From 8187cc4e4b98e40f3146ce787b71f262b62b3abd Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 10:51:11 -0400 Subject: [PATCH 01/10] research: freeze five structural hypotheses before execution --- graph_synthesis/post_certificate/PROTOCOL.md | 149 +++++++++++++++++++ 1 file changed, 149 insertions(+) create mode 100644 graph_synthesis/post_certificate/PROTOCOL.md diff --git a/graph_synthesis/post_certificate/PROTOCOL.md b/graph_synthesis/post_certificate/PROTOCOL.md new file mode 100644 index 0000000..8b20e6a --- /dev/null +++ b/graph_synthesis/post_certificate/PROTOCOL.md @@ -0,0 +1,149 @@ +# Frozen protocol: post-certificate graph-synthesis improvements + +Baseline: `9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98` (merged PR #20) +Protocol date: 2026-09-18 +Status: frozen before implementation/execution +Fresh Jev service calls planned: 0 +New scientific documents planned: 0 +Seed: 20260923 + +## Scope and evidence boundary + +The merged evidence separates semantic extraction quality from structural correctness. Repeated prompt/routing/calibration variants have not established a robust semantic advantage, while bounded exact graph algorithms, dependence-aware certificates, repair semantics and delta maintenance have repeatedly passed controlled tests. This protocol therefore tests five **structural / decision-theoretic extensions** suggested by those results rather than another prompt variant. + +The five mechanisms below were not found as executed implementations in the baseline repository inventory. That is a repository-scope statement, not a worldwide novelty claim. Their ingredients have substantial prior art: treewidth/variable-elimination dynamic programming, incremental view maintenance and transactions, query resilience/deletion propagation, robust/minimax decision making under imprecise probabilities, and weighted constraint/hypergraph optimization. + +No experiment changes candidate extraction, accepted Jev edges, production defaults, or the frozen original evidence. Controlled priorities, probabilities, costs and constraints are supplied inputs and must not be reinterpreted as calibrated factual truth. + +## H1 - Induced-width exact conflict optimization + +### Motivation +Prior studies established exact handling for bounded enumeration, forests, cutsets and bipartite conflict graphs. A remaining gap is connected non-bipartite cyclic structure that is still tractable because its induced width is small. + +### Method +Implement max-sum variable elimination for maximum-weight compatible assertion selection over pairwise conflict factors. A deterministic min-fill elimination order is computed without using assertion weights. Execution is allowed only when: +- induced width <= 4; +- every factor scope is <= 5 variables; +- cumulative materialized factor states <= 250,000. + +Otherwise the component is staged without a partial write. The returned assignment includes objective value, selected assertion IDs, elimination order, induced width and a direct feasibility check. + +### Frozen test +- 160 seeded small connected graphs, 8-16 vertices, generated from bounded-width constructions with random non-negative integer weights. +- Independent exhaustive subset oracle for every small fixture. +- 8 connected large triangle-chain / partial-2-tree fixtures from 33 through 1,025 vertices, checked by an independently implemented chain dynamic program. +- 5 over-width clique controls (K6-K10) that must stage. +- 8 input-order permutations per first 64 small fixtures. + +### Primary target +Zero objective, feasibility, witness or permutation failures; all 8 large tractable fixtures solved; all 5 over-width controls staged. On the 12-16 vertex small subset, counted max-sum table states must be at least 90% below the full 2^n assignment count in aggregate. + +## H2 - Transactional batch delta maintenance + +### Motivation +Merged studies validate local incremental maintenance and revision checks, but explicitly stop at sequential in-memory updates. Real graph changes often arrive as batches whose derived consequences must be all-or-nothing. + +### Method +Implement an in-memory transactional dependency engine for Boolean primitives and derived facts over an acyclic dependency graph. A transaction: +1. validates expected revision and every primitive change; +2. computes the affected reverse-dependency closure; +3. evaluates the new values in topological order against a copy-on-write overlay; +4. commits the complete overlay only if every evaluation succeeds. + +Injected evaluation failure, malformed update, or stale revision must leave both values and revision unchanged. This is atomic batch behavior only; no crash durability or concurrent database-isolation claim is made. + +### Frozen test +- 128 seeded modular DAGs with 64 primitive nodes and 256 derived nodes. +- Five 1-4 primitive transactions per DAG (640 accepted local transactions total), each compared with independent full recomputation from the post-transaction primitive snapshot. +- 64 injected evaluation-failure controls, 64 stale-revision controls and 64 malformed-update controls. +- 16 connected-control DAGs in which one primitive intentionally reaches nearly every derived node. + +### Primary target +Zero snapshot mismatches and zero partial-write/revision failures. Local transactions must perform at least 80% fewer derived-node evaluations than full recomputation in aggregate. The connected control must remain reported separately and must not be used to infer universal savings. + +## H3 - Exact query-resilience certificates + +### Motivation +The repository can classify query answers across repairs, but binary certainty does not quantify how much evidence must change before a currently true monotone query becomes false. A robustness margin can distinguish a query supported by many independent proof paths from one resting on a single fragile atom. + +### Method +For a monotone DNF lineage with positive integer evidence-removal costs, compute the minimum-cost evidence set whose deletion hits every active proof clause. The implementation uses: +- incidence-component decomposition; +- forced singleton reductions; +- branch-and-bound with deterministic branching; +- a disjoint-clause lower bound; +- explicit deletion witness validation. + +Connected components above 18 distinct evidence atoms stage rather than silently approximate. + +### Frozen test +- 160 seeded small DNF fixtures with <=12 atoms and 2-10 proof clauses. +- Independent exhaustive deletion-subset oracle for every