fixtures or complete generating specifications, review IDs and scenarios, independent-oracle outputs, flow/cut witnesses, update sequences and decoded-witness hashes. The five figures are generated directly from those results as SVG and PNG pairs; summary.csv preserves target classifications without pooling them. The implementation log records validation corrections without changing the protocol or endpoints. Original evidence integrity is checked against the immutable inventory. Source, result and figure hashes are bound in the extension manifest; the complete current PDF has a separate source/renderer/PDF hash record.

Do additional Jev calls improve graph edges?

Timothy Wayne Gregg — exploratory research addendum, September 18, 2026

Design and evidence boundary

diff --git a/manuscript/paper-current.md b/manuscript/paper-current.md index 48ef5bc..4c77352 100644 --- a/manuscript/paper-current.md +++ b/manuscript/paper-current.md @@ -1049,6 +1049,172 @@ The [machine-readable results](../graph_synthesis/uncertainty/results.json) pres + + +# Structural frontiers for evidence-preserving Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five frozen extensions test whether explicit source dependence, heterogeneous review assumptions and structural recognition address limitations exposed by PR #17. Latent-source marginalization matches an independent joint-world oracle on 96 supplied-model fixtures and prevents 9 false threshold admissions made by the independent-marginal comparator. Cost- and noise-aware review allocation fails its frozen downstream target: at 40 proxy effort units it leaves 13.043478 expected contaminated source groups, identical to both comparison policies. Compile-once decision diagrams exactly evaluate ten connected lineage formulas with 17–256 primitives beyond the previous blanket component cap. Certified bipartite optimization attains nine analytic large-graph optima previously staged. A fixed-path segment tree reduces counted summary-maintenance transitions by 96.47% across a 1,024-vertex, 256-update workload, excluding separately reported linear witness decoding. These are bounded engineering results, not new Jev semantic-accuracy evidence or proof of worldwide algorithmic novelty. + +## 1. Motivation, novelty scope and frozen design + +The [preceding reliability study](../graph_synthesis/reliability/RESULTS.md) found that better average calibration bias need not improve Brier score; source identity errors invalidate independent-event probabilities; capped exact solvers leave some structured cases unresolved; review effectiveness depends on assumptions; and whole-component caching provides no solver saving on a connected graph. The five present mechanisms address these particular limitations rather than adding more correlated Jev calls. TypeSafe's typed outputs and probabilities do not themselves establish graph truth ([TypeSafe documentation](https://docs.typesafe.ai/introduction)). + +The [protocol](../graph_synthesis/frontier/PROTOCOL.md) was committed as `ef8fc308d0a50cd37a6cdd3b7546e050093349c8` before implementation and benchmark execution, against `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Prior results were inspected and informed hypothesis selection. This is exploratory follow-up, not independent preregistration. H2 reuses 73 development candidates in 40 groups and 263 evaluation candidates in 149 groups. These are already published observations, not a new holdout. **Fresh service calls: 0.** Raw requests, response parsing, group separation and input hashes are checked before scoring. The other four hypotheses evaluate supplied models or graph structures, not newly extracted scientific facts. + +| Hypothesis | Proposed extension | Frozen target | Evidence class | +|---|---|---|---| +| H1 | Latent-source-conditioned lineage | Met | Controlled algorithm | +| H2 | Cost/noise-aware group review | Not met | Replay + reviewer assumptions | +| H3 | Reusable state-bounded lineage diagrams | Met | Controlled algorithm | +| H4 | Certified bipartite conflict optimization | Met | Controlled algorithm | +| H5 | Connected-path incremental optimization | Met | Controlled algorithm | + +“Met” refers only to the predeclared conjunction on its specified fixture population. These rows must not be pooled into a semantic-success percentage. The [novelty audit](../graph_synthesis/frontier/NOVELTY.md) distinguishes repository-new implementations from established source modeling, knowledge compilation, flow optimization and dynamic programming. It does not assert that no person has ever tried an equivalent idea. + +**Concurrent integration disclosure.** While this frozen extension was executing, main advanced to `a21314d17c33c639b75522ecce120586ef8dab35` with the [structural refinement](../graph_synthesis/structural/RESULTS.md) and [source-structural](../graph_synthesis/source_structural/RESULTS.md) studies. Both are preserved unchanged. The source-structural study also implements certified bipartite optimization: H4 here is therefore a parallel implementation and additional frozen test suite, not a mechanism unique to the reconciled main branch. Its frontier-width lineage evaluator overlaps H3's goal but differs from the reusable decision-diagram state budget tested here. Source-setup review differs from H2's heterogeneous per-edge effort/noise model; source-revision invalidation differs from H5's connected-path summary maintenance. Repository-new claims are limited to the protocol's original pinned baseline, not the later integration snapshot. No thresholds, fixtures or numerical results were retuned after examining the concurrent studies. Reused semantic captures must not be pooled as independent observations. + +**Further concurrent integration.