From eba76bd2cddddf46b097e6fafd743f3caf957a8b Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:23:11 -0400 Subject: [PATCH 01/21] ci: prepare isolated execution and evidence export for next research extension --- .github/workflows/dependence-certificates.yml | 78 +++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 .github/workflows/dependence-certificates.yml diff --git a/.github/workflows/dependence-certificates.yml b/.github/workflows/dependence-certificates.yml new file mode 100644 index 0000000..6a39ce9 --- /dev/null +++ b/.github/workflows/dependence-certificates.yml @@ -0,0 +1,78 @@ +name: Dependence-aware graph certificates +on: + push: + branches: [main, research/dependence-aware-certificates] + pull_request: +permissions: + contents: read +concurrency: + group: certificates-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true +jobs: + execute: + if: github.event_name == 'push' && github.ref == 'refs/heads/research/dependence-aware-certificates' + runs-on: ubuntu-latest + timeout-minutes: 20 + permissions: + contents: write + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - name: Execute frozen research when implementation is present + shell: bash + run: | + set -euo pipefail + if [ -f graph_synthesis/certificates/run.py ]; then + python -m pip install -r graph_synthesis/requirements-figures.txt + python -B -m graph_synthesis.certificates.run + python -B -m unittest discover -s graph_synthesis/certificates/tests -v 2>&1 | tee graph_synthesis/certificates/tests.log + python -B -m graph_synthesis.certificates.run --check + python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/certificates-original.json" + cp "$RUNNER_TEMP/certificates-original.json" graph_synthesis/certificates/original-inventory.json + python -B -m graph_synthesis.certificates.report --figures --update-paper + python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf + git config user.name 'github-actions[bot]' + git config user.email '41898282+github-actions[bot]@users.noreply.github.com' + git add graph_synthesis/certificates 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 certificate benchmarks and updated manuscript' + git push origin HEAD:research/dependence-aware-certificates + fi + fi + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 + with: + name: certificates-source-and-evidence + path: | + . + !.git + retention-days: 7 + replay: + if: github.event_name == 'pull_request' || github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + timeout-minutes: 15 + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + with: + ref: ${{ github.event.pull_request.head.sha || github.sha }} + persist-credentials: false + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + - run: python -m pip install -r graph_synthesis/requirements-figures.txt + - name: Verify results, manuscript and original evidence + shell: bash + run: | + set -euo pipefail + python -B -m unittest discover -s graph_synthesis/certificates/tests -v + python -B -m graph_synthesis.certificates.run --check + python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/certificates-original.json" + python -B -m graph_synthesis.certificates.report --figures --update-paper + git diff --exit-code -- graph_synthesis/certificates manuscript/paper-current.md CURRENT_RESULTS.md + python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/certificates-paper.pdf" + git diff --exit-code -- manuscript/paper-current.html From bc70b76621daca198b3b97eb4f698b17d4c40a0b Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:27:57 -0400 Subject: [PATCH 02/21] research: freeze five dependence-aware certificate hypotheses before execution --- graph_synthesis/certificates/PROTOCOL.md | 64 ++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 graph_synthesis/certificates/PROTOCOL.md diff --git a/graph_synthesis/certificates/PROTOCOL.md b/graph_synthesis/certificates/PROTOCOL.md new file mode 100644 index 0000000..de52eaa --- /dev/null +++ b/graph_synthesis/certificates/PROTOCOL.md @@ -0,0 +1,64 @@ +# Five dependence-aware graph certificate experiments + +Baseline: `a62a3257645d8e35cd4e45be53bfa9511d27724b` (merged PR #17). This protocol is committed before implementing or executing this suite. Earlier public outcomes motivated its hypotheses; this is exploratory follow-up, not independent preregistration. Never retune the endpoints below after observing outcomes. No new Jev service calls, production policy changes, or edits to archived study evidence. + +## Scope and novelty boundary + +These five combinations were not found in the current repository's experiment inventory after inspection of the relationship, falsification, theory-suite, multicall, followup, adaptive, risk-control and reliability protocols and implementations. This is a repository-scoped claim, not proof that nobody has attempted them worldwide. Beta-binomial random effects, probability-bound linear programs, bipartite min-cut, consistent answers across repairs and incremental view maintenance have established predecessors. We test their specific integration and limitations for this Jev graph pipeline. Algorithmic correctness is not semantic accuracy. + +## H1: Source-random-effects contamination forecasting + +Motivation: the latest out-of-group multiplier reduced mean bias but worsened source-contamination Brier. Hypothesis: explicitly modeling within-source error dependence improves contamination forecasting without changing any accepted edge. + +Reuse authenticated multicall base1 outputs: 73 development candidates in 40 source groups and 263 evaluation candidates in 149 disjoint groups. These evaluation data were already inspected in prior work. A group contributes only if base1 accepted at least one SUPPORTS/REFUTES edge. Record its accepted count n and wrong-edge count k. Fit pooled marginal error mu=(sum(k)+0.5)/(sum(n)+1) on development only. Select intraclass correlation rho from {0,0.01,0.05,0.1,0.2,0.4,0.6,0.8} by maximum development beta-binomial log likelihood; tie toward smaller rho. For rho>0, alpha=mu*(1/rho-1), beta=(1-mu)*(1/rho-1); predict contamination 1-B(alpha,beta+n)/B(alpha,beta). At rho=0 use 1-(1-mu)^n. Retain all likelihoods and leave-one-source-out development sensitivity fits. Execution receives group sizes, not evaluation gold. + +Compare with the identical-marginal independent model, the prior feature-based independent model, and PR #17's development-selected multiplier model. Primary conjunction: >=10% lower Brier than the identical-marginal independent comparator, absolute mean bias <=0.03, and Brier no worse than the prior feature-based baseline. A rho=0 selection is a legitimate null result. Report sample-size denominators, proper scores, descriptive paired 95%/99% group bootstrap intervals (4000 draws, seed 20260922), and singleton-group/independence limitations. No deployed risk certificate or new semantic improvement is inferred. + +## H2: Lineage admission without an independence assumption + +Motivation: exact Shannon expansion was exact only for supplied independent primitives and falsely inflated a duplicated 0.8 source to 0.96 under corrupted independence. Hypothesis: a bounded extremal-probability program prevents independence-induced false admission while using explicit dependence information when available. + +Represent a monotone DNF lineage over <=10 Boolean atoms by <=1024 possible worlds. Constrain world masses to be nonnegative, sum to one, and match supplied marginal probabilities and optional conjunction probabilities. Minimize/maximize the lineage indicator with SciPy HiGHS. Store solver residual diagnostics and dual objective bounds; outward-pad bounds by 1e-9. Infeasible, failed, over-cap or nonfinite inputs never yield an admission certificate. Threshold: lower bound >=0.95. Values are supplied fixture probabilities, never raw Jev truth probabilities. + +Primary: zero containment failures on 128 seeded arbitrary joint distributions with 2-6 atoms, exact two-event Frechet bounds within 1e-8 on a fixed marginal grid, invariance under reordered/duplicated proofs, and prevention of all independence-induced false admissions in 20 perfectly correlated two-source controls. Explicit pair constraints must tighten or preserve interval width. Report withheld high-probability admissions, infeasible constraints, over-cap staging and a misspecified-marginal negative control. Finite-world and numerical limits are explicit; no unconditional truth guarantee. + +## H3: Proof-carrying bipartite conflict optimization + +Motivation: the four-vertex cutset cap stages dense conflict components even when they are bipartite. Hypothesis: a bipartite min-cut route solves such components exactly and provides an independently checkable flow/cover certificate. + +Use unique nonempty string IDs, nonnegative integer priorities and explicit undirected edges. For each bipartite component <=2048 vertices and <=50000 edges, reduce minimum weighted vertex cover to integer max-flow; return its complementary independent set. Use deterministic sorted traversal, original priorities (no hidden tie perturbation), and certificates verifying capacity, flow conservation, cut capacity, cover feasibility and utility. Non-bipartite components retain the actual PR #17 solver and its original caps; over-cap components stage. Empty and zero-priority inputs remain valid. + +Primary: zero utility/consistency/certificate failures on 128 seeded <=10-vertex bipartite fixtures versus independent subset enumeration; no utility regression on supported small general graphs; exact unstaged analytic results on balanced complete bipartite components of sizes 32,64,128,256 and on even cycles of sizes 512,1024,2048. Retain duplicate/order tests, odd-cycle and dense-clique controls, cap controls, and the false-high-priority semantic counterexample. Report graph capacity, not model accuracy. + +## H4: Repair-invariant query certificates + +Motivation: one deterministic optimum hides alternative equally good graphs; structural optimality cannot justify treating its arbitrary tie choices as certain. Hypothesis: constrained include/exclude re-optimization correctly distinguishes certain, ambiguous, and impossible conjunctive vertex-membership queries across all admissible repairs. + +For a graph optimum U, admissible repairs are independent sets of utility >=U-epsilon; evaluate epsilon=0 and floor(0.05*U). For a conjunction of up to eight required vertices, solve the forced-in problem to establish possibility and each forced-out problem to establish certainty. Return an explicit counterexample repair for ambiguity; propagate staged/unsupported optimization rather than making a certainty claim. All priorities remain supplied inputs. + +Primary: zero classification or witness errors on 128 seeded <=10-vertex general graphs checked by independent enumeration of all admissible independent sets; zero false certainty; correct retention of every oracle-certain answer; and prevention of a single-optimum false-certainty claim on a two-way tie control. Increasing epsilon must not create new certain answers. Include conflicting conjunctions, empty queries, zero priorities, unsupported graphs and a high-priority-false control. Certainty is conditional on the repair/priority model, not factual truth. + +## H5: Delta-indexed updates without global snapshot rescans + +Motivation: PR #17 saved solver-vertex work but still validated and discovered every component globally. Hypothesis: a validated graph invariant plus an explicit mutation API permits affected-component-only validation, discovery and solving with identical graph results. + +Build an immutable-to-callers adjacency/priority store and node-to-component index. Support weight change, vertex insert/delete and edge insert/delete. Validate every mutation before state changes; construct and solve replacement affected-component states before publishing them. Bridge insertion invalidates both endpoint components; bridge deletion/vertex removal discovers splits only inside the affected old component. Unaffected component solutions are reused; updates return deltas and aggregate utility, not a forced whole-graph materialization. Provide an explicit full snapshot audit outside the update API. + +Compare against full input validation/component discovery/recomputation with the same H3 solver, and record the distinction from PR #17. Primary: zero selected/staged/utility mismatches across 640 seeded updates including merges/splits and vertex deletion; zero state change on invalid mutations; >=75% reduction in explicitly counted graph-element visits (validation/discovery/solver-input, including cold build) on 512 components of eight vertices and 128 local updates. Record connected-graph stress where savings can vanish. Record nonbinding repeated local wall-clock measurements separately from deterministic results, with environment and timing boundary; do not infer database I/O, durable transactions, asymptotic guarantees or service latency. Oracle snapshot auditing is excluded from update timing for both policies and disclosed. + +## Evidence, implementation and reporting + +Seed 20260922. Preserve all fixed inputs and their SHA-256 hashes. Decision functions may not read evaluation gold. Reconstruct saved decisions from raw calls and verify source split before H1. Independent finite/analytic oracles must not call the proposed solver for their truth values. Freeze negative results alongside positive ones. Add regression/adversarial tests, raw per-case results, input/implementation/protocol hashes, one figure per hypothesis in SVG and PNG, a generated report, complete additive manuscript/PDF update and exact replay checks. Timing artifacts are nondeterministic and separate. Keep all prior studies and conclusions intact; no synthetic cases counted as Jev observations. Merge through a PR only after verification, figure/PDF review and required checks. + +## Primary foundations and novelty search + +- SciPy 1.17.0 beta-binomial definition: https://docs.scipy.org/doc/scipy-1.17.0/reference/generated/scipy.stats.betabinom.html +- HiGHS linear programs and dual marginals: https://docs.scipy.org/doc/scipy-1.17.0/reference/optimize.linprog-highs.html +- Kaski, Mannila and Mohapatra, Optimal Union Probability Interval Is NP-Hard (2026): https://arxiv.org/abs/2605.03556 +- Faour and Kuhn, Distributed CONGEST Approximation of Weighted Vertex Covers and Matchings (2021): https://arxiv.org/abs/2111.10577 +- Pardal et al., Computational Complexity of Preferred Subset Repairs on Data-Graphs (2024): https://arxiv.org/abs/2402.09265 +- Staworko and Chomicki, Consistent Query Answers in the Presence of Universal Constraints (2008): https://arxiv.org/abs/0809.1551 +- Macedo et al., A Feature-based Classification of Model Repair Approaches (2015): https://arxiv.org/html/1504.03947v1 +- TypeSafe typed-decision boundary: https://docs.typesafe.ai/introduction + +These references establish antecedents and constraints; they do not validate this implementation or show worldwide novelty. Independent source-disjoint Jev accuracy evaluation and matched KARMA comparisons remain outside this replay/algorithm suite. From 87c8f10d49250db2dc0ad303c6ea46119ef2276c Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:30:43 -0400 Subject: [PATCH 03/21] feat: add development-only source random-effects forecast --- graph_synthesis/certificates/risk.py | 70 ++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 graph_synthesis/certificates/risk.py diff --git a/graph_synthesis/certificates/risk.py b/graph_synthesis/certificates/risk.py new file mode 100644 index 0000000..e75041a --- /dev/null +++ b/graph_synthesis/certificates/risk.py @@ -0,0 +1,70 @@ +"""Source-count random effects; forecasting never receives evaluation labels.""" +from __future__ import annotations +from collections import defaultdict +import math + +POSITIVE = frozenset(('SUPPORTS', 'REFUTES')) +RHO_GRID = (0.0, 0.01, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8) + + +def counts(rows, *, truth=False): + """Return accepted exposure, optionally training/evaluation error counts.""" + out = defaultdict(lambda: {'n': 0, 'k': 0}) + for row in rows: + label = row['views']['base1']['label'] + if label in POSITIVE: + value = out[row['group']] + value['n'] += 1 + if truth: + value['k'] += int(label != row['gold']) + return {g: v if truth else v['n'] for g, v in sorted(out.items())} + + +def contamination(n, mu, rho): + if type(n) is not int or n < 0: + raise ValueError('Exposure must be a nonnegative integer') + if not all(isinstance(x, (int, float)) and not isinstance(x, bool) and math.isfinite(x) for x in (mu, rho)): + raise ValueError('Finite real parameters required') + if not 0 <= mu <= 1 or not 0 <= rho < 1: + raise ValueError('Require mu in [0,1] and rho in [0,1)') + if n == 0 or mu == 0: + return 0.0 + if mu == 1: + return 1.0 + if rho == 0: + return -math.expm1(n * math.log1p(-mu)) + a, b = mu * (1 / rho - 1), (1 - mu) * (1 / rho - 1) + log_zero = math.lgamma(b + n) - math.lgamma(a + b + n) + math.lgamma(a + b) - math.lgamma(b) + return min(1.0, max(0.0, -math.expm1(log_zero))) + + +def log_mass(k, n, mu, rho): + combinatorial = math.lgamma(n + 1) - math.lgamma(k + 1) - math.lgamma(n - k + 1) + if rho == 0: + return combinatorial + k * math.log(mu) + (n - k) * math.log1p(-mu) + a, b = mu * (1 / rho - 1), (1 - mu) * (1 / rho - 1) + return (combinatorial + math.lgamma(a + k) + math.lgamma(b + n - k) + - math.lgamma(a + b + n) - math.lgamma(a) - math.lgamma(b) + math.lgamma(a + b)) + + +def fit(development): + development = list(development) + if not development or any(r.get('split') != 'development' for r in development): + raise ValueError('Fit accepts nonempty development rows only') + grouped = counts(development, truth=True) + n = sum(v['n'] for v in grouped.values()) + if not n: + raise ValueError('No accepted development edges') + wrong = sum(v['k'] for v in grouped.values()) + mu = (wrong + 0.5) / (n + 1) + grid = [{'rho': rho, 'log_likelihood': sum(log_mass(v['k'], v['n'], mu, rho) for v in grouped.values())} + for rho in RHO_GRID] + best = min(grid, key=lambda v: (-v['log_likelihood'], v['rho'])) + return {'mu': mu, 'rho': best['rho'], 'grid': grid, 'accepted_edges': n, 'wrong_edges': wrong, + 'fit_groups': sorted({r['group'] for r in development}), 'counts': grouped} + + +def predict(exposures, model, *, independent=False): + """Only group IDs and counts are consumed, never responses or gold.""" + return {g: contamination(n, model['mu'], 0.0 if independent else model['rho']) + for g, n in sorted(exposures.items())} From 8926d5a6b32c1af1c312383378b5221ce301c725 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:31:14 -0400 Subject: [PATCH 04/21] feat: bound lineage probabilities under explicit dependence constraints --- graph_synthesis/certificates/lineage.py | 81 +++++++++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 graph_synthesis/certificates/lineage.py diff --git a/graph_synthesis/certificates/lineage.py b/graph_synthesis/certificates/lineage.py new file mode 100644 index 0000000..eb9164b --- /dev/null +++ b/graph_synthesis/certificates/lineage.py @@ -0,0 +1,81 @@ +"""Bounded possible-world LP; no independence assumption or Jev-score semantics.""" +from __future__ import annotations +import math +import numpy as np +from scipy.optimize import linprog + +PAD = 1e-9 + + +def _probability(value): + if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value) or not 0 <= value <= 1: + raise ValueError('Supplied probabilities must be finite reals in [0,1]') + return float(value) + + +def _stage(reason, atoms): + return {'status': reason, 'certified': False, 'lower': 0.0, 'upper': 1.0, 'atoms': atoms} + + +def bounds(proofs, probabilities, joints=(), *, atom_cap=10): + """Extremal probability of a monotone DNF given marginals/conjunctions. + + joints contains (atom_sequence, probability) pairs. These are assertions + supplied by the caller, not inferred dependencies. Empty DNF is false and + an empty conjunction is true. Over-budget/failed LPs cannot admit a fact. + """ + if type(atom_cap) is not int or not 0 <= atom_cap <= 10: + raise ValueError('Atom cap must be an integer in [0,10]') + if any(not isinstance(k, str) or not k for k in probabilities): + raise ValueError('Nonempty string atom IDs required') + atoms = sorted(probabilities) + marginals = {a: _probability(probabilities[a]) for a in atoms} + def term(value): + if isinstance(value, str): + raise ValueError('A proof must be an atom sequence, not a string') + result = tuple(sorted(set(value))) + if any(a not in marginals for a in result): + raise ValueError('Unknown proof atom') + return result + terms = sorted({term(p) for p in proofs}) + constraints = sorted((term(p), _probability(v)) for p, v in joints) + if len(atoms) > atom_cap or len(terms) > 256 or len(constraints) > 256: + return _stage('capacity_staged', len(atoms)) + positions = {a: i for i, a in enumerate(atoms)} + worlds = np.arange(1 << len(atoms), dtype=np.int64) + def indicator(p): + mask = sum(1 << positions[a] for a in p) + return ((worlds & mask) == mask).astype(float) + objective = np.zeros(len(worlds)) + for p in terms: + objective = np.maximum(objective, indicator(p)) + matrix = np.array([np.ones(len(worlds))] + [indicator((a,)) for a in atoms] + [indicator(p) for p, _ in constraints]) + target = np.array([1.0] + [marginals[a] for a in atoms] + [v for _, v in constraints]) + solutions = [] + for sign in (1, -1): + c = sign * objective + result = linprog(c, A_eq=matrix, b_eq=target, bounds=(0, None), method='highs', + options={'dual_feasibility_tolerance': 1e-9, 'primal_feasibility_tolerance': 1e-9}) + if not result.success: + return _stage('infeasible_constraints' if result.status == 2 else 'solver_staged', len(atoms)) + x, dual = np.asarray(result.x), np.asarray(result.eqlin.marginals) + if not np.isfinite(x).all() or not np.isfinite(dual).all(): + return _stage('nonfinite_staged', len(atoms)) + residual = float(np.max(np.abs(matrix @ x - target))) + if residual > 1e-7 or float(np.min(x)) < -1e-9: + return _stage('residual_staged', len(atoms)) + # For any feasible world vector z, sum(z)=1, z>=0: + # c.z >= target.dual + min(0, min(c - A.T.dual)). + # This corrects a numerically imperfect dual using the simplex mass. + slack = float(min(0.0, np.min(c - matrix.T @ dual))) + padding = PAD * (1.0 + float(np.abs(dual).sum())) + lower_dual = float(target @ dual) + slack - padding + solutions.append({'signed_primal': float(c @ x), 'dual_lower': lower_dual, + 'residual': residual, 'padding': padding, 'dual': dual.tolist(), + 'support': [[int(i), float(v)] for i, v in enumerate(x) if v > 0]}) + lo, hi = max(0.0, solutions[0]['dual_lower']), min(1.0, -solutions[1]['dual_lower']) + if not math.isfinite(lo + hi) or lo > hi + 1e-8: + return _stage('inconsistent_numerics_staged', len(atoms)) + return {'status': 'bounded', 'certified': True, 'lower': lo, 'upper': hi, 'atoms': len(atoms), + 'worlds': len(worlds), 'constraints': len(target), 'min': solutions[0], 'max': solutions[1], + 'assumption': 'Bounds conditional on exact supplied constraints; outward-padded floating-point dual bounds, not formal interval arithmetic.'