diff --git a/.github/workflows/jev-multicall.yml b/.github/workflows/jev-multicall.yml index 3a60114..d57ea66 100644 --- a/.github/workflows/jev-multicall.yml +++ b/.github/workflows/jev-multicall.yml @@ -65,7 +65,8 @@ jobs: python -B -m graph_synthesis.structural.report --update-paper python -B -m graph_synthesis.source_structural.report --update-paper python -B -m graph_synthesis.assumption_aware.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 + 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.render_current_paper --output "$RUNNER_TEMP/multicall-paper.pdf" git diff --exit-code -- manuscript/paper-current.html - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 diff --git a/.github/workflows/uncertainty-grounding.yml b/.github/workflows/uncertainty-grounding.yml new file mode 100644 index 0000000..80ef18c --- /dev/null +++ b/.github/workflows/uncertainty-grounding.yml @@ -0,0 +1,182 @@ +name: Uncertainty and grounding experiments +on: + push: + branches: [main, research/uncertainty-grounding-20260918] + pull_request: +permissions: + contents: read +concurrency: + group: uncertainty-${{ 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/uncertainty-grounding-20260918' && !startsWith(github.event.head_commit.message, 'ci-review-snapshot') + runs-on: ubuntu-latest + timeout-minutes: 20 + permissions: + contents: write + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + with: + ref: ${{ github.sha }} + fetch-depth: 0 + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r graph_synthesis/requirements-figures.txt -r graph_synthesis/uncertainty/requirements.txt + - name: Integrate preceding studies and the audited novelty disclosure + shell: bash + run: | + python - <<'PY' + from pathlib import Path + import re + index=Path('CURRENT_RESULTS.md').read_text(encoding='utf-8') + anchors=[a for a in re.findall(r'',index) if a!=''] + assert anchors, 'No preceding study anchor' + anchor=anchors[-1] + assert anchor in Path('manuscript/paper-current.md').read_text(encoding='utf-8') + p=Path('graph_synthesis/uncertainty/report.py') + text,count=re.subn(r'^ANCHOR = .+$','ANCHOR = '+repr(anchor),p.read_text(encoding='utf-8'),flags=re.M) + assert count==1 + assert Path('graph_synthesis/uncertainty/NOVELTY_AUDIT.md').is_file() + if 'NOVELTY_AUDIT.md' not in text: + lines=text.splitlines(keepends=True) + positions=[i for i,line in enumerate(lines) if line.strip().startswith('return ') and '.join(out)' in line] + assert len(positions)==1 + lines[positions[0]:positions[0]]=[ + " audit = (HERE / 'NOVELTY_AUDIT.md').read_text(encoding='utf-8').strip()\n", + " position = out.index('| Hypothesis | Frozen primary target | Outcome | Evidence population |')\n", + " out[position:position] = [audit, '']\n"] + text=''.join(lines) + p.write_text(text,encoding='utf-8',newline='\n') + p=Path('graph_synthesis/uncertainty/EXECUTION_NOTES.md') + text=p.read_text(encoding='utf-8') + heading='## Integration after '+anchor + if heading not in text: + text+='\n'+heading+'\n\nConcurrent PR #19 added source-aware and structural experiments after PR #18. Both are preserved in the combined manuscript. This extension is inserted after the final study in CURRENT_RESULTS.md. The original hypothesis baseline remains a62a3257645d8e35cd4e45be53bfa9511d27724b; no generator, algorithm, seed, endpoint or threshold changed. All five numerical result blocks are compared byte-for-value with the earlier authoritative run. Source/protocol/build hashes and the paper are regenerated. The full-manuscript CI pipeline now regenerates this extension as well as every prior section, rather than dropping it. Reader-facing research markers are hidden by the renderer, covered by two additional tests. These presentation/integration corrections do not alter scientific observations. The separately documented post-execution novelty audit explicitly identifies overlap with concurrent PR #20; these are not five independently unprecedented algorithms.\n' + p.write_text(text,encoding='utf-8',newline='\n') + PY + - name: Execute frozen benchmarks, unit tests and independent replay + shell: bash + run: | + set -euo pipefail + python -B -m graph_synthesis.uncertainty.run | tee graph_synthesis/uncertainty/execution.log + python -B -m unittest discover -s graph_synthesis/uncertainty/tests -v 2>&1 | tee graph_synthesis/uncertainty/tests.log + python -B -m graph_synthesis.uncertainty.run --check + python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/uncertainty-original-inventory.json" + cp "$RUNNER_TEMP/uncertainty-original-inventory.json" graph_synthesis/uncertainty/original-inventory.json + python -B -m graph_synthesis.uncertainty.report --figures --update-paper + python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf + - name: Preserve preceding studies and all previously executed numerical outcomes + shell: bash + run: | + python - <<'PY' + from pathlib import Path + import json,os,re,subprocess + from graph_synthesis.uncertainty.report import START, END + def strip(text): + return re.sub(re.escape(START)+r'.*?'+re.escape(END)+r'\s*','',text,flags=re.S) + for path in ('CURRENT_RESULTS.md','manuscript/paper-current.md'): + before=subprocess.check_output(['git','show',os.environ['GITHUB_SHA']+':'+path]).decode('utf-8') + after=Path(path).read_text(encoding='utf-8') + assert strip(before)==strip(after), 'Preceding text changed: '+path + before=json.loads(subprocess.check_output(['git','show','08fa7c4673bc7084574951ee2b0a426288087852:graph_synthesis/uncertainty/results.json'])) + after=json.loads(Path('graph_synthesis/uncertainty/results.json').read_text(encoding='utf-8')) + for i in range(1,6): + assert before[f'H{i}']==after[f'H{i}'],f'H{i} changed during manuscript integration' + assert 'Do not describe this suite as five independently novel' in Path('manuscript/paper-current.md').read_text(encoding='utf-8') + print('All preceding study text and all five numerical result blocks preserved; novelty overlap disclosed') + PY + - name: Commit generated evidence only to this research branch + 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/uncertainty 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 executed uncertainty benchmarks, five figures and full updated manuscript' + git push origin HEAD:research/uncertainty-grounding-20260918 + fi + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 + with: + name: uncertainty-review-package + path: | + graph_synthesis/uncertainty/ + graph_synthesis/render_current_paper.py + manuscript/paper-current.pdf + manuscript/paper-current.md + manuscript/paper-current.html + manuscript/paper-current.build.json + CURRENT_RESULTS.md + 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: ${{ github.event.pull_request.head.sha || github.sha }} + persist-credentials: false + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r reproduction/requirements-jev.txt -r graph_synthesis/uncertainty/requirements.txt + - name: Replay all results and verify source, protocol, archive and PDF provenance + run: | + python -B -m unittest discover -s graph_synthesis/uncertainty/tests -v + python -B -m graph_synthesis.uncertainty.run --check + python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/uncertainty-original.json" + python - <<'PY' + import hashlib,json + from pathlib import Path + sha=lambda p:hashlib.sha256(Path(p).read_bytes()).hexdigest() + manifest=json.loads(Path('graph_synthesis/uncertainty/artifact-manifest.json').read_text(encoding='utf-8')) + for path,want in manifest['sha256'].items(): + assert sha(path)==want,path + r=json.loads(Path('graph_synthesis/uncertainty/results.json').read_text(encoding='utf-8')) + assert r['protocol_sha256']==sha('graph_synthesis/uncertainty/PROTOCOL.md') + assert r['fresh_service_calls']==0 and r['new_scientific_documents']==0 + for name,want in r['source_sha256'].items(): + assert sha('graph_synthesis/uncertainty/'+name)==want,name + build=json.loads(Path('manuscript/paper-current.build.json').read_text(encoding='utf-8')) + assert sha('manuscript/paper-current.md')==build['source_sha256'] + assert sha('graph_synthesis/render_current_paper.py')==build['renderer_sha256'] + assert sha('manuscript/paper-current.pdf')==build['pdf_sha256'] + print('Source, protocol, result, archive and full-paper provenance verified') + PY + 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: ${{ github.event.pull_request.head.sha || github.sha }} + persist-credentials: false + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r graph_synthesis/requirements-figures.txt -r graph_synthesis/uncertainty/requirements.txt + - name: Regenerate charts and manuscript without artifact drift + run: | + python -B -m graph_synthesis.uncertainty.report --figures --update-paper + git diff --exit-code -- graph_synthesis/uncertainty manuscript/paper-current.md CURRENT_RESULTS.md + python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/uncertainty-paper.pdf" + git diff --exit-code -- manuscript/paper-current.html diff --git a/CURRENT_RESULTS.md b/CURRENT_RESULTS.md index ab65bbe..8c45443 100644 --- a/CURRENT_RESULTS.md +++ b/CURRENT_RESULTS.md @@ -50,6 +50,14 @@ Marginal-only lineage bounds tighten 4.96%, missing the frozen 10% target. Query + + +## Five uncertainty and grounding improvements + +[Executed report](graph_synthesis/uncertainty/RESULTS.md), [frozen protocol](graph_synthesis/uncertainty/PROTOCOL.md), [five figures](graph_synthesis/uncertainty/figures/) and [complete updated paper](manuscript/paper-current.pdf). H1 met its frozen target; H2 met its frozen target; H3 met its frozen target; H4 met its frozen target; H5 met its frozen target. All five are controlled algorithmic/decision-theoretic tests, not new Jev accuracy observations. Dependence uncertainty, repair ambiguity, query-directed review, collateral-aware retraction and recursive grounding are tested with explicit failure controls. No fresh Jev calls, new scientific documents, globally unprecedented algorithm claim or production-policy changes. + + + 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/render_current_paper.py b/graph_synthesis/render_current_paper.py index 91a9e50..561f2ad 100644 --- a/graph_synthesis/render_current_paper.py +++ b/graph_synthesis/render_current_paper.py @@ -9,6 +9,12 @@ NUMERIC = re.compile(r'[+-]?(?:\d[\d,]*(?:\.\d*)?|\.\d+)%?') TABLE_CSS = '\nth { overflow-wrap: normal; }\ntd.numeric { white-space: nowrap; text-align: right; font-variant-numeric: tabular-nums; }\n' +RESEARCH_MARKER = re.compile(r'') + + +def strip_research_markers(markdown): + """Keep additive source anchors out of reader-facing HTML and PDF output.""" + return RESEARCH_MARKER.sub('', markdown) def format_numeric_cells(body): @@ -37,7 +43,7 @@ def render(output): from .render_paper import CSS root = Path(__file__).resolve().parents[1] source = root / "manuscript/paper-current.md" - markdown = re.sub(r"", "", source.read_text(encoding="utf-8")) + markdown = strip_research_markers(source.read_text(encoding="utf-8")) validate_images(markdown, source, root) body = MarkdownIt("commonmark", {"html": False}).enable("table").render(markdown) body = re.sub(r"

(]+>)