small fixture. +- 8 analytic large fixtures composed of hundreds to thousands of disconnected proof components, with closed-form expected resilience. +- 6 over-cap connected controls that must stage. +- Mutation checks that every returned deletion set actually falsifies the query and removing any positive-cost proper subset does not beat the reported optimum. + +### Primary target +Zero optimum/witness/oracle failures; all 8 analytic large fixtures solved exactly; all 6 over-cap controls staged. The result is a resilience certificate relative to supplied lineage and costs, not factual truth. + +## H4 - Interval-minimax query review + +### Motivation +Prior query-directed review assumes point probabilities. Multiple merged studies show that point risk estimates and calibration can fail. Review selection should therefore be stress-tested against uncertainty in the supplied probabilities themselves. + +### Method +Each primitive has an independent probability interval [lo, hi]. For a fixed review budget of two primitives, choose the review set that minimizes the **worst-case expected residual Bernoulli variance** of supplied monotone queries across all interval endpoint vectors. Perfect review reveals the selected primitive states. The production method evaluates query probability by memoized Shannon recursion; the independent oracle enumerates complete Boolean worlds directly. + +A midpoint selector using the same expected-loss objective at interval midpoints is the comparator. This study does not remove the conditional-independence assumption; it tests robustness to marginal imprecision only. + +### Frozen test +- 96 seeded six-atom interval fixtures with 1-3 monotone DNF queries. +- 32 engineered interval controls where midpoint ranking is intentionally fragile. +- Every candidate review pair checked by the independent world-enumeration oracle. +- Degenerate point-interval controls must reduce exactly to the point-probability solution. + +### Primary target +Zero production/oracle objective discrepancies above 1e-10. The minimax selector's worst-case objective must never exceed the midpoint selector's worst-case objective, and must be strictly lower on at least 20% of the 128 non-degenerate fixtures. + +## H5 - N-ary conflict constraints + +### Motivation +Every merged graph-repair optimizer represents incompatibility as pairwise edges. Some consistency rules are genuinely n-ary (for example, “not all three assertions may coexist”) and a pairwise clique projection can remove compatible pairs unnecessarily. + +### Method +Generalize max-sum elimination to forbidden hyperedge factors: a factor contributes negative infinity only when every member of the forbidden set is selected. Pairwise conflicts are size-2 hyperedges. The same deterministic induced-width and state caps as H1 apply, and returned selections are checked directly against every hyperedge. + +### Frozen test +- 160 seeded small weighted hypergraphs with 8-14 vertices, size-2 and size-3 forbidden sets, compared with exhaustive subset optimization. +- 8 large three-consecutive-forbidden chain fixtures from 64 through 2,048 vertices. With unit weights, the known optimum is n - floor(n/3). +- Pairwise-clique projection of each size-3 hyperedge is retained as an explicit over-conservative baseline. +- 5 high-arity / over-width controls must stage. +- 8 permutations per first 64 small fixtures. + +### Primary target +Zero objective/feasibility/oracle/permutation failures; all 8 large chain fixtures solved exactly; all 5 controls staged. On every large chain, the n-ary optimizer must retain at least as much supplied utility as pairwise projection and strictly more on all fixtures. + +## Reporting and falsification rules + +- A failed conjunction remains failed even if some endpoints improve. +- No five-result success percentage may be reported. +- Negative/null outcomes remain in results and figures. +- Work counters (factor states, node evaluations) are not wall-clock, API-latency or cloud-cost measurements. +- No controlled algorithm result may be described as independent Jev semantic accuracy. +- No external system superiority is claimed without a matched executed baseline. +- The current manuscript must preserve every prior study and append this section additively. + +## Reproducibility target + +The implementation will provide: +- `graph_synthesis/post_certificate/run.py` +- `graph_synthesis/post_certificate/methods.py` +- `graph_synthesis/post_certificate/tests/` +- `graph_synthesis/post_certificate/results.json` +- `graph_synthesis/post_certificate/summary.csv` +- `graph_synthesis/post_certificate/RESULTS.md` +- five deterministic SVG figures +- additive updates to `CURRENT_RESULTS.md` and `manuscript/paper-current.md` +- a read-only GitHub Actions workflow that replays all five experiments and tests. + +Protocol thresholds, fixture counts, caps and seed must not be changed after benchmark execution except to correct an implementation bug; any such correction must be documented without changing the frozen success criteria. From 7ff43a0772f06abb121980e7f96edbc2a3b93885 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 10:52:02 -0400 Subject: [PATCH 02/10] research: implement and execute five frozen structural tests --- graph_synthesis/post_certificate/__init__.py | 0 graph_synthesis/post_certificate/methods.py | 421 +++++++++++++ graph_synthesis/post_certificate/report.py | 233 +++++++ graph_synthesis/post_certificate/results.json | 592 ++++++++++++++++++ graph_synthesis/post_certificate/run.py | 299 +++++++++ graph_synthesis/post_certificate/summary.csv | 6 + .../post_certificate/tests/test_methods.py | 115 ++++ 7 files changed, 1666 insertions(+) create mode 100644 graph_synthesis/post_certificate/__init__.py create mode 100644 graph_synthesis/post_certificate/methods.py create mode 100644 graph_synthesis/post_certificate/report.py create mode 100644 graph_synthesis/post_certificate/results.json create mode 100644 graph_synthesis/post_certificate/run.py create mode 100644 graph_synthesis/post_certificate/summary.csv create mode 100644 graph_synthesis/post_certificate/tests/test_methods.py diff --git a/graph_synthesis/post_certificate/__init__.py b/graph_synthesis/post_certificate/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/graph_synthesis/post_certificate/methods.py b/graph_synthesis/post_certificate/methods.py new file mode 100644 index 0000000..4c26055 --- /dev/null +++ b/graph_synthesis/post_certificate/methods.py @@ -0,0 +1,421 @@ +from __future__ import annotations + +from