} From 93fbab1e5a2a53a95544969d52b73def1247dcec Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:33:05 -0400 Subject: [PATCH 05/21] feat: add flow-certified repair queries and delta-indexed graph maintenance --- graph_synthesis/certificates/graphs.py | 344 +++++++++++++++++++++++++ 1 file changed, 344 insertions(+) create mode 100644 graph_synthesis/certificates/graphs.py diff --git a/graph_synthesis/certificates/graphs.py b/graph_synthesis/certificates/graphs.py new file mode 100644 index 0000000..1ac5501 --- /dev/null +++ b/graph_synthesis/certificates/graphs.py @@ -0,0 +1,344 @@ +"""Opt-in conflict solvers, repair-relative queries and an in-memory delta index. + +Weights and conflict edges are supplied, not semantic truth. The public mutation +API maintains its invariant; callers must not modify underscore-prefixed state. +""" +from __future__ import annotations +from collections import deque +from . import risk # Keeps this package importable without a network dependency. +from ..reliability.methods import graph_input, solve_component as old_component + +VERTEX_CAP = 2048 +EDGE_CAP = 50000 + + +def groups(adj): + seen, result = set(), [] + for root in sorted(adj): + if root in seen: + continue + group, queue = set(), deque([root]) + seen.add(root) + while queue: + node = queue.popleft() + group.add(node) + for other in sorted(adj[node]): + if other not in seen: + seen.add(other) + queue.append(other) + result.append(frozenset(group)) + return result + + +def coloring(adj, group): + colors = {} + for root in sorted(group): + if root in colors: + continue + colors[root], queue = 0, deque([root]) + while queue: + node = queue.popleft() + for other in sorted(adj[node]): + if other not in group: + raise ValueError('Component is not closed under adjacency') + if other in colors: + if colors[other] == colors[node]: + return None + else: + colors[other] = 1 - colors[node] + queue.append(other) + return colors + + +def network(weights, adj, group, colors): + keys = sorted(group) + index = {k: i for i, k in enumerate(keys)} + source, sink, large = len(keys), len(keys) + 1, sum(weights[k] for k in keys) + 1 + arcs = [] + for k in keys: + if colors[k] == 0: + arcs.append((source, index[k], weights[k])) + arcs.extend((index[k], index[b], large) for b in sorted(adj[k])) + else: + arcs.append((index[k], sink, weights[k])) + return keys, source, sink, sorted(arcs) + + +def max_flow(arcs, n, source, sink): + """Integer Edmonds-Karp; returns primal flow and reachable-cut witnesses.""" + residual = [dict() for _ in range(n)] + for a, b, cap in arcs: + residual[a][b] = cap + residual[b][a] = 0 + adjacency = [sorted(row) for row in residual] + total, searches = 0, 0 + while True: + parent, queue = {source: None}, deque([source]) + searches += 1 + while queue and sink not in parent: + a = queue.popleft() + for b in adjacency[a]: + if residual[a][b] > 0 and b not in parent: + parent[b] = a + queue.append(b) + if sink not in parent: + break + b, amount = sink, None + while b != source: + a = parent[b] + amount = residual[a][b] if amount is None else min(amount, residual[a][b]) + b = a + b = sink + while b != source: + a = parent[b] + residual[a][b] -= amount + residual[b][a] += amount + b = a + total += amount + return total, sorted(parent), [[a, b, cap, cap - residual[a][b]] for a, b, cap in arcs], searches + + +def verify_flow(weights, edges, result): + """Independently check the claimed network, feasible flow and matching cut.""" + try: + adj = graph_input(weights, edges) + colors = coloring(adj, frozenset(adj)) + if colors is None: + return False + keys, source, sink, expected = network(weights, adj, frozenset(adj), colors) + cert = result['certificate'] + if cert['vertex_order'] != keys or len(cert['arcs']) != len(expected): + return False + balance = [0] * (len(keys) + 2) + for want, got in zip(expected, cert['arcs']): + if len(got) != 4 or tuple(got[:3]) != want or any(type(x) is not int for x in got): + return False + a, b, cap, flow = got + if not 0 <= flow <= cap: + return False + balance[a] -= flow + balance[b] += flow + reach = set(cert['reachable']) + if source not in reach or sink in reach or not reach <= set(range(len(keys) + 2)): + return False + value = cert['flow'] + if type(value) is not int or value < 0 or balance[source] != -value or balance[sink] != value: + return False + if any(balance[i] for i in range(len(keys))): + return False + cut = sum(cap for a, b, cap in expected if a in reach and b not in reach) + if cut != value: + return False + selected = set(result['selected']) + wanted = {k for i, k in enumerate(keys) if (colors[k] == 0 and i in reach) or (colors[k] == 1 and i not in reach)} + if selected != wanted or len(selected) != len(result['selected']) or result['staged']: + return False + if any(a in selected and b in selected for a, b in edges): + return False + utility = sum(weights[k] for k in selected) + return utility == result['utility'] == sum(weights.values()) - value + except (KeyError, TypeError, ValueError, IndexError): + return False + + +def component(weights, adj, group): + colors = coloring(adj, group) + if colors is None: + return old_component(weights, adj, group) + n_edges = sum(len(adj[k]) for k in group) // 2 + if len(group) > VERTEX_CAP or n_edges > EDGE_CAP: + return {'selected': [], 'staged': sorted(group), 'utility': 0, 'method': 'capacity_staged'} + keys, source, sink, arcs = network(weights, adj, group, colors) + value, reach_list, flows, searches = max_flow(arcs, len(keys) + 2, source, sink) + reach = set(reach_list) + selected = [k for i, k in enumerate(keys) if (colors[k] == 0 and i in reach) or (colors[k] == 1 and i not in reach)] + result = {'selected': selected, 'staged': [], 'utility': sum(weights[k] for k in selected), 'method': 'bipartite_flow', + 'certificate': {'vertex_order': keys, 'arcs': flows, 'reachable': reach_list, 'flow': value, 'searches': searches}} + local_w = {k: weights[k] for k in keys} + local_e = [(a, b) for a in keys for b in sorted(adj[a]) if a < b] + if not verify_flow(local_w, local_e, result): + raise ArithmeticError('Flow/cover certificate did not verify') + return result + + +def aggregate(solutions): + solutions = list(solutions) + return {'selected': sorted(k for s in solutions for k in s['selected']), + 'staged': sorted(k for s in solutions for k in s['staged']), + 'utility': sum(s['utility'] for s in solutions)} + + +def solve(weights, edges): + edges = list(edges) + adj = graph_input(weights, edges) + values = [component(weights, adj, g) for g in groups(adj)] + return {**aggregate(values), 'components': values} + + +def forced(weights, edges, *, include=(), exclude=()): + adj = graph_input(weights, edges) + chosen, removed = set(include), set(exclude) + if not chosen | removed <= set(weights): + raise ValueError('Unknown forced vertex') + if chosen & removed or any(adj[a] & chosen for a in chosen): + return None + removed |= chosen + for node in chosen: + removed.update(adj[node]) + w = {k: v for k, v in weights.items() if k not in removed} + e = [(a, b) for a, b in edges if a in w and b in w] + result = solve(w, e) + return {**result, 'selected': sorted(chosen | set(result['selected'])), + 'utility': result['utility'] + sum(weights[k] for k in chosen)} + + +def query(weights, edges, required, *, epsilon=0): + """Classify a conjunction relative to ALL weight-tolerant independent sets.""" + if type(epsilon) is not int or epsilon < 0: + raise ValueError('Epsilon must be a nonnegative integer') + if isinstance(required, str): + raise ValueError('Required vertices must be a sequence') + required = sorted(set(required)) + if len(required) > 8: + return {'classification': 'unsupported', 'reason': 'query_capacity'} + if not set(required) <= set(weights): + raise ValueError('Unknown query vertex') + edges = list(edges) + base = solve(weights, edges) + if base['staged']: + return {'classification': 'unsupported', 'reason': 'base_staged'} + threshold = base['utility'] - epsilon + possible = forced(weights, edges, include=required) + record = {'optimum': base['utility'], 'epsilon': epsilon, 'threshold': threshold, + 'single_optimum_answer': set(required) <= set(base['selected']), 'required': required, + 'counterexample': None, 'possible_repair': None, 'exclusions': []} + if possible is not None and possible['staged']: + return {**record, 'classification': 'unsupported', 'reason': 'forced_in_staged'} + if possible is None or possible['utility'] < threshold: + return {**record, 'classification': 'impossible'} + record['possible_repair'] = possible['selected'] + for node in required: + alternative = forced(weights, edges, exclude=[node]) + if alternative['staged']: + return {**record, 'classification': 'unsupported', 'reason': 'forced_out_staged'} + record['exclusions'].append({'excluded': node, 'utility': alternative['utility']}) + if alternative['utility'] >= threshold and record['counterexample'] is None: + record['counterexample'] = alternative['selected'] + return {**record, 'classification': 'ambiguous' if record['counterexample'] is not None else 'certain'} + + +class DeltaIndex: + """Prepare affected-component replacements, then publish; not a database. + + rescan_all=True is the full-snapshot comparator using the identical solver. + Work counts explicit element passes, NOT optimizer-internal operations. + Audit materialization via snapshot() is deliberately separate from update(). + """ + def __init__(self, weights, edges, *, rescan_all=False): + adj = graph_input(weights, edges) + self._weights, self._adj, self._which, self._solutions = {}, {}, {}, {} + self._selected, self._staged, self._utility = set(), set(), 0 + self.rescan_all = bool(rescan_all) + self.total_work = 0 + prepared = self._prepare(dict(weights), {k: set(v) for k, v in adj.items()}) + self.last_work = self._publish(set(), prepared, operation_visits=0) + + @staticmethod + def _prepare(weights, adj): + # Validation is local to the replacement scope, not the entire store. + edges = [(a, b) for a in sorted(adj) for b in sorted(adj[a]) if a < b] + clean = graph_input(weights, edges) + solutions = {tuple(sorted(g)): component(weights, clean, g) for g in groups(clean)} + volume = len(weights) + sum(len(v) for v in adj.values()) + work = {'copy': volume, 'validation': volume, 'discovery': volume, 'solver_input': volume} + return weights, {k: set(v) for k, v in clean.items()}, solutions, work + + def _publish(self, old_groups, prepared, *, operation_visits): + w, adj, solutions, work = prepared + old_nodes = {k for g in old_groups for k in g} + old_s = {k for g in old_groups for k in self._solutions[g]['selected']} + old_t = {k for g in old_groups for k in self._solutions[g]['staged']} + old_utility = sum(self._solutions[g]['utility'] for g in old_groups) + new = aggregate(solutions.values()) + new_s, new_t = set(new['selected']), set(new['staged']) + # No validation/solver operation after this publication boundary. + for g in old_groups: + del self._solutions[g] + for k in old_nodes: + del self._weights[k], self._adj[k], self._which[k] + self._weights.update(w) + self._adj.update(adj) + self._solutions.update(solutions) + for g in solutions: + for k in g: + self._which[k] = g + self._selected.difference_update(old_s) + self._selected.update(new_s) + self._staged.difference_update(old_t) + self._staged.update(new_t) + self._utility += new['utility'] - old_utility + work = {**work, 'operation_validation': operation_visits, + 'publication': len(old_nodes) + len(w) + len(old_s) + len(new_s) + len(old_t) + len(new_t)} + self.total_work += sum(work.values()) + self.last_delta = {'selected_added': sorted(new_s - old_s), 'selected_removed': sorted(old_s - new_s), + 'staged_added': sorted(new_t - old_t), 'staged_removed': sorted(old_t - new_t), + 'utility': self._utility, 'work': work} + return work + + def update(self, command): + schemas = {'weight': {'kind', 'node', 'value'}, 'insert_vertex': {'kind', 'node', 'value'}, + 'delete_vertex': {'kind', 'node'}, 'insert_edge': {'kind', 'a', 'b'}, 'delete_edge': {'kind', 'a', 'b'}} + if not isinstance(command, dict) or command.get('kind') not in schemas or set(command) != schemas[command['kind']]: + raise ValueError('Malformed mutation') + kind = command['kind'] + if kind in ('weight', 'insert_vertex', 'delete_vertex'): + node = command['node'] + if not isinstance(node, str) or not node: + raise ValueError('Nonempty vertex ID required') + if kind == 'insert_vertex': + if node in self._weights: + raise ValueError('Vertex already exists') + touched = set() + else: + if node not in self._weights: + raise ValueError('Unknown vertex') + touched = {self._which[node]} + if kind != 'delete_vertex' and (type(command['value']) is not int or command['value'] < 0): + raise ValueError('Nonnegative integer priority required') + else: + a, b = command['a'], command['b'] + if not all(isinstance(k, str) and k in self._weights for k in (a, b)) or a == b: + raise ValueError('Invalid edge endpoints') + present = b in self._adj[a] + if present == (kind == 'insert_edge'): + raise ValueError('Edge insertion/deletion precondition failed') + touched = {self._which[a], self._which[b]} + if self.rescan_all: + touched = set(self._solutions) + nodes = {k for g in touched for k in g} + w = {k: self._weights[k] for k in nodes} + adj = {k: set(self._adj[k]) for k in nodes} + if kind == 'weight': + w[node] = command['value'] + elif kind == 'insert_vertex': + w[node], adj[node] = command['value'], set() + elif kind == 'delete_vertex': + for other in adj[node]: + adj[other].remove(node) + del w[node], adj[node] + elif kind == 'insert_edge': + adj[a].add(b) + adj[b].add(a) + else: + adj[a].remove(b) + adj[b].remove(a) + prepared = self._prepare(w, adj) + self.last_work = self._publish(touched, prepared, operation_visits=3) + return self.last_delta.copy() + + def summary(self): + return {'selected': sorted(self._selected), 'staged': sorted(self._staged), 'utility': self._utility} + + def snapshot(self): + return {'weights': dict(self._weights), 'edges': [[a, b] for a in sorted(self._adj) for b in sorted(self._adj[a]) if a < b], + **self.summary()} From 3189bc2c13e4c720d52fc2b549739f08731d12ac Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:35:40 -0400 Subject: [PATCH 06/21] test: add adversarial checks for forecasts, probability bounds, repair witnesses and atomic deltas --- .../certificates/tests/test_certificates.py | 320 ++++++++++++++++++ 1 file changed, 320 insertions(+) create mode 100644 graph_synthesis/certificates/tests/test_certificates.py diff --git a/graph_synthesis/certificates/tests/test_certificates.py b/graph_synthesis/certificates/tests/test_certificates.py new file mode 100644 index 0000000..a001a17 --- /dev/null +++ b/graph_synthesis/certificates/tests/test_certificates.py @@ -0,0 +1,320 @@ +"""Regression tests intentionally include false premises and unsupported inputs.""" +from __future__ import annotations +import copy +import json +import math +from pathlib import Path +import random +import unittest +from unittest.mock import patch + +from graph_synthesis.certificates import risk, lineage, graphs + + +def row(group='g', label='SUPPORTS', gold='SUPPORTS', split='development'): + return {'group': group, 'split': split, 'gold': gold, 'views': {'base1': {'label': label}}} + + +class RiskTests(unittest.TestCase): + def test_probability_boundaries(self): + for rho in risk.RHO_GRID: + self.assertEqual(risk.contamination(0, .3, rho), 0) + self.assertEqual(risk.contamination(4, 0, rho), 0) + self.assertEqual(risk.contamination(4, 1, rho), 1) + + def test_singletons_do_not_identify_dependence(self): + for rho in risk.RHO_GRID: + self.assertAlmostEqual(risk.contamination(1, .17, rho), .17, places=12) + + def test_positive_correlation_lowers_any_error_at_fixed_mean(self): + for n in (2, 4, 10): + values = [risk.contamination(n, .2, r) for r in risk.RHO_GRID] + self.assertTrue(all(b <= a + 1e-12 for a, b in zip(values, values[1:]))) + + def test_beta_binomial_mass_normalizes(self): + for rho in risk.RHO_GRID: + self.assertAlmostEqual(sum(math.exp(risk.log_mass(k, 7, .2, rho)) for k in range(8)), 1, places=12) + + def test_development_only_fit(self): + with self.assertRaises(ValueError): + risk.fit([row(split='test')]) + with self.assertRaises(ValueError): + risk.fit([]) + + def test_gold_not_read_for_forecast(self): + class Blind(dict): + def __getitem__(self, key): + if key == 'gold': + raise AssertionError('Gold was read') + return super().__getitem__(key) + exposure = risk.counts([Blind(row()), Blind(row(label='ERROR'))]) + self.assertEqual(exposure, {'g': 1}) + self.assertEqual(set(risk.predict(exposure, {'mu': .2, 'rho': .1})), {'g'}) + + def test_fit_order_invariance_and_no_mutation(self): + data = [row('a'), row('a', gold='REFUTES'), row('b')] + old = copy.deepcopy(data) + self.assertEqual(risk.fit(data), risk.fit(list(reversed(data)))) + self.assertEqual(data, old) + + def test_invalid_parameters(self): + for args in ((-1, .2, .1), (True, .2, .1), (2, float('nan'), .1), (2, .2, 1), (2, -1, 0)): + with self.subTest(args=args), self.assertRaises(ValueError): + risk.contamination(*args) + + +class LineageTests(unittest.TestCase): + def test_unknown_dependence_prevents_false_admission(self): + result = lineage.bounds([['a'], ['b']], {'a': .8, 'b': .8}) + self.assertAlmostEqual(result['lower'], .8, places=8) + self.assertAlmostEqual(result['upper'], 1, places=8) + self.assertLess(result['lower'], .95) + self.assertGreater(1 - .2**2, .95) + + def test_shared_source_constraint(self): + result = lineage.bounds([['a'], ['b']], {'a': .8, 'b': .8}, [(['a', 'b'], .8)]) + self.assertAlmostEqual(result['lower'], .8, places=8) + self.assertAlmostEqual(result['upper'], .8, places=8) + + def test_independent_pair_information_recovers_admission(self): + result = lineage.bounds([['a'], ['b']], {'a': .8, 'b': .8}, [(['a', 'b'], .64)]) + self.assertGreaterEqual(result['lower'], .95) + self.assertAlmostEqual(result['upper'], .96, places=8) + + def test_impossible_constraints_stage(self): + result = lineage.bounds([['a']], {'a': .8}, [(['a'], .7)]) + self.assertFalse(result['certified']) + self.assertEqual(result['status'], 'infeasible_constraints') + self.assertEqual((result['lower'], result['upper']), (0, 1)) + + def test_capacity_stages(self): + result = lineage.bounds([['a0']], {f'a{i}': .5 for i in range(11)}) + self.assertFalse(result['certified']) + self.assertEqual(result['status'], 'capacity_staged') + + def test_duplicate_and_order_invariance(self): + proofs, p = [['a', 'b'], ['b', 'c']], {'a': .7, 'b': .8, 'c': .9} + one = lineage.bounds(proofs, p) + for copies in (1, 2, 5, 20): + two = lineage.bounds(list(reversed(proofs)) * copies, p) + self.assertEqual(one, two) + + def test_empty_formula_and_tautology(self): + false = lineage.bounds([], {}) + true = lineage.bounds([[]], {}) + self.assertLess(false['upper'], 1e-8) + self.assertGreater(true['lower'], 1 - 1e-8) + + def test_nonfinite_invalid_and_missing_atoms(self): + for p in ({'a': float('nan')}, {'a': float('inf')}, {'a': -1}, {'a': True}): + with self.assertRaises(ValueError): + lineage.bounds([['a']], p) + with self.assertRaises(ValueError): + lineage.bounds([['missing']], {'a': .8}) + + def test_solver_failure_never_admits(self): + class Failed: + success, status = False, 4 + with patch('graph_synthesis.certificates.lineage.linprog', return_value=Failed()): + result = lineage.bounds([['a']], {'a': 1}) + self.assertFalse(result['certified']) + self.assertEqual(result['lower'], 0) + + def test_dual_witness_bounds_primal(self): + r = lineage.bounds([['a', 'b'], ['c']], {'a': .7, 'b': .8, 'c': .2}) + for side in ('min', 'max'): + self.assertLessEqual(r[side]['dual_lower'], r[side]['signed_primal'] + 1e-10) + self.assertLessEqual(r[side]['residual'], 1e-7) + + +class GraphTests(unittest.TestCase): + def test_weighted_complete_bipartite(self): + w = {**{f'l{i}': 3 for i in range(16)}, **{f'r{i}': 2 for i in range(16)}} + e = [(f'l{i}', f'r{j}') for i in range(16) for j in range(16)] + result = graphs.solve(w, e) + self.assertEqual(result['utility'], 48) + self.assertFalse(result['staged']) + self.assertTrue(graphs.verify_flow(w, e, result['components'][0])) + + def test_tampered_flow_rejected(self): + w, e = {'a': 3, 'b': 2}, [('a', 'b')] + result = graphs.solve(w, e)['components'][0] + bad = copy.deepcopy(result) + bad['certificate']['arcs'][0][3] += 100 + self.assertFalse(graphs.verify_flow(w, e, bad)) + bad = copy.deepcopy(result) + bad['utility'] += 1 + self.assertFalse(graphs.verify_flow(w, e, bad)) + + def test_zero_priorities_and_isolated_nodes(self): + r = graphs.solve({'a': 0, 'b': 0, 'c': 5}, [('a', 'b')]) + self.assertEqual(r['utility'], 5) + self.assertIn('c', r['selected']) + self.assertFalse(r['staged']) + + def test_empty_graph(self): + self.assertEqual(graphs.solve({}, []), {'selected': [], 'staged': [], 'utility': 0, 'components': []}) + + def test_odd_cycle_uses_existing_solver(self): + w = {str(i): 1 for i in range(17)} + e = [(str(i), str((i + 1) % 17)) for i in range(17)] + r = graphs.solve(w, e) + self.assertEqual(r['utility'], 8) + self.assertNotEqual(r['components'][0]['method'], 'bipartite_flow') + + def test_dense_unsupported_is_staged(self): + w = {str(i): 1 for i in range(17)} + e = [(str(i), str(j)) for i in range(17) for j in range(i)] + self.assertEqual(len(graphs.solve(w, e)['staged']), 17) + + def test_order_duplicates_and_input_immutability(self): + w, e = {'a': 5, 'b': 4, 'c': 0}, [('a', 'b'), ('b', 'c')] + old = copy.deepcopy((w, e)) + self.assertEqual(graphs.solve(w, e), graphs.solve(dict(reversed(list(w.items()))), list(reversed(e)) * 5)) + self.assertEqual((w, e), old) + + def test_invalid_graph_rejected(self): + for w, e in (({'a': -1}, []), ({'a': 1}, [('a', 'a')]), ({'a': 1}, [('a', 'z')]), ({'a': True}, [])): + with self.assertRaises(ValueError): + graphs.solve(w, e) + + def test_false_priority_is_not_truth(self): + r = graphs.solve({'false': 9, 'true': 8}, [('false', 'true')]) + self.assertEqual(r['selected'], ['false']) + + +class QueryTests(unittest.TestCase): + def test_tie_is_ambiguous_with_witness(self): + w, e = {'a': 1, 'b': 1}, [('a', 'b')] + chosen = graphs.solve(w, e)['selected'][0] + r = graphs.query(w, e, [chosen]) + self.assertEqual(r['classification'], 'ambiguous') + self.assertNotIn(chosen, r['counterexample']) + self.assertTrue(r['single_optimum_answer']) + + def test_unique_forced_assertion_is_certain(self): + self.assertEqual(graphs.query({'a': 2, 'b': 1}, [('a', 'b')], ['a'])['classification'], 'certain') + + def test_conflicting_conjunction_is_impossible(self): + self.assertEqual(graphs.query({'a': 1, 'b': 1}, [('a', 'b')], ['a', 'b'])['classification'], 'impossible') + + def test_tolerance_weakens_certainty(self): + w, e = {'a': 20, 'b': 19}, [('a', 'b')] + self.assertEqual(graphs.query(w, e, ['a'])['classification'], 'certain') + self.assertEqual(graphs.query(w, e, ['a'], epsilon=1)['classification'], 'ambiguous') + + def test_empty_query_and_zero_weights(self): + self.assertEqual(graphs.query({}, [], [])['classification'], 'certain') + self.assertEqual(graphs.query({'a': 0}, [], ['a'])['classification'], 'ambiguous') + + def test_invalid_query_and_unsupported_graph(self): + with self.assertRaises(ValueError): + graphs.query({'a': 1}, [], ['missing']) + with self.assertRaises(ValueError): + graphs.query({'a': 1}, [], ['a'], epsilon=-1) + w = {str(i): 1 for i in range(17)} + e = [(str(i), str(j)) for i in range(17) for j in range(i)] + self.assertEqual(graphs.query(w, e, ['0'])['classification'], 'unsupported') + + +class DeltaTests(unittest.TestCase): + def setUp(self): + self.w = {'a': 1, 'b': 2, 'c': 3, 'd': 4} + self.e = [('a', 'b'), ('c', 'd')] + self.engine = graphs.DeltaIndex(self.w, self.e) + + def assert_oracle(self): + s = self.engine.snapshot() + expected = graphs.solve(s['weights'], s['edges']) + self.assertEqual(self.engine.summary(), {k: expected[k] for k in ('selected', 'staged', 'utility')}) + + def test_weight_update_and_unaffected_reuse(self): + before = self.engine._solutions[self.engine._which['c']] + self.engine.update({'kind': 'weight', 'node': 'a', 'value': 10}) + self.assertIs(before, self.engine._solutions[self.engine._which['c']]) + self.assert_oracle() + + def test_bridge_merge_split_and_vertex_removal(self): + for cmd in ({'kind': 'insert_edge', 'a': 'b', 'b': 'c'}, {'kind': 'delete_edge', 'a': 'b', 'b': 'c'}, + {'kind': 'delete_vertex', 'node': 'b'}): + self.engine.update(cmd) + self.assert_oracle() + + def test_insert_delete_isolated_vertex(self): + self.engine.update({'kind': 'insert_vertex', 'node': 'new', 'value': 7}) + self.assert_oracle() + self.engine.update({'kind': 'delete_vertex', 'node': 'new'}) + self.assert_oracle() + + def test_invalid_commands_preserve_state(self): + commands = [{'kind': 'weight', 'node': 'a', 'value': -1}, {'kind': 'delete_vertex', 'node': 'missing'}, + {'kind': 'insert_edge', 'a': 'a', 'b': 'a'}, {'kind': 'insert_edge', 'a': 'a', 'b': 'b'}, + {'kind': 'delete_edge', 'a': 'a', 'b': 'd'}, {'kind': 'insert_vertex', 'node': 'a', 'value': 3}, + {'kind': 'weight', 'node': 'a', 'value': 3, 'gold': 'unused'}] + before = self.engine.snapshot() + work = self.engine.total_work + for cmd in commands: + with self.assertRaises(ValueError): + self.engine.update(cmd) + self.assertEqual(before, self.engine.snapshot()) + self.assertEqual(work, self.engine.total_work) + + def test_solver_failure_preserves_state(self): + before = self.engine.snapshot() + with patch('graph_synthesis.certificates.graphs.component', side_effect=RuntimeError('injected failure')): + with