", r"
\1
", body) diff --git a/graph_synthesis/uncertainty/EXECUTION_NOTES.md b/graph_synthesis/uncertainty/EXECUTION_NOTES.md new file mode 100644 index 0000000..0f8a16d --- /dev/null +++ b/graph_synthesis/uncertainty/EXECUTION_NOTES.md @@ -0,0 +1,19 @@ +# Execution and correction record + +The remote protocol was committed as `383bab6f559c7f14525ddcef2275f3a816c9caa3` before the first local benchmark run. The first local run exercised all five frozen fixture generators and met all five conditional engineering targets. Its workspace did not contain the full repository or a local protocol copy, so its temporary metadata had a null protocol hash; that output is not the authoritative archived run. The full repository run records the committed protocol hash and is independently replayed in CI. + +During unit-test authoring, two tests initially called existing oracle helpers with the wrong signature/return shape (the finite-model oracle accepts two arguments, and the retraction oracle returns a tuple). Those test calls were corrected. No benchmark generator, seed, primary endpoint, threshold, algorithm or unfavorable control was changed in response to observed outcomes. + +The numerical LP output is recorded to 12 decimal places for deterministic evidence serialization. Full primary comparisons occur before rounding. Separate rational small-case extrema, finite subset oracles and model-interpretation enumeration remain in the suite. Synthetic fixture counts and repeated snapshot occurrences are not counted as new Jev observations. + +## Concurrent PR #18 integration + +PR #18 merged after this protocol was frozen and while these benchmarks were executing. Its structural study is preserved in full. The reporting anchor was moved after its section so both generators are idempotent, and the paper was rebuilt from current main rather than overwriting the concurrent study. The hypothesis baseline remains a62a3257645d8e35cd4e45be53bfa9511d27724b. No benchmark generator, algorithm, seed, endpoint or threshold changed. Source hashes are refreshed because the report placement changed. PR #18 also studies dependence-robust group-review envelopes and independent-primitive exact lineage; those share prior-art foundations with this extension but do not supply its unknown-dependence DNF linear programs, all-optimal-repair backbone, downstream query-loss objective, protected minimum-collateral plans or grounded recursive-withdrawal experiments. Novelty remains bounded to the reviewed baseline, not an exhaustive worldwide or concurrent-branch claim. + +## Integration after + +Concurrent PR #19 added source-aware and structural experiments after PR #18. Both are preserved in the combined manuscript. This extension is inserted after the final study in CURRENT_RESULTS.md. The original hypothesis baseline remains a62a3257645d8e35cd4e45be53bfa9511d27724b; no generator, algorithm, seed, endpoint or threshold changed. All five numerical result blocks are compared byte-for-value with the earlier authoritative run. Source/protocol/build hashes and the paper are regenerated. The full-manuscript CI pipeline now regenerates this extension as well as every prior section, rather than dropping it. Reader-facing research markers are hidden by the renderer, covered by two additional tests. These presentation/integration corrections do not alter scientific observations. Package-level novelty remains scoped to the reviewed frozen baseline; no worldwide or unreviewed concurrent-branch novelty claim is made. + +## Integration after + +Concurrent PR #19 added source-aware and structural experiments after PR #18. Both are preserved in the combined manuscript. This extension is inserted after the final study in CURRENT_RESULTS.md. The original hypothesis baseline remains a62a3257645d8e35cd4e45be53bfa9511d27724b; no generator, algorithm, seed, endpoint or threshold changed. All five numerical result blocks are compared byte-for-value with the earlier authoritative run. Source/protocol/build hashes and the paper are regenerated. The full-manuscript CI pipeline now regenerates this extension as well as every prior section, rather than dropping it. Reader-facing research markers are hidden by the renderer, covered by two additional tests. These presentation/integration corrections do not alter scientific observations. The separately documented post-execution novelty audit explicitly identifies overlap with concurrent PR #20; these are not five independently unprecedented algorithms. diff --git a/graph_synthesis/uncertainty/NOVELTY_AUDIT.md b/graph_synthesis/uncertainty/NOVELTY_AUDIT.md new file mode 100644 index 0000000..37fb9cb --- /dev/null +++ b/graph_synthesis/uncertainty/NOVELTY_AUDIT.md @@ -0,0 +1,9 @@ +### Concurrent-study overlap: post-execution audit + +After this suite was executed, a cross-branch audit examined the [PR #20 certificate-study protocol at commit 8fd9bba](https://github.com/CompleteDotTech/paper-package/blob/8fd9bba028473f581e489ad5a902878245e53759/graph_synthesis/certificates/PROTOCOL.md). That study also starts from baseline `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Its H2 substantially overlaps this study's H1: both bound DNF-lineage probabilities over possible-world distributions without assuming independent primitives. Its H4 overlaps this study's H2: both use constrained re-optimization to distinguish repair-invariant conclusions from arbitrary optimal tie choices. + +There are implementation and test-scope differences. This study's lineage prototype caps at eight used atoms, provides dependence-free analytic fallback and checks rational small-case extrema; the certificate prototype permits ten atoms and optional conjunction-probability constraints. This study returns an all-assertion optimal-repair backbone, whereas the certificate study tests conjunctive queries and an additional five-percent utility tolerance. Those differences do not justify claiming two new foundational mechanisms. + +The subsequently merged [PR #24 assumption-aware protocol at commit 6530321](https://github.com/CompleteDotTech/paper-package/blob/6530321dd0060c9c7f13bb69d6f1c88346a8e1e2/graph_synthesis/assumption_aware/PROTOCOL.md) adds further overlap: its H1/H2 share this study's dependence-bound and repair-invariance mechanisms, and its H5 also recomputes externally grounded least-fixed-point closure after withdrawals. This study's H5 uses finite-model intersection as its independent oracle and measures correctness/phantom retention rather than a primary work-reduction target. Those are useful validation differences, not a new grounding algorithm. Its H3 and this study's H3 both motivate query-aware review, but use different objectives and evidence: source-exposure weighting of saved predictions versus exact expected query Brier loss on controlled event models. Their outcome percentages and pass/fail criteria are not interchangeable. + +**Do not describe this suite as five independently novel or never-before-attempted algorithms.** At least three of its five core mechanisms substantially overlap these concurrent studies. Its contribution is five executed, evidence-bounded implementations and falsification experiments. The original bounded inventory statement refers only to the reviewed baseline, not every concurrent branch or worldwide literature. The overlapping fixture counts must not be pooled as independent semantic validation or independent external replication. This study's exact query-loss objective and protected minimum-collateral retraction address different primary objectives from the reviewed concurrent experiments, but worldwide novelty is not established for them either. All prior study sections and their negative outcomes are preserved. No hypothesis, generator, threshold or numerical outcome was changed in response to this audit. diff --git a/graph_synthesis/uncertainty/PROTOCOL.md b/graph_synthesis/uncertainty/PROTOCOL.md new file mode 100644 index 0000000..09ffaf1 --- /dev/null +++ b/graph_synthesis/uncertainty/PROTOCOL.md @@ -0,0 +1,58 @@ +# Five uncertainty and grounding improvements: frozen exploratory protocol + +Date: September 18, 2026. Baseline commit: `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Seed: 20260922 (an arbitrary random seed, not a collection date). This file is committed before executing the new benchmarks. Existing published findings informed hypothesis selection; this is exploratory engineering research, not independent preregistration. + +## Evidence and novelty boundary + +The latest reliability study found that exact independent-primitive lineage still overstates confidence when independence is wrong, a maximum-priority conflict solver can choose a false assertion, review priorities optimize source contamination rather than downstream answers, and graph maintenance correctness depends on explicit dependency semantics. This extension tests five different responses to those limitations. Every new benchmark is a controlled algorithmic or decision-theoretic fixture. **Fresh Jev calls: zero. New independently adjudicated scientific documents: zero. Production-policy changes: zero.** Do not pool fixture counts with model observations, claim measured human-review benefit, or claim improved extraction accuracy. + +The reviewed package protocols cover calibration, routing, agreement, graph-risk budgets, identity, provenance, interval conflicts, review allocation, independent-event lineage, one-best conflict solving and incremental component maintenance. The specific five mechanisms below were not found in that review. This is bounded package-level novelty, not evidence that nobody has attempted the ideas. Consistent query answering, probabilistic logic, value of information, deletion propagation and least-fixed-point inference are established prior art. New contributions are the implementations, assumption controls and linked experiments for this package; no globally unprecedented algorithm claim is authorized. + +## H1: Dependence-agnostic lineage admission + +Hypothesis: replacing an unjustified product-world assumption with bounds over all joint distributions compatible with supplied primitive marginals prevents correlation-induced false admissions. For a monotone DNF of sufficient proofs, enumerate worlds for at most eight distinct atoms and solve two linear programs over nonnegative world masses: normalization and supplied one-atom marginals are equality constraints; minimize/maximize the probability of the DNF. Canonicalize duplicate and subsumed proofs. Validate finite inputs, numerical feasibility and solver success. Numerical bounds carry a 1e-8 outward allowance; they are not formal real-arithmetic certificates. Above the atom cap or on solver failure, use dependency-free Frechet/union bounds on conjunctions and their disjunction, never an independence estimate. + +Compare to exact independent-world evaluation and the dependency-free analytic fallback. Execute 128 seeded joint-distribution fixtures with two to six atoms, including sparse correlated worlds; report actual joint probability, independent estimate, both bounds and interval width. Check 16 two/three-atom cases against a separate rational vertex-enumeration oracle. Check duplicate/order invariance and threshold 0.95. Primary: zero containment/oracle violations beyond 1e-8; zero lower-bound false admissions on these fixtures; prevent the analytic shared-source false admission (two 0.8 marginal events, identical in reality: independent OR=0.96, actual=0.8); retain a valid singleton admission at 0.99. Include bad-marginal, malformed-input and over-cap controls. Correctness depends on accurate marginals and complete supplied proofs; no raw Jev score is treated as a calibrated source probability. + +## H2: Skeptical optimal-repair backbone + +Hypothesis: admitting only assertions present in every maximum-priority consistent repair avoids treating an arbitrary optimum tie-break as evidence. Input is a supplied undirected conflict graph and nonnegative integer priorities. For components of at most 16 vertices, use memoized maximum-weight independent-set recurrence and constrained re-optimization to compute necessary, possible and excluded assertions, with a witness optimum including/excluding every ambiguous assertion. Larger components stage; this is a new ambiguity capability, not a scalability improvement over the preceding cutset solver. + +Compare the backbone with one deterministic optimum and independent exhaustive enumeration on 128 seeded graphs with two to twelve vertices. Execute four input-order permutations per case. Primary: zero necessary/possible/utility or witness mismatches, no loss relative to the unique optimum when unique, no arbitrary member admitted from an equal-priority conflicting pair, and preservation of an isolated required assertion. Report admitted-priority/coverage loss, ambiguity frequency and the 32-cycle unsupported control. The deliberately false assertion of priority 9 against true priority 8 remains necessary in the unique optimal repair: skepticism about ties is not semantic verification. + +## H3: Query-loss-directed evidence review + +Hypothesis: review selection aimed at downstream Boolean-query loss is more useful than ranking primitive error risk. Use three monotone DNF queries over six to eight independent primitive events with supplied probabilities. Review reveals a primitive perfectly at equal unit cost. Choose a nonadaptive subset of budget one or two minimizing expected residual sum of query Brier risks (conditional Bernoulli variance). Enumerate all subsets under a fixed eight-atom cap, no hidden truth labels. Compare with highest primitive-error probability and highest primitive entropy; also report no review. Evaluate 64 seeded fixtures. A separate direct world/observation Brier calculation verifies the selected loss and optimum. + +Primary: zero oracle/regret failures within 1e-10 AND at least 20% lower aggregate residual query Brier loss than primitive-risk ranking at budget two. Keep the outcome even if the efficiency target fails. Report selected IDs, all subset scores, both budgets and paired fixture differences; any bootstrap interval is descriptive of this artificial generator, not a deployment guarantee. Include a misspecified-prior control and noisy-review sensitivity. Perfect review, known query lineage and correct probabilistic inputs are explicit assumptions; costs are review counts, not invented time or money. + +## H4: Minimum-collateral assertion retraction plans + +Hypothesis: provenance-guided hitting-set search can invalidate a designated erroneous conclusion while preserving protected conclusions and avoiding the collateral damage of withdrawing every supporting candidate assertion. Inputs are complete monotone proof DNFs over at most 14 candidate-commitment atoms, positive integer withdrawal costs, an externally designated target and protected conclusions. The output is a proposed staging/retraction plan, never deletion of source documents. Search hits every target proof, preserves at least one proof for each protected conclusion, and lexicographically minimizes (number of other lost conclusions, withdrawal cost, number of withdrawals, sorted IDs). Bounded unsupported or infeasible inputs stage without mutation. + +Execute 128 seeded fixtures over four to ten atoms and six conclusions. Compare with withdraw-the-union, a cost-first greedy hitting set and independent enumeration of every withdrawal subset. Primary: zero feasibility/objective mismatches; no protected loss; at least 25% lower mean collateral than union withdrawal on cases where both return a feasible plan (if its aggregate collateral is zero, the improvement target fails). Report feasibility/coverage on all cases separately so staging cannot masquerade as quality. Include impossible protection, an omitted alternative proof, incorrect target adjudication, an over-cap input and permutation invariance. Minimal intervention is not proof that the chosen candidate assertions are factually false. + +## H5: Grounded recursive evidence materialization + +Hypothesis: least-fixed-point grounding removes self-supporting inference cycles after external support is withdrawn while retaining conclusions with an alternative grounded proof. Inputs are finite ground positive Horn rules and explicit base assertions; no negation, function symbols or ontology induction. A worklist with body counters starts only from current base assertions and fires grounded rules. Compare with naive support-count deletion from the previous materialization, which can retain cycles, and an independent finite-model oracle: enumerate every interpretation satisfying all rules and bases and intersect them. + +Execute 96 seeded four-to-eight-atom rule programs, each with 12 base snapshots (1,152 snapshot evaluations), including additions and withdrawals. Primary: zero finite-model-oracle mismatches; remove every unsupported assertion in externally seeded cycles of sizes 2,4,8,16,32,64 after the only seed is removed; retain the independently anchored alternative; eliminate at least one phantom assertion left by naive support counts. Report rule/body processing work without equating it to service latency. Include unsupported cyclic proofs with no seed, duplicate/order invariance and a false-base semantic control. Grounded entailment is truth only relative to the supplied bases and rules; this prototype does not verify their real-world correctness. + +## Reproducibility, analysis and merge gate + +Store per-case inputs, outputs, independent oracle values, negative controls, seed, protocol/source hashes and fixed primary-target evaluations in machine-readable results. No endpoint changes after first execution; implementation corrections must be logged without changing fixtures or thresholds. Generate five separate SVG/PNG figures, results report, summary CSV and an additive section in the full current manuscript; preserve all earlier evidence and author-review limitations. Re-run benchmarks byte-identically, execute regression tests, verify the archived 161-file inventory, inspect rendered paper/figures and merge only after applicable checks succeed. Mathematical counterexamples and finite-oracle checks are not statistical population claims. Reject missing, invalid and unsupported inputs explicitly rather than silently admitting assertions. + +## Primary-source context and search record + +Reviewed on September 18, 2026. Searches included package terms skeptical/credal/pivotal/hitting/unfounded, consistent query answering, probabilistic logic, query causality/resilience, value of information and recursive Datalog. Search is bounded, not an exhaustive novelty proof. + +- Package motivation: `graph_synthesis/reliability/PROTOCOL.md` and `RESULTS.md`; `graph_synthesis/followup/PROTOCOL.md`; `CURRENT_RESULTS.md` at the baseline commit. +- Grosof, An Inequality Paradigm for Probabilistic Knowledge: https://arxiv.org/abs/1304.3418 . Bounds over probabilistic logical knowledge are prior art. +- Bacchus, Probability Distributions Over Possible Worlds: https://arxiv.org/abs/1304.2341 . Possible-world probabilistic semantics and limitations are prior art. +- Query answering over inconsistent knowledge bases: A probabilistic approach, Theoretical Computer Science (2022): https://doi.org/10.1016/j.tcs.2022.09.005 . Repair-based query semantics are prior art; this study does not reproduce that system. +- Meliou et al., Why so? or Why no? Functional Causality for Explaining Query Answers: https://arxiv.org/abs/0912.5340 . Query-aware provenance and intervention are prior art. +- Freire et al., A Characterization of the Complexity of Resilience and Responsibility for Self-join-free Conjunctive Queries: https://arxiv.org/abs/1507.00674 . Minimal deletion and resilience are prior art. +- Jackson et al., Value of Information Analysis in Models to Inform Health Policy: https://pmc.ncbi.nlm.nih.gov/articles/PMC7612603/ . Expected-loss reduction for information acquisition is prior art; no healthcare inference is undertaken here. +- W3C RIF Core Dialect: https://www.w3.org/TR/rif-core/ . Horn-rule semantics are prior art; this small ground-positive prototype is not a full RIF implementation. +- SciPy linear programming implementation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html . Solver success and residuals require checking. +- KARMA: https://arxiv.org/abs/2502.06472 . No head-to-head run or external superiority claim is made. diff --git a/graph_synthesis/uncertainty/RESULTS.md b/graph_synthesis/uncertainty/RESULTS.md new file mode 100644 index 0000000..5e80b45 --- /dev/null +++ b/graph_synthesis/uncertainty/RESULTS.md @@ -0,0 +1,142 @@ +# Uncertainty, repair ambiguity and grounded evidence in Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five controlled experiments address assumptions exposed by the preceding reliability study. Dependence-agnostic lineage yields 0 lower-bound false admissions versus 10 under an independence model on 128 supplied joint-distribution fixtures. Skeptical repair keeps 375 necessary assertions rather than 442 assertions from deterministic single optima; this is an explicit ambiguity/coverage tradeoff. Query-loss-directed review lowers aggregate residual query Brier loss by 70.64% versus primitive-risk ranking at two ideal reviews. Minimum-collateral retraction lowers collateral by 56.96% on 57 matched feasible fixtures. Grounded materialization matches finite-model entailment across 1,152 snapshots and removes cyclic phantom support. All results are conditional engineering outcomes, not new Jev accuracy measurements. Wrong marginals, false priorities, misspecified review priors, incomplete provenance and false base assertions remain falsifying controls. + +## 1. Research question, prior evidence and novelty boundary + +The [preceding reliability study](../reliability/RESULTS.md) demonstrated that exact lineage arithmetic cannot repair false independence, priority-optimal graph repair can choose a false assertion, and incremental correctness depends on explicit dependencies. Its review experiments measured source contamination rather than loss in downstream queries. These observations motivate the present five hypotheses. We do not rerun calibration, voting, routing or the previous cycle-cutset benchmark under new names. + +The [protocol](PROTOCOL.md) was committed as `383bab6f559c7f14525ddcef2275f3a816c9caa3` before execution, against baseline `a62a3257645d8e35cd4e45be53bfa9511d27724b`. The seed 20260922 is an arbitrary integer, not a collection date. Existing results informed the hypotheses; this is exploratory follow-up, not independent preregistration. **Fresh Jev calls: 0. New scientific documents: 0.