dataclasses import dataclass +from functools import lru_cache +from itertools import combinations, product +from math import inf +from typing import Dict, Iterable, List, Mapping, Sequence, Tuple, FrozenSet, Set + +NEG_INF = -10**30 + + +@dataclass(frozen=True) +class Factor: + scope: Tuple[int, ...] + table: Mapping[Tuple[int, ...], int] + + def value(self, assignment: Mapping[int, int]) -> int: + key = tuple(assignment[v] for v in self.scope) + return self.table[key] + + +@dataclass +class EliminationResult: + status: str + objective: int | None + selected: Tuple[int, ...] + order: Tuple[int, ...] + induced_width: int + factor_states: int + + +def _scope_graph(n: int, factors: Sequence[Factor]) -> List[Set[int]]: + adj=[set() for _ in range(n)] + for f in factors: + for a,b in combinations(f.scope,2): + adj[a].add(b); adj[b].add(a) + return adj + + +def min_fill_order(n: int, factors: Sequence[Factor]) -> Tuple[List[int], int]: + adj=_scope_graph(n,factors) + active=set(range(n)) + order=[]; width=0 + while active: + best=None + for v in active: + neigh=sorted(adj[v] & active) + fill=0 + for i,a in enumerate(neigh): + aa=adj[a] + for b in neigh[i+1:]: + if b not in aa: + fill+=1 + key=(fill,len(neigh),v) + if best is None or key EliminationResult: + n=len(weights) + factors: List[Factor]=[] + for v,w in enumerate(weights): + factors.append(Factor((v,),{(0,):0,(1,):int(w)})) + for raw in forbidden_scopes: + scope=tuple(sorted(set(raw))) + if len(scope)<2: + raise ValueError('forbidden scope must have at least 2 vertices') + table={bits:(NEG_INF if all(bits) else 0) for bits in product((0,1), repeat=len(scope))} + factors.append(Factor(scope,table)) + # Exact fast path for the long path/triangle-chain fixtures used by the + # protocol: every non-unary scope lies inside a three-vertex sliding + # window, so 0..n-1 is the same endpoint-first min-fill elimination. + nonunary=[f.scope for f in factors if len(f.scope)>1] + if n>64 and nonunary and all(max(s)-min(s)<=2 for s in nonunary): + order=list(range(n)); width=max(max(s)-min(s) for s in nonunary) + else: + order,width=min_fill_order(n,factors) + if width>width_cap: + return EliminationResult('staged',None,tuple(),tuple(order),width,0) + work=list(factors) + back=[] + states=0 + for v in order: + touched=[f for f in work if v in f.scope] + work=[f for f in work if v not in f.scope] + union=sorted({x for f in touched for x in f.scope if x!=v}) + if len(union)>width_cap: + return EliminationResult('staged',None,tuple(),tuple(order),max(width,len(union)),states) + choices={} + new_table={} + for bits in product((0,1), repeat=len(union)): + base=dict(zip(union,bits)) + vals=[] + for xv in (0,1): + a=dict(base); a[v]=xv + score=0 + for f in touched: + fv=f.value(a) + if fv<=NEG_INF: + score=NEG_INF; break + score+=fv + vals.append(score) + states+=1 + # deterministic tie: prefer selected only when strictly better + if vals[1]>vals[0]: + best=1; val=vals[1] + else: + best=0; val=vals[0] + choices[bits]=best + new_table[bits]=val + if states>state_cap: + return EliminationResult('staged',None,tuple(),tuple(order),width,states) + back.append((v,tuple(union),choices)) + work.append(Factor(tuple(union),new_table)) + total=0 + for f in work: + if f.scope: + raise AssertionError('nonconstant factor remains') + total+=f.table[()] + assignment={} + for v,scope,choices in reversed(back): + key=tuple(assignment[x] for x in scope) + assignment[v]=choices[key] + selected=tuple(v for v in range(n) if assignment.get(v,0)) + # direct feasibility + for scope in forbidden_scopes: + if all(assignment.get(v,0) for v in scope): + raise AssertionError('returned infeasible assignment') + obj=sum(weights[v] for v in selected) + if obj!=total: + raise AssertionError((obj,total)) + return EliminationResult('solved',obj,selected,tuple(order),width,states) + + +def brute_force_optimum(weights: Sequence[int], forbidden_scopes: Sequence[Sequence[int]]) -> Tuple[int,Tuple[int,...]]: + n=len(weights); best=-1; best_sel=tuple() + for mask in range(1<>v)&1 for v in scope): + ok=False; break + if not ok: continue + score=sum(weights[v] for v in range(n) if (mask>>v)&1) + sel=tuple(v for v in range(n) if (mask>>v)&1) + if score>best or (score==best and sel bool: + if op=='and': return all(vals) + if op=='or': return any(vals) + if op=='xor': + x=False + for v in vals: x ^= bool(v) + return x + raise ValueError(op) + + def full_recompute(self, primitive_values: Mapping[int,bool]) -> Dict[int,bool]: + values={i:bool(primitive_values[i]) for i in range(self.primitive_count)} + for node in self.derived_order: + op,deps=self.definitions[node] + values[node]=self.eval_op(op,[values[d] for d in deps]) + return values + + def transact(self, values: Mapping[int,bool], revision: int, changes: Mapping[int,bool], *, expected_revision: int, fail_node: int|None=None): + if expected_revision!=revision: + raise RuntimeError('stale revision') + for p,v in changes.items(): + if not isinstance(p,int) or p<0 or p>=self.primitive_count or not isinstance(v,bool): + raise ValueError('malformed update') + overlay=dict(values) + changed=[] + for p,v in changes.items(): + if overlay[p]!=v: + overlay[p]=v; changed.append(p) + affected=set() + frontier=list(changed) + while frontier: + x=frontier.pop() + for y in self.reverse[x]: + if y not in affected: + affected.add(y); frontier.append(y) + evals=0 + for node in self.derived_order: + if node not in affected: continue + if fail_node is not None and node==fail_node: + raise RuntimeError('injected evaluation failure') + op,deps=self.definitions[node] + overlay[node]=self.eval_op(op,[overlay[d] for d in deps]) + evals+=1 + return overlay,revision+1,evals + + +def _normalize_clauses(clauses: Iterable[Iterable[int]]) -> Tuple[FrozenSet[int],...]: + xs=sorted({frozenset(c) for c in clauses}, key=lambda s:(len(s),tuple(sorted(s)))) + out=[] + for c in xs: + if not c: + return (frozenset(),) + if any(d.issubset(c) for d in out): + continue + out.append(c) + return tuple(out) + + +def _incidence_components(clauses: Sequence[FrozenSet[int]]) -> List[Tuple[FrozenSet[int],Tuple[FrozenSet[int],...]]]: + atoms=set().union(*clauses) if clauses else set() + adj={a:set() for a in atoms} + for c in clauses: + for a in c: + adj[a].update(c-{a}) + comps=[]; seen=set() + for a in sorted(atoms): + if a in seen: continue + stack=[a]; seen.add(a); comp=set() + while stack: + x=stack.pop(); comp.add(x) + for y in adj[x]: + if y not in seen: seen.add(y); stack.append(y) + cclauses=tuple(c for c in clauses if c & comp) + comps.append((frozenset(comp),cclauses)) + return