self.assertRaises(RuntimeError): + self.engine.update({'kind': 'weight', 'node': 'a', 'value': 10}) + self.assertEqual(before, self.engine.snapshot()) + + def test_snapshot_is_not_mutable_internal_state(self): + before = self.engine.snapshot() + altered = self.engine.snapshot() + altered['weights']['a'] = 999 + altered['edges'].clear() + altered['selected'].clear() + self.assertEqual(before, self.engine.snapshot()) + + def test_delta_outputs_reconstruct_selection(self): + selected = set(self.engine.summary()['selected']) + delta = self.engine.update({'kind': 'weight', 'node': 'a', 'value': 10}) + selected.difference_update(delta['selected_removed']) + selected.update(delta['selected_added']) + self.assertEqual(selected, set(self.engine.summary()['selected'])) + + def test_full_comparator_same_output_more_local_work(self): + full = graphs.DeltaIndex(self.w, self.e, rescan_all=True) + cmd = {'kind': 'weight', 'node': 'a', 'value': 10} + self.engine.update(cmd) + full.update(cmd) + self.assertEqual(self.engine.snapshot(), full.snapshot()) + self.assertLess(sum(self.engine.last_work.values()), sum(full.last_work.values())) + + +class ArtifactTests(unittest.TestCase): + def test_result_invariants_when_present(self): + path = Path(__file__).resolve().parents[1] / 'results.json' + if not path.exists(): + self.skipTest('Executed artifact is not yet present') + r = json.loads(path.read_text(encoding='utf-8')) + self.assertEqual(r['fresh_service_calls'], 0) + self.assertEqual(r['protocol_commit'], 'bc70b76621daca198b3b97eb4f698b17d4c40a0b') + self.assertFalse(set(r['H1']['fit']['fit_groups']) & set(r['H1']['test_exposures'])) + for h in ('H2', 'H3', 'H4', 'H5'): + self.assertEqual(r[h]['failures'], 0) + for fold in r['H1']['leave_group_out']: + self.assertNotIn(fold['held_group'], fold['fit_groups']) + + def test_manuscript_insertion_is_idempotent(self): + from graph_synthesis.certificates.report import insert, START, END, ANCHOR + text = '# Existing\n' + ANCHOR + '\nTail\n' + first = insert(text, 'New findings') + self.assertEqual(first, insert(first, 'New findings')) + self.assertEqual(first.count(START), 1) + self.assertEqual(first.count(END), 1) + self.assertIn('Tail', first) + with self.assertRaises(ValueError): + insert('No anchor', 'body') + + +if __name__ == '__main__': + unittest.main() From 5c50b27783563dc959e958b02f30e4deb68d41f9 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:48:24 -0400 Subject: [PATCH 07/21] docs: generate evidence-linked certificate report, five figures and complete paper section --- graph_synthesis/certificates/report.py | 238 +++++++++++++++++++++++++ 1 file changed, 238 insertions(+) create mode 100644 graph_synthesis/certificates/report.py diff --git a/graph_synthesis/certificates/report.py b/graph_synthesis/certificates/report.py new file mode 100644 index 0000000..173e2fd --- /dev/null +++ b/graph_synthesis/certificates/report.py @@ -0,0 +1,238 @@ +"""Evidence-derived figures and an additive complete-manuscript update.""" +from __future__ import annotations +import argparse +import csv +import json +from pathlib import Path +import re +from .run import ROOT, HERE, sha, write + +START = '' +END = '' +ANCHOR = '' +TITLES = ['Source random-effects forecasting', 'Dependence-aware lineage bounds', 'Bipartite flow certificates', 'Repair-invariant queries', 'Delta-indexed graph updates'] +CLASSES = ['Previously inspected saved-response forecasts', 'Supplied finite probability models', 'Supplied conflict graphs and integer priorities', 'Priority-relative graph-repair semantics', 'In-memory controlled graph mutations'] + + +def outcome(h): + return 'Met' if h['primary_target_met'] else 'Not met' + + +def insert(text, body): + 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 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 render(r): + a, b, c, d, e = [r[f'H{i}'] for i in range(1, 6)] + old, new = a['scores']['pooled_independent'], a['scores']['random_effects'] + ci = lambda xs: '[' + ', '.join(f'{v:+.6f}' for v in xs) + ']' + out = ['# Dependence-aware certificates for Jev graph synthesis', '', + 'Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026', '', + '## Abstract', '', + f"Five frozen exploratory tests extend the graph pipeline beyond independent-source assumptions and a single chosen repair. A development-fitted beta-binomial source model changes contamination Brier from {old['brier']:.6f} to {new['brier']:.6f}, a {a['brier_relative_improvement']:.2%} reduction; its primary target is {outcome(a).lower()}. Dependence-aware probability bounds prevent {b['false_admissions_prevented']}/20 independence-induced false admissions in supplied correlated-source controls. Integer flow certificates recover all seven analytic bipartite optima, including 2,048-vertex cycles previously staged. Query classification agrees with finite enumeration, and delta-indexed updates match full recomputation across {e['mutations']} random mutations. Declared boundary graph-element visits fall by {e['local_workload']['saving']:.2%} on the fixed local-update workload, but by {e['connected_control']['saving']:.2%} on its connected control. These are conditional algorithm and saved-response findings, not new Jev semantic-accuracy evidence or a comparison against KARMA.", '', + '## 1. Motivation, novelty boundary and frozen methodology', '', + 'The preceding [reliability study](../reliability/RESULTS.md) exposed three limitations: its calibration multiplier worsened Brier; exact lineage probabilities remained conditional on independent primitives; and incremental maintenance still scanned the global snapshot. Its conflict optimizer also returned one optimum and staged components outside its cutset/cap limits. The five extensions here test these specific gaps without changing candidate extraction, accepted Jev edges or production policy.', '', + 'The inspected repository inventory did not contain these five integrated experiments. This is a **repository-scoped new-experiment claim, not a worldwide first-attempt claim**. Beta-binomial random effects, extremal-probability linear programs, weighted bipartite covers, consistent query answering across repairs and incremental maintenance all have antecedents. A targeted primary-source search and its references are recorded in the [protocol](PROTOCOL.md). Established ingredients do not become novel algorithms merely through new names or integration.', '', + f"Protocol commit `{r['protocol_commit']}` precedes this suite's implementation/execution; the inspected baseline is `{r['baseline_commit']}`. Hypotheses were informed by already public results, so this is exploratory follow-up rather than independent preregistration. H1 reuses {r['development']['n']} development candidates in {r['development']['groups']} groups and {r['test']['n']} previously inspected evaluation candidates in {r['test']['groups']} disjoint groups. Saved calls are reconstructed and hashes checked before use. **Fresh service calls: {r['fresh_service_calls']}.** H2-H5 use controlled fixtures rather than additional Jev observations.", '', + '| Hypothesis | Frozen primary requirement | Result | Evidence class |', + '|---|---|---|---|'] + targets = ['At least 10% lower Brier than same-marginal independence, absolute bias <=0.03, and no regression versus prior feature risk', + 'Zero finite-oracle/bound/invariance failures; prevent all 20 correlated-source false admissions', + 'Zero finite-oracle/certificate failures, no small-case regression, all seven large analytic optima recovered', + 'Zero classification/witness failures; preserve all oracle-certain answers and reject the tie-control false certainty', + 'Zero mutation mismatches/invalid-state changes; at least 75% fewer declared boundary visits on the frozen local workload'] + for i in range(1, 6): + out.append(f'| H{i}: {TITLES[i-1]} | {targets[i-1]} | {outcome(r[f"H{i}"])} | {CLASSES[i-1]} |') + out += ['', 'These outcomes must not be combined into a semantic success percentage. A failed conjunction remains failed even when one endpoint improves.', '', + '## 2. H1: Source-random-effects contamination forecasts', '', + 'For each source, n counts base1 SUPPORTS/REFUTES decisions and k counts disagreements with the stored gold relation label. Empty accepted groups are excluded. The development-only marginal error estimate is (sum(k)+0.5)/(sum(n)+1). A grid of intraclass correlations {0,0.01,0.05,0.1,0.2,0.4,0.6,0.8} is selected by beta-binomial development log likelihood with a lower-correlation tie rule. At positive rho, alpha=mu(1/rho-1) and beta=(1-mu)(1/rho-1); contamination is 1-B(alpha,beta+n)/B(alpha,beta). The rho=0 comparator has the identical marginal estimate and uses 1-(1-mu)^n. Predictions consume only source exposure counts, not evaluation labels.', '', + f"Development selects mu={a['fit']['mu']:.6f} and rho={a['fit']['rho']:g}. All {len(a['leave_group_out'])} source-excluded development sensitivity fits are retained; held-out source IDs do not enter their training fits. Evaluation uses {new['n']} nonempty source groups, of which {a['test_singleton_groups']} are singletons. Singletons cannot distinguish these dependence models at fixed marginal error rate.", '', + '| Forecast | Brier | Clipped log loss | Predicted contaminated | Observed contaminated | Bias |', + '|---|---:|---:|---:|---:|---:|'] + for key, name in [('prior_features', 'Prior feature-risk model'), ('prior_multiplier', 'Prior development-selected multiplier'), ('pooled_independent', 'Same-marginal independence'), ('random_effects', 'Source random effects')]: + s = a['scores'][key] + out.append(f"| {name} | {s['brier']:.6f} | {s['log_loss']:.6f} | {s['mean_predicted']:.2%} | {s['mean_observed']:.2%} | {s['bias']:+.2%} |") + out += ['', f"The new-minus-same-marginal Brier difference is {a['paired_brier']['point']:+.6f}; its descriptive paired 95% interval is {ci(a['paired_brier']['95'])}, and its 99% interval is {ci(a['paired_brier']['99'])}. The frozen conjunction is **{outcome(a).lower()}**. The forecast can improve relative to its matched marginal comparator while still failing the improvement threshold or regressing versus the richer prior feature model. No accepted edge is changed.", '', + 'Positive correlation lowers the probability of at least one error at fixed marginal error probability and exposure, because errors cluster rather than spread across groups. It does not universally make an underpredicted contamination rate more accurate. The fitted exchangeable beta-binomial model is neither a distribution-shift guarantee nor a calibration certificate.', '', + '![H1. All forecasts scored on the same 98 nonempty evaluation source groups.](figures/01_source_risk.png)', '', + '## 3. H2: Lineage bounds without assumed independence', '', + 'A monotone DNF formula is evaluated over at most 1,024 Boolean worlds for at most ten atoms. Nonnegative world masses sum to one and satisfy supplied marginal and optional conjunction constraints. Two linear programs minimize and maximize the DNF truth indicator. They use SciPy HiGHS; solver failures, infeasibility and capacity excess return [0,1] with no admission certificate. These probabilities are supplied model inputs, not Jev scores reinterpreted as fact probabilities.', '', + 'For a minimization objective c, equality matrix A, target b and returned dual y, c dot z >= b dot y + min(0,min(c-A-transpose y)) for every feasible simplex vector z. The implementation subtracts an additional 1e-9 times (1+sum(abs(y))) outward pad. It saves primal support, duals and residuals. This is a checked, outward-padded floating-point bound conditional on supplied constraints, **not formal interval-arithmetic verification or an unconditional truth guarantee**.', '', + f"The {len(b['random_cases'])} seeded arbitrary-joint fixtures have {b['failures']} containment, constraint-monotonicity, invariance or control failures. The {len(b['frechet_grid'])} two-event OR/AND grid cases match their analytic Frechet bounds within 1e-8. Mean interval width is {b['mean_width_marginals']:.6f} using marginals alone and {b['mean_width_with_joint']:.6f} with one valid conjunction constraint. The primary target is **{outcome(b).lower()}**.", '', + '| Two 0.8-marginal sources supporting an OR | Probability or interval | Admission at 0.95 |', + '|---|---:|---|', + '| Assume independence without justification | 0.96 | Yes; false under perfect correlation |', + '| Actual perfectly correlated sources | 0.80 | No |', + '| Marginals only, arbitrary dependence | Approximately [0.80,1.00] | No |', + '| Explicit joint probability 0.80 | Approximately [0.80,0.80] | No |', + '| Explicit joint probability 0.64 | Approximately [0.96,0.96] | Yes, conditional on this constraint |', '', + f"Across 20 marginal settings, the method prevents {b['false_admissions_prevented']} false admissions from an unjustified independence assumption. It also withholds {b['independent_true_admissions_withheld_without_joint']} genuinely high-probability admissions when only marginals are provided, and recovers {b['known_independence_admissions_recovered']} when the correct joint constraint is supplied. That conservatism is a real information tradeoff, not free accuracy. Infeasible and 11-atom cases stage. The false-premise control supplies 0.99 for an actually 0.50-probability atom; the resulting conditional bound still admits it. Arithmetic cannot repair incorrect evidence metadata.", '', + '![H2. The correlated-source negative control separates assumed independence from valid lower bounds.](figures/02_dependence_bounds.png)', '', + '## 4. H3: Integer-flow certificates for bipartite conflicts', '', + 'A bipartite conflict component is reduced to minimum weighted vertex cover: source-to-left and right-to-sink capacities are supplied priorities, and conflict arcs have capacity one greater than total priority. An integer max-flow/min-cut certificate gives a minimum cover; its complement is a maximum-weight independent set. The verifier reconstructs the original network, checks capacity bounds and conservation, proves flow equals cut capacity, checks the cover/independent-set relationship and verifies the reported utility. No hidden tie-breaking weight perturbation changes the objective.', '', + 'The new route supports components up to 2,048 vertices and 50,000 edges. Non-bipartite components use the actual prior cutset solver with its original limits; unsupported cases stage rather than silently accepting a heuristic solution. These are explicit conflict graphs, not newly extracted relationships.', '', + f"All {len(c['random_bipartite'])} small bipartite cases match independent subset enumeration and their flow certificates. Another {len(c['general_small'])} small general graphs have no utility regression. Reordering or repeating edges preserves outputs. Total failures: {c['failures']}. The primary target is **{outcome(c).lower()}**.", '', + '| Analytic topology | Vertices | Edges | Previous utility | New utility | Oracle utility |', + '|---|---:|---:|---:|---:|---:|'] + for row in c['large']: + out.append(f"| {row['kind'].replace('_',' ')} | {row['n']} | {row['edges']} | {row['prior_utility']} | {row['result']['utility']} | {row['oracle']} |") + out += ['', 'Complete-bipartite fixtures assign priority 3 on one balanced side and 2 on the other; cycles use unit priorities. Prior zero utility means staged, not an incorrect accepted graph. The 2,050-cycle and 50,176-edge complete-bipartite controls stage; a 17-cycle still uses the prior exact route, while the dense 17-clique remains unsupported. A false assertion of priority 9 still defeats a true conflicting assertion of priority 8. Certified optimality concerns supplied priorities, not semantic truth.', '', + '![H3. Previously staged analytic conflict components are solved exactly with integer certificates.](figures/03_bipartite_capacity.png)', '', + '## 5. H4: Queries invariant across admissible graph repairs', '', + 'Let U be maximum supplied priority. Admissible repairs are all independent sets with utility at least U-epsilon, using epsilon=0 and floor(0.05U). For a conjunction of up to eight required vertices, constrained inclusion tests whether some admissible repair contains the conjunction. Constrained exclusion of each required vertex tests whether any admissible repair falsifies it. Answers are certain, ambiguous or impossible, with an explicit counterexample for ambiguity. Staged optimization propagates unsupported status rather than false certainty.', '', + f"Across {len(d['cases'])} seeded general graphs and two tolerance settings, classifications and witnesses have {d['failures']} oracle failures. Every oracle-certain answer is retained and larger tolerance creates no new certain answers. Among exact-optimum random cases, {d['random_single_optimum_false_certainty']} affirmative answers from one selected optimum are not invariant across all optima. The explicit equal-weight conflict control also returns an alternative repair that falsifies the chosen optimum's affirmative answer. Primary target: **{outcome(d).lower()}**.", '', + '| Repair tolerance | Certain conjunctions | Ambiguous conjunctions | Impossible conjunctions |', + '|---|---:|---:|---:|'] + for key, label in [('exact', 'Exact maximum priority'), ('tolerant', 'Within floor(5% of optimum)')]: + counts = d['counts'][key] + out.append(f"| {label} | {counts['certain']} | {counts['ambiguous']} | {counts['impossible']} |") + out += ['', 'Some sampled conjunctions are empty and hence tautological; these counts describe the fixture distribution, not a population query-success rate. Conflicting conjunctions are impossible. Zero-priority optional vertices can be ambiguous. The high-priority false assertion is still repair-certain under its supplied objective: repair certainty must not be presented as factual certainty.', '', + '![H4. Query status across exact and near-optimal admissible repairs.](figures/04_repair_queries.png)', '', + '## 6. H5: Delta-indexed maintenance without global snapshot rescans', '', + 'The in-memory index validates an initial graph and maintains private adjacency, priorities, component membership and cached solutions. An explicit mutation API supports weight changes, vertex insertion/deletion and edge insertion/deletion. It copies, validates, discovers and solves only affected old components and their replacements before publication. Bridge insertion joins two scopes; bridge or vertex deletion discovers splits inside the affected old scope. Unaffected solutions are reused. Mutations return selection/staging deltas and aggregate utility rather than forcing a full graph materialization. Snapshot audit is a separate operation.', '', + f"Independent full-snapshot mutation and optimization checks find {e['failures']} failures across {e['mutations']} seeded mutations, deliberate bridge controls and locality/stress workloads. Each of the five mutation types appears {e['operation_counts']['weight']} times in the randomized test. Invalid mutations produce {e['invalid_state_changes']} state changes; tests also inject a solver exception before publication. This is not a concurrent or durable database transaction protocol.", '', + '| Workload, including cold build | Delta-index boundary visits | Full-rescan boundary visits | Reduction |', + '|---|---:|---:|---:|'] + for key, name in [('local_workload', '512 eight-vertex components; 128 local updates'), ('connected_control', 'One 64-vertex component; 128 updates')]: + v = e[key] + out.append(f"| {name} | {v['local_work']:,} | {v['full_work']:,} | {v['saving']:.2%} |") + out += ['', f"The frozen primary target is **{outcome(e).lower()}**. The comparator uses the same optimizer but copies, validates, discovers and prepares every component on every update. The boundary metric counts declared passes for graph copying, validation, discovery, solver input, mutation validation and publication. It explicitly excludes sorting, optimizer-internal work, certificate-check work and the audit materialization. Therefore the percentage is **not** a measured reduction in all CPU operations, service latency or database I/O. It improves on the previous study's solver-vertex-only scope, but is still an instrumented boundary measure.", ''] + timing_path = HERE / 'timings.json' + if timing_path.exists(): + timing = json.loads(timing_path.read_text(encoding='utf-8')) + out += ['### Separate single-host timing measurements', '', + 'Three repetitions alternate policy order. Timings include cold construction and 64 updates, but exclude snapshot comparison for both policies. Medians below are descriptive, not a primary endpoint or a portable speed guarantee. All individual measurements and environment details are retained in [timings.json](timings.json).', '', + '| Components | Vertices | Delta-index median seconds | Full-rescan median seconds | Ratio |', + '|---:|---:|---:|---:|---:|'] + for row in timing['cases']: + local, full = row['median_seconds']['local'], row['median_seconds']['full'] + out.append(f"| {row['components']} | {row['vertices']} | {local:.6f} | {full:.6f} | {full/local:.2f}x |") + out += [''] + out += ['![H5. Benefits depend on component locality; the connected control has no counted-work saving.](figures/05_delta_locality.png)', '', + '## 7. Interpretation, uncertainty and falsifying controls', '', + 'H1 uses 4,000 paired source-group bootstrap draws with seed 20260922 and fixed fitted policies. The 95% and 99% percentile intervals are descriptive, not simultaneous; they omit fitting uncertainty and cannot undo repeated use of the same evaluation corpus. Neither a favorable score nor a passed point threshold would establish out-of-distribution calibration. H2-H5 finite/analytic fixtures test implementation behavior under supplied assumptions rather than estimates of Jev accuracy.', '', + 'The retained negative controls are essential: incorrect marginal probabilities defeat lineage certificates; false priorities defeat semantic interpretation of flow certificates and repair certainty; uncertain dependence withholds some valid admissions; unsupported topologies stage; and connected updates remove locality savings. The structural methods remain opt-in research prototypes. Candidate-generation recall, semantic relation qualifiers, source independence discovery and real database behavior are not solved by this suite.', '', + 'The next independent semantic claim still requires a policy frozen before inspecting a new source-disjoint, independently adjudicated corpus, and matched evidence/resource budgets against an implemented external system such as KARMA. This suite does not claim that such a comparison ran.', '', + '## 8. Reproducibility and artifact integrity', '', + '```bash', + 'python -m pip install -r graph_synthesis/certificates/requirements.txt', + 'python -B -m graph_synthesis.certificates.run --timings', + 'python -B -m unittest discover -s graph_synthesis/certificates/tests -v', + 'python -B -m graph_synthesis.certificates.run --check', + 'python -B -m graph_synthesis.certificates.report --figures --update-paper', + 'python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf', + '```', '', + 'The [machine-readable results](results.json) preserve fit candidates, held-source exclusions, predictions, complete fixture inputs, joint-world probabilities, LP witnesses, graph certificates, mutation events, oracle outputs and source hashes. Timings are deliberately separate from deterministic replay. Five SVG/PNG figure pairs and a summary CSV are regenerated from recorded results; the extension manifest hashes its source and artifacts. Original raw evidence and the archived manuscript remain unchanged, and the complete current manuscript retains every preceding study.', '', + '## 9. Primary foundations', '', + '- [SciPy beta-binomial definition](https://docs.scipy.org/doc/scipy-1.17.0/reference/generated/scipy.stats.betabinom.html) and [HiGHS linear programming and dual marginals](https://docs.scipy.org/doc/scipy-1.17.0/reference/optimize.linprog-highs.html).', + '- [Optimal Union Probability Interval Is NP-Hard](https://arxiv.org/abs/2605.03556), situating finite-world extremal-probability programs and their complexity.', + '- [Distributed CONGEST Approximation of Weighted Vertex Covers and Matchings](https://arxiv.org/abs/2111.10577), an antecedent for weighted bipartite cover formulations.', + '- [Computational Complexity of Preferred Subset Repairs on Data-Graphs](https://arxiv.org/abs/2402.09265) and [Consistent Query Answers in the Presence of Universal Constraints](https://arxiv.org/abs/0809.1551).', + '- [A Feature-based Classification of Model Repair Approaches](https://arxiv.org/html/1504.03947v1) and the [TypeSafe typed-decision interface](https://docs.typesafe.ai/introduction).', '', + 'These sources motivate established components; they do not validate this implementation or certify worldwide novelty.'] + return '\n'.join(out) + '\n' + + +def figures(r): + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + import numpy as np + plt.rcParams['svg.hashsalt'] = 'jev-certificates-20260922' + directory = HERE / 'figures' + directory.mkdir(exist_ok=True) + def save(fig, name, note): + fig.text(.5, .025, note, ha='center', va='bottom', fontsize=9) + fig.tight_layout(rect=(0, .15, 1, 1)) + fig.savefig(directory / (name + '.svg'), metadata={'Date': None}) + fig.savefig(directory / (name + '.png'), dpi=200, metadata={'Software': 'Jev certificate research'}) + plt.close(fig) + h = r['H1'] + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + keys = ['prior_features', 'prior_multiplier', 'pooled_independent', 'random_effects'] + bars = ax.bar(['Prior features', 'Prior multiplier', 'Same-marginal\nindependence', 'Source random\neffects'], [h['scores'][k]['brier'] for k in keys]) + ax.bar_label(bars, fmt='%.4f') + ax.set(ylabel='Group-contamination Brier (lower is better)', ylim=(0, .18), title='H1 | Dependence helps one comparator, not the frozen target') + save(fig, '01_source_risk', f"{h['scores']['random_effects']['n']} previously inspected nonempty source groups; rho={h['fit']['rho']:g} fit on development only.