** No default compiler or production graph policy changes. Every benchmark here uses supplied algorithmic fixtures or decision-theoretic models, not extraction from new papers or observed human review. + +These specific integrations were not found in the reviewed package protocols. A bounded search cannot establish that an idea has never been attempted anywhere. Possible-world probabilistic reasoning is established prior art ([Grosof](https://arxiv.org/abs/1304.3418), [Bacchus](https://arxiv.org/abs/1304.2341)); so are [repair-based query answering](https://doi.org/10.1016/j.tcs.2022.09.005), [query causality](https://arxiv.org/abs/0912.5340), [minimal-deletion resilience](https://arxiv.org/abs/1507.00674), [value of information](https://pmc.ncbi.nlm.nih.gov/articles/PMC7612603/) and [Horn-rule semantics](https://www.w3.org/TR/rif-core/). The contribution is a new package-level implementation and falsification suite, not invention of those methods. No head-to-head comparison with [KARMA](https://arxiv.org/abs/2502.06472) is performed. + +### Concurrent-study overlap: post-execution audit + +After this suite was executed, a cross-branch audit examined the [PR #20 certificate-study protocol at commit 8fd9bba](https://github.com/CompleteDotTech/paper-package/blob/8fd9bba028473f581e489ad5a902878245e53759/graph_synthesis/certificates/PROTOCOL.md). That study also starts from baseline `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Its H2 substantially overlaps this study's H1: both bound DNF-lineage probabilities over possible-world distributions without assuming independent primitives. Its H4 overlaps this study's H2: both use constrained re-optimization to distinguish repair-invariant conclusions from arbitrary optimal tie choices. + +There are implementation and test-scope differences. This study's lineage prototype caps at eight used atoms, provides dependence-free analytic fallback and checks rational small-case extrema; the certificate prototype permits ten atoms and optional conjunction-probability constraints. This study returns an all-assertion optimal-repair backbone, whereas the certificate study tests conjunctive queries and an additional five-percent utility tolerance. Those differences do not justify claiming two new foundational mechanisms. + +The subsequently merged [PR #24 assumption-aware protocol at commit 6530321](https://github.com/CompleteDotTech/paper-package/blob/6530321dd0060c9c7f13bb69d6f1c88346a8e1e2/graph_synthesis/assumption_aware/PROTOCOL.md) adds further overlap: its H1/H2 share this study's dependence-bound and repair-invariance mechanisms, and its H5 also recomputes externally grounded least-fixed-point closure after withdrawals. This study's H5 uses finite-model intersection as its independent oracle and measures correctness/phantom retention rather than a primary work-reduction target. Those are useful validation differences, not a new grounding algorithm. Its H3 and this study's H3 both motivate query-aware review, but use different objectives and evidence: source-exposure weighting of saved predictions versus exact expected query Brier loss on controlled event models. Their outcome percentages and pass/fail criteria are not interchangeable. + +**Do not describe this suite as five independently novel or never-before-attempted algorithms.** At least three of its five core mechanisms substantially overlap these concurrent studies. Its contribution is five executed, evidence-bounded implementations and falsification experiments. The original bounded inventory statement refers only to the reviewed baseline, not every concurrent branch or worldwide literature. The overlapping fixture counts must not be pooled as independent semantic validation or independent external replication. This study's exact query-loss objective and protected minimum-collateral retraction address different primary objectives from the reviewed concurrent experiments, but worldwide novelty is not established for them either. All prior study sections and their negative outcomes are preserved. No hypothesis, generator, threshold or numerical outcome was changed in response to this audit. + +| Hypothesis | Frozen primary target | Outcome | Evidence population | +|---|---|---|---| +| H1: Dependence-agnostic lineage | No containment/oracle/false-admission errors; prevent shared-source control and retain valid singleton | Met | 128 joint fixtures + 16 rational oracles | +| H2: Skeptical repair backbone | Exact all-optima agreement; preserve unique optimum; stage arbitrary tie members | Met | 128 conflict graphs + 512 order checks | +| H3: Query-loss-directed review | Zero oracle regret and at least 20% lower query loss at budget two | Met | 64 independent-event/query fixtures | +| H4: Minimum-collateral retraction | Exact feasible objective, no protected loss, at least 25% less matched collateral | Met | 128 proof-lineage fixtures | +| H5: Grounded recursive evidence | Exact finite-model entailment, eliminate unsupported cycles, preserve grounded backup | Met | 96 rule programs / 1,152 snapshots | + +Meeting an engineering target is not evidence that five new semantic improvements have been discovered. Generator-specific effects and assumption-breaking controls must be read together. + +## 2. H1: Dependence-agnostic lineage admission + +A conclusion has a monotone disjunctive-normal-form proof: any listed conjunction of primitive events is sufficient. The former exact-lineage method multiplied independent primitive probabilities. Here the one-atom marginals are supplied, but dependence is left unspecified. With one nonnegative mass per Boolean world, impose total mass one and each marginal as an equality. Minimize and maximize the conclusion indicator over this feasible polytope. The resulting interval asks what is justified across all joint distributions compatible with those premises. + +The numerical prototype uses [SciPy linear programming](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html), at most eight used atoms, primal feasibility checks, repaired dual objective bounds and a 1e-8 outward allowance. These are numerical bounds, not formal real-arithmetic certificates. Failed or over-cap solves return dependence-free Frechet conjunction and union bounds. Duplicate and subsumed proofs are removed. No raw Jev score is promoted to a calibrated source reliability. + +Across 128 seeded joint-distribution fixtures there are 0 containment errors, 0 duplicate/order errors and 0 false lower-bound admissions at threshold 0.95. Exact independent-world evaluation instead gives 10 false admissions. On 16 two/three-atom cases, a separate rational vertex-enumeration oracle finds 0 discrepancies beyond 1e-8. Mean interval width is 0.190534 for the analytic fallback and 0.166133 for the LP bounds. Primary target: **met**. + +| Control | Result | Interpretation | +|---|---|---| +| Two 0.8-marginal events are actually identical | Independence gives 0.96; robust interval [0.8, 1.0]; actual 0.8 | Stage, rather than admit at 0.95 | +| Accurate singleton marginal 0.99 | Lower bound 0.99 | A justified high-probability admission is retained | +| Nine used atoms | Analytic fallback | No over-cap exactness claim | +| Supplied singleton 0.99, actual 0.5 | Lower bound still 0.99 | Wrong marginals invalidate the premises | + +This is improved resistance to an unjustified independence assumption, not a guarantee against inaccurate priors, incomplete proofs or unknown extraction errors. A wide interval is intentionally retained when dependence is unidentified. + +![H1. False admissions at threshold 0.95 on supplied joint-distribution fixtures.](figures/01_dependence.png) + +## 3. H2: Skeptical optimal-repair backbone + +A one-best conflict optimizer selects one maximum-priority independent set, resolving ties by identifier. A tie-break is not evidence for one assertion over another. The proposed policy partitions assertions into necessary (in every optimum), possible (in at least one optimum) and excluded (in none). Only necessary assertions are unambiguously admissible under that objective. Constrained re-optimization produces an including and excluding witness for each ambiguous assertion. + +The solver is bounded to connected components of at most 16 vertices. Larger components stage explicitly and the returned full-graph utility is unknown, while solved-component results remain labeled as partial. This improves ambiguity representation, not scalability relative to the previous 256-vertex cutset solver. Priorities and conflicts are supplied; neither is inferred from semantic truth. + +On 128 seeded graphs, independent exhaustive subset enumeration finds 0 utility/membership/witness errors and 0 unique-optimum losses. There are 0 failures over 512 order checks. Multiple optimal repairs occur in 50 cases. Deterministic single repairs select 442 assertions in total; the skeptical backbone retains 375, withholding 67 tie-dependent selections. Primary target: **met**. + +An equal-priority conflicting pair is ambiguous while an isolated positive-priority assertion remains necessary. A 32-cycle is unsupported at the new cap. Critically, the unique false-priority control (false=9, true=8) still makes the false assertion necessary. Skepticism about optimization ties does not validate priorities or facts; coverage and supplied-priority utility can decrease. + +![H2. Selected versus necessary assertions across the supplied conflict graphs.](figures/02_repair_backbone.png) + +## 4. H3: Query-loss-directed evidence review + +Reviewing the most likely wrong primitive need not improve the answers that matter. Given supplied independent-event probabilities and three Boolean queries, choose a nonadaptive set of one or two primitive reviews minimizing expected residual query Brier loss. Perfect review reveals each selected event truthfully. The objective is the sum of expected conditional Bernoulli variances, equivalently expected squared error of the posterior query probabilities. Enumerate all review subsets within an eight-atom cap. No realized truth label enters selection. + +Compare against lowest primitive truth probability (highest error risk for an asserted primitive), highest primitive entropy, and no review. A separate world/observation squared-error computation checks every subset and optimum. The primary effect is against risk ranking; entropy is an additional, more uncertainty-aligned comparator, not silently omitted. + +| Reviews per fixture | Risk ranking | Entropy ranking | Query-directed | Query-directed with unmodeled 10% review noise | +|---:|---:|---:|---:|---:| +| 1 | 0.378866 | 0.313358 | 0.186337 | 0.300979 | +| 2 | 0.333855 | 0.239755 | 0.098034 | 0.289846 | + +Values are mean residual *summed* Brier loss for three queries per fixture, not classification error rates. Across 64 fixtures, budget-two total loss falls from 21.366751 to 6.274170, a 70.64% reduction. Oracle failures: 0. Primary target: **met**. The paired mean difference has a descriptive 95% fixture-bootstrap interval [-0.270651, -0.202129] (2,000 draws). This interval describes the artificial generator only, not scientific-paper performance or human reviewers. + +**Assumption-breaking result:** the misspecified-prior control makes the chosen review's actual loss 0.4525, worse than risk ranking's 0.1800. The noisy-review rows keep the selector/posterior fixed while reviews undergo independent 10% bit flips; the model does not know this noise. Thus perfect-review gains are not robust guarantees. Costs are review counts only. No reviewer time, money, Jev-token cost or live service latency is inferred. + +![H3. Downstream query loss under equal ideal-review counts.](figures/03_query_review.png) + +## 5. H4: Minimum-collateral assertion retraction plans + +After external adjudication designates a conclusion for withdrawal, which candidate commitments should be staged? A plan must hit every supplied sufficient proof of the target while leaving at least one proof for each protected conclusion. Branching on an unhit target proof explores candidate withdrawals. Lexicographically minimize other lost conclusions, withdrawal cost, withdrawal count and sorted identifiers. Positive costs and monotone proof semantics justify pruning dominated partial plans. The cap is 14 atoms; unsupported or infeasible plans return no mutation. + +This is an opt-in plan over candidate commitments, not deletion of source documents or a claim that the selected commitments are false. Compare with withdrawing the union of every target-proof atom and a cost-first greedy hitting set. The independent oracle enumerates every possible withdrawal subset. + +| Method | Feasible plans / 128 | Collateral on the same matched 57 cases | +|---|---:|---:| +| Union withdrawal | 57 | 79 | +| Cost-first greedy | 96 | Not the frozen primary comparator | +| Minimum-collateral | 112 | 34 | + +There are 0 oracle/objective errors, 0 protected/feasibility violations and 0 order errors. On the 57 cases where both proposed and union plans are feasible, total collateral falls from 79 to 34, or 56.96%. Primary target: **met**. The proposed method finds 112 feasible cases; the remaining 16 are infeasible under the supplied protection constraints, matching exhaustive enumeration. Coverage is reported separately so abstention cannot masquerade as quality. + +The impossible-protection control stages rather than sacrificing the protected fact. The omitted-proof control leaves the target supported by an undisclosed alternative after a seemingly valid retraction. Incorrect external target adjudication can withdraw true commitments. These failures are not fixed by combinatorial optimality; complete lineage and correct intervention goals are essential. + +![H4. Collateral at matched feasible coverage; total feasibility is reported separately.](figures/04_retraction.png) + +## 6. H5: Grounded recursive evidence materialization + +Local support counts can leave a cycle apparently supported after its only external evidence disappears: A supports B and B supports A. For finite ground positive Horn rules, restart a worklist from current base assertions, decrement per-rule body counters as grounded atoms arrive, and fire a head only after every body atom is grounded. The process computes the least fixed point. It does not treat previously derived assertions as current bases. A separately implemented oracle intersects all finite interpretations satisfying the supplied bases and rules. + +Across 96 seeded programs and 1,152 base snapshots, there are 0 finite-model disagreements and 0 duplicate/order disagreements. The local-support comparator leaves 943 phantom assertion occurrences over these snapshots; these are repeated assertion/snapshot occurrences, not unique facts or observed model hallucinations. Comparator updates begin from the preceding correct state, isolating each withdrawal failure rather than accumulating prior mistakes. Primary target: **met**. + +| Cycle size | Phantom assertions after sole seed withdrawal: local support | Grounded materialization | Grounded with independent backup | +|---:|---:|---:|---:| +| 2 | 2 | 0 | 2 | +| 4 | 4 | 0 | 4 | +| 8 | 8 | 0 | 8 | +| 16 | 16 | 0 | 16 | +| 32 | 32 | 0 | 32 | +| 64 | 64 | 0 | 64 | + +The backup column excludes the external backup atom itself. Every unsupported cycle is removed and every independently re-anchored cycle survives. Unseeded self-support also produces no fact. However, a false base assertion still grounds its rule consequences: entailment is conditional on the supplied bases and rules, not verification of real-world truth. Body-visit counts measure this in-memory materialization work, not database I/O, end-to-end update speed or Jev latency. This work recomputes grounding; it does not claim an incremental complexity improvement. + +![H5. Unsupported cycle members retained after the only external seed is removed.](figures/05_grounding.png) + +## 7. Interpretation, limitations and next falsification boundary + +The results support five bounded engineering capabilities: represent unidentified dependence honestly; distinguish necessary from arbitrary optimal selections; aim ideal reviews at downstream queries; minimize intervention collateral under explicit protection; and reject cyclic self-support without an external base. They do not establish new relation-extraction accuracy, calibrated real-world primitive reliabilities, automatic conflict discovery, trustworthy adjudication or better performance than KARMA. Exact small-instance objectives and known query priors favor these exhaustive prototypes, whose cost and coverage limits are explicit. + +All five primary criteria are conjunctions frozen before execution and are evaluated without changing their thresholds. They are not five independent scientific discoveries or a pooled success rate. H1/H2/H4/H5 use finite-oracle tests rather than statistical population tests. H3 uses one descriptive, unadjusted generator-level bootstrap interval; it is not a prospective semantic confidence interval. Negative controls remain alongside favorable primary results. + +A future semantic test must freeze policy and resource budgets before new source-disjoint, independently adjudicated graph data are observed. It must measure both correct retained edges and wrong edges, downstream query loss, intervention harm, actual reviewer errors, incomplete provenance, marginal misspecification and cap-driven staging. That study has not been performed here. + +## 8. Reproducibility and artifact integrity + +```bash +python -m pip install -r graph_synthesis/uncertainty/requirements.txt +python -B -m graph_synthesis.uncertainty.run +python -B -m unittest discover -s graph_synthesis/uncertainty/tests -v +python -B -m graph_synthesis.uncertainty.run --check +python -m pip install -r graph_synthesis/requirements-figures.txt +python -B -m graph_synthesis.uncertainty.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +``` + +The [machine-readable results](results.json) preserve fixture inputs, oracle values, all review-subset scores, repair witnesses, retraction feasibility, snapshot outcomes, controls and source/protocol hashes. The [execution notes](EXECUTION_NOTES.md) 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. diff --git a/graph_synthesis/uncertainty/__init__.py b/graph_synthesis/uncertainty/__init__.py new file mode 100644 index 0000000..de14e9d --- /dev/null +++ b/graph_synthesis/uncertainty/__init__.py @@ -0,0 +1 @@ +"""Opt-in controlled research; no production graph policy changes.""" diff --git a/graph_synthesis/uncertainty/artifact-manifest.json b/graph_synthesis/uncertainty/artifact-manifest.json new file mode 100644 index 0000000..5f2cc1c --- /dev/null +++ b/graph_synthesis/uncertainty/artifact-manifest.json @@ -0,0 +1,31 @@ +{ + "note": "Uncertainty extension only, excluding self. 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/graph_synthesis/uncertainty/methods.py b/graph_synthesis/uncertainty/methods.py new file mode 100644 index 0000000..f9f0922 --- /dev/null +++ b/graph_synthesis/uncertainty/methods.py @@ -0,0 +1,312 @@ +"""Bounded uncertainty/grounding prototypes with explicit assumption boundaries.""" +from __future__ import annotations +from collections import defaultdict, deque +from functools import lru_cache +from itertools import combinations +import math +from typing import Iterable, Mapping + +EPS = 1e-8 + + +def identifiers(values: Iterable[str]) -> tuple[str, ...]: + values = tuple(values) + if any(not isinstance(x, str) or not x for x in values): + raise ValueError('Identifiers must be nonempty strings') + return tuple(sorted(set(values))) + + +def probabilities(values: Mapping[str, float]) -> dict[str, float]: + identifiers(values) + if not values: + raise ValueError('At least one primitive is required') + result = {} + for key in sorted(values): + x = values[key] + if isinstance(x, bool) or not isinstance(x, (int, float)) or not math.isfinite(x) or not 0 <= x <= 1: + raise ValueError('Probabilities must be finite numbers in [0,1]') + result[key] = float(x) + return result + + +def dnf(proofs: Iterable[Iterable[str]], atoms: Iterable[str]) -> tuple[tuple[str, ...], ...]: + known = set(atoms) + terms = set() + for proof in proofs: + if isinstance(proof, str): + raise ValueError('A proof must be a collection, not a string') + term = identifiers(proof) + if not term or not set(term) <= known: + raise ValueError('Empty proof or unknown primitive') + terms.add(term) + ordered = sorted(terms, key=lambda t: (len(t), t)) + minimal = [] + for term in ordered: + if not any(set(other) <= set(term) for other in minimal): + minimal.append(term) + return tuple(minimal) + + +def holds(proofs: Iterable[Iterable[str]], active: set[str]) -> bool: + return any(set(term) <= active for term in proofs) + + +def world_table(p: Mapping[str, float]): + p = probabilities(p) + atoms = tuple(p) + if len(atoms) > 8: + raise ValueError('World enumeration cap exceeded') + worlds = [set(a for i, a in enumerate(atoms) if mask & (1 << i)) for mask in range(1 << len(atoms))] + mass = [math.prod(p[a] if a in world else 1-p[a] for a in atoms) for world in worlds] + return atoms, worlds, mass + + +def independent_probability(proofs, p): + terms = dnf(proofs, p) + _, worlds, mass = world_table(p) + return sum(m for w, m in zip(worlds, mass) if holds(terms, w)) + + +def frechet_bounds(proofs, p): + p = probabilities(p) + terms = dnf(proofs, p) + lower = max((max(0.0, sum(p[a] for a in term)-len(term)+1) for term in terms), default=0.0) + upper = min(1.0, sum(min(p[a] for a in term) for term in terms)) + return {'lower': lower, 'upper': upper, 'mode': 'analytic-dependence-free'} + + +def credal_bounds(proofs, p): + """Numerical possible-world LP; dual residual repair plus outward tolerance. + + This is not a formal real-arithmetic certificate. Incorrect marginals remain + incorrect premises. Atom-cap/solver failures return dependence-free bounds. + """ + p = probabilities(p) + terms = dnf(proofs, p) + fallback = frechet_bounds(terms, p) + used = sorted({a for term in terms for a in term}) + if not used: + return {'lower': 0.0, 'upper': 0.0, 'mode': 'constant-false'} + if len(used) > 8: + return dict(fallback, reason='atom-cap') + import numpy as np + from scipy.optimize import linprog + worlds = [set(a for i, a in enumerate(used) if mask & (1 << i)) for mask in range(1 << len(used))] + A = np.array([[1.0]*len(worlds)] + [[float(a in w) for w in worlds] for a in used]) + b = np.array([1.0]+[p[a] for a in used]) + c = np.array([float(holds(terms, w)) for w in worlds]) + try: + answers = [linprog(obj, A_eq=A, b_eq=b, bounds=(0, None), method='highs') for obj in (c, -c)] + if any(not ans.success or not np.all(np.isfinite(ans.x)) for ans in answers): + return dict(fallback, reason='solver-status') + residual = max(float(np.max(np.abs(A@ans.x-b))) for ans in answers) + if residual > EPS or any(float(np.min(ans.x)) < -EPS for ans in answers): + return dict(fallback, reason='solver-residual') + dual_bounds = [] + for obj, ans in zip((c, -c), answers): + y = np.asarray(ans.eqlin.marginals) + if not np.all(np.isfinite(y)): + return dict(fallback, reason='invalid-dual') + # Normalization fixes total mass to one, so subtracting the largest + # dual constraint violation repairs its objective bound. + violation = max(0.0, float(np.max(A.T@y-obj))) + dual_bounds.append(float(b@y)-violation-EPS) + lower = max(fallback['lower'], dual_bounds[0], 0.0) + upper = min(fallback['upper'], -dual_bounds[1], 1.0) + if lower > upper: + return dict(fallback, reason='inconsistent-numerical-bounds') + return {'lower': lower, 'upper': upper, 'mode': 'numerical-LP', + 'raw_lower': float(answers[0].fun), 'raw_upper': -float(answers[1].fun), + 'primal_residual': residual, 'worlds': len(worlds)} + except (ValueError, RuntimeError, ArithmeticError): + return dict(fallback, reason='solver-exception') + + +def graph_input(weights, edges): + nodes = identifiers(weights) + if any(isinstance(v, bool) or not isinstance(v, int) or v < 0 for v in weights.values()): + raise ValueError('Priorities must be nonnegative integers') + adjacency = {x: set() for x in nodes} + for edge in edges: + if isinstance(edge, str) or len(edge) != 2: + raise ValueError('Each conflict has two endpoints') + a, b = edge + if a not in adjacency or b not in adjacency or a == b: + raise ValueError('Unknown endpoint or self-conflict') + adjacency[a].add(b); adjacency[b].add(a) + return nodes, adjacency + + +def skeptical_repair(weights, edges): + """All-optima backbone on <=16-vertex connected components, otherwise stage.""" + nodes, adj = graph_input(weights, edges) + remaining = set(nodes) + components = [] + while remaining: + component = set(); stack = [min(remaining)] + while stack: + v = stack.pop() + if v not in component: + component.add(v); stack.extend(sorted(adj[v]-component, reverse=True)) + remaining -= component; components.append(sorted(component)) + selected, necessary, possible, excluded, staged = [], [], [], [], [] + witnesses = {}; total = 0; states = 0 + for comp in components: + if len(comp) > 16: + staged.extend(comp); continue + n = len(comp); index = {a: i for i, a in enumerate(comp)} + masks = [sum(1 << index[b] for b in adj[a]) for a in comp] + @lru_cache(None) + def optimum(mask): + if not mask: + return 0, () + bit = mask & -mask; i = bit.bit_length()-1 + score0, set0 = optimum(mask ^ bit) + score1, set1 = optimum(mask & ~bit & ~masks[i]) + score1 += weights[comp[i]]; set1 = tuple(sorted(set1+(comp[i],))) + if score1 > score0 or (score1 == score0 and set1 < set0): + return score1, set1 + return score0, set0 + full = (1 << n)-1 + best, chosen = optimum(full); total += best; selected.extend(chosen) + for i, node in enumerate(comp): + without, witness0 = optimum(full & ~(1 << i)) + inc, witness1 = optimum(full & ~(1 << i) & ~masks[i]) + inc += weights[node]; witness1 = tuple(sorted(witness1+(node,))) + if inc == best: + possible.append(node) + else: + excluded.append(node) + if without < best: + necessary.append(node) + elif inc == best: + witnesses[node] = {'component': comp, 'include': list(witness1), 'exclude': list(witness0)} + states += optimum.cache_info().currsize + for entry in witnesses.values(): + outside = set(selected)-set(entry['component']) + entry['include'] = sorted(outside | set(entry['include'])) + entry['exclude'] = sorted(outside | set(entry['exclude'])) + return {'selected': sorted(selected), 'necessary': sorted(necessary), 'possible': sorted(possible), + 'excluded': sorted(excluded), 'staged': sorted(staged), 'witnesses': witnesses, + 'utility': None if staged else total, 'solved_utility': total, 'states': states} + + +def query_loss(queries, p, reviewed): + """Sum of expected conditional variances under supplied independent inputs.""" + atoms, worlds, mass = world_table(p) + terms = [dnf(q, atoms) for q in queries] + reviewed = identifiers(reviewed) + if not set(reviewed) <= set(atoms): + raise ValueError('Review contains unknown atoms') + groups = {} + for world, m in zip(worlds, mass): + observation = tuple(a in world for a in reviewed) + entry = groups.setdefault(observation, [0.0, [0.0]*len(terms)]) + entry[0] += m + for j, query in enumerate(terms): + entry[1][j] += m*holds(query, world) + return max(0.0, sum(s-s*s/m for m, sums in groups.values() if m > 0 for s in sums)) + + +def review_plan(queries, p, budget): + p = probabilities(p) + if len(p) > 8 or isinstance(budget, bool) or not isinstance(budget, int) or not 0 <= budget <= len(p): + raise ValueError('Unsupported atom count or review budget') + terms = [dnf(q, p) for q in queries] + scores = [{'reviewed': list(subset), 'loss': query_loss(terms, p, subset)} + for subset in combinations(sorted(p), budget)] + best = min(scores, key=lambda x: (round(x['loss'], 12), x['reviewed'])) + return dict(best, candidates=scores) + + +def retraction_input(facts, target, protected, costs): + atoms = identifiers(costs) + if not atoms or any(isinstance(x, bool) or not isinstance(x, int) or x <= 0 for x in costs.values()): + raise ValueError('Withdrawal costs must be positive integers') + identifiers(facts); protected = identifiers(protected) + if target not in facts or not set(protected) <= set(facts): + raise ValueError('Unknown target or protected conclusion') + terms = {f: dnf(proof, atoms) for f, proof in facts.items()} + if any(not proof for proof in terms.values()): + raise ValueError('Every supplied conclusion must initially have a proof') + return atoms, terms, protected + + +def retraction_plan(facts, target, protected, costs): + """Propose staging candidate commitments, never delete underlying evidence.""" + atoms, terms, protected = retraction_input(facts, target, protected, costs) + if len(atoms) > 14: + return {'status': 'staged-cap', 'removed': None} + best = None; best_removed = None; seen = set() + def search(removed): + nonlocal best, best_removed + key = tuple(sorted(removed)) + if key in seen: + return + seen.add(key) + active = set(atoms)-removed + if any(not holds(terms[f], active) for f in protected): + return + lost = sorted(f for f in terms if f != target and f not in protected and not holds(terms[f], active)) + cost = sum(costs[a] for a in removed) + if best is not None and (len(lost), cost) > best[:2]: + return + unhit = [term for term in terms[target] if not set(term) & removed] + if not unhit: + objective = (len(lost), cost, len(removed), key) + if best is None or objective < best: + best, best_removed = objective, key + return + for atom in min(unhit, key=lambda term: (len(term), term)): + search(removed | {atom}) + search(set()) + if best is None: + return {'status': 'infeasible', 'removed': None, 'states': len(seen)} + return {'status': 'exact', 'removed': list(best_removed), 'collateral': best[0], + 'cost': best[1], 'count': best[2], 'states': len(seen)} + + +def horn_input(bases, rules): + base = set(identifiers(bases)); canonical = set() + for head, body in rules: + identifiers([head]) + if isinstance(body, str): + raise ValueError('Rule body must be a collection') + body = identifiers(body) + if not body: + raise ValueError('Use explicit base assertions, not empty rule bodies') + canonical.add((head, body)) + return base, sorted(canonical) + + +def grounded(bases, rules): + """Least fixed point of finite ground positive rules using grounded events.""" + base, rules = horn_input(bases, rules) + active = set(base); queue = deque(sorted(base)); consumers = defaultdict(list) + remaining = [] + for i, (_, body) in enumerate(rules): + remaining.append(len(body)) + for atom in body: + consumers[atom].append(i) + visits = 0; witnesses = {} + while queue: + atom = queue.popleft() + for i in consumers[atom]: + visits += 1; remaining[i] -= 1 + head, body = rules[i] + if remaining[i] == 0 and head not in active: + active.add(head); witnesses[head] = list(body); queue.append(head) + return {'active': sorted(active), 'body_visits': visits, 'witnesses': witnesses, + 'body_incidences': sum(len(body) for _, body in rules)} + + +def support_prune(previous, bases, rules): + """Deliberately naive comparator: local support can retain unfounded cycles.""" + base, rules = horn_input(bases, rules) + active = set(previous) | base + while True: + supported = base | {head for head, body in rules if set(body) <= active} + remove = active-supported + if not remove: + return sorted(active) + active -= remove diff --git a/graph_synthesis/uncertainty/original-inventory.json b/graph_synthesis/uncertainty/original-inventory.json new file mode 100644 index 0000000..28e1a16 --- /dev/null +++ b/graph_synthesis/uncertainty/original-inventory.json @@ -0,0 +1,4 @@ +{ + "archive_files_verified": 161, + "original_files_changed": 0 +} diff --git a/graph_synthesis/uncertainty/report.py b/graph_synthesis/uncertainty/report.py new file mode 100644 index 0000000..cf5fa2c --- /dev/null +++ b/graph_synthesis/uncertainty/report.py @@ -0,0 +1,225 @@ +"""Regenerate five figures and an additive, evidence-bounded full-paper section.""" +from __future__ import annotations +import argparse +import csv +import json +import re +from statistics import mean +from .run import HERE, ROOT, sha, write + +START = '' +END = '' +ANCHOR = '' +TITLES = ['Dependence-agnostic lineage', 'Skeptical repair backbone', 'Query-loss-directed review', + 'Minimum-collateral retraction', 'Grounded recursive evidence'] +FIGURES = ['01_dependence', '02_repair_backbone', '03_query_review', '04_retraction', '05_grounding'] + + +def review_means(h): + rows = [] + for j, budget in enumerate((1, 2)): + rows.append({'budget': budget, **{p: mean(c['budgets'][j][p]['loss'] for c in h['cases']) + for p in ('risk', 'entropy', 'proposed')}, + 'noisy_proposed': mean(c['budgets'][j]['noisy_proposed_loss'] for c in h['cases']), + 'noisy_risk': mean(c['budgets'][j]['noisy_risk_loss'] for c in h['cases'])}) + return rows + + +def render(r): + a, b, c, d, e = [r[f'H{i}'] for i in range(1, 6)] + status = lambda h: 'Met' if h['primary_target_met'] else 'Not met' + out = ['# Uncertainty, repair ambiguity and grounded evidence in Jev graph synthesis', '', + 'Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026', '', + '## Abstract', '', + f"Five controlled experiments address assumptions exposed by the preceding reliability study. Dependence-agnostic lineage yields {a['false_admissions']} lower-bound false admissions versus {a['independent_false_admissions']} under an independence model on {len(a['cases'])} supplied joint-distribution fixtures. Skeptical repair keeps {b['necessary_assertions']} necessary assertions rather than {b['selected_assertions']} assertions from deterministic single optima; this is an explicit ambiguity/coverage tradeoff. Query-loss-directed review lowers aggregate residual query Brier loss by {c['relative_loss_reduction']:.2%} versus primitive-risk ranking at two ideal reviews. Minimum-collateral retraction lowers collateral by {d['relative_collateral_reduction']:.2%} on {d['matched_feasible_cases']} matched feasible fixtures. Grounded materialization matches finite-model entailment across {e['snapshot_evaluations']:,} snapshots and removes cyclic phantom support. All results are conditional engineering outcomes, not new Jev accuracy measurements. Wrong marginals, false priorities, misspecified review priors, incomplete provenance and false base assertions remain falsifying controls.", '', + '## 1. Research question, prior evidence and novelty boundary', '', + 'The [preceding reliability study](../reliability/RESULTS.md) demonstrated that exact lineage arithmetic cannot repair false independence, priority-optimal graph repair can choose a false assertion, and incremental correctness depends on explicit dependencies. Its review experiments measured source contamination rather than loss in downstream queries. These observations motivate the present five hypotheses. We do not rerun calibration, voting, routing or the previous cycle-cutset benchmark under new names.', '', + f"The [protocol](PROTOCOL.md) was committed as `{r['protocol_commit']}` before execution, against baseline `{r['baseline_commit']}`. The seed {r['seed']} is an arbitrary integer, not a collection date. Existing results informed the hypotheses; this is exploratory follow-up, not independent preregistration. **Fresh Jev calls: {r['fresh_service_calls']}. New scientific documents: {r['new_scientific_documents']}.** No default compiler or production graph policy changes. Every benchmark here uses supplied algorithmic fixtures or decision-theoretic models, not extraction from new papers or observed human review.", '', + 'These specific integrations were not found in the reviewed package protocols. A bounded search cannot establish that an idea has never been attempted anywhere. Possible-world probabilistic reasoning is established prior art ([Grosof](https://arxiv.org/abs/1304.3418), [Bacchus](https://arxiv.org/abs/1304.2341)); so are [repair-based query answering](https://doi.org/10.1016/j.tcs.2022.09.005), [query causality](https://arxiv.org/abs/0912.5340), [minimal-deletion resilience](https://arxiv.org/abs/1507.00674), [value of information](https://pmc.ncbi.nlm.nih.gov/articles/PMC7612603/) and [Horn-rule semantics](https://www.w3.org/TR/rif-core/). The contribution is a new package-level implementation and falsification suite, not invention of those methods. No head-to-head comparison with [KARMA](https://arxiv.org/abs/2502.06472) is performed.', '', + '| Hypothesis | Frozen primary target | Outcome | Evidence population |', + '|---|---|---|---|'] + targets = ['No containment/oracle/false-admission errors; prevent shared-source control and retain valid singleton', + 'Exact all-optima agreement; preserve unique optimum; stage arbitrary tie members', + 'Zero oracle regret and at least 20% lower query loss at budget two', + 'Exact feasible objective, no protected loss, at least 25% less matched collateral', + 'Exact finite-model entailment, eliminate unsupported cycles, preserve grounded backup'] + populations = ['128 joint fixtures + 16 rational oracles', '128 conflict graphs + 512 order checks', + '64 independent-event/query fixtures', '128 proof-lineage fixtures', '96 rule programs / 1,152 snapshots'] + for i in range(5): + out.append(f'| H{i+1}: {TITLES[i]} | {targets[i]} | {status(r[f"H{i+1}"])} | {populations[i]} |') + out += ['', 'Meeting an engineering target is not evidence that five new semantic improvements have been discovered. Generator-specific effects and assumption-breaking controls must be read together.', '', + '## 2. H1: Dependence-agnostic lineage admission', '', + 'A conclusion has a monotone disjunctive-normal-form proof: any listed conjunction of primitive events is sufficient. The former exact-lineage method multiplied independent primitive probabilities. Here the one-atom marginals are supplied, but dependence is left unspecified. With one nonnegative mass per Boolean world, impose total mass one and each marginal as an equality. Minimize and maximize the conclusion indicator over this feasible polytope. The resulting interval asks what is justified across all joint distributions compatible with those premises.', '', + 'The numerical prototype uses [SciPy linear programming](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html), at most eight used atoms, primal feasibility checks, repaired dual objective bounds and a 1e-8 outward allowance. These are numerical bounds, not formal real-arithmetic certificates. Failed or over-cap solves return dependence-free Frechet conjunction and union bounds. Duplicate and subsumed proofs are removed. No raw Jev score is promoted to a calibrated source reliability.', '', + f"Across {len(a['cases'])} seeded joint-distribution fixtures there are {a['containment_failures']} containment errors, {a['invariance_failures']} duplicate/order errors and {a['false_admissions']} false lower-bound admissions at threshold 0.95. Exact independent-world evaluation instead gives {a['independent_false_admissions']} false admissions. On {len(a['oracle_cases'])} two/three-atom cases, a separate rational vertex-enumeration oracle finds {a['oracle_failures']} discrepancies beyond 1e-8. Mean interval width is {a['mean_analytic_width']:.6f} for the analytic fallback and {a['mean_credal_width']:.6f} for the LP bounds. Primary target: **{status(a).lower()}**.", '', + '| Control | Result | Interpretation |', '|---|---|---|', + '| Two 0.8-marginal events are actually identical | Independence gives 0.96; robust interval [0.8, 1.0]; actual 0.8 | Stage, rather than admit at 0.95 |', + '| Accurate singleton marginal 0.99 | Lower bound 0.99 | A justified high-probability admission is retained |', + '| Nine used atoms | Analytic fallback | No over-cap exactness claim |', + '| Supplied singleton 0.99, actual 0.5 | Lower bound still 0.99 | Wrong marginals invalidate the premises |', '', + 'This is improved resistance to an unjustified independence assumption, not a guarantee against inaccurate priors, incomplete proofs or unknown extraction errors. A wide interval is intentionally retained when dependence is unidentified.', '', + '![H1. False admissions at threshold 0.95 on supplied joint-distribution fixtures.](figures/01_dependence.png)', '', + '## 3. H2: Skeptical optimal-repair backbone', '', + 'A one-best conflict optimizer selects one maximum-priority independent set, resolving ties by identifier. A tie-break is not evidence for one assertion over another. The proposed policy partitions assertions into necessary (in every optimum), possible (in at least one optimum) and excluded (in none). Only necessary assertions are unambiguously admissible under that objective. Constrained re-optimization produces an including and excluding witness for each ambiguous assertion.', '', + 'The solver is bounded to connected components of at most 16 vertices. Larger components stage explicitly and the returned full-graph utility is unknown, while solved-component results remain labeled as partial. This improves ambiguity representation, not scalability relative to the previous 256-vertex cutset solver. Priorities and conflicts are supplied; neither is inferred from semantic truth.', '', + f"On {len(b['cases'])} seeded graphs, independent exhaustive subset enumeration finds {b['oracle_failures']} utility/membership/witness errors and {b['unique_optimum_losses']} unique-optimum losses. There are {b['invariance_failures']} failures over 512 order checks. Multiple optimal repairs occur in {b['ambiguous_cases']} cases. Deterministic single repairs select {b['selected_assertions']} assertions in total; the skeptical backbone retains {b['necessary_assertions']}, withholding {b['selected_assertions']-b['necessary_assertions']} tie-dependent selections. Primary target: **{status(b).lower()}**.", '', + 'An equal-priority conflicting pair is ambiguous while an isolated positive-priority assertion remains necessary. A 32-cycle is unsupported at the new cap. Critically, the unique false-priority control (false=9, true=8) still makes the false assertion necessary. Skepticism about optimization ties does not validate priorities or facts; coverage and supplied-priority utility can decrease.', '', + '![H2. Selected versus necessary assertions across the supplied conflict graphs.](figures/02_repair_backbone.png)', '', + '## 4. H3: Query-loss-directed evidence review', '', + 'Reviewing the most likely wrong primitive need not improve the answers that matter. Given supplied independent-event probabilities and three Boolean queries, choose a nonadaptive set of one or two primitive reviews minimizing expected residual query Brier loss. Perfect review reveals each selected event truthfully. The objective is the sum of expected conditional Bernoulli variances, equivalently expected squared error of the posterior query probabilities. Enumerate all review subsets within an eight-atom cap. No realized truth label enters selection.', '', + 'Compare against lowest primitive truth probability (highest error risk for an asserted primitive), highest primitive entropy, and no review. A separate world/observation squared-error computation checks every subset and optimum. The primary effect is against risk ranking; entropy is an additional, more uncertainty-aligned comparator, not silently omitted.', '', + '| Reviews per fixture | Risk ranking | Entropy ranking | Query-directed | Query-directed with unmodeled 10% review noise |', + '|---:|---:|---:|---:|---:|'] + for row in review_means(c): + out.append(f"| {row['budget']} | {row['risk']:.6f} | {row['entropy']:.6f} | {row['proposed']:.6f} | {row['noisy_proposed']:.6f} |") + ctrl = c['controls']['misspecified_prior'] + out += ['', f"Values are mean residual *summed* Brier loss for three queries per fixture, not classification error rates. Across {len(c['cases'])} fixtures, budget-two total loss falls from {c['budget2_risk_total_loss']:.6f} to {c['budget2_proposed_total_loss']:.6f}, a {c['relative_loss_reduction']:.2%} reduction. Oracle failures: {c['oracle_failures']}. Primary target: **{status(c).lower()}**. The paired mean difference has a descriptive 95% fixture-bootstrap interval [{c['paired_fixture_difference_95'][0]:.6f}, {c['paired_fixture_difference_95'][1]:.6f}] (2,000 draws). This interval describes the artificial generator only, not scientific-paper performance or human reviewers.", '', + f"**Assumption-breaking result:** the misspecified-prior control makes the chosen review's actual loss {ctrl['actual_proposed_loss']:.4f}, worse than risk ranking's {ctrl['actual_risk_loss']:.4f}. The noisy-review rows keep the selector/posterior fixed while reviews undergo independent 10% bit flips; the model does not know this noise. Thus perfect-review gains are not robust guarantees. Costs are review counts only. No reviewer time, money, Jev-token cost or live service latency is inferred.", '', + '![H3. Downstream query loss under equal ideal-review counts.](figures/03_query_review.png)', '', + '## 5. H4: Minimum-collateral assertion retraction plans', '', + 'After external adjudication designates a conclusion for withdrawal, which candidate commitments should be staged? A plan must hit every supplied sufficient proof of the target while leaving at least one proof for each protected conclusion. Branching on an unhit target proof explores candidate withdrawals. Lexicographically minimize other lost conclusions, withdrawal cost, withdrawal count and sorted identifiers. Positive costs and monotone proof semantics justify pruning dominated partial plans. The cap is 14 atoms; unsupported or infeasible plans return no mutation.', '', + 'This is an opt-in plan over candidate commitments, not deletion of source documents or a claim that the selected commitments are false. Compare with withdrawing the union of every target-proof atom and a cost-first greedy hitting set. The independent oracle enumerates every possible withdrawal subset.', '', + f"| Method | Feasible plans / 128 | Collateral on the same matched {d['matched_feasible_cases']} cases |", '|---|---:|---:|', + f"| Union withdrawal | {d['union_feasible_cases']} | {d['union_collateral']} |", + f"| Cost-first greedy | {d['greedy_feasible_cases']} | Not the frozen primary comparator |", + f"| Minimum-collateral | {d['proposed_feasible_cases']} | {d['proposed_collateral']} |", '', + f"There are {d['oracle_failures']} oracle/objective errors, {d['protected_losses']} protected/feasibility violations and {d['invariance_failures']} order errors. On the {d['matched_feasible_cases']} cases where both proposed and union plans are feasible, total collateral falls from {d['union_collateral']} to {d['proposed_collateral']}, or {d['relative_collateral_reduction']:.2%}. Primary target: **{status(d).lower()}**. The proposed method finds {d['proposed_feasible_cases']} feasible cases; the remaining {len(d['cases'])-d['proposed_feasible_cases']} are infeasible under the supplied protection constraints, matching exhaustive enumeration. Coverage is reported separately so abstention cannot masquerade as quality.", '', + 'The impossible-protection control stages rather than sacrificing the protected fact. The omitted-proof control leaves the target supported by an undisclosed alternative after a seemingly valid retraction. Incorrect external target adjudication can withdraw true commitments. These failures are not fixed by combinatorial optimality; complete lineage and correct intervention goals are essential.', '', + '![H4. Collateral at matched feasible coverage; total feasibility is reported separately.](figures/04_retraction.png)', '', + '## 6. H5: Grounded recursive evidence materialization', '', + 'Local support counts can leave a cycle apparently supported after its only external evidence disappears: A supports B and B supports A. For finite ground positive Horn rules, restart a worklist from current base assertions, decrement per-rule body counters as grounded atoms arrive, and fire a head only after every body atom is grounded. The process computes the least fixed point. It does not treat previously derived assertions as current bases. A separately implemented oracle intersects all finite interpretations satisfying the supplied bases and rules.', '', + f"Across {len(e['cases'])} seeded programs and {e['snapshot_evaluations']:,} base snapshots, there are {e['oracle_failures']} finite-model disagreements and {e['invariance_failures']} duplicate/order disagreements. The local-support comparator leaves {e['naive_phantom_occurrences']} phantom assertion occurrences over these snapshots; these are repeated assertion/snapshot occurrences, not unique facts or observed model hallucinations. Comparator updates begin from the preceding correct state, isolating each withdrawal failure rather than accumulating prior mistakes. Primary target: **{status(e).lower()}**.", '', + '| Cycle size | Phantom assertions after sole seed withdrawal: local support | Grounded materialization | Grounded with independent backup |', + '|---:|---:|---:|---:|'] + for row in e['controls']['cycles']: + out.append(f"| {row['n']} | {len(row['naive_after_withdrawal'])} | {len(row['proposed_after_withdrawal']['active'])} | {len(row['alternative_support']['active'])-1} |") + out += ['', 'The backup column excludes the external backup atom itself. Every unsupported cycle is removed and every independently re-anchored cycle survives. Unseeded self-support also produces no fact. However, a false base assertion still grounds its rule consequences: entailment is conditional on the supplied bases and