comps + + +def _disjoint_clause_lb(clauses: Sequence[FrozenSet[int]], costs: Mapping[int,int]) -> int: + used=set(); lb=0 + for c in sorted(clauses,key=lambda x:(len(x),min(costs[a] for a in x))): + if c.isdisjoint(used): + lb+=min(costs[a] for a in c) + used.update(c) + return lb + + +def _solve_hitting_component(clauses: Sequence[FrozenSet[int]], costs: Mapping[int,int]) -> Tuple[int,Set[int]]: + clauses=_normalize_clauses(clauses) + best_cost=10**18; best_set:set[int]=set() + memo={} + def rec(cs: Tuple[FrozenSet[int],...], chosen: FrozenSet[int], cost: int): + nonlocal best_cost,best_set + if not cs: + if cost=best_cost: + return + # forced singleton first, else shortest cheapest clause + target=min(cs,key=lambda c:(len(c),sum(costs[a] for a in c),tuple(sorted(c)))) + for a in sorted(target,key=lambda x:(costs[x],x)): + nc=tuple(c for c in cs if a not in c) + rec(_normalize_clauses(nc), chosen|frozenset((a,)), cost+costs[a]) + rec(tuple(clauses),frozenset(),0) + return best_cost,best_set + + +def query_resilience(clauses: Sequence[Sequence[int]], costs: Mapping[int,int], *, component_cap:int=18): + norm=_normalize_clauses(clauses) + if not norm: + return {'status':'already_false','cost':0,'delete':tuple()} + if norm==(frozenset(),): + return {'status':'unstageable_true','cost':None,'delete':tuple()} + total=0; chosen=set() + for atoms,cc in _incidence_components(norm): + if len(atoms)>component_cap: + return {'status':'staged','cost':None,'delete':tuple()} + c,s=_solve_hitting_component(cc,costs) + total+=c; chosen.update(s) + delete=tuple(sorted(chosen)) + # witness: all clauses hit + if any(set(c).isdisjoint(chosen) for c in norm): + raise AssertionError('invalid deletion witness') + return {'status':'solved','cost':total,'delete':delete} + + +def brute_force_resilience(clauses: Sequence[Sequence[int]], costs: Mapping[int,int]): + atoms=sorted(set().union(*(set(c) for c in clauses))) + best=10**18; best_set=tuple() + for mask in range(1<>i)&1} + if all(set(c)&chosen for c in clauses): + cost=sum(costs[a] for a in chosen) + sel=tuple(sorted(chosen)) + if cost Tuple[Tuple[int,...],...]: + out=[] + if value: + for c in clauses: + if atom in c: + nc=tuple(x for x in c if x!=atom) + if not nc: return (tuple(),) + out.append(nc) + else: + out.append(c) + else: + for c in clauses: + if atom not in c: out.append(c) + # remove duplicates and supersets + sets=_normalize_clauses(out) + return tuple(tuple(sorted(s)) for s in sets) + + +def dnf_probability_shannon(clauses: Sequence[Sequence[int]], probs: Mapping[int,float]) -> float: + norm=tuple(tuple(sorted(s)) for s in _normalize_clauses(clauses)) + @lru_cache(maxsize=None) + def rec(cs: Tuple[Tuple[int,...],...]) -> float: + if not cs: return 0.0 + if cs==(tuple(),): return 1.0 + atoms=sorted({a for c in cs for a in c}) + a=atoms[0]; p=probs[a] + return p*rec(_condition_dnf(cs,a,1))+(1-p)*rec(_condition_dnf(cs,a,0)) + return rec(norm) + + +def dnf_probability_worlds(clauses: Sequence[Sequence[int]], probs: Mapping[int,float], fixed: Mapping[int,int]|None=None) -> float: + fixed=dict(fixed or {}) + atoms=sorted(set(probs)-set(fixed)) + total=0.0 + for bits in product((0,1),repeat=len(atoms)): + ass={**fixed,**dict(zip(atoms,bits))} + pw=1.0 + for a,b in zip(atoms,bits): + p=probs[a]; pw*=p if b else (1-p) + truth=any(all(ass.get(a,0) for a in c) for c in clauses) + if truth: total+=pw + return total + + +def expected_residual_variance(queries: Sequence[Sequence[Sequence[int]]], probs: Mapping[int,float], review: Sequence[int], *, method='shannon') -> float: + review=tuple(review) + total=0.0 + for obs in product((0,1), repeat=len(review)): + fixed=dict(zip(review,obs)) + pobs=1.0 + for a,b in fixed.items(): + p=probs[a]; pobs*=p if b else (1-p) + if pobs==0: continue + for q in queries: + cond=[] + forced_true=False + for c in q: + impossible=False; rem=[] + for a in c: + if a in fixed: + if not fixed[a]: impossible=True; break + else: rem.append(a) + if not impossible: + if not rem: forced_true=True; break + cond.append(rem) + if forced_true: + qp=1.0 + elif not cond: + qp=0.0 + else: + if method=='shannon': + qp=dnf_probability_shannon(cond,probs) + else: + qp=dnf_probability_worlds(cond,probs) + total+=pobs*qp*(1-qp) + return total + + +def select_interval_minimax(queries, intervals: Mapping[int,Tuple[float,float]], budget:int=2): + atoms=tuple(sorted(intervals)) + candidates=list(combinations(atoms,budget)) + endpoints=[] + for bits in product((0,1),repeat=len(atoms)): + endpoints.append({a:intervals[a][bits[i]] for i,a in enumerate(atoms)}) + worst={} + for r in candidates: + vals=[expected_residual_variance(queries,p,r,method='shannon') for p in endpoints] + worst[r]=max(vals) + minimax=min(candidates,key=lambda r:(worst[r],r)) + mid={a:(lo+hi)/2 for a,(lo,hi) in intervals.items()} + point_obj={r:expected_residual_variance(queries,mid,r,method='shannon') for r in candidates} + midpoint=min(candidates,key=lambda r:(point_obj[r],r)) + return {'minimax':minimax,'minimax_worst':worst[minimax], 'midpoint':midpoint,'midpoint_worst':worst[midpoint], 'all_worst':worst} + + +def oracle_interval_worst(queries, intervals: Mapping[int,Tuple[float,float]], review: Sequence[int]) -> float: + atoms=tuple(sorted(intervals)); worst=0.0 + for bits in product((0,1),repeat=len(atoms)): + p={a:intervals[a][bits[i]] for i,a in enumerate(atoms)} + v=expected_residual_variance(queries,p,review,method='world') + worst=max(worst,v) + return worst diff --git a/graph_synthesis/post_certificate/report.py b/graph_synthesis/post_certificate/report.py new file mode 100644 index 0000000..0024329 --- /dev/null +++ b/graph_synthesis/post_certificate/report.py @@ -0,0 +1,233 @@ +from __future__ import annotations + +import argparse, csv, hashlib, json, math, sys +from pathlib import Path + +HERE=Path(__file__).resolve().parent +ROOT=HERE.parents[1] +RESULTS=HERE/'results.json' +FIGDIR=HERE/'figures' +START='' +END='' + + +def pct(x): return f"{100*x:.2f}%" + +def fmt(x): + if isinstance(x,float): return f"{x:.6g}" + return str(x) + + +def svg_bar(title, labels, values, ylabel, note=''): + W,H=1200,700; ml,mr,mt,mb=120,60,100,150 + pw=W-ml-mr; ph=H-mt-mb + vmax=max(values) if values else 1 + if vmax<=0: vmax=1 + n=len(values); gap=24; bw=(pw-gap*(n+1))/max(1,n) + out=[f'', + '', + f'{title}', + f'', + f''] + for t in range(6): + y=mt+ph-(ph*t/5); val=vmax*t/5 + out += [f'', + f'{val:.3g}'] + for i,(lab,val) in enumerate(zip(labels,values)): + x=ml+gap+i*(bw+gap); h=ph*val/vmax; y=mt+ph-h + out += [f'', + f'{val:.4g}', + f'{lab}'] + out += [f'{ylabel}'] + if note: + out += [f'{note}'] + out.append('') + return '\n'.join(out)+'\n' + + +def svg_lines(title, xs, series, ylabel, note=''): + W,H=1200,700; ml,mr,mt,mb=120,70,100,150; pw=W-ml-mr; ph=H-mt-mb + ymax=max(max(vals) for _,vals in series) if series else 1; xmax=max(xs); xmin=min(xs) + if ymax<=0:ymax=1 + out=[f'','', + f'{title}', + f''] + dash=['','6,5','2,5','10,4'] + for si,(name,vals) in enumerate(series): + pts=[] + for x,v in zip(xs,vals): + xx=ml+pw*(x-xmin)/(xmax-xmin if xmax!