\nImprovement threshold and stronger prior comparator remain part of the conjunction.") + h = r['H2']['dependence_controls'][0] + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + bars = ax.bar(['Unjustified\nindependence', 'Actual correlated\nprobability', 'Dependence-aware\nlower bound'], [h['independence_assumed'], h['correlated_truth'], h['unknown_dependence']['lower']]) + ax.bar_label(bars, fmt='%.2f') + ax.axhline(.95, linestyle='--', label='Admission threshold 0.95') + ax.set(ylabel='Supplied-model probability', ylim=(0, 1.12), title='H2 | Unknown dependence prevents a false high-confidence admission') + ax.legend(loc='lower right') + save(fig, '02_dependence_bounds', 'Two perfectly correlated sources each have marginal probability 0.80.\nMarginals alone imply approximately [0.80, 1.00], not 0.96; bad supplied probabilities remain unsafe.') + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + for kind, label, marker in [('complete_bipartite', 'Complete bipartite: solver = analytic optimum', 's'), ('even_cycle', 'Even cycles: solver = analytic optimum', 'o')]: + rows = [x for x in r['H3']['large'] if x['kind'] == kind] + ax.plot([x['n'] for x in rows], [x['result']['utility'] for x in rows], marker=marker, label=label) + rows = r['H3']['large'] + ax.plot([x['n'] for x in rows], [x['prior_utility'] for x in rows], linestyle='--', label='Prior policy: all staged') + ax.set(xscale='log', xlabel='Vertices in one conflict component (log scale)', ylabel='Supplied-priority utility', title='H3 | Exact integer-flow route expands supported graph capacity') + ax.legend(fontsize=9) + save(fig, '03_bipartite_capacity', 'Seven analytic fixtures; complete bipartite priorities 3 versus 2, cycle priorities 1.\nCapacity is not evidence of better semantic relation extraction.') + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + x = np.arange(3) + for offset, key, label in [(-.2, 'exact', 'Exact-optimum repairs'), (.2, 'tolerant', 'Within floor(5% of optimum)')]: + bars = ax.bar(x + offset, [r['H4']['counts'][key][k] for k in ('certain', 'ambiguous', 'impossible')], width=.4, label=label) + ax.bar_label(bars) + ax.set(xticks=x, xticklabels=['Certain', 'Ambiguous', 'Impossible'], ylabel='Conjunctive query fixtures', ylim=(0, 90), title='H4 | Certainty is evaluated across repairs, not one chosen graph') + ax.legend() + save(fig, '04_repair_queries', '128 fixed small-graph queries; includes empty conjunctions.\nRepair-relative certainty remains conditional on supplied priorities, not factual truth.') + fig, ax = plt.subplots(figsize=(8.5, 4.8)) + h = r['H5'] + bars = ax.bar(['512-component local workload', 'Single connected component'], [100 * h['local_workload']['saving'], 100 * h['connected_control']['saving']]) + ax.bar_label(bars, fmt='%.2f%%') + ax.set(ylabel='Reduction in declared boundary visits (%)', ylim=(0, 115), title='H5 | Delta-index benefits disappear when every update touches the graph') + save(fig, '05_delta_locality', 'Cold build plus 128 updates; counts include validation/discovery/publication boundaries.\nOptimizer internals and sorting are excluded; these are not end-to-end latency percentages.') + + +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() + ')' + body = re.sub(r'\]\(([^)]+)\)', rebase, render(r)) + path = ROOT / 'manuscript/paper-current.md' + path.write_text(insert(path.read_text(encoding='utf-8'), body), encoding='utf-8', newline='\n') + outcomes = '; '.join(f"H{i} {outcome(r[f'H{i}']).lower()}" for i in range(1, 6)) + note = ('## Five dependence-aware certificate experiments after PR #17\n\n' + '[Executed report](graph_synthesis/certificates/RESULTS.md), [frozen protocol](graph_synthesis/certificates/PROTOCOL.md), ' + '[five figures](graph_synthesis/certificates/figures/) and [complete updated paper](manuscript/paper-current.pdf). ' + + outcomes + '. Source-random-effects forecasting is saved-response reuse; lineage bounds, flow certificates, repair queries and delta updates are controlled algorithms. ' + 'No fresh Jev calls, worldwide novelty claim, new semantic-accuracy claim or production-policy change. Negative and assumption-breaking controls are retained.') + path = ROOT / 'CURRENT_RESULTS.md' + path.write_text(insert(path.read_text(encoding='utf-8'), note), encoding='utf-8', newline='\n') + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--figures', action='store_true') + parser.add_argument('--update-paper', action='store_true') + args = parser.parse_args() + r = json.loads((HERE / 'results.json').read_text(encoding='utf-8')) + (HERE / 'RESULTS.md').write_text(render(r), encoding='utf-8', newline='\n') + with (HERE / 'summary.csv').open('w', encoding='utf-8', newline='') as handle: + writer = csv.writer(handle, lineterminator='\n') + writer.writerow(['hypothesis', 'title', 'primary_target', 'evidence_class']) + for i in range(1, 6): + writer.writerow([f'H{i}', TITLES[i-1], outcome(r[f'H{i}']), CLASSES[i-1]]) + if args.figures: + figures(r) + if args.update_paper: + update_paper(r) + paths = sorted(p for p in HERE.rglob('*') if p.is_file() and p.name != 'artifact-manifest.json' and '__pycache__' not in p.parts) + write(HERE / 'artifact-manifest.json', {'sha256': {p.relative_to(ROOT).as_posix(): sha(p) for p in paths}}) + print('Certificate report, figures and manuscript section generated from recorded evidence') + + +if __name__ == '__main__': + main() From 60baab5ae980496d1a7b84cc6adb4fbdd5d18b1f Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:48:31 -0400 Subject: [PATCH 08/21] build: pin linear-programming dependency for certificate benchmarks --- graph_synthesis/certificates/requirements.txt | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 graph_synthesis/certificates/requirements.txt diff --git a/graph_synthesis/certificates/requirements.txt b/graph_synthesis/certificates/requirements.txt new file mode 100644 index 0000000..61c3c35 --- /dev/null +++ b/graph_synthesis/certificates/requirements.txt @@ -0,0 +1,2 @@ +-r ../requirements-figures.txt +scipy==1.17.0 From cf2d6a28c120f3261e1763a0fc975316332879aa Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:48:40 -0400 Subject: [PATCH 09/21] build: expose opt-in certificate research package --- graph_synthesis/certificates/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 graph_synthesis/certificates/__init__.py diff --git a/graph_synthesis/certificates/__init__.py b/graph_synthesis/certificates/__init__.py new file mode 100644 index 0000000..291f6db --- /dev/null +++ b/graph_synthesis/certificates/__init__.py @@ -0,0 +1 @@ +"""Exploratory dependence-aware graph certificates; opt-in research only.""" From 9e86508b1b2026e65df66486862cdcca8f9ad5aa Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:49:04 -0400 Subject: [PATCH 10/21] ci: execute pinned LP benchmarks, timings and evidence replay before publication --- .github/workflows/dependence-certificates.yml | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/.github/workflows/dependence-certificates.yml b/.github/workflows/dependence-certificates.yml index 6a39ce9..b87ed1e 100644 --- a/.github/workflows/dependence-certificates.yml +++ b/.github/workflows/dependence-certificates.yml @@ -27,8 +27,8 @@ jobs: run: | set -euo pipefail if [ -f graph_synthesis/certificates/run.py ]; then - python -m pip install -r graph_synthesis/requirements-figures.txt - python -B -m graph_synthesis.certificates.run + python -m pip install -r graph_synthesis/certificates/requirements.txt + python -B -m graph_synthesis.certificates.run --timings | tee graph_synthesis/certificates/execution.log python -B -m unittest discover -s graph_synthesis/certificates/tests -v 2>&1 | tee graph_synthesis/certificates/tests.log python -B -m graph_synthesis.certificates.run --check python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/certificates-original.json" @@ -64,7 +64,7 @@ jobs: - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 with: python-version: '3.12' - - run: python -m pip install -r graph_synthesis/requirements-figures.txt + - run: python -m pip install -r graph_synthesis/certificates/requirements.txt - name: Verify results, manuscript and original evidence shell: bash run: | @@ -72,6 +72,18 @@ jobs: python -B -m unittest discover -s graph_synthesis/certificates/tests -v python -B -m graph_synthesis.certificates.run --check python -B -m graph_synthesis.verify --output "$RUNNER_TEMP/certificates-original.json" + python - <<'PY' + import hashlib,json + from pathlib import Path + sha=lambda p:hashlib.sha256(Path(p).read_bytes()).hexdigest() + for path,want in json.loads(Path('graph_synthesis/certificates/artifact-manifest.json').read_text())['sha256'].items(): + assert sha(path)==want,path + build=json.loads(Path('manuscript/paper-current.build.json').read_text()) + 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('Certificate artifacts and current PDF hashes verified') + PY python -B -m graph_synthesis.certificates.report --figures --update-paper git diff --exit-code -- graph_synthesis/certificates manuscript/paper-current.md CURRENT_RESULTS.md python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/certificates-paper.pdf" From 23f739fd7862223a0b90e3476301351a34afd729 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:49:25 -0400 Subject: [PATCH 11/21] ci: preserve certificate extension in complete manuscript regeneration --- .github/workflows/jev-multicall.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/jev-multicall.yml b/.github/workflows/jev-multicall.yml index 536a39c..48e647f 100644 --- a/.github/workflows/jev-multicall.yml +++ b/.github/workflows/jev-multicall.yml @@ -55,14 +55,15 @@ jobs: - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 with: python-version: '3.12' - - run: python -m pip install -r graph_synthesis/requirements-figures.txt + - run: python -m pip install -r graph_synthesis/certificates/requirements.txt - name: Regenerate report, graphs and complete manuscript run: | python -B -m graph_synthesis.report_multicall --directory experiments/jev-multicall-20260918 python -B -m graph_synthesis.adaptive.report --update-paper python -B -m graph_synthesis.risk_control.report --update-paper python -B -m graph_synthesis.reliability.report --update-paper - git diff --exit-code -- experiments/jev-multicall-20260918/REPORT.md manuscript/paper-current.md CURRENT_RESULTS.md graph_synthesis/risk_control graph_synthesis/reliability + python -B -m graph_synthesis.certificates.report --update-paper + git diff --exit-code -- experiments/jev-multicall-20260918/REPORT.md manuscript/paper-current.md CURRENT_RESULTS.md graph_synthesis/risk_control graph_synthesis/reliability graph_synthesis/certificates python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/multicall-paper.pdf" git diff --exit-code -- manuscript/paper-current.html - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 From 03634555d8272e4b04517822c695f2cb95940752 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:49:50 -0400 Subject: [PATCH 12/21] fix: omit certificate section control markers from rendered manuscript --- graph_synthesis/render_current_paper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graph_synthesis/render_current_paper.py b/graph_synthesis/render_current_paper.py index 45df8e5..b3ff6c7 100644 --- a/graph_synthesis/render_current_paper.py +++ b/graph_synthesis/render_current_paper.py @@ -37,7 +37,7 @@ 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 = re.sub(r"", "", 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) From 69c6feae08fe97fee50bc40f72dd81c97dc6bb08 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 04:51:44 -0400 Subject: [PATCH 13/21] research: execute frozen five-hypothesis certificate suite with finite oracles and separate timings --- graph_synthesis/certificates/run.py | 460 ++++++++++++++++++++++++++++ 1 file changed, 460 insertions(+) create mode 100644 graph_synthesis/certificates/run.py diff --git a/graph_synthesis/certificates/run.py b/graph_synthesis/certificates/run.py new file mode 100644 index 0000000..7dff741 --- /dev/null +++ b/graph_synthesis/certificates/run.py @@ -0,0 +1,460 @@ +"""Run the frozen certificate suite without service calls; keep timings separate.""" +from __future__ import annotations +import argparse +from collections import Counter +import hashlib +from itertools import combinations +import json +import math +from pathlib import Path +import platform +import random +import socket +import statistics +import sys +import time +from unittest.mock import patch +import numpy as np +from . import risk, lineage, graphs +from ..adaptive.run import load +from ..reliability.run import calibration, proper_scores +from ..reliability.methods import solve_graph as prior_solve + +ROOT = Path(__file__).resolve().parents[2] +HERE = Path(__file__).resolve().parent +SEED = 20260922 +PROTOCOL_COMMIT = 'bc70b76621daca198b3b97eb4f698b17d4c40a0b' +BASELINE_COMMIT = 'a62a3257645d8e35cd4e45be53bfa9511d27724b' +DRAWS = 4000 + + +def sha(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def normalized(value): + return json.loads(json.dumps(value, sort_keys=True, allow_nan=False)) + + +def write(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, sort_keys=True, indent=2, allow_nan=False) + '\n', encoding='utf-8', newline='\n') + + +def interval(deltas): + values = np.asarray(deltas, dtype=float) + rng = np.random.default_rng(SEED) + sample = values[rng.integers(0, len(values), size=(DRAWS, len(values)))].mean(axis=1) + return {'point': float(values.mean()), '95': np.quantile(sample, [.025, .975]).tolist(), + '99': np.quantile(sample, [.005, .995]).tolist(), 'groups': len(values), 'draws': DRAWS} + + +def experiment_risk(development, test): + fitted = risk.fit(development) + exposure = risk.counts(test) + actual = risk.counts(test, truth=True) + truth = {g: int(v['k'] > 0) for g, v in actual.items()} + proposed = risk.predict(exposure, fitted) + independent = risk.predict(exposure, fitted, independent=True) + previous = calibration(development, test) + scores = {'pooled_independent': proper_scores(independent, truth), 'random_effects': proper_scores(proposed, truth), + 'prior_features': previous['baseline'], 'prior_multiplier': previous['proposed']} + folds = [] + for group in sorted({r['group'] for r in development}): + fit = risk.fit([r for r in development if r['group'] != group]) + held = risk.counts([r for r in development if r['group'] == group]) + folds.append({'held_group': group, 'fit_groups': fit['fit_groups'], 'mu': fit['mu'], 'rho': fit['rho'], + 'prediction': risk.predict(held, fit)}) + new, old = scores['random_effects'], scores['pooled_independent'] + return {'primary_target_met': new['brier'] <= .9 * old['brier'] and abs(new['bias']) <= .03 + and new['brier'] <= scores['prior_features']['brier'], + 'fit': fitted, 'test_exposures': exposure, 'test_counts': actual, 'scores': scores, + 'prior_multiplier': previous['multiplier'], 'leave_group_out': folds, + 'test_singleton_groups': sum(n == 1 for n in exposure.values()), + 'group_predictions': [{'group': g, 'truth': truth[g], 'n': exposure[g], 'random_effects': proposed[g], + 'pooled_independent': independent[g]} for g in sorted(truth)], + 'paired_brier': interval([(proposed[g] - truth[g])**2 - (independent[g] - truth[g])**2 for g in sorted(truth)]), + 'brier_relative_improvement': 1 - new['brier'] / old['brier']} + + +def formula_holds(world, proofs, positions): + return any(all(world & (1 << positions[a]) for a in proof) for proof in proofs) + + +def experiment_lineage(): + rng = random.Random(SEED + 2) + cases, grid, controls = [], [], [] + failures = 0 + widths, narrower = [], [] + for i in range(128): + n = rng.randrange(2, 7) + names = [f'a{j}' for j in range(n)] + pos = {a: j for j, a in enumerate(names)} + masses = [rng.randrange(1, 100) for _ in range(1 << n)] + total = sum(masses) + joint = [v / total for v in masses] + probabilities = {a: sum(v for world, v in enumerate(joint) if world & (1 << j)) for a, j in pos.items()} + proofs = [sorted(rng.sample(names, rng.randrange(1, min(3, n) + 1))) for _ in range(rng.randrange(1, 7))] + actual = sum(v for world, v in enumerate(joint) if formula_holds(world, proofs, pos)) + pair = sorted(rng.sample(names, 2)) + pair_p = sum(v for world, v in enumerate(joint) if all(world & (1 << pos[a]) for a in pair)) + base = lineage.bounds(proofs, probabilities) + informed = lineage.bounds(proofs, probabilities, [(pair, pair_p)]) + shuffled = lineage.bounds(list(reversed(proofs)) * 3, dict(reversed(list(probabilities.items())))) + errors = [] + for key, value in [('marginals', base), ('joint', informed)]: + if not value['certified'] or not value['lower'] - 1e-8 <= actual <= value['upper'] + 1e-8: + errors.append(key + '_containment') + if informed['upper'] - informed['lower'] > base['upper'] - base['lower'] + 1e-8: + errors.append('constraint_widening') + if any(abs(base[k] - shuffled[k]) > 1e-8 for k in ('lower', 'upper')): + errors.append('duplicate_invariance') + failures += len(errors) + widths.append(base['upper'] - base['lower']) + narrower.append(informed['upper'] - informed['lower']) + cases.append({'id': i, 'probabilities': probabilities, 'proofs': proofs, 'actual_joint': joint, 'truth': actual, + 'pair_constraint': [pair, pair_p], 'marginals': base, 'with_joint': informed, 'errors': errors}) + for p in (0., .2, .5, .8, 1.): + for q in (0., .2, .5, .8, 1.): + for kind, proofs, expected in [('OR', [['a'], ['b']], (max(p, q), min(1., p + q))), + ('AND', [['a', 'b']], (max(0., p + q - 1), min(p, q)))]: + result = lineage.bounds(proofs, {'a': p, 'b': q}) + ok = result['certified'] and all(abs(result[k] - want) <= 1e-8 for k, want in zip(('lower', 'upper'), expected)) + failures += int(not ok) + grid.append({'p': p, 'q': q, 'kind': kind, 'expected': expected, 'result': result, 'ok': ok}) + prevented, recovered, withheld = 0, 0, 0 + for i in range(20): + p = .8 + .005 * i + naive = 1 - (1 - p)**2 + robust = lineage.bounds([['a'], ['b']], {'a': p, 'b': p}) + correlated = lineage.bounds([['a'], ['b']], {'a': p, 'b': p}, [(['a', 'b'], p)]) + independent = lineage.bounds([['a'], ['b']], {'a': p, 'b': p}, [(['a', 'b'], p * p)]) + blocked = naive >= .95 and p < .95 and robust['lower'] < .95 + recovered_here = independent['certified'] and independent['lower'] >= .95 + prevented += int(blocked) + recovered += int(recovered_here) + withheld += int(naive >= .95 and robust['lower'] < .95) + ok = blocked and correlated['upper'] < .95 and recovered_here + failures += int(not ok) + controls.append({'p': p, 'correlated_truth': p, 'independence_assumed': naive, 'unknown_dependence': robust, + 'known_correlation': correlated, 'known_independence': independent, 'ok': ok}) + infeasible = lineage.bounds([['a']], {'a': .8}, [(['a'], .7)]) + capacity = lineage.bounds([['a0']], {f'a{i}': .5 for i in range(11)}) + bad_input = lineage.bounds([['a']], {'a': .99}) + failures += int(infeasible['certified'] or capacity['certified']) + return {'primary_target_met': failures == 0 and prevented == 20, 'failures': failures, 'random_cases': cases, + 'frechet_grid': grid, 'dependence_controls': controls, 'false_admissions_prevented': prevented, + 'known_independence_admissions_recovered': recovered, 'independent_true_admissions_withheld_without_joint': withheld, + 'mean_width_marginals': statistics.mean(widths), 'mean_width_with_joint': statistics.mean(narrower), + 'infeasible_control': infeasible, 'capacity_control': capacity, + 'wrong_marginal_control': {'actual_probability': .5, 'supplied_probability': .99, 'result': bad_input, + 'false_admission_under_wrong_premise': bad_input['lower'] >= .95}} + + +def independent_sets(weights, edges): + """Independent exhaustive oracle: no use of the proposed optimizer.""" + keys = sorted(weights) + if len(keys) > 12: + raise ValueError('Finite oracle cap exceeded') + index = {k: i for i, k in enumerate(keys)} + masks = [(1 << index[a]) | (1 << index[b]) for a, b in edges] + answers = [] + for mask in range(1 << len(keys)): + if all(mask & pair != pair for pair in masks): + selected = [k for i, k in enumerate(keys) if mask & (1 << i)] + answers.append((sum(weights[k] for k in selected), selected)) + return answers + + +def valid_repair(weights, edges, selected, threshold): + chosen = set(selected) + return len(chosen) == len(selected) and chosen <= set(weights) and not any(a in chosen and b in chosen for a, b in edges) and sum(weights[k] for k in chosen) >= threshold + + +def certificate_checks(weights, edges, result): + checks = [] + for c in result['components']: + if c['method'] == 'bipartite_flow': + nodes = set(c['certificate']['vertex_order']) + w = {k: weights[k] for k in nodes} + e = [(a, b) for a, b in edges if a in nodes and b in nodes] + checks.append(graphs.verify_flow(w, e, c)) + return all(checks) + + +def experiment_graphs(): + rng = random.Random(SEED + 3) + cases, general, large = [], [], [] + failures = 0 + for i in range(128): + n = rng.randrange(2, 11) + left = rng.randrange(1, n) + w = {f'v{j}': rng.randrange(10) for j in range(n)} + e = [(f'v{a}', f'v{b}') for a in range(left) for b in range(left, n) if rng.random() < .45] + oracle = max(v for v, _ in independent_sets(w, e)) + result = graphs.solve(w, e) + reordered = graphs.solve(dict(reversed(list(w.items()))), list(reversed(e)) * 3) + ok = result['utility'] == oracle and not result['staged'] and valid_repair(w, e, result['selected'], oracle) and certificate_checks(w, e, result) and result == reordered + failures += int(not ok) + cases.append({'id': i, 'weights': w, 'edges': e, 'oracle': oracle, 'result': result, 'ok': ok}) + all_edges = [(a, b) for a, b in combinations(w, 2) if rng.random() < .35] + old, new = prior_solve(w, all_edges), graphs.solve(w, all_edges) + optimum = max(v for v, _ in independent_sets(w, all_edges)) + ok = new['utility'] >= old['utility'] and new['utility'] == optimum and not new['staged'] + failures += int(not ok) + general.append({'weights': w, 'edges': all_edges, 'oracle': optimum, 'prior_utility': old['utility'], 'new_utility': new['utility'], 'ok': ok}) + for n in (32, 64, 128, 256): + half = n // 2 + w = {**{f'l{i:04}': 3 for i in range(half)}, **{f'r{i:04}': 2 for i in range(half)}} + e = [(f'l{i:04}', f'r{j:04}') for i in range(half) for j in range(half)] + oracle = 3 * half + old, new = prior_solve(w, e), graphs.solve(w, e) + ok = new['utility'] == oracle and not new['staged'] and certificate_checks(w, e, new) + failures += int(not ok) + large.append({'kind': 'complete_bipartite', 'n': n, 'edges': len(e), 'oracle': oracle, 'prior_utility': old['utility'], 'prior_staged': len(old['staged']), 'result': new, 'ok': ok}) + for n in (512, 1024, 2048): + w = {f'v{i:04}': 