** Main subsequently advanced through PR #24 to `6530321dd0060c9c7f13bb69d6f1c88346a8e1e2`. Its [assumption-aware study](../graph_synthesis/assumption_aware/RESULTS.md) is also preserved unchanged. Its dependence-robust probability envelopes address unknown joint dependence rather than H1's supplied latent-source conditional model. Its connected-tree ancestor-message updates overlap H5's objective, but H5 uses a balanced segment tree on a fixed path, with a different update mechanism and counted work metric. These results are parallel extensions of the same pinned baseline, not independent replications, combined semantic observations, or claims of unique worldwide invention. Frozen frontier hypotheses, implementations, fixture seeds and numerical results are unchanged; only additive reporting and combined regression coverage expand. + +**Final integration snapshot.** Main also received PR #21, the [uncertainty and grounding study](../graph_synthesis/uncertainty/RESULTS.md), at `114450ef08736c21c97c8725de700d8d2c6ca94d`. That study and all three earlier concurrent extensions are preserved unchanged. Its dependence-agnostic bounds and query-loss-directed review further overlap the broad research questions here, but use different supplied-information assumptions and controlled objectives. This frontier study does not claim to be the unique first repository implementation on the reconciled main branch; its dated novelty comparison remains the frozen PR #17 baseline. All frozen numerical outcomes remain unchanged and overlapping captured observations are not pooled. + +## 2. H1: Source-conditioned evidence probability + +Distinct evidence atoms can still depend on a common unreliable source. Instead of treating their marginal probabilities as independent, the proposed model supplies a binary quality state for each source, a prior for that state, and two conditional probabilities for each assigned atom. Source states are assumed mutually independent; primitive events are independent only conditional on those states. For a monotone proof formula F, compute P(F) = sum_z P(z) P(F given z). Each conditional formula uses the existing bounded exact lineage evaluator. This is exact marginalization within the supplied model, not discovery of the correct dependence structure. + +The 96 seeded fixtures contain 2–8 primitives and 1–3 sources. The independent oracle enumerates joint source-plus-primitive assignments, rather than calling the proposed marginalization/evaluation routine. There are **0 oracle discrepancies above 1e-12**, **0 failures in 288 order/duplicate checks**, and **0 proposed false admissions at 0.95**. Independent marginals make 9 false admissions on these random fixtures. Mean absolute probability error is 0.055531 for independent marginals and 1.31e-16 for the supplied dependence model. + +| Shared-source query | Independent marginals | Conditioned model | Joint-world truth | +|---|---:|---:|---:| +| At least one of two copies (OR) | 0.96 | 0.80 | 0.80 | +| Both copies (AND) | 0.64 | 0.80 | 0.80 | + +The OR control prevents an unjustified 0.95 admission; the AND control shows that dependence errors do not always inflate probability. The target is **met**. Inputs over eight sources or sixteen primitives are explicitly staged with [0,1], not assigned an exact probability. Conditional evaluator exhaustion retains conservative bounds. + +![H1. Declared common-source dependence corrects both OR inflation and AND deflation on the supplied two-copy control. These are model probabilities, not observed Jev accuracies.](../graph_synthesis/frontier/figures/01_source_dependence.png) + +**Assumption-breaking control:** two declared independent latent sources that actually share one cause still yield 0.96 when the actual probability is 0.80. The extension therefore moves the necessary independence assumption to the source layer; it does not remove it. Primitive reliabilities and source assignments are fixture inputs, not calibrated model scores or independently adjudicated provenance. Bayesian source-quality modeling already has substantial prior art, including [Zhao et al. (2012)](https://arxiv.org/abs/1203.0058); this experiment does not reproduce or outperform their truth-discovery system. + +## 3. H2: Review allocation under effort and reviewer errors + +The proposed allocator uses the prior study's development-only single-view risk estimator. Evaluation gold, compact-view predictions and observed review outcomes are unavailable to selection. The supplied per-edge effort is min(8, 1 + floor(recorded single-call input tokens / 1000)). This is a bounded proxy, not measured annotation time, cognitive difficulty, monetary cost or new service