rules, not verification of real-world truth. Body-visit counts measure this in-memory materialization work, not database I/O, end-to-end update speed or Jev latency. This work recomputes grounding; it does not claim an incremental complexity improvement.', '', + '![H5. Unsupported cycle members retained after the only external seed is removed.](figures/05_grounding.png)', '', + '## 7. Interpretation, limitations and next falsification boundary', '', + 'The results support five bounded engineering capabilities: represent unidentified dependence honestly; distinguish necessary from arbitrary optimal selections; aim ideal reviews at downstream queries; minimize intervention collateral under explicit protection; and reject cyclic self-support without an external base. They do not establish new relation-extraction accuracy, calibrated real-world primitive reliabilities, automatic conflict discovery, trustworthy adjudication or better performance than KARMA. Exact small-instance objectives and known query priors favor these exhaustive prototypes, whose cost and coverage limits are explicit.', '', + 'All five primary criteria are conjunctions frozen before execution and are evaluated without changing their thresholds. They are not five independent scientific discoveries or a pooled success rate. H1/H2/H4/H5 use finite-oracle tests rather than statistical population tests. H3 uses one descriptive, unadjusted generator-level bootstrap interval; it is not a prospective semantic confidence interval. Negative controls remain alongside favorable primary results.', '', + 'A future semantic test must freeze policy and resource budgets before new source-disjoint, independently adjudicated graph data are observed. It must measure both correct retained edges and wrong edges, downstream query loss, intervention harm, actual reviewer errors, incomplete provenance, marginal misspecification and cap-driven staging. That study has not been performed here.', '', + '## 8. Reproducibility and artifact integrity', '', + '```bash', + 'python -m pip install -r graph_synthesis/uncertainty/requirements.txt', + 'python -B -m graph_synthesis.uncertainty.run', + 'python -B -m unittest discover -s graph_synthesis/uncertainty/tests -v', + 'python -B -m graph_synthesis.uncertainty.run --check', + 'python -m pip install -r graph_synthesis/requirements-figures.txt', + 'python -B -m graph_synthesis.uncertainty.report --figures --update-paper', + 'python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf', + '```', '', + 'The [machine-readable results](results.json) preserve fixture inputs, oracle values, all review-subset scores, repair witnesses, retraction feasibility, snapshot outcomes, controls and source/protocol hashes. The [execution notes](EXECUTION_NOTES.md) 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.', ''] + audit = (HERE / 'NOVELTY_AUDIT.md').read_text(encoding='utf-8').strip() + position = out.index('| Hypothesis | Frozen primary target | Outcome | Evidence population |') + out[position:position] = [audit, ''] + return '\n'.join(out) + + +def figures(r): + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + matplotlib.rcParams['svg.hashsalt'] = 'uncertainty-20260918' + directory = HERE/'figures'; directory.mkdir(exist_ok=True) + def save(fig, name, note): + fig.text(.08, .025, note, fontsize=9, va='bottom') + fig.tight_layout(rect=(0, .13, 1, 1)) + fig.savefig(directory/(name+'.png'), dpi=180) + fig.savefig(directory/(name+'.svg'), metadata={'Date': None}) + plt.close(fig) + a, b, c, d, e = [r[f'H{i}'] for i in range(1, 6)] + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + values = [a['independent_false_admissions'], a['false_admissions']] + bars = ax.bar(['Assumed independence', 'Dependence-agnostic lower bound'], values) + ax.bar_label(bars); ax.set(ylim=(0, max(values+[1])*1.2), ylabel='False admissions at threshold 0.95', + title='H1 | Unknown dependence is not independent evidence') + save(fig, FIGURES[0], '128 supplied joint-distribution fixtures; zero fresh Jev calls.\nValid marginals remain an assumption; numerical bounds are not formal certificates.') + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + values = [b['selected_assertions'], b['necessary_assertions']] + bars = ax.bar(['One deterministic optimum', 'Necessary in every optimum'], values) + ax.bar_label(bars); ax.set(ylim=(0, max(values)*1.18), ylabel='Total selected assertions across fixtures', + title='H2 | Expose tie-dependent choices instead of treating them as certain') + save(fig, FIGURES[1], f"128 supplied conflict graphs; {b['ambiguous_cases']} have multiple optima.\nWithheld assertions reflect ambiguity, not proven errors; false priorities can still win.") + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + means = review_means(c); zero = mean(x['no_review_loss'] for x in c['cases']) + for key, label, marker in [('risk', 'Primitive error risk', 'o'), ('entropy', 'Primitive entropy', 's'), ('proposed', 'Query-loss objective', '^')]: + ax.plot([0, 1, 2], [zero]+[x[key] for x in means], marker=marker, label=label) + ax.set(xticks=[0, 1, 2], ylim=(0, None), xlabel='Perfect primitive reviews per fixture', + ylabel='Mean residual summed Brier loss (three queries)', title='H3 | Allocate review to downstream query uncertainty') + ax.legend() + save(fig, FIGURES[2], '64 independent-event fixtures; supplied correct priors and perfect review.\nThe paper retains noisy-review and misspecified-prior counterexamples.') + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + values = [d['union_collateral'], d['proposed_collateral']] + bars = ax.bar(['Withdraw all target-proof atoms', 'Minimum-collateral plan'], values) + ax.bar_label(bars); ax.set(ylim=(0, max(values+[1])*1.2), ylabel='Total other conclusions lost', + title='H4 | Retract target support without unnecessary collateral') + save(fig, FIGURES[3], f"Same {d['matched_feasible_cases']} cases where both plans preserve protected conclusions.\nOverall feasible plans: union {d['union_feasible_cases']}/128, proposed {d['proposed_feasible_cases']}/128. Complete lineage assumed.") + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + rows = e['controls']['cycles']; sizes = [x['n'] for x in rows] + ax.plot(sizes, [len(x['naive_after_withdrawal']) for x in rows], marker='o', label='Local support-count pruning') + ax.plot(sizes, [len(x['proposed_after_withdrawal']['active']) for x in rows], marker='s', label='Grounded least fixed point') + ax.set(xlabel='Cycle size', ylabel='Unsupported cycle assertions retained', + title='H5 | Circular support does not replace external grounding', ylim=(-2, 70)) + ax.legend() + save(fig, FIGURES[4], 'Controlled seed-withdrawal cases; independent backup support is preserved.\n1,152 random snapshots also match finite-model entailment; false bases remain unsafe.') + + +def replace_after_anchor(text, body): + if text.count(START) != text.count(END) or text.count(START) > 1: + raise ValueError('Malformed uncertainty section markers') + text = re.sub(re.escape(START)+r'.*?'+re.escape(END)+r'\s*', '', text, flags=re.S) + if text.count(ANCHOR) != 1: + raise ValueError('Expected exactly one preceding reliability section anchor') + before, after = text.split(ANCHOR, 1) + return before+ANCHOR+'\n\n'+START+'\n\n'+body.strip()+'\n\n'+END+'\n\n'+after.lstrip() + + +def update_paper(r): + def rebase(match): + url = match.group(1) + if '://' in url or url.startswith('#'): + return ']('+url+')' + path = (HERE/url).resolve() + if not path.is_relative_to(ROOT): + raise ValueError('Link outside repository') + return '](../'+path.relative_to(ROOT).as_posix()+')' + section = re.sub(r'\]\(([^)]+)\)', rebase, render(r)) + path = ROOT/'manuscript/paper-current.md' + path.write_text(replace_after_anchor(path.read_text(encoding='utf-8'), section), encoding='utf-8', newline='\n') + note = ('## Five uncertainty and grounding improvements\n\n' + '[Executed report](graph_synthesis/uncertainty/RESULTS.md), [frozen protocol](graph_synthesis/uncertainty/PROTOCOL.md), ' + '[five figures](graph_synthesis/uncertainty/figures/) and [complete updated paper](manuscript/paper-current.pdf). ' + +'; '.join(f"H{i} {'met' if r[f'H{i}']['primary_target_met'] else 'did not meet'} its frozen target" for i in range(1, 6)) + +'. All five are controlled algorithmic/decision-theoretic tests, not new Jev accuracy observations. ' + 'Dependence uncertainty, repair ambiguity, query-directed review, collateral-aware retraction and recursive grounding are tested with explicit failure controls. ' + 'No fresh Jev calls, new scientific documents, globally unprecedented algorithm claim or production-policy changes.') + path = ROOT/'CURRENT_RESULTS.md' + path.write_text(replace_after_anchor(path.read_text(encoding='utf-8'), note), encoding='utf-8', newline='\n') + + +def manifest(): + files = {p.relative_to(ROOT).as_posix(): sha(p) for p in sorted(HERE.rglob('*')) + if p.is_file() and '__pycache__' not in p.parts and p.name != 'artifact-manifest.json'} + write(HERE/'artifact-manifest.json', {'sha256': files, + 'note': 'Uncertainty extension only, excluding self. The mutable shared paper has its own build manifest.'}) + + +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')) + (HERE/'RESULTS.md').write_text(render(r), encoding='utf-8', newline='\n') + with (HERE/'summary.csv').open('w', encoding='utf-8', newline='') as stream: + writer = csv.writer(stream); writer.writerow(['hypothesis', 'title', 'primary_target_met', 'fresh_service_calls']) + for i, title in enumerate(TITLES, 1): + writer.writerow([f'H{i}', title, r[f'H{i}']['primary_target_met'], r['fresh_service_calls']]) + if args.figures: + figures(r) + if args.update_paper: + update_paper(r) + manifest(); print('Generated uncertainty report, table, figures and requested additive paper section') + + +if __name__ == '__main__': + main() diff --git a/graph_synthesis/uncertainty/requirements.txt b/graph_synthesis/uncertainty/requirements.txt new file mode 100644 index 0000000..5e3abf0 --- /dev/null +++ b/graph_synthesis/uncertainty/requirements.txt @@ -0,0 +1,2 @@ +# Bounded world-mass linear programs. No inference service dependency. +scipy==1.17.0 diff --git a/graph_synthesis/uncertainty/results.json b/graph_synthesis/uncertainty/results.json new file mode 100644 index 0000000..4f2ea25 --- /dev/null +++ b/graph_synthesis/uncertainty/results.json @@ -0,0 +1,115939 @@ +{ + "H1": { + "cases": [ + { + "actual": 0.904255319149, + "analytic": { + "lower": 0.904255319149, + "mode": "analytic-dependence-free", + "upper": 0.904255319149 + }, + "id": 0, + "independent": 0.904255319149, + "joint_integer_masses": [ + 3, + 0, + 3, + 6, + 0, + 6, + 4, + 5, + 0, + 2, + 0, + 4, + 0, + 5, + 0, + 150 + ], + "probabilities": { + "a0": 0.946808510638, + "a1": 0.914893617021, + "a2": 0.904255319149, + "a3": 0.856382978723 + }, + "proofs": [ + [ + "a1", + "a2", + "a3" + ], + [ + "a1", + "a2", + "a3" + ], + [ + "a2" + ], + [ + "a0", + "a2", + "a3" + ] + ], + "proposed": { + "lower": 0.904255319149, + "mode": "numerical-LP", + "primal_residual": 0.0, + "raw_lower": 0.904255319149, + "raw_upper": 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holds, independent_probability, + query_loss, retraction_plan, review_plan, skeptical_repair, support_prune, + world_table) + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +SEED = 20260922 +BASELINE = 'a62a3257645d8e35cd4e45be53bfa9511d27724b' +PROTOCOL_COMMIT = '383bab6f559c7f14525ddcef2275f3a816c9caa3' + + +def sha(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def clean(value): + if isinstance(value, float): + return round(value, 12) + if isinstance(value, dict): + return {str(k): clean(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [clean(v) for v in value] + return value + + +def write(path, value): + Path(path).write_text(json.dumps(clean(value), sort_keys=True, indent=2, allow_nan=False)+'\n', encoding='utf-8', newline='\n') + + +def random_proofs(rng, atoms, maximum=4): + return [sorted(rng.sample(atoms, rng.randint(1, min(3, len(atoms))))) + for _ in range(rng.randint(1, maximum))] + + +def solve_rational(matrix, rhs): + """Independent exact Gaussian elimination for tiny vertex-oracle systems.""" + n = len(rhs) + rows = [[Fraction(x) for x in row]+[Fraction(y)] for row, y in zip(matrix, rhs)] + for col in range(n): + pivot = next((i for i in range(col, n) if rows[i][col]), None) + if pivot is None: + return None + rows[col], rows[pivot] = rows[pivot], rows[col] + scale = rows[col][col]; rows[col] = [x/scale for x in rows[col]] + for i in range(n): + if i != col: + factor = rows[i][col] + rows[i] = [x-factor*y for x, y in zip(rows[i], rows[col])] + return [row[-1] for row in rows] + + +def rational_lineage_oracle(proofs, p): + atoms = sorted(p); size = 1 << len(atoms) + matrix = [[1]*size]+[[int(mask & (1 << i) != 0) for mask in range(size)] for i in range(len(atoms))] + rhs = [Fraction(1)]+[Fraction(p[a]) for a in atoms] + value = [int(any(all(mask & (1 << atoms.index(a)) for a in term) for term in proofs)) for mask in range(size)] + extrema = [] + for columns in combinations(range(size), len(rhs)): + solution = solve_rational([[row[c] for c in columns] for row in matrix], rhs) + if solution is not None and all(x >= 0 for x in solution): + extrema.append(sum(x*value[c] for c, x in zip(columns, solution))) + if not extrema: + raise AssertionError('No feasible rational vertex') + return float(min(extrema)), float(max(extrema)) + + +def h1(): + rng = random.Random(SEED+1); cases = []; failures = false_admissions = invariance = 0 + for i in range(128): + n = rng.randint(2, 6); atoms = [f'a{j}' for j in range(n)] + weights = [rng.randrange(0, 7) if rng.random() < 0.55 else 0 for _ in range(1 << n)] + weights[0] += 1; weights[-1] += 10 if i % 3 else 150 + total = sum(weights) + p = {a: sum(w for mask, w in enumerate(weights) if mask & (1 << j))/total for j, a in enumerate(atoms)} + proofs = random_proofs(rng, atoms) + actual = sum(w for mask, w in enumerate(weights) if any(all(mask & (1 << atoms.index(a)) for a in term) for term in proofs))/total + result = credal_bounds(proofs, p); coarse = frechet_bounds(proofs, p) + independent = independent_probability(proofs, p) + failures += not result['lower']-1e-8 <= actual <= result['upper']+1e-8 + false_admissions += result['lower'] >= 0.95 and actual < 0.95-1e-8 + duplicate = credal_bounds(list(reversed(proofs))*5, dict(reversed(list(p.items())))) + invariance += abs(duplicate['lower']-result['lower']) > 1e-10 or abs(duplicate['upper']-result['upper']) > 1e-10 + cases.append({'id': i, 'probabilities': p, 'joint_integer_masses': weights, 'proofs': proofs, + 'actual': actual, 'independent': independent, 'proposed': result, 'analytic': coarse}) + oracle_cases = []; oracle_failures = 0 + for i in range(16): + atoms = [f'a{j}' for j in range(2+i % 2)] + p = {a: Fraction(rng.randint(1, 9), 10) for a in atoms} + proofs = random_proofs(rng, atoms) + lower, upper = rational_lineage_oracle(proofs, p) + result = credal_bounds(proofs, {a: float(x) for a, x in p.items()}) + oracle_failures += (abs(result.get('raw_lower', result['lower'])-lower) > 1e-8 or + abs(result.get('raw_upper', result['upper'])-upper) > 1e-8) + oracle_cases.append({'proofs': proofs, 'probabilities': {a: str(x) for a, x in p.items()}, + 'oracle': [lower, upper], 'proposed': result}) + shared = credal_bounds([['a'], ['b']], {'a': 0.8, 'b': 0.8}) + singleton = credal_bounds([['a']], {'a': 0.99}) + overcap = credal_bounds([[f'a{i}'] for i in range(9)], {f'a{i}': 0.8 for i in range(9)}) + bad_marginal = credal_bounds([['a']], {'a': 0.99}) + return {'cases': cases, 'oracle_cases': oracle_cases, 'containment_failures': failures, + 'oracle_failures': oracle_failures, 'invariance_failures': invariance, 'false_admissions': false_admissions, + 'independent_false_admissions': sum(c['independent'] >= 0.95 and c['actual'] < 0.95 for c in cases), + 'mean_analytic_width': mean(c['analytic']['upper']-c['analytic']['lower'] for c in cases), + 'mean_credal_width': mean(c['proposed']['upper']-c['proposed']['lower'] for c in cases), + 'controls': {'shared_source': {'independent': 0.96, 'actual': 0.8, 'proposed': shared}, + 'singleton': singleton, 'over_cap': overcap, + 'bad_marginal': {'supplied': 0.99, 'actual': 0.5, 'proposed': bad_marginal}}, + 'primary_target_met': not (failures or oracle_failures or invariance or false_admissions) + and shared['lower'] < 0.95 and singleton['lower'] >= 0.95} + + +def independent_sets_oracle(weights, edges): + nodes = sorted(weights); best = -1; optima = [] + for mask in range(1 << len(nodes)): + chosen = {node for i, node in enumerate(nodes) if mask & (1 << i)} + if any(a in chosen and b in chosen for a, b in edges): + continue + value = sum(weights[a] for a in chosen) + if value > best: + best = value; optima = [chosen] + elif value == best: + optima.append(chosen) + return best, optima + + +def h2(): + rng = random.Random(SEED+2); cases = []; failures = invariance = 0 + for i in range(128): + nodes = [f'v{j:02}' for j in range(rng.randint(2, 12))] + weights = {a: 1 if i % 4 == 0 else rng.randint(0, 9) for a in nodes} + density = rng.choice([0.1, 0.3, 0.6, 0.9]) + edges = [list(pair) for pair in combinations(nodes, 2) if rng.random() < density] + result = skeptical_repair(weights, edges) + utility, optima = independent_sets_oracle(weights, edges) + necessary = sorted(set.intersection(*optima)); possible = sorted(set.union(*optima)) + invalid = result['utility'] != utility or result['necessary'] != necessary or result['possible'] != possible + for node, witness in result['witnesses'].items(): + invalid |= (set(witness['include']) not in optima or node not in witness['include'] or + set(witness['exclude']) not in optima or node in witness['exclude']) + failures += bool(invalid) + for _ in range(4): + order = nodes[:]; rng.shuffle(order); permuted = edges[:]; rng.shuffle(permuted) + invariance += skeptical_repair({a: weights[a] for a in order}, permuted) != result + cases.append({'id': i, 'weights': weights, 'edges': edges, 'proposed': result, + 'oracle_utility': utility, 'oracle_necessary': necessary, 'oracle_possible': possible, + 'optimal_repairs': len(optima), 'backbone_utility': sum(weights[a] for a in necessary)}) + tie = skeptical_repair({'a': 5, 'b': 5, 'certain': 2}, [('a', 'b')]) + misleading = skeptical_repair({'false': 9, 'true': 8}, [('false', 'true')]) + overcap = skeptical_repair({f'v{i}': 1 for i in range(32)}, [(f'v{i}', f'v{(i+1)%32}') for i in range(32)]) + unique_loss = sum(c['optimal_repairs'] == 1 and c['backbone_utility'] != c['oracle_utility'] for c in cases) + return {'cases': cases, 'oracle_failures': failures, 'invariance_failures': invariance, + 'unique_optimum_losses': unique_loss, 'ambiguous_cases': sum(c['optimal_repairs'] > 1 for c in cases), + 'selected_assertions': sum(len(c['proposed']['selected']) for c in cases), + 'necessary_assertions': sum(len(c['proposed']['necessary']) for c in cases), + 'controls': {'tie': tie, 'false_priority': misleading, 'over_cap': overcap}, + 'primary_target_met': not (failures or invariance or unique_loss) and tie['necessary'] == ['certain']} + + +def direct_brier(queries, assumed, reviewed, actual=None, noise=0.0): + """Independent oracle via explicit squared errors, not conditional variance.""" + atoms, worlds, mass = world_table(assumed) + reviewed = sorted(reviewed) + _, _, actual_mass = world_table(actual if actual is not None else assumed) + posterior = {} + for obs in product((False, True), repeat=len(reviewed)): + matching = [i for i, w in enumerate(worlds) if all((a in w) == b for a, b in zip(reviewed, obs))] + denominator = sum(mass[i] for i in matching) + posterior[obs] = [sum(mass[i]*any(all(a in worlds[i] for a in t) for t in q) for i in matching)/denominator + if denominator else 0.5 for q in queries] + total = 0.0 + for world, m in zip(worlds, actual_mass): + truth = [any(all(a in world for a in term) for term in q) for q in queries] + for obs, predictions in posterior.items(): + likelihood = 1.0 + for a, b in zip(reviewed, obs): + likelihood *= (1-noise) if ((a in world) == b) else noise + total += m*likelihood*sum((float(y)-p)**2 for y, p in zip(truth, predictions)) + return total + + +def bootstrap_difference(values): + rng = random.Random(SEED+30) + draws = sorted(mean(rng.choices(values, k=len(values))) for _ in range(2000)) + return [draws[49], draws[1949]] + + +def h3(): + rng = random.Random(SEED+3); cases = []; failures = 0 + for i in range(64): + atoms = [f'a{j}' for j in range(rng.randint(6, 8))] + p = {a: rng.choice([0.05, 0.15, 0.3, 0.5, 0.7, 0.85, 0.95]) for a in atoms} + queries = [random_proofs(rng, atoms, 3) for _ in range(3)] + budgets = [] + for budget in (1, 2): + proposed = review_plan(queries, p, budget) + risk = sorted(p, key=lambda a: (p[a], a))[:budget] + entropy = sorted(p, key=lambda a: (-p[a]*(1-p[a]), a))[:budget] + oracle_scores = [direct_brier(queries, p, subset) for subset in combinations(atoms, budget)] + proposed_direct = direct_brier(queries, p, proposed['reviewed']) + failures += abs(proposed_direct-proposed['loss']) > 1e-10 or proposed_direct-min(oracle_scores) > 1e-10 + budgets.append({'budget': budget, 'proposed': proposed, 'oracle_minimum': min(oracle_scores), + 'risk': {'reviewed': risk, 'loss': direct_brier(queries, p, risk)}, + 'entropy': {'reviewed': entropy, 'loss': direct_brier(queries, p, entropy)}, + 'noisy_proposed_loss': direct_brier(queries, p, proposed['reviewed'], noise=0.1), + 'noisy_risk_loss': direct_brier(queries, p, risk, noise=0.1)}) + cases.append({'id': i, 'probabilities': p, 'queries': queries, + 'no_review_loss': direct_brier(queries, p, []), 'budgets': budgets}) + proposed_sum = sum(c['budgets'][1]['proposed']['loss'] for c in cases) + risk_sum = sum(c['budgets'][1]['risk']['loss'] for c in cases) + improvement = 1-proposed_sum/risk_sum if risk_sum else 0.0 + assumed = {'a': 0.9, 'b': 0.05}; actual = {'a': 0.9, 'b': 0.5} + queries = [[['a']], [['a']], [['b']]] + chosen = review_plan(queries, assumed, 1) + control = {'assumed': assumed, 'actual': actual, 'queries': queries, 'proposed': chosen, + 'actual_proposed_loss': direct_brier(queries, assumed, chosen['reviewed'], actual), + 'risk_reviewed': ['b'], 'actual_risk_loss': direct_brier(queries, assumed, ['b'], actual)} + return {'cases': cases, 'oracle_failures': failures, 'budget2_proposed_total_loss': proposed_sum, + 'budget2_risk_total_loss': risk_sum, 'relative_loss_reduction': improvement, + 'paired_fixture_difference_95': bootstrap_difference([c['budgets'][1]['proposed']['loss']-c['budgets'][1]['risk']['loss'] for c in cases]), + 'controls': {'misspecified_prior': control, 'noise_rate': 0.1, 'noise_model': 'unmodeled independent reviewer bit flips'}, + 'primary_target_met': failures == 0 and improvement >= 0.20} + + +def retraction_oracle(facts, target, protected, costs): + atoms = sorted(costs); best = None; best_removed = None + for mask in range(1 << len(atoms)): + removed = {a for j, a in enumerate(atoms) if mask & (1 << j)} + truth = {f: any(not any(a in removed for a in term) for term in proofs) for f, proofs in facts.items()} + if truth[target] or any(not truth[f] for f in protected): + continue + collateral = sum(not value for f, value in truth.items() if f != target and f not in protected) + objective = (collateral, sum(costs[a] for a in removed), len(removed), tuple(sorted(removed))) + if best is None or objective < best: + best, best_removed = objective, sorted(removed) + return best, best_removed + + +def evaluate_removal(facts, target, protected, costs, removed): + if removed is None: + return {'feasible': False} + active = set(costs)-set(removed) + feasible = not holds(facts[target], active) and all(holds(facts[f], active) for f in protected) + return {'feasible': feasible, 'removed': sorted(removed), + 'collateral': sum(not holds(proofs, active) for f, proofs in facts.items() if f != target and f not in protected), + 'cost': sum(costs[a] for a in removed)} + + +def h4(): + rng = random.Random(SEED+4); cases = []; failures = protected_losses = invariance = 0 + for i in range(128): + atoms = [f'a{j}' for