1 for i in range(n)} + e = [(f'v{i:04}', f'v{(i + 1) % n:04}') for i in range(n)] + old, new = prior_solve(w, e), graphs.solve(w, e) + ok = new['utility'] == n // 2 and not new['staged'] and certificate_checks(w, e, new) + failures += int(not ok) + large.append({'kind': 'even_cycle', 'n': n, 'edges': len(e), 'oracle': n // 2, 'prior_utility': old['utility'], 'prior_staged': len(old['staged']), 'result': new, 'ok': ok}) + n = 2050 + cap_w = {f'v{i:04}': 1 for i in range(n)} + cap_e = [(f'v{i:04}', f'v{(i + 1) % n:04}') for i in range(n)] + vertex_cap = graphs.solve(cap_w, cap_e) + w = {**{f'l{i}': 1 for i in range(224)}, **{f'r{i}': 1 for i in range(224)}} + e = [(f'l{i}', f'r{j}') for i in range(224) for j in range(224)] + edge_cap = graphs.solve(w, e) + w = {f'v{i:02}': 1 for i in range(17)} + odd = graphs.solve(w, [(f'v{i:02}', f'v{(i + 1) % 17:02}') for i in range(17)]) + dense = graphs.solve(w, list(combinations(w, 2))) + false = graphs.solve({'false': 9, 'true': 8}, [('false', 'true')]) + controls_ok = len(vertex_cap['staged']) == 2050 and len(edge_cap['staged']) == 448 and odd['utility'] == 8 and len(dense['staged']) == 17 + failures += int(not controls_ok) + return {'primary_target_met': failures == 0 and all(x['ok'] for x in large), 'failures': failures, + 'random_bipartite': cases, 'general_small': general, 'large': large, + 'controls': {'vertex_cap_staged': len(vertex_cap['staged']), 'edge_cap_staged': len(edge_cap['staged']), + 'edge_cap_edges': len(e), 'odd_cycle_utility': odd['utility'], 'dense_staged': len(dense['staged']), + 'false_priority_selected': false['selected']}} + + +def experiment_queries(): + rng = random.Random(SEED + 4) + cases, totals = [], {'exact': Counter(), 'tolerant': Counter()} + failures, point_false_certainty = 0, 0 + for i in range(128): + n = rng.randrange(2, 11) + w = {f'v{j}': rng.randrange(10) for j in range(n)} + e = [(a, b) for a, b in combinations(w, 2) if rng.random() < .3] + q = sorted(rng.sample(list(w), rng.randrange(min(3, n) + 1))) + all_repairs = independent_sets(w, e) + optimum = max(v for v, _ in all_repairs) + trials = [] + for name, epsilon in [('exact', 0), ('tolerant', math.floor(.05 * optimum))]: + repairs = [selected for v, selected in all_repairs if v >= optimum - epsilon] + truth = [set(q) <= set(s) for s in repairs] + oracle = 'certain' if all(truth) else 'ambiguous' if any(truth) else 'impossible' + result = graphs.query(w, e, q, epsilon=epsilon) + ok = result['classification'] == oracle + if oracle in ('certain', 'ambiguous'): + witness = result.get('possible_repair', []) + ok = ok and valid_repair(w, e, witness, optimum - epsilon) and set(q) <= set(witness) + if oracle == 'ambiguous': + witness = result.get('counterexample') + ok = ok and witness is not None and valid_repair(w, e, witness, optimum - epsilon) and not set(q) <= set(witness) + failures += int(not ok) + point_false_certainty += int(name == 'exact' and result.get('single_optimum_answer', False) and oracle != 'certain') + totals[name][oracle] += 1 + trials.append({'name': name, 'epsilon': epsilon, 'oracle': oracle, 'admissible_repair_count': len(repairs), 'result': result, 'ok': ok}) + failures += int(trials[1]['oracle'] == 'certain' and trials[0]['oracle'] != 'certain') + cases.append({'id': i, 'weights': w, 'edges': e, 'required': q, 'optimum': optimum, 'trials': trials}) + w, e = {'a': 1, 'b': 1}, [('a', 'b')] + chosen = graphs.solve(w, e)['selected'][0] + tie = graphs.query(w, e, [chosen]) + bad_truth = graphs.query({'false': 9, 'true': 8}, [('false', 'true')], ['false']) + failures += int(tie['classification'] != 'ambiguous' or not tie['single_optimum_answer']) + return {'primary_target_met': failures == 0, 'failures': failures, 'cases': cases, + 'counts': {k: {label: v[label] for label in ('certain', 'ambiguous', 'impossible')} for k, v in totals.items()}, + 'random_single_optimum_false_certainty': point_false_certainty, 'tie_control': tie, + 'false_priority_control': {'semantic_truth': False, 'result': bad_truth}} + + +def path_components(count, size=8): + w = {f'g{i:04}v{j:02}': 1 for i in range(count) for j in range(size)} + e = [(f'g{i:04}v{j:02}', f'g{i:04}v{j + 1:02}') for i in range(count) for j in range(size - 1)] + return w, e + + +def workload(count, updates, *, size=8): + w, e = path_components(count, size) + commands = [{'kind': 'weight', 'node': f'g{i % count:04}v{i % size:02}', 'value': 2 + i // count} for i in range(updates)] + return w, e, commands + + +def apply_reference(w, e, command): + """Independent full snapshot mutation oracle, not a DeltaIndex operation.""" + w, e = dict(w), {tuple(sorted(pair)) for pair in e} + kind = command['kind'] + if kind in ('weight', 'insert_vertex'): + w[command['node']] = command['value'] + elif kind == 'delete_vertex': + del w[command['node']] + e = {pair for pair in e if command['node'] not in pair} + elif kind == 'insert_edge': + e.add(tuple(sorted((command['a'], command['b'])))) + else: + e.remove(tuple(sorted((command['a'], command['b'])))) + return w, sorted(e) + + +def paired_work(w, e, commands): + local, full = graphs.DeltaIndex(w, e), graphs.DeltaIndex(w, e, rescan_all=True) + failures, steps = 0, [] + cold = {'local': local.total_work, 'full': full.total_work} + for command in commands: + ld, fd = local.update(command), full.update(command) + ok = local.snapshot() == full.snapshot() + failures += int(not ok) + steps.append({'command': command, 'local_work': ld['work'], 'full_work': fd['work'], 'utility': ld['utility'], 'ok': ok}) + return {'vertices': len(w), 'edges': len(e), 'updates': len(commands), 'cold': cold, 'steps': steps, 'failures': failures, + 'local_work': local.total_work, 'full_work': full.total_work, 'saving': 1 - local.total_work / full.total_work} + + +def experiment_delta(): + rng = random.Random(SEED + 5) + cases, failures, invalid_changes = [], 0, 0 + operation_counts = Counter() + for trial in range(32): + w = {f'v{i:02}': rng.randrange(10) for i in range(8)} + e = [(a, b) for a, b in combinations(w, 2) if rng.random() < .15] + original_w, original_e = dict(w), list(e) + local = graphs.DeltaIndex(w, e) + events = [] + for step in range(20): + kind = ('weight', 'insert_edge', 'delete_edge', 'insert_vertex', 'delete_vertex')[step % 5] + if kind == 'insert_edge': + options = sorted(set(combinations(sorted(w), 2)) - {tuple(sorted(x)) for x in e}) + if options: + a, b = rng.choice(options) + cmd = {'kind': kind, 'a': a, 'b': b} + else: + kind = 'weight' + if kind == 'delete_edge': + if e: + a, b = rng.choice(e) + cmd = {'kind': kind, 'a': a, 'b': b} + else: + kind = 'weight' + if kind == 'weight': + cmd = {'kind': kind, 'node': rng.choice(sorted(w)), 'value': rng.randrange(10)} + elif kind == 'insert_vertex': + cmd = {'kind': kind, 'node': f'new{step:02}', 'value': rng.randrange(10)} + elif kind == 'delete_vertex': + cmd = {'kind': kind, 'node': rng.choice(sorted(w))} + delta = local.update(cmd) + w, e = apply_reference(w, e, cmd) + expected = graphs.solve(w, e) + snapshot = local.snapshot() + ok = snapshot['weights'] == w and snapshot['edges'] == [list(pair) for pair in sorted(tuple(sorted(pair)) for pair in e)] and local.summary() == {k: expected[k] for k in ('selected', 'staged', 'utility')} + failures += int(not ok) + operation_counts[kind] += 1 + events.append({'command': cmd, 'delta': delta, 'oracle': {k: expected[k] for k in ('selected', 'staged', 'utility')}, 'ok': ok}) + before = local.snapshot() + try: + local.update({'kind': 'weight', 'node': next(iter(w)), 'value': -1}) + invalid_changes += 1 + except ValueError: + invalid_changes += int(local.snapshot() != before) + cases.append({'trial': trial, 'weights': original_w, 'edges': original_e, 'events': events}) + w, e, commands = workload(512, 128) + locality = paired_work(w, e, commands) + w, e, commands = workload(1, 128, size=64) + connected = paired_work(w, e, commands) + w, e = path_components(2, 4) + bridge_commands = [{'kind': 'insert_edge', 'a': 'g0000v03', 'b': 'g0001v00'}, + {'kind': 'delete_edge', 'a': 'g0000v03', 'b': 'g0001v00'}, + {'kind': 'insert_edge', 'a': 'g0000v03', 'b': 'g0001v00'}, + {'kind': 'delete_vertex', 'node': 'g0000v03'}] + bridges = paired_work(w, e, bridge_commands) + failures += locality['failures'] + connected['failures'] + bridges['failures'] + invalid_changes + return {'primary_target_met': failures == 0 and locality['saving'] >= .75, 'failures': failures, + 'random_cases': cases, 'mutations': sum(operation_counts.values()), 'operation_counts': dict(operation_counts), + 'invalid_state_changes': invalid_changes, 'local_workload': locality, 'connected_control': connected, 'bridge_control': bridges, + 'metric_boundary': 'Declared graph-element passes for copy, validation, discovery, solver input, operation validation and publication; excludes sorting, optimizer-internal and certificate-check work. Not total CPU work.'} + + +def timing_experiment(): + import scipy + cases = [] + for count in (16, 64, 256, 512): + w, e, commands = workload(count, 64) + samples = {'local': [], 'full': []} + for repetition in range(3): + engines = {} + for policy in (('local', 'full') if repetition % 2 == 0 else ('full', 'local')): + start = time.perf_counter() + engine = graphs.DeltaIndex(w, e, rescan_all=policy == 'full') + for cmd in commands: + engine.update(cmd) + samples[policy].append(time.perf_counter() - start) + engines[policy] = engine + if engines['local'].snapshot() != engines['full'].snapshot(): + raise ValueError('Timing policies disagree') + cases.append({'components': count, 'vertices': len(w), 'updates': len(commands), 'seconds': samples, + 'median_seconds': {k: statistics.median(v) for k, v in samples.items()}}) + return {'python': sys.version, 'platform': platform.platform(), 'numpy': np.__version__, 'scipy': scipy.__version__, + 'clock': 'perf_counter; cold construction plus updates; full snapshot comparison outside both timing boundaries', + 'repetitions': 3, 'cases': cases, 'qualification': 'Single-host in-memory measurements; not a service or durable database benchmark.'} + + +def execute(): + rows, calls, verification = load() + development = [r for r in rows if r['split'] == 'development'] + test = [r for r in rows if r['split'] == 'test'] + dev_groups, test_groups = {r['group'] for r in development}, {r['group'] for r in test} + if (len(development), len(test), len(dev_groups), len(test_groups)) != (73, 263, 40, 149) or dev_groups & test_groups or len({r['id'] for r in rows}) != len(rows): + raise ValueError('Source-group split or record identity changed') + source_paths = sorted(p for p in HERE.rglob('*') if p.is_file() and (p.suffix == '.py' or p.name in ('PROTOCOL.md', 'requirements.txt'))) + inputs = ROOT / 'experiments/jev-multicall-20260918' + hashes = {p.relative_to(ROOT).as_posix(): sha(p) for p in source_paths} + for name in ('artifact-manifest.json', 'calls.jsonl', 'predictions.json'): + p = inputs / name + hashes[p.relative_to(ROOT).as_posix()] = sha(p) + for p in (ROOT / 'graph_synthesis/reliability/methods.py', ROOT / 'graph_synthesis/reliability/run.py', ROOT / 'graph_synthesis/adaptive/run.py'): + hashes[p.relative_to(ROOT).as_posix()] = sha(p) + result = {'protocol_commit': PROTOCOL_COMMIT, 'baseline_commit': BASELINE_COMMIT, 'seed': SEED, + 'fresh_service_calls': 0, 'evidence_class': 'H1 reused saved-response forecasts; H2-H5 controlled algorithms', + 'development': {'n': len(development), 'groups': len(dev_groups)}, 'test': {'n': len(test), 'groups': len(test_groups)}, + 'input_verification': verification, 'sha256': hashes} + for name, function in [('H1', lambda: experiment_risk(development, test)), ('H2', experiment_lineage), + ('H3', experiment_graphs), ('H4', experiment_queries), ('H5', experiment_delta)]: + result[name] = function() + print(name + ': ' + ('target met' if result[name]['primary_target_met'] else 'target not met'), flush=True) + return normalized(result) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--check', action='store_true') + parser.add_argument('--timings', action='store_true') + args = parser.parse_args() + def blocked(*args, **kwargs): + raise RuntimeError('No network calls are permitted in this benchmark') + with patch.object(socket.socket, 'connect', blocked), patch.object(socket.socket, 'connect_ex', blocked), patch.object(socket, 'create_connection', blocked): + result = execute() + if args.check: + from ..verify import compare_json + changes = compare_json(json.loads((HERE / 'results.json').read_text(encoding='utf-8')), result) + print('Replay verified; floating-point roundoff entries:', len(changes)) + else: + write(HERE / 'results.json', result) + if args.timings: + if args.check: + parser.error('Timings are separate from deterministic replay') + write(HERE / 'timings.json', timing_experiment()) + print(json.dumps({'status': 'reproduced' if args.check else 'executed', 'targets': {k: result[k]['primary_target_met'] for k in ('H1', 'H2', 'H3', 'H4', 'H5')}, 'fresh_service_calls': 0})) + + +if __name__ == '__main__': + main() From b555e49cb3ebf5c0074df5bee70deb113c2b686d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 18 Sep 2026 08:53:36 +0000 Subject: [PATCH 14/21] research: record executed certificate benchmarks and updated manuscript --- CURRENT_RESULTS.md | 8 + graph_synthesis/certificates/RESULTS.md | 158 + .../certificates/artifact-manifest.json | 30 + graph_synthesis/certificates/execution.log | 6 + .../certificates/figures/01_source_risk.png | Bin 0 -> 107839 bytes .../certificates/figures/01_source_risk.svg | 1691 + .../figures/02_dependence_bounds.png | Bin 0 -> 107637 bytes .../figures/02_dependence_bounds.svg | 1611 + .../figures/03_bipartite_capacity.png | Bin 0 -> 120807 bytes .../figures/03_bipartite_capacity.svg | 1720 + .../figures/04_repair_queries.png | Bin 0 -> 99741 bytes .../figures/04_repair_queries.svg | 1732 + .../figures/05_delta_locality.png | Bin 0 -> 98275 bytes .../figures/05_delta_locality.svg | 1561 + .../certificates/original-inventory.json | 4 + graph_synthesis/certificates/results.json | 294019 +++++++++++++++ graph_synthesis/certificates/summary.csv | 6 + graph_synthesis/certificates/tests.log | 48 + graph_synthesis/certificates/timings.json | 95 + manuscript/paper-current.build.json | 6 +- manuscript/paper-current.html | 344 + manuscript/paper-current.md | 163 + manuscript/paper-current.pdf | Bin 2886764 -> 3406799 bytes 23 files changed, 303199 insertions(+), 3 deletions(-) create mode 100644 graph_synthesis/certificates/RESULTS.md create mode 100644 graph_synthesis/certificates/artifact-manifest.json create mode 100644 graph_synthesis/certificates/execution.log create mode 100644 graph_synthesis/certificates/figures/01_source_risk.png create mode 100644 graph_synthesis/certificates/figures/01_source_risk.svg create mode 100644 graph_synthesis/certificates/figures/02_dependence_bounds.png create mode 100644 graph_synthesis/certificates/figures/02_dependence_bounds.svg create mode 100644 graph_synthesis/certificates/figures/03_bipartite_capacity.png create mode 100644 graph_synthesis/certificates/figures/03_bipartite_capacity.svg create mode 100644 graph_synthesis/certificates/figures/04_repair_queries.png create mode 100644 graph_synthesis/certificates/figures/04_repair_queries.svg create mode 100644 graph_synthesis/certificates/figures/05_delta_locality.png create mode 100644 graph_synthesis/certificates/figures/05_delta_locality.svg create mode 100644 graph_synthesis/certificates/original-inventory.json create mode 100644 graph_synthesis/certificates/results.json create mode 100644 graph_synthesis/certificates/summary.csv create mode 100644 graph_synthesis/certificates/tests.log create mode 100644 graph_synthesis/certificates/timings.json diff --git a/CURRENT_RESULTS.md b/CURRENT_RESULTS.md index e22d418..d94441a 100644 --- a/CURRENT_RESULTS.md +++ b/CURRENT_RESULTS.md @@ -24,6 +24,14 @@ + + +## Five dependence-aware certificate experiments after PR #17 + +[Executed report](graph_synthesis/certificates/RESULTS.md), [frozen protocol](graph_synthesis/certificates/PROTOCOL.md), [five figures](graph_synthesis/certificates/figures/) and [complete updated paper](manuscript/paper-current.pdf). H1 not met; H2 met; H3 met; H4 met; H5 met. Source-random-effects forecasting is saved-response reuse; lineage bounds, flow certificates, repair queries and delta updates are controlled algorithms. No fresh Jev calls, worldwide novelty claim, new semantic-accuracy claim or production-policy change. Negative and assumption-breaking controls are retained. + + + 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/certificates/RESULTS.md b/graph_synthesis/certificates/RESULTS.md new file mode 100644 index 0000000..8d26213 --- /dev/null +++ b/graph_synthesis/certificates/RESULTS.md @@ -0,0 +1,158 @@ +# Dependence-aware certificates for Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five frozen exploratory tests extend the graph pipeline beyond independent-source assumptions and a single chosen repair. A development-fitted beta-binomial source model changes contamination Brier from 0.140410 to 0.131444, a 6.39% reduction; its primary target is not met. Dependence-aware probability bounds prevent 20/20 independence-induced false admissions in supplied correlated-source controls. Integer flow certificates recover all seven analytic bipartite optima, including 2,048-vertex cycles previously staged. Query classification agrees with finite enumeration, and delta-indexed updates match full recomputation across 640 random mutations. Declared boundary graph-element visits fall by 99.11% on the fixed local-update workload, but by 0.00% on its connected control. These are conditional algorithm and saved-response findings, not new Jev semantic-accuracy evidence or a comparison against KARMA. + +## 1. Motivation, novelty boundary and frozen methodology + +The preceding [reliability study](../reliability/RESULTS.md) exposed three limitations: its calibration multiplier worsened Brier; exact lineage probabilities remained conditional on independent primitives; and incremental maintenance still scanned the global snapshot. Its conflict optimizer also returned one optimum and staged components outside its cutset/cap limits. The five extensions here test these specific gaps without changing candidate extraction, accepted Jev edges or production policy. + +The inspected repository inventory did not contain these five integrated experiments. This is a **repository-scoped new-experiment claim, not a worldwide first-attempt claim**. Beta-binomial random effects, extremal-probability linear programs, weighted bipartite covers, consistent query answering across repairs and incremental maintenance all have antecedents. A targeted primary-source search and its references are recorded in the [protocol](PROTOCOL.md). Established ingredients do not become novel algorithms merely through new names or integration. + +Protocol commit `bc70b76621daca198b3b97eb4f698b17d4c40a0b` precedes this suite's implementation/execution; the inspected baseline is `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Hypotheses were informed by already public results, so this is exploratory follow-up rather than independent preregistration. H1 reuses 73 development candidates in 40 groups and 263 previously inspected evaluation candidates in 149 disjoint groups. Saved calls are reconstructed and hashes checked before use. **Fresh service calls: 0.** H2-H5 use controlled fixtures rather than additional Jev observations. + +| Hypothesis | Frozen primary requirement | Result | Evidence class | +|---|---|---|---| +| H1: Source random-effects forecasting | At least 10% lower Brier than same-marginal independence, absolute bias <=0.03, and no regression versus prior feature risk | Not met | Previously inspected saved-response forecasts | +| H2: Dependence-aware lineage bounds | Zero finite-oracle/bound/invariance failures; prevent all 20 correlated-source false admissions | Met | Supplied finite probability models | +| H3: Bipartite flow certificates | Zero finite-oracle/certificate failures, no small-case regression, all seven large analytic optima recovered | Met | Supplied conflict graphs and integer priorities | +| H4: Repair-invariant queries | Zero classification/witness failures; preserve all oracle-certain answers and reject the tie-control false certainty | Met | Priority-relative graph-repair semantics | +| H5: Delta-indexed graph updates | Zero mutation mismatches/invalid-state changes; at least 75% fewer declared boundary visits on the frozen local workload | Met | In-memory controlled graph mutations | + +These outcomes must not be combined into a semantic success percentage. A failed conjunction remains failed even when one endpoint improves. + +## 2. H1: Source-random-effects contamination forecasts + +For each source, n counts base1 SUPPORTS/REFUTES decisions and k counts disagreements with the stored gold relation label. Empty accepted groups are excluded. The development-only marginal error estimate is (sum(k)+0.5)/(sum(n)+1). A grid of intraclass correlations {0,0.01,0.05,0.1,0.2,0.4,0.6,0.8} is selected by beta-binomial development log likelihood with a lower-correlation tie rule. At positive rho, alpha=mu(1/rho-1) and beta=(1-mu)(1/rho-1); contamination is 1-B(alpha,beta+n)/B(alpha,beta). The rho=0 comparator has the identical marginal estimate and uses 1-(1-mu)^n. Predictions consume only source exposure counts, not evaluation labels. + +Development selects mu=0.121622 and rho=0.6. All 40 source-excluded development sensitivity fits are retained; held-out source IDs do not enter their training fits. Evaluation uses 98 nonempty source groups, of which 60 are singletons. Singletons cannot distinguish these dependence models at fixed marginal error rate. + +| Forecast | Brier | Clipped log loss | Predicted contaminated | Observed contaminated | Bias | +|---|---:|---:|---:|---:|---:| +| Prior feature-risk model | 0.128923 | 0.467074 | 8.04% | 15.31% | -7.26% | +| Prior development-selected multiplier | 0.134900 | 0.441344 | 13.64% | 15.31% | -1.67% | +| Same-marginal independence | 0.140410 | 0.457499 | 17.38% | 15.31% | +2.07% | +| Source random effects | 0.131444 | 0.435042 | 14.07% | 15.31% | -1.24% | + +The new-minus-same-marginal Brier difference is -0.008966; its descriptive paired 95% interval is [-0.021889, +0.001383], and its 99% interval is [-0.026211, +0.004667]. The frozen conjunction is **not met**. The forecast can improve relative to its matched marginal comparator while still failing the improvement threshold or regressing versus the richer prior feature model. No accepted edge is changed. + +Positive correlation lowers the probability of at least one error at fixed marginal error probability and exposure, because errors cluster rather than spread across groups. It does not universally make an underpredicted contamination rate more accurate. The fitted exchangeable beta-binomial model is neither a distribution-shift guarantee nor a calibration certificate. + +![H1. All forecasts scored on the same 98 nonempty evaluation source groups.](figures/01_source_risk.png) + +## 3. H2: Lineage bounds without assumed independence + +A monotone DNF formula is evaluated over at most 1,024 Boolean worlds for at most ten atoms. Nonnegative world masses sum to one and satisfy supplied marginal and optional conjunction constraints. Two linear programs minimize and maximize the DNF truth indicator. They use SciPy HiGHS; solver failures, infeasibility and capacity excess return [0,1] with no admission certificate. These probabilities are supplied model inputs, not Jev scores reinterpreted as fact probabilities. + +For a minimization objective c, equality matrix A, target b and returned dual y, c dot z >= b dot y + min(0,min(c-A-transpose y)) for every feasible simplex vector z. The implementation subtracts an additional 1e-9 times (1+sum(abs(y))) outward pad. It saves primal support, duals and residuals. This is a checked, outward-padded floating-point bound conditional on supplied constraints, **not formal interval-arithmetic verification or an unconditional truth guarantee**. + +The 128 seeded arbitrary-joint fixtures have 0 containment, constraint-monotonicity, invariance or control failures. The 50 two-event OR/AND grid cases match their analytic Frechet bounds within 1e-8. Mean interval width is 0.326563 using marginals alone and 0.239902 