expenditure. At base detection s0, detection for cost c is s0 / (1 + 0.05(c−1)); a supplied false-removal probability applies to correct reviewed edges. + +For selected review IDs A, the modeled source-group contamination is 1 − product_i[1 − p_i(1 − s_i I(i in A))]. The objective is total modeled group cleaning minus expected correct-edge removals with unit penalty. Enumerate subsets within each group of at most twelve candidates, then allocate across groups using integer-budget multiple-choice knapsack. Larger groups receive no optimized review; **0 primary groups exceed the cap**. No-review remains an available option. The baselines are the actual previous group-aware order packed into the same budget, and marginal-model-benefit-per-cost greedy. Equal budgets do not require identical spending. + +The fixed acceptance population contains 150 single-view positive judgments: 132 correct and 18 wrong, spread across 15 contaminated groups before review. This is not the earlier study's separate matched-volume top-145 comparison. Primary scoring uses all 149 evaluation source groups, including groups with no accepted edges. The reviewer simulation removes an actually wrong edge with its supplied detection probability and removes a correct one with its supplied false-removal probability. No human reviewed these edges in this experiment. + +The frozen primary scenario is budget 40, s0 = 0.75 and false-removal probability 0.01. The target requires at least 5% fewer expected contaminated groups than **both** comparators, while allowing at most 0.25 additional expected correct-edge removals versus either. + +| Policy | Effort spent | Edges reviewed | Expected contaminated groups | Expected correct removed | +|---|---:|---:|---:|---:| +| no review | 0 | 0 | 15.000000 | 0.0000 | +| optimal | 40 | 10 | 13.043478 | 0.0600 | +| packed prior | 37 | 9 | 13.043478 | 0.0600 | +| ratio greedy | 40 | 10 | 13.043478 | 0.0600 | + +The target is **not met**. All three review policies have the same primary expected contamination and correct-edge loss. The optimizer's modeled gain (3.078385) exceeds packed prior (3.060146) but ties ratio greedy (3.078385). Improved optimization of an imperfect model is not a demonstrated graph-quality gain. There are 0 objective discrepancies on 64 independent finite-enumeration checks; solver correctness does not rescue the empirical target. + +![H2. Frozen budget-40 reviewer scenario. All three selectors tie on expected source contamination despite different selected IDs and effort spending. Values are scenario expectations, not observed human outcomes.](../graph_synthesis/frontier/figures/02_noisy_review.png) + +All 36 combinations of budgets 20/40/80/160 and detection/false-removal scenarios are retained in results.json; selection is recalculated from features and supplied assumptions only. The 4,000 paired group-bootstrap draws at fixed primary selections give a 95% and 99% proposed-minus-comparator contamination-rate interval of [0,0] for both comparisons because their per-group expected outcomes are identical. **This degeneracy is not evidence of known population equivalence.** These descriptive intervals omit model-fitting and reviewer-model uncertainty, and published-data reuse prevents independent confirmation. A deliberately incorrect-risk control spends its only review on an actually correct edge and misses the wrong one. Prior research already demonstrates the relevance of real annotation costs ([Settles et al., 2008](https://burrsettles.com/pub/settles.nips08ws.pdf)); our token-based proxy is not a measurement of those costs. + +## 4. H3: Compile once, bound by decision-diagram state count + +The previous evaluator rejects connected lineage components above sixteen atoms even when their Boolean structure is simple. The proposed compiler canonicalizes a monotone disjunction of conjunctions, chooses frequency-descending/lexical atom order, memoizes residual formulas and merges identical decision nodes. A compiled reduced ordered binary decision diagram can then be evaluated with changing supplied probabilities without recompiling its structure. Children precede parents, so evaluation is a bottom-up weighted sum. Conditional expansion preserves the represented Boolean function; merging identical subfunctions preserves every probability assignment under independent primitive events. + +The safety boundaries are 512 atoms, 4,096 input proofs and 32,768 visited residual states. Input-cap violations stage. State-budget exhaustion returns conservative previous lineage bounds with an explicit nonexact status, never a partially evaluated point estimate. Compilation remains potentially exponential; these caps are operational safeguards, not a polynomial-time theorem. + +There are 0 probability discrepancies across 128 random formulas with five assignments each, ten analytic large fixtures, 64 updates on one reused 128-atom chain, and the state-cap control. All ten connected large fixtures are exact under the new compiler and nonexact under the actual previous evaluator. Duplicate/input-order checks have 0 failures. + +| Formula | Primitives | Decision nodes | Prior lower bound | Exact probability | +|---|---:|---:|---:|---:| +| fan | 17 | 