=xmin else 1); yy=mt+ph-ph*v/ymax; pts.append(f'{xx:.1f},{yy:.1f}') + out.append(f'') + out.append(f'{name}') + for x in xs: + xx=ml+pw*(x-xmin)/(xmax-xmin if xmax!=xmin else 1) + out.append(f'{x}') + out.append(f'{ylabel}') + if note: out.append(f'{note}') + out.append('') + return '\n'.join(out)+'\n' + + +def generated(r): + h1,h2,h3,h4,h5=[r[f'H{i}'] for i in range(1,6)] + files={} + files['figures/01_induced_width.svg']=svg_bar( + 'H1 | Exact repair work versus full enumeration', + ['factor states','2^n assignments'],[h1['counted_factor_states'],h1['full_assignment_count']], 'counted states', + f"Aggregate 12-16 vertex fixtures; reduction {pct(h1['state_reduction'])}. Work count is not wall-clock speedup.") + files['figures/02_transactional_delta.svg']=svg_bar( + 'H2 | Transactional delta evaluations', ['local delta','full recompute','connected control'], + [h2['local_evaluations']/h2['accepted_transactions'],256,256], 'derived-node evaluations per transaction', + f"Local aggregate reduction {pct(h2['evaluation_reduction'])}; connected control reduction {pct(h2['connected_mean_reduction'])}.") + xs=[x['proof_components'] for x in h3['large']] + files['figures/03_resilience.svg']=svg_lines('H3 | Exact resilience on decomposable proof families',xs,[('certified cost',[x['cost'] for x in h3['large']])],'minimum deletion cost','All analytic large fixtures match the independent closed form.') + files['figures/04_interval_review.svg']=svg_bar('H4 | Interval-minimax review outcomes', + ['strict better','ties','worse'],[h4['strict_improvements'],h4['fixtures']-h4['strict_improvements']-h4['worse_than_midpoint'],h4['worse_than_midpoint']], 'fixtures', + f"Strict minimax improvement on {pct(h4['strict_fraction'])}; 0 oracle discrepancies.") + xs=[x['n'] for x in h5['large']] + files['figures/05_hypergraph.svg']=svg_lines('H5 | N-ary versus pairwise-projected repair',xs, + [('n-ary exact',[x['objective'] for x in h5['large']]),('pairwise projection',[x['pairwise_projection'] for x in h5['large']])], 'retained unit utility', + 'Pairwise clique projection is intentionally over-conservative for three-way-only conflicts.') + + summary=[ + ['hypothesis','result','primary_measure','value','boundary'], + ['H1','met','factor-state reduction',f"{h1['state_reduction']:.12f}",'counted DP states, not wall-clock'], + ['H2','met','local evaluation reduction',f"{h2['evaluation_reduction']:.12f}",'connected control saves 0%'], + ['H3','met','small/large oracle failures','0','supplied lineage and costs only'], + ['H4','met','strict minimax improvements',str(h4['strict_improvements']),'independent marginals with interval uncertainty'], + ['H5','met','large exact hypergraph fixtures',str(len(h5['large'])),'supplied n-ary constraints and utility'], + ] + files['summary.csv']='\n'.join(','.join(row) for row in summary)+'\n' + + report=f'''# Post-certificate structural extensions for Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five precommitted controlled extensions were executed after the dependence-aware certificate study. All five frozen **algorithmic** targets were met: bounded induced-width max-sum repair matched exhaustive oracles and solved connected non-bipartite triangle chains through 1,025 vertices; atomic batch delta maintenance matched full recomputation across 640 accepted transactions while reducing counted local derived-node evaluations by {pct(h2['evaluation_reduction'])}; exact query resilience matched 160 exhaustive small oracles and eight large analytic decompositions; interval-minimax review never worsened the frozen worst-case objective and strictly improved {h4['strict_improvements']}/{h4['fixtures']} fixtures ({pct(h4['strict_fraction'])}); and n-ary forbidden-set repair matched all small oracles and retained strictly more supplied utility than a pairwise clique projection on every large three-way-conflict chain. These are structural and decision-theoretic findings. They are not new Jev semantic-accuracy observations, deployment guarantees, or evidence of superiority to an external graph-synthesis system. + +## 1. Frozen protocol and evidence boundary + +The protocol was committed as `{r['protocol_commit']}` against baseline `{r['baseline_commit']}` before implementation and execution. Seed `{r['seed']}` is an arbitrary reproducibility seed. **Fresh Jev calls: {r['fresh_jev_calls']}. New scientific documents: {r['new_scientific_documents']}.** Existing negative semantic/routing/calibration results motivated this round, so this is exploratory follow-up rather than independent preregistration. + +The package-level additions are not worldwide novelty claims. Treewidth-aware dynamic programming, incremental view maintenance, query resilience/deletion propagation, minimax decision rules under imprecise probabilities, and hypergraph/constraint optimization all have prior art. The contribution here is the frozen integration and falsification suite for this repository. + +| Hypothesis | Frozen target | Outcome | +|---|---|---| +| H1: induced-width exact conflict optimization | zero small/oracle/permutation failures; solve 8 large width-2 chains; stage all over-width controls; >=90% fewer counted states | **Met** | +| H2: transactional batch delta maintenance | zero snapshot or atomicity failures; >=80% fewer local derived evaluations | **Met** | +| H3: exact query-resilience certificates | zero small oracle/witness failures; solve 8 analytic large fixtures; stage 6 over-cap controls | **Met** | +| H4: interval-minimax query review | zero oracle discrepancies; never worse than midpoint worst case; strict gain on >=20% | **Met** | +| H5: n-ary conflict constraints | zero small oracle/permutation failures; solve 8 large chains; strict utility gain vs pairwise projection; stage controls | **Met** | + +“Met” refers only to the predeclared controlled fixture conjunction. These rows must not be pooled into a semantic-success percentage. + +## 2. H1 - induced-width exact conflict optimization + +A max-sum variable-elimination solver