with one valid conjunction constraint. The primary target is **met**. + +| Two 0.8-marginal sources supporting an OR | Probability or interval | Admission at 0.95 | +|---|---:|---| +| Assume independence without justification | 0.96 | Yes; false under perfect correlation | +| Actual perfectly correlated sources | 0.80 | No | +| Marginals only, arbitrary dependence | Approximately [0.80,1.00] | No | +| Explicit joint probability 0.80 | Approximately [0.80,0.80] | No | +| Explicit joint probability 0.64 | Approximately [0.96,0.96] | Yes, conditional on this constraint | + +Across 20 marginal settings, the method prevents 20 false admissions from an unjustified independence assumption. It also withholds 20 genuinely high-probability admissions when only marginals are provided, and recovers 20 when the correct joint constraint is supplied. That conservatism is a real information tradeoff, not free accuracy. Infeasible and 11-atom cases stage. The false-premise control supplies 0.99 for an actually 0.50-probability atom; the resulting conditional bound still admits it. Arithmetic cannot repair incorrect evidence metadata. + +![H2. The correlated-source negative control separates assumed independence from valid lower bounds.](figures/02_dependence_bounds.png) + +## 4. H3: Integer-flow certificates for bipartite conflicts + +A bipartite conflict component is reduced to minimum weighted vertex cover: source-to-left and right-to-sink capacities are supplied priorities, and conflict arcs have capacity one greater than total priority. An integer max-flow/min-cut certificate gives a minimum cover; its complement is a maximum-weight independent set. The verifier reconstructs the original network, checks capacity bounds and conservation, proves flow equals cut capacity, checks the cover/independent-set relationship and verifies the reported utility. No hidden tie-breaking weight perturbation changes the objective. + +The new route supports components up to 2,048 vertices and 50,000 edges. Non-bipartite components use the actual prior cutset solver with its original limits; unsupported cases stage rather than silently accepting a heuristic solution. These are explicit conflict graphs, not newly extracted relationships. + +All 128 small bipartite cases match independent subset enumeration and their flow certificates. Another 128 small general graphs have no utility regression. Reordering or repeating edges preserves outputs. Total failures: 0. The primary target is **met**. + +| Analytic topology | Vertices | Edges | Previous utility | New utility | Oracle utility | +|---|---:|---:|---:|---:|---:| +| complete bipartite | 32 | 256 | 0 | 48 | 48 | +| complete bipartite | 64 | 1024 | 0 | 96 | 96 | +| complete bipartite | 128 | 4096 | 0 | 192 | 192 | +| complete bipartite | 256 | 16384 | 0 | 384 | 384 | +| even cycle | 512 | 512 | 0 | 256 | 256 | +| even cycle | 1024 | 1024 | 0 | 512 | 512 | +| even cycle | 2048 | 2048 | 0 | 1024 | 1024 | + +Complete-bipartite fixtures assign priority 3 on one balanced side and 2 on the other; cycles use unit priorities. Prior zero utility means staged, not an incorrect accepted graph. The 2,050-cycle and 50,176-edge complete-bipartite controls stage; a 17-cycle still uses the prior exact route, while the dense 17-clique remains unsupported. A false assertion of priority 9 still defeats a true conflicting assertion of priority 8. Certified optimality concerns supplied priorities, not semantic truth. + +![H3. Previously staged analytic conflict components are solved exactly with integer certificates.](figures/03_bipartite_capacity.png) + +## 5. H4: Queries invariant across admissible graph repairs + +Let U be maximum supplied priority. Admissible repairs are all independent sets with utility at least U-epsilon, using epsilon=0 and floor(0.05U). For a conjunction of up to eight required vertices, constrained inclusion tests whether some admissible repair contains the conjunction. Constrained exclusion of each required vertex tests whether any admissible repair falsifies it. Answers are certain, ambiguous or impossible, with an explicit counterexample for ambiguity. Staged optimization propagates unsupported status rather than false certainty. + +Across 128 seeded general graphs and two tolerance settings, classifications and witnesses have 0 oracle failures. Every oracle-certain answer is retained and larger tolerance creates no new certain answers. Among exact-optimum random cases, 2 affirmative answers from one selected optimum are not invariant across all optima. The explicit equal-weight conflict control also returns an alternative repair that falsifies the chosen optimum's affirmative answer. Primary target: **met**. + +| Repair tolerance | Certain conjunctions | Ambiguous conjunctions | Impossible conjunctions | +|---|---:|---:|---:| +| Exact maximum priority | 69 | 7 | 52 | +| Within floor(5% of optimum) | 65 | 12 | 51 | + +Some sampled conjunctions are empty and hence tautological; these counts describe the fixture distribution, not a population query-success rate. Conflicting conjunctions are impossible. Zero-priority optional vertices can be ambiguous. The high-priority false assertion is still repair-certain under its supplied objective: repair certainty must not be presented as factual certainty. + +![H4. Query status across exact and near-optimal admissible repairs.](figures/04_repair_queries.png) + +## 6. H5: Delta-indexed maintenance without global snapshot rescans + +The in-memory index validates an initial graph and maintains private adjacency, priorities, component membership and cached solutions. An explicit mutation API supports weight changes, vertex insertion/deletion and edge insertion/deletion. It copies, validates, discovers and solves only affected old components and their replacements before publication. Bridge insertion joins two scopes; bridge or vertex deletion discovers splits inside the affected old scope. Unaffected solutions are reused. Mutations return selection/staging deltas and aggregate utility rather than forcing a full graph materialization. Snapshot audit is a separate operation. + +Independent full-snapshot mutation and optimization checks find 0 failures across 640 seeded mutations, deliberate bridge controls and locality/stress workloads. Each of the five mutation types appears 128 times in the randomized test. Invalid mutations produce 0 state changes; tests also inject a solver exception before publication. This is not a concurrent or durable database transaction protocol. + +| Workload, including cold build | Delta-index boundary visits | Full-rescan boundary visits | Reduction | +|---|---:|---:|---:| +| 512 eight-vertex components; 128 local updates | 65,920 | 7,391,616 | 99.11% | +| One 64-vertex component; 128 updates | 123,096 | 123,096 | 0.00% | + +The frozen primary target is **met**. The comparator uses the same optimizer but copies, validates, discovers and prepares every component on every update. The boundary metric counts declared passes for graph copying, validation, discovery, solver input, mutation validation and publication. It explicitly excludes sorting, optimizer-internal work, certificate-check work and the audit materialization. Therefore the percentage is **not** a measured reduction in all CPU operations, service latency or database I/O. It improves on the previous study's solver-vertex-only scope, but is still an instrumented boundary measure. + +### Separate single-host timing measurements + +Three repetitions alternate policy order. Timings include cold construction and 64 updates, but exclude snapshot comparison for both policies. Medians below are descriptive, not a primary endpoint or a portable speed guarantee. All individual measurements and environment details are retained in [timings.json](timings.json). + +| Components | Vertices | Delta-index median seconds | Full-rescan median seconds | Ratio | +|---:|---:|---:|---:|---:| +| 16 | 128 | 0.011026 | 0.125704 | 11.40x | +| 64 | 512 | 0.017190 | 0.555562 | 32.32x | +| 256 | 2048 | 0.042154 | 2.336416 | 55.43x | +| 512 | 4096 | 0.075171 | 4.910725 | 65.33x | + +![H5. Benefits depend on component locality; the connected control has no counted-work saving.](figures/05_delta_locality.png) + +## 7. Interpretation, uncertainty and falsifying controls + +H1 uses 4,000 paired source-group bootstrap draws with seed 20260922 and fixed fitted policies. The 95% and 99% percentile intervals are descriptive, not simultaneous; they omit fitting uncertainty and cannot undo repeated use of the same evaluation corpus. Neither a favorable score nor a passed point threshold would establish out-of-distribution calibration. H2-H5 finite/analytic fixtures test implementation behavior under supplied assumptions rather than estimates of Jev accuracy. + +The retained negative controls are essential: incorrect marginal probabilities defeat lineage certificates; false priorities defeat semantic interpretation of flow certificates and repair certainty; uncertain dependence withholds some valid admissions; unsupported topologies stage; and connected updates remove locality savings. The structural methods remain opt-in research prototypes. Candidate-generation recall, semantic relation qualifiers, source independence discovery and real database behavior are not solved by this suite. + +The next independent semantic claim still requires a policy frozen before inspecting a new source-disjoint, independently adjudicated corpus, and matched evidence/resource budgets against an implemented external system such as KARMA. This suite does not claim that such a comparison ran. + +## 8. Reproducibility and artifact integrity + +```bash +python -m pip install -r graph_synthesis/certificates/requirements.txt +python -B -m graph_synthesis.certificates.run --timings +python -B -m unittest discover -s graph_synthesis/certificates/tests -v +python -B -m graph_synthesis.certificates.run --check +python -B -m graph_synthesis.certificates.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +``` + +The [machine-readable results](results.json) preserve fit candidates, held-source exclusions, predictions, complete fixture inputs, joint-world probabilities, LP witnesses, graph certificates, mutation events, oracle outputs and source hashes. Timings are deliberately separate from deterministic replay. Five SVG/PNG figure pairs and a summary CSV are regenerated from recorded results; the extension manifest hashes its source and artifacts. Original raw evidence and the archived manuscript remain unchanged, and the complete current manuscript retains every preceding study. + +## 9. Primary foundations + +- [SciPy beta-binomial definition](https://docs.scipy.org/doc/scipy-1.17.0/reference/generated/scipy.stats.betabinom.html) and [HiGHS linear programming and dual marginals](https://docs.scipy.org/doc/scipy-1.17.0/reference/optimize.linprog-highs.html). +- [Optimal Union Probability Interval Is NP-Hard](https://arxiv.org/abs/2605.03556), situating finite-world extremal-probability programs and their complexity. +- [Distributed CONGEST Approximation of Weighted Vertex Covers and Matchings](https://arxiv.org/abs/2111.10577), an antecedent for weighted bipartite cover formulations. +- [Computational Complexity of Preferred Subset Repairs on Data-Graphs](https://arxiv.org/abs/2402.09265) and [Consistent Query Answers in the Presence of Universal Constraints](https://arxiv.org/abs/0809.1551). +- [A Feature-based Classification of Model Repair Approaches](https://arxiv.org/html/1504.03947v1) and the [TypeSafe typed-decision interface](https://docs.typesafe.ai/introduction). + +These sources motivate established components; they do not validate this implementation or certify worldwide novelty. diff --git a/graph_synthesis/certificates/artifact-manifest.json b/graph_synthesis/certificates/artifact-manifest.json new file mode 100644 index 0000000..da2118c --- /dev/null +++ b/graph_synthesis/certificates/artifact-manifest.json @@ -0,0 +1,30 @@ +{ + "sha256": { + "graph_synthesis/certificates/PROTOCOL.md": "7b638984125e4861768a72f664ac447c174574026627d2fec33e8fbf8aa4cf0a", + "graph_synthesis/certificates/RESULTS.md": "fba7ae2dda5f85c0576111e73f2f2e2de4a37330dade057ccf041a84989c882a", + "graph_synthesis/certificates/__init__.py": "5dfc4d3b8c5dfa3d035892e9882d9899c659485af81fa5931c1d0685e3cfb156", + "graph_synthesis/certificates/execution.log": "6ebc40f34f2659a44e967585bfdc6ba2b59ae01d3dfde56248ac2ca13ce59bb2", + "graph_synthesis/certificates/figures/01_source_risk.png": 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+ "discovery": 1, + "operation_validation": 3, + "publication": 2, + "solver_input": 1, + "validation": 1 + } + }, + "ok": true, + "oracle": { + "selected": [ + "new03", + "new08", + "new13", + "new18", + "v01", + "v02", + "v07" + ], + "staged": [], + "utility": 47 + } + }, + { + "command": { + "kind": "delete_vertex", + "node": "new08" + }, + "delta": { + "selected_added": [], + "selected_removed": [ + "new08" + ], + "staged_added": [], + "staged_removed": [], + "utility": 38, + "work": { + "copy": 0, + "discovery": 0, + "operation_validation": 3, + "publication": 2, + "solver_input": 0, + "validation": 0 + } + }, + "ok": true, + "oracle": { + "selected": [ + "new03", + "new13", + "new18", + "v01", + "v02", + "v07" + ], + "staged": [], + "utility": 38 + } + } + ], + "trial": 31, + "weights": { + "v00": 4, + "v01": 7, + "v02": 8, + "v03": 1, + "v04": 2, + "v05": 0, + "v06": 6, + "v07": 9 + } + } + ] + }, + "baseline_commit": "a62a3257645d8e35cd4e45be53bfa9511d27724b", + "development": { + "groups": 40, + "n": 73 + }, + "evidence_class": "H1 reused saved-response forecasts; H2-H5 controlled algorithms", + "fresh_service_calls": 0, + "input_verification": { + "analysis_comparison": "exact counts/labels/structure; float atol 1e-14, rtol 1e-12 for cross-platform roundoff", + "analysis_reproduced": true, + "calls": 3024, + "cases": 336, + "distinct_repeat_sites": true, + "gold_free_requests": true, + "group_disjoint": true, + "limitation": "Request/response journals are local provenance, not provider-signed attestations; failed responses remain failures.", + "network_disabled_replay": "identical predictions and execution after CRLF/LF normalization", + "raw_response_validation": true, + "status": "passed" + }, + "protocol_commit": "bc70b76621daca198b3b97eb4f698b17d4c40a0b", + "seed": 20260922, + "sha256": { + "experiments/jev-multicall-20260918/artifact-manifest.json": "53c993dfa17a587c65221ca549e01db240b1f8ccdff514ed09908619bcadff02", + "experiments/jev-multicall-20260918/calls.jsonl": "7e628ea16e8b6e3561060a752c03451f7426253fbb5496b2c2ff9a09c13e8187", + "experiments/jev-multicall-20260918/predictions.json": "8372542d67c3365c1374467700b2f21fc220c274d549ae3e1f070a2d8df58c7a", + "graph_synthesis/adaptive/run.py": "1d3bf47a54735cbc071deb166d52330bf5704914edf98a10dd31c89f2c8fed68", + "graph_synthesis/certificates/PROTOCOL.md": "7b638984125e4861768a72f664ac447c174574026627d2fec33e8fbf8aa4cf0a", + "graph_synthesis/certificates/__init__.py": "5dfc4d3b8c5dfa3d035892e9882d9899c659485af81fa5931c1d0685e3cfb156", + "graph_synthesis/certificates/graphs.py": "eb0b52d370dbc5a301769784fbc92a467a6da9c5c2d8e0b052f5d33c34668458", + "graph_synthesis/certificates/lineage.py": "e32d834f11a2fb9ac9b661fd816e378bf618a311eb4d9141490cee6487b3bcfc", + "graph_synthesis/certificates/report.py": "4349ffe7dd269833e8144401af3f0a487cf170e80a517d68e0f1eb642047176e", + "graph_synthesis/certificates/requirements.txt": "65dc02c36f5b601cb14c191d83442bc70ce93bd812b0b81fc5204d7925ae3105", + "graph_synthesis/certificates/risk.py": "98b07bc50ff482015003d13cb112ffadd9b4602655c933840464c951a20e4113", + "graph_synthesis/certificates/run.py": "471cca0e1b3eb2fa579401371d1cedd0de2754582367ac5ca6d7186b3192c7dc", + "graph_synthesis/certificates/tests/test_certificates.py": "e2c8a78d23237285a88098a8c70b92894685e7bcd4eb011921517151d2434326", + "graph_synthesis/reliability/methods.py": "c0552448f2daf44609949486b0f765039fe3b7c85f50d517d736cb07d551be98", + "graph_synthesis/reliability/run.py": "627905f35276e31bab49c2e3402dc529d9a0a2bbd64070bf9e559a8ce4bb0fba" + }, + "test": { + "groups": 149, + "n": 263 + } +} diff --git a/graph_synthesis/certificates/summary.csv b/graph_synthesis/certificates/summary.csv new file mode 100644 index 0000000..859ec2c --- /dev/null +++ b/graph_synthesis/certificates/summary.csv @@ -0,0 +1,6 @@ +hypothesis,title,primary_target,evidence_class +H1,Source random-effects forecasting,Not met,Previously inspected saved-response forecasts +H2,Dependence-aware lineage bounds,Met,Supplied finite probability models +H3,Bipartite flow certificates,Met,Supplied conflict graphs and integer priorities +H4,Repair-invariant queries,Met,Priority-relative graph-repair semantics +H5,Delta-indexed graph updates,Met,In-memory controlled graph mutations diff --git a/graph_synthesis/certificates/tests.log b/graph_synthesis/certificates/tests.log new file mode 100644 index 0000000..fbc88dd --- /dev/null +++ b/graph_synthesis/certificates/tests.log @@ -0,0 +1,48 @@ +test_manuscript_insertion_is_idempotent (test_certificates.ArtifactTests.test_manuscript_insertion_is_idempotent) ... ok +test_result_invariants_when_present (test_certificates.ArtifactTests.test_result_invariants_when_present) ... ok +test_bridge_merge_split_and_vertex_removal (test_certificates.DeltaTests.test_bridge_merge_split_and_vertex_removal) ... ok +test_delta_outputs_reconstruct_selection (test_certificates.DeltaTests.test_delta_outputs_reconstruct_selection) ... ok +test_full_comparator_same_output_more_local_work (test_certificates.DeltaTests.test_full_comparator_same_output_more_local_work) ... ok +test_insert_delete_isolated_vertex (test_certificates.DeltaTests.test_insert_delete_isolated_vertex) ... ok +test_invalid_commands_preserve_state (test_certificates.DeltaTests.test_invalid_commands_preserve_state) ... ok +test_snapshot_is_not_mutable_internal_state (test_certificates.DeltaTests.test_snapshot_is_not_mutable_internal_state) ... ok +test_solver_failure_preserves_state (test_certificates.DeltaTests.test_solver_failure_preserves_state) ... ok +test_weight_update_and_unaffected_reuse (test_certificates.DeltaTests.test_weight_update_and_unaffected_reuse) ... ok +test_dense_unsupported_is_staged (test_certificates.GraphTests.test_dense_unsupported_is_staged) ... ok +test_empty_graph (test_certificates.GraphTests.test_empty_graph) ... ok +test_false_priority_is_not_truth (test_certificates.GraphTests.test_false_priority_is_not_truth) ... ok +test_invalid_graph_rejected (test_certificates.GraphTests.test_invalid_graph_rejected) ... ok +test_odd_cycle_uses_existing_solver (test_certificates.GraphTests.test_odd_cycle_uses_existing_solver) ... ok +test_order_duplicates_and_input_immutability (test_certificates.GraphTests.test_order_duplicates_and_input_immutability) ... ok +test_tampered_flow_rejected (test_certificates.GraphTests.test_tampered_flow_rejected) ... ok +test_weighted_complete_bipartite (test_certificates.GraphTests.test_weighted_complete_bipartite) ... ok +test_zero_priorities_and_isolated_nodes (test_certificates.GraphTests.test_zero_priorities_and_isolated_nodes) ... ok +test_capacity_stages (test_certificates.LineageTests.test_capacity_stages) ... ok +test_dual_witness_bounds_primal (test_certificates.LineageTests.test_dual_witness_bounds_primal) ... ok +test_duplicate_and_order_invariance (test_certificates.LineageTests.test_duplicate_and_order_invariance) ... ok +test_empty_formula_and_tautology (test_certificates.LineageTests.test_empty_formula_and_tautology) ... ok +test_impossible_constraints_stage (test_certificates.LineageTests.test_impossible_constraints_stage) ... ok +test_independent_pair_information_recovers_admission (test_certificates.LineageTests.test_independent_pair_information_recovers_admission) ... ok +test_nonfinite_invalid_and_missing_atoms (test_certificates.LineageTests.test_nonfinite_invalid_and_missing_atoms) ... ok +test_shared_source_constraint (test_certificates.LineageTests.test_shared_source_constraint) ... ok +test_solver_failure_never_admits (test_certificates.LineageTests.test_solver_failure_never_admits) ... ok +test_unknown_dependence_prevents_false_admission (test_certificates.LineageTests.test_unknown_dependence_prevents_false_admission) ... ok +test_conflicting_conjunction_is_impossible (test_certificates.QueryTests.test_conflicting_conjunction_is_impossible) ... ok +test_empty_query_and_zero_weights (test_certificates.QueryTests.test_empty_query_and_zero_weights) ... ok +test_invalid_query_and_unsupported_graph (test_certificates.QueryTests.test_invalid_query_and_unsupported_graph) ... ok +test_tie_is_ambiguous_with_witness (test_certificates.QueryTests.test_tie_is_ambiguous_with_witness) ... ok +test_tolerance_weakens_certainty (test_certificates.QueryTests.test_tolerance_weakens_certainty) ... ok +test_unique_forced_assertion_is_certain (test_certificates.QueryTests.test_unique_forced_assertion_is_certain) ... ok +test_beta_binomial_mass_normalizes (test_certificates.RiskTests.test_beta_binomial_mass_normalizes) ... ok +test_development_only_fit (test_certificates.RiskTests.test_development_only_fit) ... ok +test_fit_order_invariance_and_no_mutation (test_certificates.RiskTests.test_fit_order_invariance_and_no_mutation) ... ok +test_gold_not_read_for_forecast (test_certificates.RiskTests.test_gold_not_read_for_forecast) ... ok +test_invalid_parameters (test_certificates.RiskTests.test_invalid_parameters) ... ok +test_positive_correlation_lowers_any_error_at_fixed_mean (test_certificates.RiskTests.test_positive_correlation_lowers_any_error_at_fixed_mean) ... ok +test_probability_boundaries (test_certificates.RiskTests.test_probability_boundaries) ... ok +test_singletons_do_not_identify_dependence (test_certificates.RiskTests.test_singletons_do_not_identify_dependence) ... ok + +---------------------------------------------------------------------- +Ran 43 tests in 0.196s + +OK diff --git a/graph_synthesis/certificates/timings.json b/graph_synthesis/certificates/timings.json new file mode 100644 index 0000000..9be6dc3 --- /dev/null +++ b/graph_synthesis/certificates/timings.json @@ -0,0 +1,95 @@ +{ + "cases": [ + { + "components": 16, + "median_seconds": { + "full": 0.12570409500000324, + "local": 0.011025625000002037 + }, + "seconds": { + "full": [ + 0.12570409500000324, + 0.12562665099999037, + 0.12962008000000935 + ], + "local": [ + 0.011402658999998039, + 0.010957887999992977, + 0.011025625000002037 + ] + }, + "updates": 64, + "vertices": 128 + }, + { + "components": 64, + "median_seconds": { + "full": 0.5555617600000033, + "local": 0.017190087999992443 + }, + "seconds": { + "full": [ + 0.5506035490000016, + 0.5564781019999998, + 0.5555617600000033 + ], + "local": [ + 0.017680665000000317, + 0.016802734999998847, + 0.017190087999992443 + ] + }, + "updates": 64, + "vertices": 512 + }, + { + "components": 256, + "median_seconds": { + "full": 2.3364158739999965, + "local": 0.04215446400000644 + }, + "seconds": { + "full": [ + 2.3364158739999965, + 2.3347134500000095, + 2.369167206 + ], + "local": [ + 0.0417307269999867, + 0.04215446400000644, + 0.04237294200000008 + ] + }, + "updates": 64, + "vertices": 2048 + }, + { + "components": 512, + "median_seconds": { + "full": 4.910724754, + "local": 0.07517117300000109 + }, + "seconds": { + "full": [ + 4.930171537999996, + 4.910724754, + 4.910309710000007 + ], + "local": [ + 0.07517117300000109, + 0.07501426499999297, + 0.10281746100000078 + ] + }, + "updates": 64, + "vertices": 4096 + } + ], + "clock": "perf_counter; cold construction plus updates; full snapshot comparison outside both timing boundaries", + "numpy": "2.5.3", + "platform": "Linux-6.17.0-1022-azure-x86_64-with-glibc2.39", + "python": "3.12.14 (main, Aug 13 2026, 02:47:42) [GCC 13.3.0]", + "qualification": "Single-host in-memory measurements; not a service or durable database benchmark.", + "repetitions": 3, + "scipy": "1.17.0" +} diff --git a/manuscript/paper-current.build.json b/manuscript/paper-current.build.json index b65dc8f..5b705ea 100644 --- a/manuscript/paper-current.build.json +++ b/manuscript/paper-current.build.json @@ -1,6 +1,6 @@ { - "source_sha256": "461bbdddfe20b0450578b7d76c9142c472440f01a8dcccae5a6fb84bf988ea3c", - "renderer_sha256": 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8. Reproducibility