17 | 0.495 | 0.989984894 | +| fan | 32 | 32 | 0.495 | 0.990000000 | +| fan | 64 | 64 | 0.495 | 0.990000000 | +| fan | 128 | 128 | 0.495 | 0.990000000 | +| fan | 256 | 256 | 0.495 | 0.990000000 | +| chain | 17 | 57 | 0.250 | 0.968101501 | +| chain | 32 | 117 | 0.250 | 0.998672193 | +| chain | 64 | 245 | 0.250 | 0.999998494 | +| chain | 128 | 501 | 0.250 | 1.000000000 | +| chain | 256 | 1013 | 0.250 | 1.000000000 | + +The shared-fan oracle is 0.99(1−0.5^(n−1)). The adjacent-pair chain oracle uses an independent two-state recurrence for the probability of no adjacent successes. The 128-atom reuse workload performs 1 compilation with 501 decision nodes, then 64 changed-probability evaluations totaling 32,064 decision-node visits. The target is **met**. Diagram size, compile-state visits and evaluation-node visits are separate quantities; none is a measured service-latency saving. + +![H3. Decision nodes required for exact connected formulas beyond the previous sixteen-primitive component cap. Lineage probability updates reuse these compiled structures.](../graph_synthesis/frontier/figures/03_lineage_capacity.png) + +Knowledge compilation for probabilistic data is established ([Fink et al., 2012](https://arxiv.org/abs/1201.6569)); provenance maintenance and hybrid compilation approaches for uncertain knowledge graphs are also established ([Gaur et al., 2021](https://arxiv.org/abs/2108.07758)). The contribution here is the bounded reusable implementation and its tests against this repository's earlier cap, not invention of decision diagrams or superiority over those systems. H3 still assumes independent primitive inputs; it does not automatically incorporate H1's source model. + +## 5. H4: Certified bipartite conflict optimization + +The cycle-cutset solver in PR #17 can stage a bipartite graph with many cycles even though its supplied-priority maximum independent set has a tractable reduction. The extension first recognizes bipartite components with at most 512 vertices. For bipartition L/R, create source-to-L and R-to-sink capacities equal to nonnegative integer priorities and L-to-R conflict arcs of capacity total priority plus one. A minimum cut cannot profitably cross a conflict arc, since cutting every priority arc is cheaper. Its vertex-side choices therefore give a minimum-weight vertex cover; the complement is a maximum-weight independent set. + +The returned certificate contains a capacity-feasible flow, flow conservation at every internal node, a source/sink separating cut of equal value, and the complementary selected witness. A separate verifier checks these conditions without invoking an optimizer. A feasible flow lower-bounds any cut; equality certifies cut optimality, and the cover reduction certifies the selected supplied-priority objective. Nonbipartite components retain the unmodified prior solver and its staging behavior. No additional budget constraint is imposed; adding such constraints changes the complexity ([Doron-Arad and Shachnai, 2023](https://arxiv.org/abs/2307.08592)). + +There are 0 optimum discrepancies, 0 certificate errors and 0 prior-utility regressions across 128 random bipartite and 64 general small graphs plus nine analytic large fixtures. The large comparator values below come from the actual imported previous solver, not a renamed reimplementation. Zero previous utility means the unsupported component was staged, not that no positive-utility solution exists. + +| Structure | Vertices | Prior utility | Greedy utility | Certified utility | Oracle | +|---|---:|---:|---:|---:|---:| +| complete bipartite | 18 | 0 | 18 | 18 | 18 | +| complete bipartite | 32 | 0 | 32 | 32 | 32 | +| complete bipartite | 64 | 0 | 64 | 64 | 64 | +| complete bipartite | 128 | 0 | 128 | 128 | 128 | +| complete bipartite | 256 | 0 | 256 | 256 | 256 | +| grid | 32 | 0 | 16 | 16 | 16 | +| grid | 64 | 0 | 32 | 32 | 32 | +| grid | 128 | 0 | 64 | 64 | 64 | +| grid | 256 | 0 | 128 | 128 | 128 | + +The target is **met**. Complete bipartite fixtures have side weights one and two, giving optimum total weight on the heavier side. Rectangular unit grids have an independent checkerboard half; a perfect matching gives the corresponding upper bound. Certificate-tampering tests, zero weights, invalid endpoints, odd cycles and a dense 17-clique are retained. The odd clique still stages. Priority greedy also attains all nine large analytic optima; the extension adds an optimality certificate and recovery versus prior staging, not an observed large-fixture utility advantage over greedy. + +![H4. Fraction of analytically optimal supplied-priority utility on nine structured conflict fixtures. Prior staging is zero recovered utility, not extraction failure.](../graph_synthesis/frontier/figures/04_bipartite_capacity.png) + +The semantic negative control remains decisive: a false assertion with supplied priority 9 defeats a conflicting true assertion with priority 8. An exact optimizer can be exactly wrong about factual truth when its priorities are wrong. No graph-extraction, clinical-validity or KARMA-superiority claim follows. + +## 6. H5: Connected-path summary maintenance + +Whole-component invalidation cannot exploit a local weight update inside one connected