j in range(rng.randint(4, 10))] + facts = {f'q{j}': random_proofs(rng, atoms, 3) for j in range(6)} + costs = {a: rng.randint(1, 5) for a in atoms}; protected = ['q1']; target = 'q0' + result = retraction_plan(facts, target, protected, costs) + objective, oracle_removed = retraction_oracle(facts, target, protected, costs) + failures += result['removed'] != oracle_removed + proposed = evaluate_removal(facts, target, protected, costs, result['removed']) + protected_losses += result['removed'] is not None and not proposed['feasible'] + union = {a for term in facts[target] for a in term} + greedy = set() + while any(not set(term) & greedy for term in facts[target]): + candidates = {a for term in facts[target] if not set(term) & greedy for a in term} + greedy.add(min(candidates, key=lambda a: (costs[a], a))) + shuffled = list(facts); rng.shuffle(shuffled) + invariance += retraction_plan({f: list(reversed(facts[f])) for f in shuffled}, target, protected, + dict(reversed(list(costs.items())))) != result + cases.append({'id': i, 'facts': facts, 'costs': costs, 'target': target, 'protected': protected, + 'proposed': result, 'oracle_objective': objective, 'oracle_removed': oracle_removed, + 'union': evaluate_removal(facts, target, protected, costs, union), + 'greedy': evaluate_removal(facts, target, protected, costs, greedy)}) + matched = [c for c in cases if c['proposed']['removed'] is not None and c['union']['feasible']] + union_sum = sum(c['union']['collateral'] for c in matched) + proposed_sum = sum(c['proposed']['collateral'] for c in matched) + improvement = 1-proposed_sum/union_sum if union_sum else 0.0 + incomplete = retraction_plan({'target': [['a']], 'other': [['b']]}, 'target', [], {'a': 1, 'b': 1}) + incomplete_still_true = holds([['a'], ['b']], {'a', 'b'}-set(incomplete['removed'])) + return {'cases': cases, 'oracle_failures': failures, 'protected_losses': protected_losses, + 'invariance_failures': invariance, 'matched_feasible_cases': len(matched), + 'union_collateral': union_sum, 'proposed_collateral': proposed_sum, 'relative_collateral_reduction': improvement, + 'proposed_feasible_cases': sum(c['proposed']['removed'] is not None for c in cases), + 'union_feasible_cases': sum(c['union']['feasible'] for c in cases), + 'greedy_feasible_cases': sum(c['greedy']['feasible'] for c in cases), + 'controls': {'impossible': retraction_plan({'t': [['a']], 'p': [['a']]}, 't', ['p'], {'a': 1}), + 'omitted_proof': {'plan': incomplete, 'target_still_true_under_complete_lineage': incomplete_still_true}, + 'wrong_target': {'plan': incomplete, 'all_supplied_atoms_actually_true': True}, + 'over_cap': retraction_plan({'t': [['a0']]}, 't', [], {f'a{i}': 1 for i in range(15)})}, + 'primary_target_met': not (failures or protected_losses or invariance) and improvement >= 0.25} + + +def finite_model_oracle(bases, rules): + atoms = sorted(set(bases) | {head for head, _ in rules} | {a for _, body in rules for a in body}) + intersection = set(atoms) + for bits in product((False, True), repeat=len(atoms)): + interpretation = dict(zip(atoms, bits)) + if not all(interpretation[a] for a in bases): + continue + if all(interpretation[head] or not all(interpretation[a] for a in body) for head, body in rules): + intersection &= {a for a in atoms if interpretation[a]} + return sorted(intersection) + + +def h5(): + rng = random.Random(SEED+5); cases = []; failures = invariance = 0; phantom = 0 + for i in range(96): + atoms = [f'a{j}' for j in range(rng.randint(4, 8))] + rules = [(rng.choice(atoms), sorted(rng.sample(atoms, rng.randint(1, min(3, len(atoms)))))) for _ in range(2*len(atoms))] + if i % 2 == 0: + rules.extend((atoms[(j+1)%3], [atoms[j]]) for j in range(3)) + previous = []; snapshots = [] + for step in range(12): + bases = [a for a in atoms if rng.random() < 0.3] + result = grounded(bases, rules); oracle = finite_model_oracle(bases, rules) + failures += result['active'] != oracle + expanded = grounded(set(previous) | set(bases), rules)['active'] + naive = support_prune(expanded, bases, rules) + extra = sorted(set(naive)-set(oracle)); phantom += len(extra) + invariance += grounded(list(reversed(bases)), list(reversed(rules))*2) != result + snapshots.append({'step': step, 'bases': bases, 'proposed': result, 'oracle': oracle, 'naive': naive, 'phantom': extra}) + previous = result['active'] + cases.append({'id': i, 'rules': rules, 'snapshots': snapshots}) + cycles = [] + for n in (2, 4, 8, 16, 32, 64): + rules = [(f'v{(i+1)%n}', [f'v{i}']) for i in range(n)]+[('v0', ['seed'])] + previous = grounded(['seed'], rules)['active'] + after = grounded([], rules) + naive = support_prune(previous, [], rules) + alternative = grounded(['backup'], rules+[('v0', ['backup'])]) + cycles.append({'n': n, 'proposed_after_withdrawal': after, 'naive_after_withdrawal': naive, + 'alternative_support': alternative}) + controls_pass = all(not c['proposed_after_withdrawal']['active'] and len(c['alternative_support']['active']) == c['n']+1 for c in cycles) + return {'cases': cases, 'snapshot_evaluations': 1152, 'oracle_failures': failures, + 'invariance_failures': invariance, 'naive_phantom_occurrences': phantom, + 'controls': {'cycles': cycles, 'unseeded_self_support': grounded([], [('a', ['a'])]), + 'false_base': {'actual_false': ['a', 'b'], 'proposed': grounded(['a'], [('b', ['a'])])}}, + 'primary_target_met': not (failures or invariance) and controls_pass and phantom > 0} + + +def execute(): + result = {'study': 'Five uncertainty and grounding improvements', 'date': '2026-09-18', 'seed': SEED, + 'baseline_commit': BASELINE, 'protocol_commit': PROTOCOL_COMMIT, 'fresh_service_calls': 0, + 'new_scientific_documents': 0, 'evidence_class': 'controlled fixtures only; no semantic validation', + 'protocol_sha256': sha(HERE/'PROTOCOL.md') if (HERE/'PROTOCOL.md').exists() else None, + 'source_sha256': {p.name: sha(p) for p in sorted(HERE.glob('*.py'))}} + for i, function in enumerate((h1, h2, h3, h4, h5), 1): + result[f'H{i}'] = function() + print(f"H{i}: primary target {'met' if result[f'H{i}']['primary_target_met'] else 'NOT met'}", flush=True) + return clean(result) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__); parser.add_argument('--check', action='store_true') + args = parser.parse_args(); result = execute(); path = HERE/'results.json' + if args.check: + if json.loads(path.read_text(encoding='utf-8')) != result: + raise SystemExit('Deterministic replay differs from committed results') + print('Exact machine-readable replay verified') + else: + write(path, result) + print(f'Wrote {path}') + + +if __name__ == '__main__': + main() diff --git a/graph_synthesis/uncertainty/summary.csv b/graph_synthesis/uncertainty/summary.csv new file mode 100644 index 0000000..094b118 --- /dev/null +++ b/graph_synthesis/uncertainty/summary.csv @@ -0,0 +1,6 @@ +hypothesis,title,primary_target_met,fresh_service_calls +H1,Dependence-agnostic lineage,True,0 +H2,Skeptical repair backbone,True,0 +H3,Query-loss-directed review,True,0 +H4,Minimum-collateral retraction,True,0 +H5,Grounded recursive evidence,True,0 diff --git a/graph_synthesis/uncertainty/tests.log b/graph_synthesis/uncertainty/tests.log new file mode 100644 index 0000000..a988534 --- /dev/null +++ b/graph_synthesis/uncertainty/tests.log @@ -0,0 +1,41 @@ +test_all_study_markers_are_hidden (test_render_markers.RenderMarkerTests.test_all_study_markers_are_hidden) ... ok +test_reader_text_and_other_comments_are_preserved (test_render_markers.RenderMarkerTests.test_reader_text_and_other_comments_are_preserved) ... ok +test_conjunctive_body_needs_all_atoms (test_uncertainty.GroundingTests.test_conjunctive_body_needs_all_atoms) ... ok +test_false_base_semantic_control (test_uncertainty.GroundingTests.test_false_base_semantic_control) ... ok +test_finite_model_oracle (test_uncertainty.GroundingTests.test_finite_model_oracle) ... ok +test_invalid_ground_rules (test_uncertainty.GroundingTests.test_invalid_ground_rules) ... ok +test_no_unseeded_self_support (test_uncertainty.GroundingTests.test_no_unseeded_self_support) ... ok +test_rule_duplicate_order_and_work_bound (test_uncertainty.GroundingTests.test_rule_duplicate_order_and_work_bound) ... ok +test_withdrawn_cycle_and_backup (test_uncertainty.GroundingTests.test_withdrawn_cycle_and_backup) ... ok +test_canonicalization (test_uncertainty.LineageTests.test_canonicalization) ... ok +test_cap_and_unused_atoms (test_uncertainty.LineageTests.test_cap_and_unused_atoms) ... ok +test_correlated_control (test_uncertainty.LineageTests.test_correlated_control) ... ok +test_invalid_probabilities (test_uncertainty.LineageTests.test_invalid_probabilities) ... ok +test_invalid_proofs (test_uncertainty.LineageTests.test_invalid_proofs) ... ok +test_rational_extrema (test_uncertainty.LineageTests.test_rational_extrema) ... ok +test_solver_exception_fallback (test_uncertainty.LineageTests.test_solver_exception_fallback) ... ok +test_solver_status_fallback (test_uncertainty.LineageTests.test_solver_status_fallback) ... ok +test_wrong_marginal_is_not_corrected (test_uncertainty.LineageTests.test_wrong_marginal_is_not_corrected) ... ok +test_all_small_unweighted_graphs (test_uncertainty.RepairTests.test_all_small_unweighted_graphs) ... ok +test_invalid_graph (test_uncertainty.RepairTests.test_invalid_graph) ... ok +test_large_component_is_explicitly_partial (test_uncertainty.RepairTests.test_large_component_is_explicitly_partial) ... ok +test_tie_and_isolated_backbone (test_uncertainty.RepairTests.test_tie_and_isolated_backbone) ... ok +test_unique_false_priority_control (test_uncertainty.RepairTests.test_unique_false_priority_control) ... ok +test_zero_priority_is_optional (test_uncertainty.RepairTests.test_zero_priority_is_optional) ... ok +test_additive_update_is_idempotent (test_uncertainty.ReportTests.test_additive_update_is_idempotent) ... ok +test_missing_or_malformed_anchor_is_rejected (test_uncertainty.ReportTests.test_missing_or_malformed_anchor_is_rejected) ... ok +test_cap (test_uncertainty.RetractionTests.test_cap) ... ok +test_exact_plan_and_no_mutation (test_uncertainty.RetractionTests.test_exact_plan_and_no_mutation) ... ok +test_impossible_protection (test_uncertainty.RetractionTests.test_impossible_protection) ... ok +test_invalid_retraction_inputs (test_uncertainty.RetractionTests.test_invalid_retraction_inputs) ... ok +test_missing_proof_control (test_uncertainty.RetractionTests.test_missing_proof_control) ... ok +test_degenerate_probabilities (test_uncertainty.ReviewTests.test_degenerate_probabilities) ... ok +test_direct_brier_and_monotonicity (test_uncertainty.ReviewTests.test_direct_brier_and_monotonicity) ... ok +test_invalid_budgets (test_uncertainty.ReviewTests.test_invalid_budgets) ... ok +test_irrelevant_high_risk_atom (test_uncertainty.ReviewTests.test_irrelevant_high_risk_atom) ... ok +test_misspecification_counterexample (test_uncertainty.ReviewTests.test_misspecification_counterexample) ... ok + +---------------------------------------------------------------------- +Ran 36 tests in 0.479s + +OK diff --git a/graph_synthesis/uncertainty/tests/test_render_markers.py b/graph_synthesis/uncertainty/tests/test_render_markers.py new file mode 100644 index 0000000..878a83f --- /dev/null +++ b/graph_synthesis/uncertainty/tests/test_render_markers.py @@ -0,0 +1,21 @@ +"""Reader-facing rendering must not expose manuscript insertion metadata.""" +import unittest +from graph_synthesis.render_current_paper import strip_research_markers + + +class RenderMarkerTests(unittest.TestCase): + def test_all_study_markers_are_hidden(self): + for study in ('FOLLOWUP', 'ADAPTIVE', 'RISK_CONTROL', 'RELIABILITY', 'STRUCTURAL', 'UNCERTAINTY', 'FUTURE_STUDY'): + for boundary in ('START', 'END'): + with self.subTest(study=study, boundary=boundary): + self.assertEqual(strip_research_markers(f'beforeafter'), 'beforeafter') + + def test_reader_text_and_other_comments_are_preserved(self): + text = '# Evidence\n\nUNCERTAINTY_RESEARCH_START is an example identifier.\n\n' + self.assertEqual(strip_research_markers(text), text) + cleaned = strip_research_markers('A\n\nB') + self.assertEqual(strip_research_markers(cleaned), cleaned) + + +if __name__ == '__main__': + unittest.main() diff --git a/graph_synthesis/uncertainty/tests/test_uncertainty.py b/graph_synthesis/uncertainty/tests/test_uncertainty.py new file mode 100644 index 0000000..dfb067e --- /dev/null +++ b/graph_synthesis/uncertainty/tests/test_uncertainty.py @@ -0,0 +1,245 @@ +"""Contracts, independent oracles, falsifying assumptions and additive reporting.""" +from copy import deepcopy +from fractions import Fraction +from itertools import combinations +import math +from types import SimpleNamespace +import unittest +from unittest.mock import patch +from graph_synthesis.uncertainty.methods import ( + credal_bounds, dnf, frechet_bounds, grounded, independent_probability, + probabilities, query_loss, retraction_plan, review_plan, skeptical_repair, + support_prune, +) +from graph_synthesis.uncertainty.run import ( + direct_brier, finite_model_oracle, independent_sets_oracle, + rational_lineage_oracle, retraction_oracle, +) + + +class LineageTests(unittest.TestCase): + def test_invalid_probabilities(self): + for value in (-1, 1.1, math.nan, math.inf, True, '0.5'): + with self.subTest(value=value), self.assertRaises(ValueError): + probabilities({'a': value}) + with self.assertRaises(ValueError): + probabilities({}) + + def test_invalid_proofs(self): + for proof in ([[]], [['unknown']], ['a']): + with self.subTest(proof=proof), self.assertRaises(ValueError): + credal_bounds(proof, {'a': .5}) + + def test_canonicalization(self): + self.assertEqual(dnf([['b', 'a'], ['a'], ['a']], ['a', 'b']), (('a',),)) + self.assertEqual(credal_bounds([], {'a': .2})['upper'], 0) + + def test_correlated_control(self): + proofs = [['a'], ['b']]; p = {'a': .8, 'b': .8} + self.assertAlmostEqual(independent_probability(proofs, p), .96) + result = credal_bounds(proofs, p) + self.assertLess(result['lower'], .95) + self.assertAlmostEqual(result['lower'], .8, places=7) + self.assertGreaterEqual(result['upper'], .8) + + def test_rational_extrema(self): + for proof in ([['a', 'b']], [['a'], ['b']], [['a', 'b'], ['b', 'c']]): + p = {'a': Fraction(7, 10), 'b': Fraction(4, 10), 'c': Fraction(9, 10)} + lo, hi = rational_lineage_oracle(proof, p) + result = credal_bounds(proof, {a: float(x) for a, x in p.items()}) + self.assertAlmostEqual(result['raw_lower'], lo) + self.assertAlmostEqual(result['raw_upper'], hi) + + def test_solver_status_fallback(self): + with patch('scipy.optimize.linprog', return_value=SimpleNamespace(success=False)): + result = credal_bounds([['a'], ['b']], {'a': .8, 'b': .8}) + self.assertEqual(result['reason'], 'solver-status') + self.assertEqual(result['mode'], 'analytic-dependence-free') + + def test_solver_exception_fallback(self): + with patch('scipy.optimize.linprog', side_effect=RuntimeError('controlled failure')): + result = credal_bounds([['a', 'b']], {'a': .8, 'b': .8}) + self.assertEqual(result['reason'], 'solver-exception') + self.assertAlmostEqual(result['lower'], .6) + + def test_cap_and_unused_atoms(self): + p = {f'a{i}': .8 for i in range(9)} + result = credal_bounds([[a] for a in p], p) + self.assertEqual(result['reason'], 'atom-cap') + self.assertEqual(credal_bounds([['a0']], p)['mode'], 'numerical-LP') + + def test_wrong_marginal_is_not_corrected(self): + self.assertGreater(credal_bounds([['a']], {'a': .99})['lower'], .5) + + +class RepairTests(unittest.TestCase): + def test_tie_and_isolated_backbone(self): + result = skeptical_repair({'a': 5, 'b': 5, 'c': 2}, [('a', 'b')]) + self.assertEqual(result['necessary'], ['c']) + self.assertEqual(result['possible'], ['a', 'b', 'c']) + self.assertEqual(result['witnesses']['a']['include'], ['a', 'c']) + self.assertEqual(result['witnesses']['a']['exclude'], ['b', 'c']) + + def test_all_small_unweighted_graphs(self): + nodes = ['a', 'b', 'c', 'd']; edges = list(combinations(nodes, 2)) + for mask in range(1 << len(edges)): + chosen = [edge for i, edge in enumerate(edges) if mask & (1 << i)] + weights = {a: 1 for a in nodes}; best, optima = independent_sets_oracle(weights, chosen) + result = skeptical_repair(weights, chosen) + self.assertEqual(result['utility'], best) + self.assertEqual(result['necessary'], sorted(set.intersection(*optima))) + self.assertEqual(result['possible'], sorted(set.union(*optima))) + + def test_zero_priority_is_optional(self): + result = skeptical_repair({'a': 0}, []) + self.assertEqual(result['necessary'], []) + self.assertEqual(result['possible'], ['a']) + self.assertEqual(result['selected'], []) + + def test_invalid_graph(self): + for weights, edges in (({'a': -1}, []), ({'a': True}, []), ({'a': 1}, [('a', 'a')]), + ({'a': 1}, [('a', 'b')]), ({'a': 1}, ['a'])): + with self.subTest(weights=weights, edges=edges), self.assertRaises(ValueError): + skeptical_repair(weights, edges) + + def test_large_component_is_explicitly_partial(self): + weights = {f'a{i}': 1 for i in range(17)}; weights['isolated'] = 2 + edges = [(f'a{i}', f'a{(i+1)%17}') for i in range(17)] + result = skeptical_repair(weights, edges) + self.assertIsNone(result['utility']) + self.assertEqual(len(result['staged']), 17) + self.assertEqual(result['necessary'], ['isolated']) + + def test_unique_false_priority_control(self): + result = skeptical_repair({'false': 9, 'true': 8}, [('false', 'true')]) + self.assertEqual(result['necessary'], ['false']) + + +class ReviewTests(unittest.TestCase): + def test_direct_brier_and_monotonicity(self): + p = {'a': .4, 'b': .7, 'c': .9}; queries = [[['a', 'b'], ['c']], [['b']]] + losses = [] + for k in range(4): + result = review_plan(queries, p, k) + losses.append(result['loss']) + self.assertAlmostEqual(result['loss'], direct_brier(queries, p, result['reviewed'])) + self.assertTrue(all(a+1e-12 >= b for a, b in zip(losses, losses[1:]))) + self.assertAlmostEqual(losses[-1], 0) + + def test_irrelevant_high_risk_atom(self): + result = review_plan([[['important']]], {'important': .5, 'irrelevant': .01}, 1) + self.assertEqual(result['reviewed'], ['important']) + self.assertAlmostEqual(result['loss'], 0) + + def test_degenerate_probabilities(self): + self.assertAlmostEqual(query_loss([[['a']], [['b']]], {'a': 0., 'b': 1.}, []), 0) + + def test_invalid_budgets(self): + for budget in (-1, 2, True, .5): + with self.subTest(budget=budget), self.assertRaises(ValueError): + review_plan([[['a']]], {'a': .5}, budget) + with self.assertRaises(ValueError): + query_loss([[['a']]], {'a': .5}, ['unknown']) + + def test_misspecification_counterexample(self): + q = [[['a']], [['a']], [['b']]]; p = {'a': .9, 'b': .05}; actual = {'a': .9, 'b': .5} + proposed = review_plan(q, p, 1)['reviewed'] + self.assertGreater(direct_brier(q, p, proposed, actual), direct_brier(q, p, ['b'], actual)) + + +class RetractionTests(unittest.TestCase): + def setUp(self): + self.facts = {'target': [['a', 'b'], ['a', 'c']], 'protected': [['b']], 'other': [['c']]} + self.costs = {'a': 2, 'b': 1, 'c': 1} + + def test_exact_plan_and_no_mutation(self): + before = deepcopy((self.facts, self.costs)) + result = retraction_plan(self.facts, 'target', ['protected'], self.costs) + self.assertEqual(result['removed'], ['a']) + self.assertEqual(result['collateral'], 0) + self.assertEqual((self.facts, self.costs), before) + oracle = retraction_oracle(self.facts, 'target', ['protected'], self.costs) + self.assertEqual(result['removed'], oracle[1]) + + def test_impossible_protection(self): + result = retraction_plan({'t': [['a']], 'p': [['a']]}, 't', ['p'], {'a': 1}) + self.assertEqual(result['status'], 'infeasible') + self.assertIsNone(result['removed']) + + def test_cap(self): + costs = {f'a{i}': 1 for i in range(15)} + result = retraction_plan({'t': [['a0']]}, 't', [], costs) + self.assertEqual(result['status'], 'staged-cap') + + def test_invalid_retraction_inputs(self): + for costs in ({'a': 0}, {'a': -1}, {'a': True}): + with self.subTest(costs=costs), self.assertRaises(ValueError): + retraction_plan({'t': [['a']]}, 't', [], costs) + with self.assertRaises(ValueError): + retraction_plan({'t': []}, 't', [], {'a': 1}) + with self.assertRaises(ValueError): + retraction_plan({'t': [['a']]}, 'missing', [], {'a': 1}) + + def test_missing_proof_control(self): + plan = retraction_plan({'t': [['a']]}, 't', [], {'a': 1, 'b': 1}) + self.assertEqual(plan['removed'], ['a']) + self.assertNotIn('b', plan['removed']) # Undisclosed proof b survives. + + +class GroundingTests(unittest.TestCase): + def test_withdrawn_cycle_and_backup(self): + rules = [('a', ['seed']), ('b', ['a']), ('a', ['b'])] + old = grounded(['seed'], rules)['active'] + self.assertEqual(grounded([], rules)['active'], []) + self.assertEqual(support_prune(old, [], rules), ['a', 'b']) + self.assertEqual(grounded(['backup'], rules+[('a', ['backup'])])['active'], ['a', 'b', 'backup']) + + def test_conjunctive_body_needs_all_atoms(self): + rules = [('head', ['a', 'b'])] + self.assertEqual(grounded(['a'], rules)['active'], ['a']) + self.assertEqual(grounded(['a', 'b'], rules)['active'], ['a', 'b', 'head']) + + def test_finite_model_oracle(self): + atoms = ['a', 'b', 'c']; rules = [('b', ['a']), ('c', ['b']), ('a', ['c'])] + for bases in ([], ['a'], ['b'], ['a', 'c']): + self.assertEqual(grounded(bases, rules)['active'], finite_model_oracle(bases, rules)) + + def test_no_unseeded_self_support(self): + self.assertEqual(grounded([], [('a', ['a'])])['active'], []) + + def test_invalid_ground_rules(self): + for rules in ([('a', [])], [('a', 'b')], [('', ['a'])], [('a', [None])]): + with self.subTest(rules=rules), self.assertRaises(ValueError): + grounded([], rules) + + def test_rule_duplicate_order_and_work_bound(self): + rules = [('b', ['a']), ('c', ['b', 'a']), ('a', ['c'])] + result = grounded(['a'], rules) + self.assertEqual(result, grounded(['a', 'a'], list(reversed(rules))*5)) + self.assertLessEqual(result['body_visits'], result['body_incidences']) + + def test_false_base_semantic_control(self): + self.assertEqual(grounded(['false_base'], [('unsupported_in_reality', ['false_base'])])['active'], + ['false_base', 'unsupported_in_reality']) + + +class ReportTests(unittest.TestCase): + def test_additive_update_is_idempotent(self): + from graph_synthesis.uncertainty.report import replace_after_anchor, ANCHOR, START, END + original = 'prior study content\n'+ANCHOR+'\n\nfollowing study content\n' + updated = replace_after_anchor(original, 'new study content') + self.assertEqual(updated, replace_after_anchor(updated, 'new study content')) + self.assertTrue(updated.startswith('prior study content\n'+ANCHOR)) + self.assertTrue(updated.endswith('following study content\n')) + self.assertEqual(updated.count(START), 1) + self.assertEqual(updated.count(END), 1) + + def test_missing_or_malformed_anchor_is_rejected(self): + from graph_synthesis.uncertainty.report import replace_after_anchor, ANCHOR, START + for text in ('no anchor', ANCHOR+ANCHOR, ANCHOR+START): + with self.subTest(text=text), self.assertRaises(ValueError): + replace_after_anchor(text, 'content') + + +if __name__ == '__main__': + unittest.main() diff --git a/manuscript/paper-current.build.json b/manuscript/paper-current.build.json index b2bd563..9ebd6b4 100644 --- a/manuscript/paper-current.build.json +++ b/manuscript/paper-current.build.json @@ -1,6 +1,6 @@ { - "source_sha256": "8658c87dc90697cc9a4afeb7de58b85b871f7c8868f32bd0eb5512028c7d69c3", - "renderer_sha256": "79b125f1e5ff975aeb8596fd4b3333ba4d7d75cacc2aa6243169bd61f39d3f59", - "pdf_sha256": "4385d4c1fd67da63c7d9910189507fff4a6218824439aa4a16e4e7045a19e1cd", + "source_sha256": "418891ab916ecea5892075bb31b1cbdd6870a52e58406907c0b64ed0c6a06348", + "renderer_sha256": "b1ecb302903b01907b9c4dbd931862ef0f62c50804ca92077fd9ac26eb1354a4", + "pdf_sha256": "ebad9b2d9874b674a327c0d57fb4431cfded511df014eb15bff38d75c33aa9a5", "note": "Local-only assets; PDF bytes may vary with renderer and fonts." } diff --git a/manuscript/paper-current.html b/manuscript/paper-current.html index a161ba6..635af5d 100644 --- a/manuscript/paper-current.html +++ b/manuscript/paper-current.html @@ -1954,6 +1954,237 @@