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

The machine-readable results preserve development fold exclusions, all calibration candidates, group predictions, review IDs, sensitivity assumptions, random fixtures, mutation events, oracle outputs and source hashes. Five SVG/PNG figure pairs and summary.csv are regenerated from results.json. The extension manifest binds source, results, figures and validation records. Original raw calls and the archived original manuscript are untouched. The complete current manuscript retains all preceding studies and their limitations.

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Dependence-aware certificates for Jev graph synthesis

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

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Abstract

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Five frozen exploratory tests extend the graph pipeline beyond independent-source assumptions and a single chosen repair. A development-fitted beta-binomial source model changes contamination Brier from 0.140410 to 0.131444, a 6.39% reduction; its primary target is not met. Dependence-aware probability bounds prevent 20/20 independence-induced false admissions in supplied correlated-source controls. Integer flow certificates recover all seven analytic bipartite optima, including 2,048-vertex cycles previously staged. Query classification agrees with finite enumeration, and delta-indexed updates match full recomputation across 640 random mutations. Declared boundary graph-element visits fall by 99.11% on the fixed local-update workload, but by 0.00% on its connected control. These are conditional algorithm and saved-response findings, not new Jev semantic-accuracy evidence or a comparison against KARMA.

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1. Motivation, novelty boundary and frozen methodology

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The preceding reliability study exposed three limitations: its calibration multiplier worsened Brier; exact lineage probabilities remained conditional on independent primitives; and incremental maintenance still scanned the global snapshot. Its conflict optimizer also returned one optimum and staged components outside its cutset/cap limits. The five extensions here test these specific gaps without changing candidate extraction, accepted Jev edges or production policy.

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The inspected repository inventory did not contain these five integrated experiments. This is a repository-scoped new-experiment claim, not a worldwide first-attempt claim. Beta-binomial random effects, extremal-probability linear programs, weighted bipartite covers, consistent query answering across repairs and incremental maintenance all have antecedents. A targeted primary-source search and its references are recorded in the protocol. Established ingredients do not become novel algorithms merely through new names or integration.

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Protocol commit bc70b76621daca198b3b97eb4f698b17d4c40a0b precedes this suite's implementation/execution; the inspected baseline is a62a3257645d8e35cd4e45be53bfa9511d27724b. Hypotheses were informed by already public results, so this is exploratory follow-up rather than independent preregistration. H1 reuses 73 development candidates in 40 groups and 263 previously inspected evaluation candidates in 149 disjoint groups. Saved calls are reconstructed and hashes checked before use. Fresh service calls: 0. H2-H5 use controlled fixtures rather than additional Jev observations.

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HypothesisFrozen primary requirementResultEvidence class
H1: Source random-effects forecastingAt least 10% lower Brier than same-marginal independence, absolute bias <=0.03, and no regression versus prior feature riskNot metPreviously inspected saved-response forecasts
H2: Dependence-aware lineage boundsZero finite-oracle/bound/invariance failures; prevent all 20 correlated-source false admissionsMetSupplied finite probability models
H3: Bipartite flow certificatesZero finite-oracle/certificate failures, no small-case regression, all seven large analytic optima recoveredMetSupplied conflict graphs and integer priorities
H4: Repair-invariant queriesZero classification/witness failures; preserve all oracle-certain answers and reject the tie-control false certaintyMetPriority-relative graph-repair semantics
H5: Delta-indexed graph updatesZero mutation mismatches/invalid-state changes; at least 75% fewer declared boundary visits on the frozen local workloadMetIn-memory controlled graph mutations
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These outcomes must not be combined into a semantic success percentage. A failed conjunction remains failed even when one endpoint improves.

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2. H1: Source-random-effects contamination forecasts

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For each source, n counts base1 SUPPORTS/REFUTES decisions and k counts disagreements with the stored gold relation label. Empty accepted groups are excluded. The development-only marginal error estimate is (sum(k)+0.5)/(sum(n)+1). A grid of intraclass correlations {0,0.01,0.05,0.1,0.2,0.4,0.6,0.8} is selected by beta-binomial development log likelihood with a lower-correlation tie rule. At positive rho, alpha=mu(1/rho-1) and beta=(1-mu)(1/rho-1); contamination is 1-B(alpha,beta+n)/B(alpha,beta). The rho=0 comparator has the identical marginal estimate and uses 1-(1-mu)^n. Predictions consume only source exposure counts, not evaluation labels.

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Development selects mu=0.121622 and rho=0.6. All 40 source-excluded development sensitivity fits are retained; held-out source IDs do not enter their training fits. Evaluation uses 98 nonempty source groups, of which 60 are singletons. Singletons cannot distinguish these dependence models at fixed marginal error rate.

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ForecastBrierClipped log lossPredicted contaminatedObserved contaminatedBias
Prior feature-risk model0.1289230.4670748.04%15.31%-7.26%
Prior development-selected multiplier0.1349000.44134413.64%15.31%-1.67%
Same-marginal independence0.1404100.45749917.38%15.31%+2.07%
Source random effects0.1314440.43504214.07%15.31%-1.24%
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The new-minus-same-marginal Brier difference is -0.008966; its descriptive paired 95% interval is [-0.021889, +0.001383], and its 99% interval is [-0.026211, +0.004667]. The frozen conjunction is not met. The forecast can improve relative to its matched marginal comparator while still failing the improvement threshold or regressing versus the richer prior feature model. No accepted edge is changed.

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Positive correlation lowers the probability of at least one error at fixed marginal error probability and exposure, because errors cluster rather than spread across groups. It does not universally make an underpredicted contamination rate more accurate. The fitted exchangeable beta-binomial model is neither a distribution-shift guarantee nor a calibration certificate.

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H1. All forecasts scored on the same 98 nonempty evaluation source groups.
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3. H2: Lineage bounds without assumed independence

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A monotone DNF formula is evaluated over at most 1,024 Boolean worlds for at most ten atoms. Nonnegative world masses sum to one and satisfy supplied marginal and optional conjunction constraints. Two linear programs minimize and maximize the DNF truth indicator. They use SciPy HiGHS; solver failures, infeasibility and capacity excess return [0,1] with no admission certificate. These probabilities are supplied model inputs, not Jev scores reinterpreted as fact probabilities.

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For a minimization objective c, equality matrix A, target b and returned dual y, c dot z >= b dot y + min(0,min(c-A-transpose y)) for every feasible simplex vector z. The implementation subtracts an additional 1e-9 times (1+sum(abs(y))) outward pad. It saves primal support, duals and residuals. This is a checked, outward-padded floating-point bound conditional on supplied constraints, not formal interval-arithmetic verification or an unconditional truth guarantee.

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The 128 seeded arbitrary-joint fixtures have 0 containment, constraint-monotonicity, invariance or control failures. The 50 two-event OR/AND grid cases match their analytic Frechet bounds within 1e-8. Mean interval width is 0.326563 using marginals alone and 0.239902 with one valid conjunction constraint. The primary target is met.

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Two 0.8-marginal sources supporting an ORProbability or intervalAdmission at 0.95
Assume independence without justification0.96Yes; false under perfect correlation
Actual perfectly correlated sources0.80No
Marginals only, arbitrary dependenceApproximately [0.80,1.00]No
Explicit joint probability 0.80Approximately [0.80,0.80]No
Explicit joint probability 0.64Approximately [0.96,0.96]Yes, conditional on this constraint
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Across 20 marginal settings, the method prevents 20 false admissions from an unjustified independence assumption. It also withholds 20 genuinely high-probability admissions when only marginals are provided, and recovers 20 when the correct joint constraint is supplied. That conservatism is a real information tradeoff, not free accuracy. Infeasible and 11-atom cases stage. The false-premise control supplies 0.99 for an actually 0.50-probability atom; the resulting conditional bound still admits it. Arithmetic cannot repair incorrect evidence metadata.

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H2. The correlated-source negative control separates assumed independence from valid lower bounds.
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4. H3: Integer-flow certificates for bipartite conflicts

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A bipartite conflict component is reduced to minimum weighted vertex cover: source-to-left and right-to-sink capacities are supplied priorities, and conflict arcs have capacity one greater than total priority. An integer max-flow/min-cut certificate gives a minimum cover; its complement is a maximum-weight independent set. The verifier reconstructs the original network, checks capacity bounds and conservation, proves flow equals cut capacity, checks the cover/independent-set relationship and verifies the reported utility. No hidden tie-breaking weight perturbation changes the objective.

+

The new route supports components up to 2,048 vertices and 50,000 edges. Non-bipartite components use the actual prior cutset solver with its original limits; unsupported cases stage rather than silently accepting a heuristic solution. These are explicit conflict graphs, not newly extracted relationships.

+

All 128 small bipartite cases match independent subset enumeration and their flow certificates. Another 128 small general graphs have no utility regression. Reordering or repeating edges preserves outputs. Total failures: 0. The primary target is met.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Analytic topologyVerticesEdgesPrevious utilityNew utilityOracle utility
complete bipartite3225604848
complete bipartite64102409696
complete bipartite12840960192192
complete bipartite256163840384384
even cycle5125120256256
even cycle102410240512512
even cycle20482048010241024
+

Complete-bipartite fixtures assign priority 3 on one balanced side and 2 on the other; cycles use unit priorities. Prior zero utility means staged, not an incorrect accepted graph. The 2,050-cycle and 50,176-edge complete-bipartite controls stage; a 17-cycle still uses the prior exact route, while the dense 17-clique remains unsupported. A false assertion of priority 9 still defeats a true conflicting assertion of priority 8. Certified optimality concerns supplied priorities, not semantic truth.

+
H3. Previously staged analytic conflict components are solved exactly with integer certificates.
+

5. H4: Queries invariant across admissible graph repairs

+

Let U be maximum supplied priority. Admissible repairs are all independent sets with utility at least U-epsilon, using epsilon=0 and floor(0.05U). For a conjunction of up to eight required vertices, constrained inclusion tests whether some admissible repair contains the conjunction. Constrained exclusion of each required vertex tests whether any admissible repair falsifies it. Answers are certain, ambiguous or impossible, with an explicit counterexample for ambiguity. Staged optimization propagates unsupported status rather than false certainty.

+

Across 128 seeded general graphs and two tolerance settings, classifications and witnesses have 0 oracle failures. Every oracle-certain answer is retained and larger tolerance creates no new certain answers. Among exact-optimum random cases, 2 affirmative answers from one selected optimum are not invariant across all optima. The explicit equal-weight conflict control also returns an alternative repair that falsifies the chosen optimum's affirmative answer. Primary target: met.