graph. For the deliberately narrower case of a fixed connected path, the proposed data structure stores the skip/take dynamic-programming transition for each vertex in a max-plus segment tree. Two-state transitions compose associatively; a leaf update requires recomputing only its ancestors. The root gives optimal utility without emitting the entire selected graph. Topology is validated at construction. Invalid or unknown-vertex weight updates are rejected before mutation; changing topology requires rebuilding. Stars and cycles are rejected rather than silently treated as paths. + +Across 64 random paths with sixteen updates each and the 1,024-vertex path with 256 updates, there are **0 utility or independent-witness discrepancies** against independent full linear dynamic programming. The primary workload includes cold construction and every update. + +| Work quantity | Count | Interpretation | +|---|---:|---| +| Full recomputation transition candidates | 789,504 | Three scalar candidates per vertex per build/update | +| Segment-tree transition candidates | 27,864 | Eight per internal 2-by-2 composition, including cold build | +| Segment summary-work reduction | 96.47% | Only the two transition-candidate counts above | +| Cold topology validation visits | 3,070 | Vertex plus adjacency visits, separately recorded | +| Full witness-decoding node visits | 526,079 | Every output decoded for validation; remains linear | + +The frozen 90% counted summary-work reduction target is **met**. The 27,864 versus 789,504 transition comparison is not a total runtime or database-I/O measurement. Witness decoding adds 526,079 tree-node visits under a different operation metric and is explicitly not included in that percentage. A consumer needing the complete selected graph after every update still pays linear output/decoding work. Python overhead, memory traffic, initial sorting, dictionary operations and persistence are not captured by scalar transition counts. + +![H5. Counted optimal-utility summary maintenance on a fixed connected path, including cold build. Full witness decoding is separately reported and excluded from the percentage.](../graph_synthesis/frontier/figures/05_path_work.png) + +## 7. Interpretation and next falsification boundary + +The results favor identifying the tractable structure of a supplied problem over imposing blanket size restrictions: declared dependence changes which probabilities are valid; compact formula structure permits reusable exact evaluation; bipartiteness permits certified optimization despite many cycles; and a fixed path permits local summary updates despite being one connected component. These conclusions are conditional on correct metadata and supported topology. They do not establish that those structures dominate real Jev-generated graphs. + +The review result is a useful negative finding. A more exact allocator can improve a modeled objective yet fail to improve the frozen downstream source-contamination measure. This counsels against promoting the allocator on optimization quality alone. Prospective human-cost/detection measurements, source-disjoint independently adjudicated documents, policies frozen before those labels are seen, and matched evidence/resource budgets against external graph-synthesis systems remain necessary for semantic or deployment claims. H1 and H3 reliability inputs must be estimated and audited separately; raw model confidence cannot simply be substituted. + +The methods remain opt-in research utilities. No default compiler policy or production graph is changed. Failure controls and all prior studies remain in the complete manuscript. Each target is falsifiable on its specified population, but a passed finite benchmark is not a proof of correctness for all inputs or real-world superiority. + +## 8. Reproducibility and audit + +```bash +python -B -m graph_synthesis.frontier.run +python -B -m unittest discover -s graph_synthesis/frontier/tests -v +python -B -m graph_synthesis.frontier.run --check +python -B -m graph_synthesis.frontier.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +python -B -m graph_synthesis.frontier.verify +``` + +The machine-readable results retain source and code hashes, source-group counts, all random fixtures or complete generating specifications, review IDs and scenarios, independent-oracle outputs, flow/cut witnesses, update sequences and decoded-witness hashes. The five figures are generated directly from those results as SVG and PNG pairs; summary.csv preserves target classifications without pooling them. The [implementation log](../graph_synthesis/frontier/IMPLEMENTATION.md) records validation corrections without changing the protocol or endpoints. Original evidence integrity is checked against the immutable inventory. Source, result and figure hashes are bound in the extension manifest; the complete current PDF has a separate source/renderer/PDF hash record. + + + # Do additional Jev calls improve graph edges? Timothy Wayne Gregg — exploratory research addendum, September 18, 2026 diff --git a/manuscript/paper-current.pdf b/manuscript/paper-current.pdf index 41ab32f..ea5c445 100644 Binary files a/manuscript/paper-current.pdf and b/manuscript/paper-current.pdf differ