8. Reproducibility and evidence links

python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf

Running without --check intentionally regenerates results. Network connection methods are blocked during benchmark execution; saved API failures remain charged in source evidence. Fixtures, updates, endpoint witnesses, fits, exposure weights, selected review IDs, bootstrap intervals, resource boundaries and source hashes are retained. Result replay checks exact structure/counts and narrowly bounded floating-point differences; the PDF build manifest binds the mutable full-paper source, renderer and PDF. The extension manifest excludes its own hash and does not rewrite earlier study manifests.

+

Uncertainty, repair ambiguity and grounded evidence in Jev graph synthesis

+

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

+

Abstract

+

Five controlled experiments address assumptions exposed by the preceding reliability study. Dependence-agnostic lineage yields 0 lower-bound false admissions versus 10 under an independence model on 128 supplied joint-distribution fixtures. Skeptical repair keeps 375 necessary assertions rather than 442 assertions from deterministic single optima; this is an explicit ambiguity/coverage tradeoff. Query-loss-directed review lowers aggregate residual query Brier loss by 70.64% versus primitive-risk ranking at two ideal reviews. Minimum-collateral retraction lowers collateral by 56.96% on 57 matched feasible fixtures. Grounded materialization matches finite-model entailment across 1,152 snapshots and removes cyclic phantom support. All results are conditional engineering outcomes, not new Jev accuracy measurements. Wrong marginals, false priorities, misspecified review priors, incomplete provenance and false base assertions remain falsifying controls.

+

1. Research question, prior evidence and novelty boundary

+

The preceding reliability study demonstrated that exact lineage arithmetic cannot repair false independence, priority-optimal graph repair can choose a false assertion, and incremental correctness depends on explicit dependencies. Its review experiments measured source contamination rather than loss in downstream queries. These observations motivate the present five hypotheses. We do not rerun calibration, voting, routing or the previous cycle-cutset benchmark under new names.

+

The protocol was committed as 383bab6f559c7f14525ddcef2275f3a816c9caa3 before execution, against baseline a62a3257645d8e35cd4e45be53bfa9511d27724b. The seed 20260922 is an arbitrary integer, not a collection date. Existing results informed the hypotheses; this is exploratory follow-up, not independent preregistration. Fresh Jev calls: 0. New scientific documents: 0. No default compiler or production graph policy changes. Every benchmark here uses supplied algorithmic fixtures or decision-theoretic models, not extraction from new papers or observed human review.

+

These specific integrations were not found in the reviewed package protocols. A bounded search cannot establish that an idea has never been attempted anywhere. Possible-world probabilistic reasoning is established prior art (Grosof, Bacchus); so are repair-based query answering, query causality, minimal-deletion resilience, value of information and Horn-rule semantics. The contribution is a new package-level implementation and falsification suite, not invention of those methods. No head-to-head comparison with KARMA is performed.

+

Concurrent-study overlap: post-execution audit

+

After this suite was executed, a cross-branch audit examined the PR #20 certificate-study protocol at commit 8fd9bba. That study also starts from baseline a62a3257645d8e35cd4e45be53bfa9511d27724b. Its H2 substantially overlaps this study's H1: both bound DNF-lineage probabilities over possible-world distributions without assuming independent primitives. Its H4 overlaps this study's H2: both use constrained re-optimization to distinguish repair-invariant conclusions from arbitrary optimal tie choices.

+

There are implementation and test-scope differences. This study's lineage prototype caps at eight used atoms, provides dependence-free analytic fallback and checks rational small-case extrema; the certificate prototype permits ten atoms and optional conjunction-probability constraints. This study returns an all-assertion optimal-repair backbone, whereas the certificate study tests conjunctive queries and an additional five-percent utility tolerance. Those differences do not justify claiming two new foundational mechanisms.

+

The subsequently merged PR #24 assumption-aware protocol at commit 6530321 adds further overlap: its H1/H2 share this study's dependence-bound and repair-invariance mechanisms, and its H5 also recomputes externally grounded least-fixed-point closure after withdrawals. This study's H5 uses finite-model intersection as its independent oracle and measures correctness/phantom retention rather than a primary work-reduction target. Those are useful validation differences, not a new grounding algorithm. Its H3 and this study's H3 both motivate query-aware review, but use different objectives and evidence: source-exposure weighting of saved predictions versus exact expected query Brier loss on controlled event models. Their outcome percentages and pass/fail criteria are not interchangeable.

+

Do not describe this suite as five independently novel or never-before-attempted algorithms. At least three of its five core mechanisms substantially overlap these concurrent studies. Its contribution is five executed, evidence-bounded implementations and falsification experiments. The original bounded inventory statement refers only to the reviewed baseline, not every concurrent branch or worldwide literature. The overlapping fixture counts must not be pooled as independent semantic validation or independent external replication. This study's exact query-loss objective and protected minimum-collateral retraction address different primary objectives from the reviewed concurrent experiments, but worldwide novelty is not established for them either. All prior study sections and their negative outcomes are preserved. No hypothesis, generator, threshold or numerical outcome was changed in response to this audit.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HypothesisFrozen primary targetOutcomeEvidence population
H1: Dependence-agnostic lineageNo containment/oracle/false-admission errors; prevent shared-source control and retain valid singletonMet128 joint fixtures + 16 rational oracles
H2: Skeptical repair backboneExact all-optima agreement; preserve unique optimum; stage arbitrary tie membersMet128 conflict graphs + 512 order checks
H3: Query-loss-directed reviewZero oracle regret and at least 20% lower query loss at budget twoMet64 independent-event/query fixtures
H4: Minimum-collateral retractionExact feasible objective, no protected loss, at least 25% less matched collateralMet128 proof-lineage fixtures
H5: Grounded recursive evidenceExact finite-model entailment, eliminate unsupported cycles, preserve grounded backupMet96 rule programs / 1,152 snapshots
+

Meeting an engineering target is not evidence that five new semantic improvements have been discovered. Generator-specific effects and assumption-breaking controls must be read together.

+

2. H1: Dependence-agnostic lineage admission

+

A conclusion has a monotone disjunctive-normal-form proof: any listed conjunction of primitive events is sufficient. The former exact-lineage method multiplied independent primitive probabilities. Here the one-atom marginals are supplied, but dependence is left unspecified. With one nonnegative mass per Boolean world, impose total mass one and each marginal as an equality. Minimize and maximize the conclusion indicator over this feasible polytope. The resulting interval asks what is justified across all joint distributions compatible with those premises.

+

The numerical prototype uses SciPy linear programming, at most eight used atoms, primal feasibility checks, repaired dual objective bounds and a 1e-8 outward allowance. These are numerical bounds, not formal real-arithmetic certificates. Failed or over-cap solves return dependence-free Frechet conjunction and union bounds. Duplicate and subsumed proofs are removed. No raw Jev score is promoted to a calibrated source reliability.

+

Across 128 seeded joint-distribution fixtures there are 0 containment errors, 0 duplicate/order errors and 0 false lower-bound admissions at threshold 0.95. Exact independent-world evaluation instead gives 10 false admissions. On 16 two/three-atom cases, a separate rational vertex-enumeration oracle finds 0 discrepancies beyond 1e-8. Mean interval width is 0.190534 for the analytic fallback and 0.166133 for the LP bounds. Primary target: met.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ControlResultInterpretation
Two 0.8-marginal events are actually identicalIndependence gives 0.96; robust interval [0.8, 1.0]; actual 0.8Stage, rather than admit at 0.95
Accurate singleton marginal 0.99Lower bound 0.99A justified high-probability admission is retained
Nine used atomsAnalytic fallbackNo over-cap exactness claim
Supplied singleton 0.99, actual 0.5Lower bound still 0.99Wrong marginals invalidate the premises
+

This is improved resistance to an unjustified independence assumption, not a guarantee against inaccurate priors, incomplete proofs or unknown extraction errors. A wide interval is intentionally retained when dependence is unidentified.

+
H1. False admissions at threshold 0.95 on supplied joint-distribution fixtures.
+

3. H2: Skeptical optimal-repair backbone

+

A one-best conflict optimizer selects one maximum-priority independent set, resolving ties by identifier. A tie-break is not evidence for one assertion over another. The proposed policy partitions assertions into necessary (in every optimum), possible (in at least one optimum) and excluded (in none). Only necessary assertions are unambiguously admissible under that objective. Constrained re-optimization produces an including and excluding witness for each ambiguous assertion.

+

The solver is bounded to connected components of at most 16 vertices. Larger components stage explicitly and the returned full-graph utility is unknown, while solved-component results remain labeled as partial. This improves ambiguity representation, not scalability relative to the previous 256-vertex cutset solver. Priorities and conflicts are supplied; neither is inferred from semantic truth.

+

On 128 seeded graphs, independent exhaustive subset enumeration finds 0 utility/membership/witness errors and 0 unique-optimum losses. There are 0 failures over 512 order checks. Multiple optimal repairs occur in 50 cases. Deterministic single repairs select 442 assertions in total; the skeptical backbone retains 375, withholding 67 tie-dependent selections. Primary target: met.

+

An equal-priority conflicting pair is ambiguous while an isolated positive-priority assertion remains necessary. A 32-cycle is unsupported at the new cap. Critically, the unique false-priority control (false=9, true=8) still makes the false assertion necessary. Skepticism about optimization ties does not validate priorities or facts; coverage and supplied-priority utility can decrease.

+
H2. Selected versus necessary assertions across the supplied conflict graphs.
+

4. H3: Query-loss-directed evidence review

+

Reviewing the most likely wrong primitive need not improve the answers that matter. Given supplied independent-event probabilities and three Boolean queries, choose a nonadaptive set of one or two primitive reviews minimizing expected residual query Brier loss. Perfect review reveals each selected event truthfully. The objective is the sum of expected conditional Bernoulli variances, equivalently expected squared error of the posterior query probabilities. Enumerate all review subsets within an eight-atom cap. No realized truth label enters selection.

+

Compare against lowest primitive truth probability (highest error risk for an asserted primitive), highest primitive entropy, and no review. A separate world/observation squared-error computation checks every subset and optimum. The primary effect is against risk ranking; entropy is an additional, more uncertainty-aligned comparator, not silently omitted.

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Reviews per fixtureRisk rankingEntropy rankingQuery-directedQuery-directed with unmodeled 10% review noise
10.3788660.3133580.1863370.300979
20.3338550.2397550.0980340.289846
+

Values are mean residual summed Brier loss for three queries per fixture, not classification error rates. Across 64 fixtures, budget-two total loss falls from 21.366751 to 6.274170, a 70.64% reduction. Oracle failures: 0. Primary target: met. The paired mean difference has a descriptive 95% fixture-bootstrap interval [-0.270651, -0.202129] (2,000 draws). This interval describes the artificial generator only, not scientific-paper performance or human reviewers.

+

Assumption-breaking result: the misspecified-prior control makes the chosen review's actual loss 0.4525, worse than risk ranking's 0.1800. The noisy-review rows keep the selector/posterior fixed while reviews undergo independent 10% bit flips; the model does not know this noise. Thus perfect-review gains are not robust guarantees. Costs are review counts only. No reviewer time, money, Jev-token cost or live service latency is inferred.

+
H3. Downstream query loss under equal ideal-review counts.
+

5. H4: Minimum-collateral assertion retraction plans

+

After external adjudication designates a conclusion for withdrawal, which candidate commitments should be staged? A plan must hit every supplied sufficient proof of the target while leaving at least one proof for each protected conclusion. Branching on an unhit target proof explores candidate withdrawals. Lexicographically minimize other lost conclusions, withdrawal cost, withdrawal count and sorted identifiers. Positive costs and monotone proof semantics justify pruning dominated partial plans. The cap is 14 atoms; unsupported or infeasible plans return no mutation.

+

This is an opt-in plan over candidate commitments, not deletion of source documents or a claim that the selected commitments are false. Compare with withdrawing the union of every target-proof atom and a cost-first greedy hitting set. The independent oracle enumerates every possible withdrawal subset.

+ + + + + + + + + + + + + + + + + + + + + + + + + +
MethodFeasible plans / 128Collateral on the same matched 57 cases
Union withdrawal5779
Cost-first greedy96Not the frozen primary comparator
Minimum-collateral11234
+

There are 0 oracle/objective errors, 0 protected/feasibility violations and 0 order errors. On the 57 cases where both proposed and union plans are feasible, total collateral falls from 79 to 34, or 56.96%. Primary target: met. The proposed method finds 112 feasible cases; the remaining 16 are infeasible under the supplied protection constraints, matching exhaustive enumeration. Coverage is reported separately so abstention cannot masquerade as quality.

+

The impossible-protection control stages rather than sacrificing the protected fact. The omitted-proof control leaves the target supported by an undisclosed alternative after a seemingly valid retraction. Incorrect external target adjudication can withdraw true commitments. These failures are not fixed by combinatorial optimality; complete lineage and correct intervention goals are essential.

+
H4. Collateral at matched feasible coverage; total feasibility is reported separately.
+

6. H5: Grounded recursive evidence materialization

+

Local support counts can leave a cycle apparently supported after its only external evidence disappears: A supports B and B supports A. For finite ground positive Horn rules, restart a worklist from current base assertions, decrement per-rule body counters as grounded atoms arrive, and fire a head only after every body atom is grounded. The process computes the least fixed point. It does not treat previously derived assertions as current bases. A separately implemented oracle intersects all finite interpretations satisfying the supplied bases and rules.