+ + + + + + + + + + + + + + + + + + + + + + + +
Repair toleranceCertain conjunctionsAmbiguous conjunctionsImpossible conjunctions
Exact maximum priority69752
Within floor(5% of optimum)651251
+

Some sampled conjunctions are empty and hence tautological; these counts describe the fixture distribution, not a population query-success rate. Conflicting conjunctions are impossible. Zero-priority optional vertices can be ambiguous. The high-priority false assertion is still repair-certain under its supplied objective: repair certainty must not be presented as factual certainty.

+
H4. Query status across exact and near-optimal admissible repairs.
+

6. H5: Delta-indexed maintenance without global snapshot rescans

+

The in-memory index validates an initial graph and maintains private adjacency, priorities, component membership and cached solutions. An explicit mutation API supports weight changes, vertex insertion/deletion and edge insertion/deletion. It copies, validates, discovers and solves only affected old components and their replacements before publication. Bridge insertion joins two scopes; bridge or vertex deletion discovers splits inside the affected old scope. Unaffected solutions are reused. Mutations return selection/staging deltas and aggregate utility rather than forcing a full graph materialization. Snapshot audit is a separate operation.

+

Independent full-snapshot mutation and optimization checks find 0 failures across 640 seeded mutations, deliberate bridge controls and locality/stress workloads. Each of the five mutation types appears 128 times in the randomized test. Invalid mutations produce 0 state changes; tests also inject a solver exception before publication. This is not a concurrent or durable database transaction protocol.

+ + + + + + + + + + + + + + + + + + + + + + + +
Workload, including cold buildDelta-index boundary visitsFull-rescan boundary visitsReduction
512 eight-vertex components; 128 local updates65,9207,391,61699.11%
One 64-vertex component; 128 updates123,096123,0960.00%
+

The frozen primary target is met. The comparator uses the same optimizer but copies, validates, discovers and prepares every component on every update. The boundary metric counts declared passes for graph copying, validation, discovery, solver input, mutation validation and publication. It explicitly excludes sorting, optimizer-internal work, certificate-check work and the audit materialization. Therefore the percentage is not a measured reduction in all CPU operations, service latency or database I/O. It improves on the previous study's solver-vertex-only scope, but is still an instrumented boundary measure.

+

Separate single-host timing measurements

+

Three repetitions alternate policy order. Timings include cold construction and 64 updates, but exclude snapshot comparison for both policies. Medians below are descriptive, not a primary endpoint or a portable speed guarantee. All individual measurements and environment details are retained in timings.json.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ComponentsVerticesDelta-index median secondsFull-rescan median secondsRatio
161280.0110260.12570411.40x
645120.0171900.55556232.32x
25620480.0421542.33641655.43x
51240960.0751714.91072565.33x
+
H5. Benefits depend on component locality; the connected control has no counted-work saving.
+

7. Interpretation, uncertainty and falsifying controls

+

H1 uses 4,000 paired source-group bootstrap draws with seed 20260922 and fixed fitted policies. The 95% and 99% percentile intervals are descriptive, not simultaneous; they omit fitting uncertainty and cannot undo repeated use of the same evaluation corpus. Neither a favorable score nor a passed point threshold would establish out-of-distribution calibration. H2-H5 finite/analytic fixtures test implementation behavior under supplied assumptions rather than estimates of Jev accuracy.

+

The retained negative controls are essential: incorrect marginal probabilities defeat lineage certificates; false priorities defeat semantic interpretation of flow certificates and repair certainty; uncertain dependence withholds some valid admissions; unsupported topologies stage; and connected updates remove locality savings. The structural methods remain opt-in research prototypes. Candidate-generation recall, semantic relation qualifiers, source independence discovery and real database behavior are not solved by this suite.

+

The next independent semantic claim still requires a policy frozen before inspecting a new source-disjoint, independently adjudicated corpus, and matched evidence/resource budgets against an implemented external system such as KARMA. This suite does not claim that such a comparison ran.

+

8. Reproducibility and artifact integrity

+
python -m pip install -r graph_synthesis/certificates/requirements.txt
+python -B -m graph_synthesis.certificates.run --timings
+python -B -m unittest discover -s graph_synthesis/certificates/tests -v
+python -B -m graph_synthesis.certificates.run --check
+python -B -m graph_synthesis.certificates.report --figures --update-paper
+python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf
+
+

The machine-readable results preserve fit candidates, held-source exclusions, predictions, complete fixture inputs, joint-world probabilities, LP witnesses, graph certificates, mutation events, oracle outputs and source hashes. Timings are deliberately separate from deterministic replay. Five SVG/PNG figure pairs and a summary CSV are regenerated from recorded results; the extension manifest hashes its source and artifacts. Original raw evidence and the archived manuscript remain unchanged, and the complete current manuscript retains every preceding study.

+

9. Primary foundations

+ +

These sources motivate established components; they do not validate this implementation or certify worldwide novelty.

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 83a93c9..10a1bfd 100644 --- a/manuscript/paper-current.md +++ b/manuscript/paper-current.md @@ -448,6 +448,169 @@ The machine-readable results preserve development fold exclusions, all calibrati + + +# Dependence-aware certificates for Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five frozen exploratory tests extend the graph pipeline beyond independent-source assumptions and a single chosen repair. A development-fitted beta-binomial source model changes contamination Brier from 0.140410 to 0.131444, a 6.39% reduction; its primary target is not met. Dependence-aware probability bounds prevent 20/20 independence-induced false admissions in supplied correlated-source controls. Integer flow certificates recover all seven analytic bipartite optima, including 2,048-vertex cycles previously staged. Query classification agrees with finite enumeration, and delta-indexed updates match full recomputation across 640 random mutations. Declared boundary graph-element visits fall by 99.11% on the fixed local-update workload, but by 0.00% on its connected control. These are conditional algorithm and saved-response findings, not new Jev semantic-accuracy evidence or a comparison against KARMA. + +## 1. Motivation, novelty boundary and frozen methodology + +The preceding [reliability study](../graph_synthesis/reliability/RESULTS.md) exposed three limitations: its calibration multiplier worsened Brier; exact lineage probabilities remained conditional on independent primitives; and incremental maintenance still scanned the global snapshot. Its conflict optimizer also returned one optimum and staged components outside its cutset/cap limits. The five extensions here test these specific gaps without changing candidate extraction, accepted Jev edges or production policy. + +The inspected repository inventory did not contain these five integrated experiments. This is a **repository-scoped new-experiment claim, not a worldwide first-attempt claim**. Beta-binomial random effects, extremal-probability linear programs, weighted bipartite covers, consistent query answering across repairs and incremental maintenance all have antecedents. A targeted primary-source search and its references are recorded in the [protocol](../graph_synthesis/certificates/PROTOCOL.md). Established ingredients do not become novel algorithms merely through new names or integration. + +Protocol commit `bc70b76621daca198b3b97eb4f698b17d4c40a0b` precedes this suite's implementation/execution; the inspected baseline is `a62a3257645d8e35cd4e45be53bfa9511d27724b`. Hypotheses were informed by already public results, so this is exploratory follow-up rather than independent preregistration. H1 reuses 73 development candidates in 40 groups and 263 previously inspected evaluation candidates in 149 disjoint groups. Saved calls are reconstructed and hashes checked before use. **Fresh service calls: 0.** H2-H5 use controlled fixtures rather than additional Jev observations. + +| Hypothesis | Frozen primary requirement | Result | Evidence class | +|---|---|---|---| +| H1: Source random-effects forecasting | At least 10% lower Brier than same-marginal independence, absolute bias <=0.03, and no regression versus prior feature risk | Not met | Previously inspected saved-response forecasts | +| H2: Dependence-aware lineage bounds | Zero finite-oracle/bound/invariance failures; prevent all 20 correlated-source false admissions | Met | Supplied finite probability models | +| H3: Bipartite flow certificates | Zero finite-oracle/certificate failures, no small-case regression, all seven large analytic optima recovered | Met | Supplied conflict graphs and integer priorities | +| H4: Repair-invariant queries | Zero classification/witness failures; preserve all oracle-certain answers and reject the tie-control false certainty | Met | Priority-relative graph-repair semantics | +| H5: Delta-indexed graph updates | Zero mutation mismatches/invalid-state changes; at least 75% fewer declared boundary visits on the frozen local workload | Met | In-memory controlled graph mutations | + +These outcomes must not be combined into a semantic success percentage. A failed conjunction remains failed even when one endpoint improves. + +## 2. H1: Source-random-effects contamination forecasts + +For each source, n counts base1 SUPPORTS/REFUTES decisions and k counts disagreements with the stored gold relation label. Empty accepted groups are excluded. The development-only marginal error estimate is (sum(k)+0.5)/(sum(n)+1). A grid of intraclass correlations {0,0.01,0.05,0.1,0.2,0.4,0.6,0.8} is selected by beta-binomial development log likelihood with a lower-correlation tie rule. At positive rho, alpha=mu(1/rho-1) and beta=(1-mu)(1/rho-1); contamination is 1-B(alpha,beta+n)/B(alpha,beta). The rho=0 comparator has the identical marginal estimate and uses 1-(1-mu)^n. Predictions consume only source exposure counts, not evaluation labels. + +Development selects mu=0.121622 and rho=0.6. All 40 source-excluded development sensitivity fits are retained; held-out source IDs do not enter their training fits. Evaluation uses 98 nonempty source groups, of which 60 are singletons. Singletons cannot distinguish these dependence models at fixed marginal error rate. + +| Forecast | Brier | Clipped log loss | Predicted contaminated | Observed contaminated | Bias | +|---|---:|---:|---:|---:|---:| +| Prior feature-risk model | 0.128923 | 0.467074 | 8.04% | 15.31% | -7.26% | +| Prior development-selected multiplier | 0.134900 | 0.441344 | 13.64% | 15.31% | -1.67% | +| Same-marginal independence | 0.140410 | 0.457499 | 17.38% | 15.31% | +2.07% | +| Source random effects | 0.131444 | 0.435042 | 14.07% | 15.31% | -1.24% | + +The new-minus-same-marginal Brier difference is -0.008966; its descriptive paired 95% interval is [-0.021889, +0.001383], and its 99% interval is [-0.026211, +0.004667]. The frozen conjunction is **not met**. The forecast can improve relative to its matched marginal comparator while still failing the improvement threshold or regressing versus the richer prior feature model. No accepted edge is changed. + +Positive correlation lowers the probability of at least one error at fixed marginal error probability and exposure, because errors cluster rather than spread across groups. It does not universally make an underpredicted contamination rate more accurate. The fitted exchangeable beta-binomial model is neither a distribution-shift guarantee nor a calibration certificate. + +![H1. All forecasts scored on the same 98 nonempty evaluation source groups.](../graph_synthesis/certificates/figures/01_source_risk.png) + +## 3. H2: Lineage bounds without assumed independence + +A monotone DNF formula is evaluated over at most 1,024 Boolean worlds for at most ten atoms. Nonnegative world masses sum to one and satisfy supplied marginal and optional conjunction constraints. Two linear programs minimize and maximize the DNF truth indicator. They use SciPy HiGHS; solver failures, infeasibility and capacity excess return [0,1] with no admission certificate. These probabilities are supplied model inputs, not Jev scores reinterpreted as fact probabilities. + +For a minimization objective c, equality matrix A, target b and returned dual y, c dot z >= b dot y + min(0,min(c-A-transpose y)) for every feasible simplex vector z. The implementation subtracts an additional 1e-9 times (1+sum(abs(y))) outward pad. It saves primal support, duals and residuals. This is a checked, outward-padded floating-point bound conditional on supplied constraints, **not formal interval-arithmetic verification or an unconditional truth guarantee**. + +The 128 seeded arbitrary-joint fixtures have 0 containment, constraint-monotonicity, invariance or control failures. The 50 two-event OR/AND grid cases match their analytic Frechet bounds within 1e-8. Mean interval width is 0.326563 using marginals alone and 0.239902 with one valid conjunction constraint. The primary target is **met**. + +| Two 0.8-marginal sources supporting an OR | Probability or interval | Admission at 0.95 | +|---|---:|---| +| Assume independence without justification | 0.96 | Yes; false under perfect correlation | +| Actual perfectly correlated sources | 0.80 | No | +| Marginals only, arbitrary dependence | Approximately [0.80,1.00] | No | +| Explicit joint probability 0.80 | Approximately [0.80,0.80] | No | +| Explicit joint probability 0.64 | Approximately [0.96,0.96] | Yes, conditional on this constraint | + +Across 20 marginal settings, the method prevents 20 false admissions from an unjustified independence assumption. It also withholds 20 genuinely high-probability admissions when only marginals are provided, and recovers 20 when the correct joint constraint is supplied. That conservatism is a real information tradeoff, not free accuracy. Infeasible and 11-atom cases stage. The false-premise control supplies 0.99 for an actually 0.50-probability atom; the resulting conditional bound still admits it. Arithmetic cannot repair incorrect evidence metadata. + +![H2. The correlated-source negative control separates assumed independence from valid lower bounds.](../graph_synthesis/certificates/figures/02_dependence_bounds.png) + +## 4. H3: Integer-flow certificates for bipartite conflicts + +A bipartite conflict component is reduced to minimum weighted vertex cover: source-to-left and right-to-sink capacities are supplied priorities, and conflict arcs have capacity one greater than total priority. An integer max-flow/min-cut certificate gives a minimum cover; its complement is a maximum-weight independent set. The verifier reconstructs the original network, checks capacity bounds and conservation, proves flow equals cut capacity, checks the cover/independent-set relationship and verifies the reported utility. No hidden tie-breaking weight perturbation changes the objective. + +The new route supports components up to 2,048 vertices and 50,000 edges. Non-bipartite components use the actual prior cutset solver with its original limits; unsupported cases stage rather than silently accepting a heuristic solution. These are explicit conflict graphs, not newly extracted relationships. + +All 128 small bipartite cases match independent subset enumeration and their flow certificates. Another 128 small general graphs have no utility regression. Reordering or repeating edges preserves outputs. Total failures: 0. The primary target is **met**. + +| Analytic topology | Vertices | Edges | Previous utility | New utility | Oracle utility | +|---|---:|---:|---:|---:|---:| +| complete bipartite | 32 | 256 | 0 | 48 | 48 | +| complete bipartite | 64 | 1024 | 0 | 96 | 96 | +| complete bipartite | 128 | 4096 | 0 | 192 | 192 | +| complete bipartite | 256 | 16384 | 0 | 384 | 384 | +| even cycle | 512 | 512 | 0 | 256 | 256 | +| even cycle | 1024 | 1024 | 0 | 512 | 512 | +| even cycle | 2048 | 2048 | 0 | 1024 | 1024 | + +Complete-bipartite fixtures assign priority 3 on one balanced side and 2 on the other; cycles use unit priorities. Prior zero utility means staged, not an incorrect accepted graph. The 2,050-cycle and 50,176-edge complete-bipartite controls stage; a 17-cycle still uses the prior exact route, while the dense 17-clique remains unsupported. A false assertion of priority 9 still defeats a true conflicting assertion of priority 8. Certified optimality concerns supplied priorities, not semantic truth. + +![H3. Previously staged analytic conflict components are solved exactly with integer certificates.](../graph_synthesis/certificates/figures/03_bipartite_capacity.png) + +## 5. H4: Queries invariant across admissible graph repairs + +Let U be maximum supplied priority. Admissible repairs are all independent sets with utility at least U-epsilon, using epsilon=0 and floor(0.05U). For a conjunction of up to eight required vertices, constrained inclusion tests whether some admissible repair contains the conjunction. Constrained exclusion of each required vertex tests whether any admissible repair falsifies it. Answers are certain, ambiguous or impossible, with an explicit counterexample for ambiguity. Staged optimization propagates unsupported status rather than false certainty. + +Across 128 seeded general graphs and two tolerance settings, classifications and witnesses have 0 oracle failures. Every oracle-certain answer is retained and larger tolerance creates no new certain answers. Among exact-optimum random cases, 2 affirmative answers from one selected optimum are not invariant across all optima. The explicit equal-weight conflict control also returns an alternative repair that falsifies the chosen optimum's affirmative answer. Primary target: **met**. + +| Repair tolerance | Certain conjunctions | Ambiguous conjunctions | Impossible conjunctions | +|---|---:|---:|---:| +| Exact maximum priority | 69 | 7 | 52 | +| Within floor(5% of optimum) | 65 | 12 | 51 | + +Some sampled conjunctions are empty and hence tautological; these counts describe the fixture distribution, not a population query-success rate. Conflicting conjunctions are impossible. Zero-priority optional vertices can be ambiguous. The high-priority false assertion is still repair-certain under its supplied objective: repair certainty must not be presented as factual certainty. + +![H4. Query status across exact and near-optimal admissible repairs.](../graph_synthesis/certificates/figures/04_repair_queries.png) + +## 6. H5: Delta-indexed maintenance without global snapshot rescans + +The in-memory index validates an initial graph and maintains private adjacency, priorities, component membership and cached solutions. An explicit mutation API supports weight changes, vertex insertion/deletion and edge insertion/deletion. It copies, validates, discovers and solves only affected old components and their replacements before publication. Bridge insertion joins two scopes; bridge or vertex deletion discovers splits inside the affected old scope. Unaffected solutions are reused. Mutations return selection/staging deltas and aggregate utility rather than forcing a full graph materialization. Snapshot audit is a separate operation. + +Independent full-snapshot mutation and optimization checks find 0 failures across 640 seeded mutations, deliberate bridge controls and locality/stress workloads. Each of the five mutation types appears 128 times in the randomized test. Invalid mutations produce 0 state changes; tests also inject a solver exception before publication. This is not a concurrent or durable database transaction protocol. + +| Workload, including cold build | Delta-index boundary visits | Full-rescan boundary visits | Reduction | +|---|---:|---:|---:| +| 512 eight-vertex components; 128 local updates | 65,920 | 7,391,616 | 99.11% | +| One 64-vertex component; 128 updates | 123,096 | 123,096 | 0.00% | + +The frozen primary target is **met**. The comparator uses the same optimizer but copies, validates, discovers and prepares every component on every update. The boundary metric counts declared passes for graph copying, validation, discovery, solver input, mutation validation and publication. It explicitly excludes sorting, optimizer-internal work, certificate-check work and the audit materialization. Therefore the percentage is **not** a measured reduction in all CPU operations, service latency or database I/O. It improves on the previous study's solver-vertex-only scope, but is still an instrumented boundary measure. + +### Separate single-host timing measurements + +Three repetitions alternate policy order. Timings include cold construction and 64 updates, but exclude snapshot comparison for both policies. Medians below are descriptive, not a primary endpoint or a portable speed guarantee. All individual measurements and environment details are retained in [timings.json](../graph_synthesis/certificates/timings.json). + +| Components | Vertices | Delta-index median seconds | Full-rescan median seconds | Ratio | +|---:|---:|---:|---:|---:| +| 16 | 128 | 0.011026 | 0.125704 | 11.40x | +| 64 | 512 | 0.017190 | 0.555562 | 32.32x | +| 256 | 2048 | 0.042154 | 2.336416 | 55.43x | +| 512 | 4096 | 0.075171 | 4.910725 | 65.33x | + +![H5. Benefits depend on component locality; the connected control has no counted-work saving.](../graph_synthesis/certificates/figures/05_delta_locality.png) + +## 7. Interpretation, uncertainty and falsifying controls + +H1 uses 4,000 paired source-group bootstrap draws with seed 20260922 and fixed fitted policies. The 95% and 99% percentile intervals are descriptive, not simultaneous; they omit fitting uncertainty and cannot undo repeated use of the same evaluation corpus. Neither a favorable score nor a passed point threshold would establish out-of-distribution calibration. H2-H5 finite/analytic fixtures test implementation behavior under supplied assumptions rather than estimates of Jev accuracy. + +The retained negative controls are essential: incorrect marginal probabilities defeat lineage certificates; false priorities defeat semantic interpretation of flow certificates and repair certainty; uncertain dependence withholds some valid admissions; unsupported topologies stage; and connected updates remove locality savings. The structural methods remain opt-in research prototypes. Candidate-generation recall, semantic relation qualifiers, source independence discovery and real database behavior are not solved by this suite. + +The next independent semantic claim still requires a policy frozen before inspecting a new source-disjoint, independently adjudicated corpus, and matched evidence/resource budgets against an implemented external system such as KARMA. This suite does not claim that such a comparison ran. + +## 8. Reproducibility and artifact integrity + +```bash +python -m pip install -r graph_synthesis/certificates/requirements.txt +python -B -m graph_synthesis.certificates.run --timings +python -B -m unittest discover -s graph_synthesis/certificates/tests -v +python -B -m graph_synthesis.certificates.run --check +python -B -m graph_synthesis.certificates.report --figures --update-paper +python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf +``` + +The [machine-readable results](../graph_synthesis/certificates/results.json) preserve fit candidates, held-source exclusions, predictions, complete fixture inputs, joint-world probabilities, LP witnesses, graph certificates, mutation events, oracle outputs and source hashes. Timings are deliberately separate from deterministic replay. Five SVG/PNG figure pairs and a summary CSV are regenerated from recorded results; the extension manifest hashes its source and artifacts. Original raw evidence and the archived manuscript remain unchanged, and the complete current manuscript retains every preceding study. + +## 9. Primary foundations + +- [SciPy beta-binomial definition](https://docs.scipy.org/doc/scipy-1.17.0/reference/generated/scipy.stats.betabinom.html) and [HiGHS linear programming and dual marginals](https://docs.scipy.org/doc/scipy-1.17.0/reference/optimize.linprog-highs.html). +- [Optimal Union Probability Interval Is NP-Hard](https://arxiv.org/abs/2605.03556), situating finite-world extremal-probability programs and their complexity. +- [Distributed CONGEST Approximation of Weighted Vertex Covers and Matchings](https://arxiv.org/abs/2111.10577), an antecedent for weighted bipartite cover formulations. +- [Computational Complexity of Preferred Subset Repairs on Data-Graphs](https://arxiv.org/abs/2402.09265) and [Consistent Query Answers in the Presence of Universal Constraints](https://arxiv.org/abs/0809.1551). +- [A Feature-based Classification of Model Repair Approaches](https://arxiv.org/html/1504.03947v1) and the [TypeSafe typed-decision interface](https://docs.typesafe.ai/introduction). + +These sources motivate established components; they do not validate this implementation or certify worldwide novelty. + + + # Do additional Jev calls improve graph edges? 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