+

Across 96 seeded programs and 1,152 base snapshots, there are 0 finite-model disagreements and 0 duplicate/order disagreements. The local-support comparator leaves 943 phantom assertion occurrences over these snapshots; these are repeated assertion/snapshot occurrences, not unique facts or observed model hallucinations. Comparator updates begin from the preceding correct state, isolating each withdrawal failure rather than accumulating prior mistakes. Primary target: met.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Cycle sizePhantom assertions after sole seed withdrawal: local supportGrounded materializationGrounded with independent backup
2202
4404
8808
1616016
3232032
6464064
+

The backup column excludes the external backup atom itself. Every unsupported cycle is removed and every independently re-anchored cycle survives. Unseeded self-support also produces no fact. However, a false base assertion still grounds its rule consequences: entailment is conditional on the supplied bases and rules, not verification of real-world truth. Body-visit counts measure this in-memory materialization work, not database I/O, end-to-end update speed or Jev latency. This work recomputes grounding; it does not claim an incremental complexity improvement.

+
H5. Unsupported cycle members retained after the only external seed is removed.
+

7. Interpretation, limitations and next falsification boundary

+

The results support five bounded engineering capabilities: represent unidentified dependence honestly; distinguish necessary from arbitrary optimal selections; aim ideal reviews at downstream queries; minimize intervention collateral under explicit protection; and reject cyclic self-support without an external base. They do not establish new relation-extraction accuracy, calibrated real-world primitive reliabilities, automatic conflict discovery, trustworthy adjudication or better performance than KARMA. Exact small-instance objectives and known query priors favor these exhaustive prototypes, whose cost and coverage limits are explicit.

+

All five primary criteria are conjunctions frozen before execution and are evaluated without changing their thresholds. They are not five independent scientific discoveries or a pooled success rate. H1/H2/H4/H5 use finite-oracle tests rather than statistical population tests. H3 uses one descriptive, unadjusted generator-level bootstrap interval; it is not a prospective semantic confidence interval. Negative controls remain alongside favorable primary results.

+

A future semantic test must freeze policy and resource budgets before new source-disjoint, independently adjudicated graph data are observed. It must measure both correct retained edges and wrong edges, downstream query loss, intervention harm, actual reviewer errors, incomplete provenance, marginal misspecification and cap-driven staging. That study has not been performed here.

+

8. Reproducibility and artifact integrity

+
python -m pip install -r graph_synthesis/uncertainty/requirements.txt
+python -B -m graph_synthesis.uncertainty.run
+python -B -m unittest discover -s graph_synthesis/uncertainty/tests -v
+python -B -m graph_synthesis.uncertainty.run --check
+python -m pip install -r graph_synthesis/requirements-figures.txt
+python -B -m graph_synthesis.uncertainty.report --figures --update-paper
+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.

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 564969c..48ef5bc 100644 --- a/manuscript/paper-current.md +++ b/manuscript/paper-current.md @@ -902,6 +902,153 @@ Running without `--check` intentionally regenerates results. Network connection + + +# Uncertainty, repair ambiguity and grounded evidence in Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five controlled experiments address assumptions exposed by the preceding reliability study. Dependence-agnostic lineage yields 0 lower-bound false admissions versus 10 under an independence model on 128 supplied joint-distribution fixtures. Skeptical repair keeps 375 necessary assertions rather than 442 assertions from deterministic single optima; this is an explicit ambiguity/coverage tradeoff. Query-loss-directed review lowers aggregate residual query Brier loss by 70.64% versus primitive-risk ranking at two ideal reviews. Minimum-collateral retraction lowers collateral by 56.96% on 57 matched feasible fixtures. Grounded materialization matches finite-model entailment across 1,152 snapshots and removes cyclic phantom support. All results are conditional engineering outcomes, not new Jev accuracy measurements. Wrong marginals, false priorities, misspecified review priors, incomplete provenance and false base assertions remain falsifying controls. + +## 1. Research question, prior evidence and novelty boundary + +The [preceding reliability study](../graph_synthesis/reliability/RESULTS.md) demonstrated that exact lineage arithmetic cannot repair false independence, priority-optimal graph repair can choose a false assertion, and incremental correctness depends on explicit dependencies. Its review experiments measured source contamination rather than loss in downstream queries. These observations motivate the present five hypotheses. We do not rerun calibration, voting, routing or the previous cycle-cutset benchmark under new names. + +The [protocol](../graph_synthesis/uncertainty/PROTOCOL.md) was committed as `383bab6f559c7f14525ddcef2275f3a816c9caa3` before execution, against baseline `a62a3257645d8e35cd4e45be53bfa9511d27724b`. The seed 20260922 is an arbitrary integer, not a collection date. Existing results informed the hypotheses; this is exploratory follow-up, not independent preregistration. **Fresh Jev calls: 0. New scientific documents: 0.** No default compiler or production graph policy changes. Every benchmark here uses supplied algorithmic fixtures or decision-theoretic models, not extraction from new papers or observed human review. + +These specific integrations were not found in the reviewed package protocols. A bounded search cannot establish that an idea has never been attempted anywhere. Possible-world probabilistic reasoning is established prior art ([Grosof](https://arxiv.org/abs/1304.3418), [Bacchus](https://arxiv.org/abs/1304.2341)); so are [repair-based query answering](https://doi.org/10.1016/j.tcs.2022.09.005), [query causality](https://arxiv.org/abs/0912.5340), [minimal-deletion resilience](https://arxiv.org/abs/1507.00674), [value of information](https://pmc.ncbi.nlm.nih.gov/articles/PMC7612603/) and [Horn-rule semantics](https://www.w3.org/TR/rif-core/). The contribution is a new package-level implementation and falsification suite, not invention of those methods. No head-to-head comparison with [KARMA](https://arxiv.org/abs/2502.06472) is performed. + +### Concurrent-study overlap: post-execution audit + +After this suite was executed, a cross-branch audit examined the [PR #20 certificate-study protocol at commit 8fd9bba](https://github.com/CompleteDotTech/paper-package/blob/8fd9bba028473f581e489ad5a902878245e53759/graph_synthesis/certificates/PROTOCOL.md). That study also starts from baseline `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Its H2 substantially overlaps this study's H1: both bound DNF-lineage probabilities over possible-world distributions without assuming independent primitives. Its H4 overlaps this study's H2: both use constrained re-optimization to distinguish repair-invariant conclusions from arbitrary optimal tie choices. + +There are implementation and test-scope differences. This study's lineage prototype caps at eight used atoms, provides dependence-free analytic fallback and checks rational small-case extrema; the certificate prototype permits ten atoms and optional conjunction-probability constraints. This study returns an all-assertion optimal-repair backbone, whereas the certificate study tests conjunctive queries and an additional five-percent utility tolerance. Those differences do not justify claiming two new foundational mechanisms. + +The subsequently merged [PR #24 assumption-aware protocol at commit 6530321](https://github.com/CompleteDotTech/paper-package/blob/6530321dd0060c9c7f13bb69d6f1c88346a8e1e2/graph_synthesis/assumption_aware/PROTOCOL.md) adds further overlap: its H1/H2 share this study's dependence-bound and repair-invariance mechanisms, and its H5 also recomputes externally grounded least-fixed-point closure after withdrawals. This study's H5 uses finite-model intersection as its independent oracle and measures correctness/phantom retention rather than a primary work-reduction target. Those are useful validation differences, not a new grounding algorithm. Its H3 and this study's H3 both motivate query-aware review, but use different objectives and evidence: source-exposure weighting of saved predictions versus exact expected query Brier loss on controlled event models. Their outcome percentages and pass/fail criteria are not interchangeable. + +**Do not describe this suite as five independently novel or never-before-attempted algorithms.** At least three of its five core mechanisms substantially overlap these concurrent studies. Its contribution is five executed, evidence-bounded implementations and falsification experiments. The original bounded inventory statement refers only to the reviewed baseline, not every concurrent branch or worldwide literature. The overlapping fixture counts must not be pooled as independent semantic validation or independent external replication. This study's exact query-loss objective and protected minimum-collateral retraction address different primary objectives from the reviewed concurrent experiments, but worldwide novelty is not established for them either. All prior study sections and their negative outcomes are preserved. No hypothesis, generator, threshold or numerical outcome was changed in response to this audit. + +| Hypothesis | Frozen primary target | Outcome | Evidence population | +|---|---|---|---| +| H1: Dependence-agnostic lineage | No containment/oracle/false-admission errors; prevent shared-source control and retain valid singleton | Met | 128 joint fixtures + 16 rational oracles | +| H2: Skeptical repair backbone | Exact all-optima agreement; preserve unique optimum; stage arbitrary tie members | Met | 128 conflict graphs + 512 order checks | +| H3: Query-loss-directed review | Zero oracle regret and at least 20% lower query loss at budget two | Met | 64 independent-event/query fixtures | +| H4: Minimum-collateral retraction | Exact feasible objective, no protected loss, at least 25% less matched collateral | Met | 128 proof-lineage fixtures | +| H5: Grounded recursive evidence | Exact finite-model entailment, eliminate unsupported cycles, preserve grounded backup | Met | 96 rule programs / 1,152 snapshots | + +Meeting an engineering target is not evidence that five new semantic improvements have been discovered. Generator-specific effects and assumption-breaking controls must be read together. + +## 2. H1: Dependence-agnostic lineage admission + +A conclusion has a monotone disjunctive-normal-form proof: any listed conjunction of primitive events is sufficient. The former exact-lineage method multiplied independent primitive probabilities. Here the one-atom marginals are supplied, but dependence is left unspecified. With one nonnegative mass per Boolean world, impose total mass one and each marginal as an equality. Minimize and maximize the conclusion indicator over this feasible polytope. The resulting interval asks what is justified across all joint distributions compatible with those premises. + +The numerical prototype uses [SciPy linear programming](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html), at most eight used atoms, primal feasibility checks, repaired dual objective bounds and a 1e-8 outward allowance. These are numerical bounds, not formal real-arithmetic certificates. Failed or over-cap solves return dependence-free Frechet conjunction and union bounds. Duplicate and subsumed proofs are removed. No raw Jev score is promoted to a calibrated source reliability. + +Across 128 seeded joint-distribution fixtures there are 0 containment errors, 0 duplicate/order errors and 0 false lower-bound admissions at threshold 0.95. Exact independent-world evaluation instead gives 10 false admissions. On 16 two/three-atom cases, a separate rational vertex-enumeration oracle finds 0 discrepancies beyond 1e-8. Mean interval width is 0.190534 for the analytic fallback and 0.166133 for the LP bounds. Primary target: **met**. + +| Control | Result | Interpretation | +|---|---|---| +| Two 0.8-marginal events are actually identical | Independence gives 0.96; robust interval [0.8, 1.0]; actual 0.8 | Stage, rather than admit at 0.95 | +| Accurate singleton marginal 0.99 | Lower bound 0.99 | A justified high-probability admission is retained | +| Nine used atoms | Analytic fallback | No over-cap exactness claim | +| Supplied singleton 0.99, actual 0.5 | Lower bound still 0.99 | Wrong marginals invalidate the premises | + +This is improved resistance to an unjustified independence assumption, not a guarantee against inaccurate priors, incomplete proofs or unknown extraction errors. A wide interval is intentionally retained when dependence is unidentified. + +![H1. False admissions at threshold 0.95 on supplied joint-distribution fixtures.](../graph_synthesis/uncertainty/figures/01_dependence.png) + +## 3. H2: Skeptical optimal-repair backbone + +A one-best conflict optimizer selects one maximum-priority independent set, resolving ties by identifier. A tie-break is not evidence for one assertion over another. The proposed policy partitions assertions into necessary (in every optimum), possible (in at least one optimum) and excluded (in none). Only necessary assertions are unambiguously admissible under that objective. Constrained re-optimization produces an including and excluding witness for each ambiguous assertion. + +The solver is bounded to connected components of at most 16 vertices. Larger components stage explicitly and the returned full-graph utility is unknown, while solved-component results remain labeled as partial. This improves ambiguity representation, not scalability relative to the previous 256-vertex cutset solver. Priorities and conflicts are supplied; neither is inferred from semantic truth. + +On 128 seeded graphs, independent exhaustive subset enumeration finds 0 utility/membership/witness errors and 0 unique-optimum losses. There are 0 failures over 512 order checks. Multiple optimal repairs occur in 50 cases. Deterministic single repairs select 442 assertions in total; the skeptical backbone retains 375, withholding 67 tie-dependent selections. Primary target: **met**. + +An equal-priority conflicting pair is ambiguous while an isolated positive-priority assertion remains necessary. A 32-cycle is unsupported at the new cap. Critically, the unique false-priority control (false=9, true=8) still makes the false assertion necessary. Skepticism about optimization ties does not validate priorities or facts; coverage and supplied-priority utility can decrease. + +![H2. Selected versus necessary assertions across the supplied conflict graphs.](../graph_synthesis/uncertainty/figures/02_repair_backbone.png) + +## 4. H3: Query-loss-directed evidence review + +Reviewing the most likely wrong primitive need not improve the answers that matter. Given supplied independent-event probabilities and three Boolean queries, choose a nonadaptive set of one or two primitive reviews minimizing expected residual query Brier loss. Perfect review reveals each selected event truthfully. The objective is the sum of expected conditional Bernoulli variances, equivalently expected squared error of the posterior query probabilities. Enumerate all review subsets within an eight-atom cap. No realized truth label enters selection. + +Compare against lowest primitive truth probability (highest error risk for an asserted primitive), highest primitive entropy, and no review. A separate world/observation squared-error computation checks every subset and optimum. The primary effect is against risk ranking; entropy is an additional, more uncertainty-aligned comparator, not silently omitted. + +| Reviews per fixture | Risk ranking | Entropy ranking | Query-directed | Query-directed with unmodeled 10% review noise | +|---:|---:|---:|---:|---:| +| 1 | 0.378866 | 0.313358 | 0.186337 | 0.300979 | +| 2 | 0.333855 | 0.239755 | 0.098034 | 0.289846 | + +Values are mean residual *summed* Brier loss for three queries per fixture, not classification error rates. Across 64 fixtures, budget-two total loss falls from 21.366751 to 6.274170, a 70.64% reduction. Oracle failures: 0. Primary target: **met**. The paired mean difference has a descriptive 95% fixture-bootstrap interval [-0.270651, -0.202129] (2,000 draws). This interval describes the artificial generator only, not scientific-paper performance or human reviewers. + +**Assumption-breaking result:** the misspecified-prior control makes the chosen review's actual loss 0.4525, worse than risk ranking's 0.1800. The noisy-review rows keep the selector/posterior fixed while reviews undergo independent 10% bit flips; the model does not know this noise. Thus perfect-review gains are not robust guarantees. Costs are review counts only. No reviewer time, money, Jev-token cost or live service latency is inferred. + +![H3. Downstream query loss under equal ideal-review counts.](../graph_synthesis/uncertainty/figures/03_query_review.png) + +## 5. H4: Minimum-collateral assertion retraction plans + +After external adjudication designates a conclusion for withdrawal, which candidate commitments should be staged? A plan must hit every supplied sufficient proof of the target while leaving at least one proof for each protected conclusion. Branching on an unhit target proof explores candidate withdrawals. Lexicographically minimize other lost conclusions, withdrawal cost, withdrawal count and sorted identifiers. Positive costs and monotone proof semantics justify pruning dominated partial plans. The cap is 14 atoms; unsupported or infeasible plans return no mutation. + +This is an opt-in plan over candidate commitments, not deletion of source documents or a claim that the selected commitments are false. Compare with withdrawing the union of every target-proof atom and a cost-first greedy hitting set. The independent oracle enumerates every possible withdrawal subset. + +| Method | Feasible plans / 128 | Collateral on the same matched 57 cases | +|---|---:|---:| +| Union withdrawal | 57 | 79 | +| Cost-first greedy | 96 | Not the frozen primary comparator | +| Minimum-collateral | 112 | 34 | + +There are 0 oracle/objective errors, 0 protected/feasibility violations and 0 order errors. On the 57 cases where both proposed and union plans are feasible, total collateral falls from 79 to 34, or 56.96%. Primary target: **met**. The proposed method finds 112 feasible cases; the remaining 16 are infeasible under the supplied protection constraints, matching exhaustive enumeration. Coverage is reported separately so abstention cannot masquerade as quality. + +The impossible-protection control stages rather than sacrificing the protected fact. The omitted-proof control leaves the target supported by an undisclosed alternative after a seemingly valid retraction. Incorrect external target adjudication can withdraw true commitments. These failures are not fixed by combinatorial optimality; complete lineage and correct intervention goals are essential. + +![H4. Collateral at matched feasible coverage; total feasibility is reported separately.](../graph_synthesis/uncertainty/figures/04_retraction.png) + +## 6. H5: Grounded recursive evidence materialization + +Local support counts can leave a cycle apparently supported after its only external evidence disappears: A supports B and B supports A. For finite ground positive Horn rules, restart a worklist from current base assertions, decrement per-rule body counters as grounded atoms arrive, and fire a head only after every body atom is grounded. The process computes the least fixed point. It does not treat previously derived assertions as current bases. A separately implemented oracle intersects all finite interpretations satisfying the supplied bases and rules. + +Across 96 seeded programs and 1,152 base snapshots, there are 0 finite-model disagreements and 0 duplicate/order disagreements. The local-support comparator leaves 943 phantom assertion occurrences over these snapshots; these are repeated assertion/snapshot occurrences, not unique facts or observed model hallucinations. Comparator updates begin from the preceding correct state, isolating each withdrawal failure rather than accumulating prior mistakes. Primary target: **met**. + +| Cycle size | Phantom assertions after sole seed withdrawal: local support | Grounded materialization | Grounded with independent backup | +|---:|---:|---:|---:| +| 2 | 2 | 0 | 2 | +| 4 | 4 | 0 | 4 | +| 8 | 8 | 0 | 8 | +| 16 | 16 | 0 | 16 | +| 32 | 32 | 0 | 32 | +| 64 | 64 | 0 | 64 | + +The backup column excludes the external backup atom itself. Every unsupported cycle is removed and every independently re-anchored cycle survives. Unseeded self-support also produces no fact. However, a false base assertion still grounds its rule consequences: entailment is conditional on the supplied bases and rules, not verification of real-world truth. Body-visit counts measure this in-memory materialization work, not database I/O, end-to-end update speed or Jev latency. This work recomputes grounding; it does not claim an incremental complexity improvement. + +![H5. Unsupported cycle members retained after the only external seed is removed.](../graph_synthesis/uncertainty/figures/05_grounding.png) + +## 7. Interpretation, limitations and next falsification boundary + +The results support five bounded engineering capabilities: represent unidentified dependence honestly; distinguish necessary from arbitrary optimal selections; aim ideal reviews at downstream queries; minimize intervention collateral under explicit protection; and reject cyclic self-support without an external base. They do not establish new relation-extraction accuracy, calibrated real-world primitive reliabilities, automatic conflict discovery, trustworthy adjudication or better performance than KARMA. Exact small-instance objectives and known query priors favor these exhaustive prototypes, whose cost and coverage limits are explicit. + +All five primary criteria are conjunctions frozen before execution and are evaluated without changing their thresholds. They are not five independent scientific discoveries or a pooled success rate. H1/H2/H4/H5 use finite-oracle tests rather than statistical population tests. H3 uses one descriptive, unadjusted generator-level bootstrap interval; it is not a prospective semantic confidence interval. Negative controls remain alongside favorable primary results. + +A future semantic test must freeze policy and resource budgets before new source-disjoint, independently adjudicated graph data are observed. It must measure both correct retained edges and wrong edges, downstream query loss, intervention harm, actual reviewer errors, incomplete provenance, marginal misspecification and cap-driven staging. That study has not been performed here. + +## 8. Reproducibility and artifact integrity + +```bash +python -m pip install -r graph_synthesis/uncertainty/requirements.txt +python -B -m graph_synthesis.uncertainty.run +python -B -m unittest discover -s graph_synthesis/uncertainty/tests -v +python -B -m graph_synthesis.uncertainty.run --check +python -m pip install -r graph_synthesis/requirements-figures.txt +python -B -m graph_synthesis.uncertainty.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +``` + +The [machine-readable results](../graph_synthesis/uncertainty/results.json) preserve fixture inputs, oracle values, all review-subset scores, repair witnesses, retraction feasibility, snapshot outcomes, controls and source/protocol hashes. The [execution notes](../graph_synthesis/uncertainty/EXECUTION_NOTES.md) 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. + + + # 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 6a03429..41ab32f 100644 Binary files a/manuscript/paper-current.pdf and b/manuscript/paper-current.pdf differ