uses a deterministic min-fill order and stages components whose induced width exceeds four or whose materialized state budget exceeds the frozen cap. On 160 seeded 8-16 vertex bounded-width graphs, the solver matches exhaustive maximum-weight compatible-subset objectives with **0 failures** and **0 input-order permutation failures**. Width distribution is {h1['case_width_histogram']}. + +For 12-16 vertex fixtures, counted factor states are **{h1['counted_factor_states']:,}** versus **{h1['full_assignment_count']:,}** full assignments, a {pct(h1['state_reduction'])} reduction in this work metric. All eight connected triangle-chain controls from 33 to 1,025 vertices are solved exactly at induced width two. K6-K10 controls stage at widths 5-9. State-count reduction is not a measured CPU or service-latency speedup. + +![H1. Counted exact-DP states versus full enumeration.](figures/01_induced_width.svg) + +## 3. H2 - transactional batch delta maintenance + +Each transaction validates its revision and changes, computes a reverse-dependency closure, evaluates a copy-on-write overlay in topological order, and commits only after every affected derived node succeeds. Across **{h2['accepted_transactions']}** accepted 1-4 primitive transactions on 128 modular DAGs there are **{h2['snapshot_mismatches']} snapshot mismatches** against independent full recomputation. The suite also records {h2['failure_atomic_controls']}/64 injected-evaluation failures, {h2['stale_revision_controls']}/64 stale revisions and {h2['malformed_controls']}/64 malformed updates with no partial state/revision mutation. + +Local work falls from {h2['full_recompute_evaluations']:,} full derived-node evaluations to {h2['local_evaluations']:,}, a {pct(h2['evaluation_reduction'])} reduction. Every connected control evaluates all 256 derived nodes, yielding {pct(h2['connected_mean_reduction'])} saving and making the locality boundary explicit. This is in-memory atomic batch behavior, not crash durability or concurrent database isolation. + +![H2. Local delta work and connected worst-case control.](figures/02_transactional_delta.svg) + +## 4. H3 - exact query-resilience certificates + +For monotone DNF lineage, the method computes a minimum-cost evidence deletion set that hits every active proof clause using incidence-component decomposition and bounded branch-and-bound. All **{h3['small_cases']}** small fixtures match exhaustive deletion-subset oracles with valid witnesses. Eight analytic decomposable families from 256 to 2,048 proof components (up to 4,096 atoms) match their closed-form optimum; six connected 19-24 atom controls stage under the frozen component cap. + +The output is a robustness margin relative to the supplied proof lineage and removal costs. It does not say that those proofs are factually correct or that their costs are calibrated to real review effort. + +![H3. Certified resilience cost across large decomposable proof families.](figures/03_resilience.svg) + +## 5. H4 - interval-minimax query review + +Point-risk review can be brittle when primitive probabilities are uncertain. The proposed selector chooses two reviews minimizing worst-case expected residual Bernoulli variance across all endpoint combinations of supplied marginal probability intervals. Production query probability uses memoized Shannon recursion; the independent checker enumerates Boolean worlds directly. + +Across {h4['fixtures']} non-degenerate fixtures there are **{h4['oracle_discrepancies']} oracle discrepancies** and **{h4['worse_than_midpoint']} cases worse than the midpoint selector** under the same worst-case objective. The minimax selector is strictly better on **{h4['strict_improvements']} fixtures ({pct(h4['strict_fraction'])})**, including {h4['random_strict']} seeded random fixtures and all {h4['engineered_strict']} engineered fragile-midpoint controls. All {h4['degenerate_point_controls']} point-interval controls reduce to the point solution. + +This remains conditional on independent primitives; interval robustness is not a dependence certificate or a semantic calibration guarantee. + +![H4. Minimax versus midpoint worst-case review outcomes.](figures/04_interval_review.svg) + +## 6. H5 - n-ary conflict constraints + +Pairwise conflict edges cannot faithfully represent a rule such as “not all three assertions may coexist.” The factor solver is generalized to forbidden hyperedges: a factor rejects only the all-selected assignment for that scope. Pairwise conflict is the arity-two special case. + +All **{h5['small_cases']}** weighted small hypergraphs match exhaustive subset optimization with **0 failures** and **{h5['permutation_failures']} permutation failures**. Eight three-consecutive-forbidden chains from 64 through 2,048 vertices match the analytic optimum `n - floor(n/3)`. Pairwise clique projection is deliberately over-conservative: at n=2,048 it retains {h5['large'][-1]['pairwise_projection']} unit utility versus {h5['large'][-1]['objective']} for the n-ary model. All arity-6 through arity-10 over-width controls stage. + +![H5. Exact n-ary repair versus pairwise projection.](figures/05_hypergraph.svg) + +## 7. Interpretation + +This round strengthens a repeated pattern in the package: explicit structure can create large exactness/work-count gains under supplied assumptions, while those gains do not substitute for semantic validation. H1 and H5 broaden tractable repair structure; H2 makes local maintenance atomic across batches; H3 adds a quantitative fragility certificate; H4 replaces a point-risk review objective with a worst-case interval objective. None establishes that Jev extracted the right entities, relations, qualifiers, probabilities, priorities, costs or constraints. + +A decisive next semantic study still requires a new source-disjoint corpus, frozen policy before evaluation, independent adjudication, prospective service cost/latency and matched external baselines. No production graph policy changes in this extension. + +## 8. Reproducibility + +```bash +python -B -m unittest discover -s graph_synthesis/post_certificate/tests -v +python -B -m graph_synthesis.post_certificate.run --check +python -B -m graph_synthesis.post_certificate.report --check +``` + +`results.json` contains the frozen benchmark outcomes, controls and work counters; `summary.csv` provides a compact result table; the five SVG figures are deterministic renderings of those results. The frozen protocol remains unchanged. +''' + files['RESULTS.md']=report + paper=report.replace('](figures/','](../graph_synthesis/post_certificate/figures/') + files['PAPER_SECTION.md']=paper + current=f'''{START}\n\n## Five post-certificate structural extensions\n\n[Executed report](graph_synthesis/post_certificate/RESULTS.md), [frozen protocol](graph_synthesis/post_certificate/PROTOCOL.md), [five figures](graph_synthesis/post_certificate/figures/) and the updated full paper. H1-H5 meet their frozen controlled targets. Induced-width repair reduces counted exact-DP states {pct(h1['state_reduction'])} on the measured small subset; transactional local updates reduce counted derived evaluations {pct(h2['evaluation_reduction'])} while the connected control saves 0%; query resilience matches exhaustive/analytic optima; interval-minimax review is strictly better on {h4['strict_improvements']}/{h4['fixtures']} worst-case fixtures and never worse; n-ary repair avoids pairwise over-rejection on all large controls. No fresh Jev calls, independent semantic-accuracy claim, external-system superiority, worldwide novelty claim or production-policy change.\n\n{END}\n''' + files['CURRENT_SNIPPET.md']=current + return files + + +def replace_block(text, block): + if START in text: + before,rest=text.split(START,1) + if END not in rest: raise ValueError('unterminated post-certificate block') + _,after=rest.split(END,1) + return before+block+after.lstrip('\n') + return text.rstrip()+"\n\n"+block + + +def paper_block(section): + return START+'\n\n'+section.rstrip()+'\n\n'+END+'\n' + + +def write_generated(check=False, update_paper=False): + r=json.loads(RESULTS.read_text(encoding='utf-8')) + files=generated(r); mismatches=[] + for rel,content in files.items(): + path=HERE/rel + if check: + if not path.exists() or path.read_text(encoding='utf-8')!=content: mismatches.append(str(path.relative_to(ROOT))) + else: + path.parent.mkdir(parents=True,exist_ok=True); path.write_text(content,encoding='utf-8',newline='\n') + if update_paper: + current_path=ROOT/'CURRENT_RESULTS.md'; paper_path=ROOT/'manuscript/paper-current.md' + current=replace_block(current_path.read_text(encoding='utf-8'),files['CURRENT_SNIPPET.md']) + pblock=paper_block(files['PAPER_SECTION.md']) + paper=replace_block(paper_path.read_text(encoding='utf-8'),pblock) + if check: + if current_path.read_text(encoding='utf-8')!=current: mismatches.append('CURRENT_RESULTS.md') + if paper_path.read_text(encoding='utf-8')!=paper: mismatches.append('manuscript/paper-current.md') + else: + current_path.write_text(current,encoding='utf-8',newline='\n'); paper_path.write_text(paper,encoding='utf-8',newline='\n') + if mismatches: + print('generated output mismatch: '+', '.join(mismatches),file=sys.stderr); return 2 + return 0 + + +def main(argv=None): + ap=argparse.ArgumentParser(); ap.add_argument('--check',action='store_true'); ap.add_argument('--update-paper',action='store_true'); args=ap.parse_args(argv) + return write_generated(args.check,args.update_paper) + +if __name__=='__main__': raise SystemExit(main()) diff --git a/graph_synthesis/post_certificate/results.json b/graph_synthesis/post_certificate/results.json new file mode 100644 index 0000000..5b75ae1 --- /dev/null +++ b/graph_synthesis/post_certificate/results.json @@ -0,0 +1,592 @@ +{ + "H1": { + "case_width_histogram": { + "1": 33, + "2": 45, + "3": 53, + "4": 29 + }, + "controls": [ + { + "n": 6, + "status": "staged", + "width": 5 + }, + { + "n": 7, + "status": "staged", + "width": 6 + }, + { + "n": 8, + "status": "staged", + "width": 7 + }, + { + "n": 9, + "status": "staged", + "width": 8 + }, + { + "n": 10, + "status": "staged", + "width": 9 + } + ], + "counted_factor_states": 14660, + "failures": [], + "full_assignment_count": 1986560, + "large": [ + { + "expected": 11, + "n": 33, + "objective": 11, + "states": 254, + "status": "solved", + "width": 2 + }, + { + "expected": 22, + "n": 65, + "objective": 22, + "states": 510, + "status": "solved", + "width": 2 + }, + { + "expected": 43, + "n": 129, + "objective": 43, + "states": 1022, + "status": "solved", + "width": 2 + }, + { + "expected": 86, + "n": 257, + "objective": 86, + "states": 2046, + "status": "solved", + "width": 2 + }, + { + "expected": 129, + "n": 385, + "objective": 129, + "states": 3070, + "status": "solved", + "width": 2 + }, + { + "expected": 171, + "n": 513, + "objective": 171, + "states": 4094, + "status": "solved", + "width": 2 + }, + { + "expected": 257, + "n": 769, + "objective": 257, + "states": 6142, + "status": "solved", + "width": 2 + }, + { + "expected": 342, + "n": 1025, + "objective": 342, + "states": 8190, + "status": "solved", + "width": 2 + } + ], + "permutation_failures": 0, + "primary_target_met": true, + "small_cases": 160, + "small_solved": 160, + "state_reduction": 0.9926204091494846 + }, + "H2": { + "accepted_transactions": 640, + "connected_controls": [ + { + "correct": true, + 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+ "primary_target_met": true, + "random_strict": 13, + "samples": [ + { + "kind": "random", + "midpoint": [ + 0, + 3 + ], + "midpoint_worst": 0.0, + "minimax": [ + 0, + 3 + ], + "minimax_worst": 0.0 + }, + { + "kind": "random", + "midpoint": [ + 1, + 2 + ], + "midpoint_worst": 0.09661106881133921, + "minimax": [ + 1, + 2 + ], + "minimax_worst": 0.09661106881133921 + }, + { + "kind": "random", + "midpoint": [ + 2, + 4 + ], + "midpoint_worst": 0.0, + "minimax": [ + 2, + 4 + ], + "minimax_worst": 0.0 + }, + { + "kind": "random", + "midpoint": [ + 2, + 5 + ], + "midpoint_worst": 0.0960162421762519, + "minimax": [ + 2, + 5 + ], + "minimax_worst": 0.0960162421762519 + }, + { + "kind": "random", + "midpoint": [ + 2, + 3 + ], + "midpoint_worst": 0.2680534518265011, + "minimax": [ + 1, + 2 + ], + "minimax_worst": 0.267103938659212 + }, + { + "kind": "random", + "midpoint": [ + 0, + 2 + ], + "midpoint_worst": 0.06073329931775564, + "minimax": [ + 0, + 2 + ], + "minimax_worst": 0.06073329931775564 + }, + { + "kind": "random", + "midpoint": [ + 0, + 4 + ], + "midpoint_worst": 0.03609290723701447, + "minimax": [ + 0, + 4 + ], + "minimax_worst": 0.03609290723701447 + }, + { + "kind": "random", + "midpoint": [ + 0, + 5 + ], + "midpoint_worst": 0.08814167706701849, + "minimax": [ + 0, + 5 + ], + "minimax_worst": 0.08814167706701849 + }, + { + "kind": "random", + "midpoint": [ + 2, + 3 + ], + "midpoint_worst": 0.21699798050107816, + "minimax": [ + 2, + 3 + ], + "minimax_worst": 0.21699798050107816 + }, + { + "kind": "random", + "midpoint": [ + 2, + 5 + ], + "midpoint_worst": 0.18216959634475388, + "minimax": [ + 2, + 5 + ], + "minimax_worst": 0.18216959634475388 + }, + { + "kind": "random", + "midpoint": [ + 1, + 5 + ], + "midpoint_worst": 0.24054854606815526, + "minimax": [ + 1, + 5 + ], + "minimax_worst": 0.24054854606815526 + }, + { + "kind": "random", + "midpoint": [ + 3, + 4 + ], + "midpoint_worst": 0.07124624217352793, + "minimax": [ + 0, + 4 + ], + "minimax_worst": 0.0670979332010524 + } + ], + "strict_fraction": 0.3515625, + "strict_improvements": 45, + "worse_than_midpoint": 0 + }, + "H5": { + "controls": [ + { + "arity": 6, + "status": "staged", + "width": 5 + }, + { + "arity": 7, + "status": "staged", + "width": 6 + }, + { + "arity": 8, + "status": "staged", + "width": 7 + }, + { + "arity": 9, + "status": "staged", + "width": 8 + }, + { + "arity": 10, + "status": "staged", + "width": 9 + } + ], + "failures": [], + "large": [ + { + "expected": 43, + "n": 64, + "objective": 43, + "pairwise_projection": 22, + "states": 502, + "width": 2 + }, + { + "expected": 86, + "n": 128, + "objective": 86, + "pairwise_projection": 43, + "states": 1014, + "width": 2 + }, + { + "expected": 171, + "n": 256, + "objective": 171, + "pairwise_projection": 86, + "states": 2038, + "width": 2 + }, + { + "expected": 342, + "n": 512, + "objective": 342, + "pairwise_projection": 171, + "states": 4086, + "width": 2 + }, + { + "expected": 512, + "n": 768, + "objective": 512, + "pairwise_projection": 256, + "states": 6134, + "width": 2 + }, + { + "expected": 683, + "n": 1024, + "objective": 683, + "pairwise_projection": 342, + "states": 8182, + "width": 2 + }, + { + "expected": 1024, + "n": 1536, + "objective": 1024, + "pairwise_projection": 512, + "states": 12278, + "width": 2 + }, + { + "expected": 1366, + "n": 2048, + "objective": 1366, + "pairwise_projection": 683, + "states": 16374, + "width": 2 + } + ], + "permutation_failures": 0, + "primary_target_met": true, + "small_cases": 160, + "small_solved": 160 + }, + "baseline_commit": "9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98", + "fresh_jev_calls": 0, + "new_scientific_documents": 0, + "protocol_commit": "8187cc4e4b98e40f3146ce787b71f262b62b3abd", + "seed": 20260923, + "targets_met": { + "H1": true, + "H2": true, + "H3": true, + "H4": true, + "H5": true + } +} diff --git a/graph_synthesis/post_certificate/run.py b/graph_synthesis/post_certificate/run.py new file mode 100644 index 0000000..ee0f034 --- /dev/null +++ b/graph_synthesis/post_certificate/run.py @@ -0,0 +1,299 @@ +from __future__ import annotations + +import argparse, json, random, sys +from pathlib import Path +from itertools import combinations + +from .methods import ( + max_sum_elimination, brute_force_optimum, DependencyGraph, + query_resilience, brute_force_resilience, + select_interval_minimax, oracle_interval_worst, +) + +ROOT=Path(__file__).resolve().parents[2] +OUT=Path(__file__).with_name('results.json') +SEED=20260923 +BASELINE='9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98' +PROTOCOL_COMMIT='8187cc4e4b98e40f3146ce787b71f262b62b3abd' + + +def k_tree_graph(rng,n,k): + edges=set() + initial=tuple(range(k+1)) + for a,b in combinations(initial,2): edges.add((a,b)) + kcliques={tuple(c) for c in combinations(initial,k)} + for v in range(k+1,n): + base=rng.choice(sorted(kcliques)) + for a in base: edges.add(tuple(sorted((a,v)))) + clique=tuple(sorted(base+(v,))) + for c in combinations(clique,k): kcliques.add(tuple(c)) + return sorted(edges) + + +def h1(rng): + failures=[]; perm_fail=0; state_sum=0; full_sum=0; solved=0 + cases=[] + for i in range(160): + n=rng.randint(8,16); k=rng.randint(1,min(4,n-1)) + edges=k_tree_graph(rng,n,k) + weights=[rng.randint(1,19) for _ in range(n)] + got=max_sum_elimination(weights,edges) + want,_=brute_force_optimum(weights,edges) + if got.status!='solved' or got.objective!=want: + failures.append({'case':i,'n':n,'k':k,'status':got.status,'got':got.objective,'want':want,'width':got.induced_width}) + else: solved+=1 + if n>=12: + state_sum+=got.factor_states; full_sum+=(1<=.90) + return {'primary_target_met':target,'small_cases':160,'small_solved':solved,'failures':failures,'permutation_failures':perm_fail, + 'large':large,'controls':controls,'counted_factor_states':state_sum,'full_assignment_count':full_sum,'state_reduction':saving, + 'case_width_histogram':{str(w):sum(1 for c in cases if c['width']==w) for w in sorted({c['width'] for c in cases})}} + + +def make_modular_dag(rng): + P=64; defs={} + for m in range(8): + prim=list(range(m*8,(m+1)*8)) + prior=[] + for j in range(32): + node=P+m*32+j + candidates=prim+prior + depn=2 if len(candidates)<3 else rng.choice([2,2,3]) + deps=tuple(sorted(rng.sample(candidates,depn))) + op=rng.choice(['and','or','xor']) + defs[node]=(op,deps); prior.append(node) + return DependencyGraph(P,defs) + + +def make_connected_dag(): + P=64; defs={} + for j in range(256): + node=P+j + if j==0: deps=(0,1) + else: deps=(P+j-1, 1+(j%63)) + defs[node]=('xor' if j%3==0 else ('or' if j%3==1 else 'and'),deps) + return DependencyGraph(P,defs) + + +def h2(rng): + mismatches=0; accepted=0; local_evals=0; full_evals=0 + failure_atomic=0; stale_atomic=0; malformed_atomic=0 + for gi in range(128): + g=make_modular_dag(rng) + prim={i:bool(rng.getrandbits(1)) for i in range(64)} + values=g.full_recompute(prim); rev=0 + for t in range(5): + m=rng.randrange(8); ks=rng.randint(1,4); ps=rng.sample(range(m*8,(m+1)*8),ks) + changes={p:not values[p] for p in ps} + newv,newrev,ev=g.transact(values,rev,changes,expected_revision=rev) + newprim={i:newv[i] for i in range(64)} + full=g.full_recompute(newprim) + if newv!=full: mismatches+=1 + values,rev=newv,newrev; accepted+=1; local_evals+=ev; full_evals+=256 + if gi<64: + before=dict(values); brevis=rev + p=gi%64; changes={p:not values[p]} + # choose an affected derived node + affected=[]; stack=[p]; seen=set() + while stack: + x=stack.pop() + for y in g.reverse[x]: + if y not in seen: seen.add(y); affected.append(y); stack.append(y) + fail=affected[len(affected)//2] if affected else None + try: g.transact(values,rev,changes,expected_revision=rev,fail_node=fail) + except RuntimeError: pass + if values==before and rev==brevis: failure_atomic+=1 + try: g.transact(values,rev,changes,expected_revision=rev-1) + except RuntimeError: pass + if values==before and rev==brevis: stale_atomic+=1 + try: g.transact(values,rev,{64:True},expected_revision=rev) + except ValueError: pass + if values==before and rev==brevis: malformed_atomic+=1 + connected=[] + for i in range(16): + g=make_connected_dag(); prim={j:bool((j+i)%2) for j in range(64)}; values=g.full_recompute(prim) + newv,_,ev=g.transact(values,0,{0:not values[0]},expected_revision=0) + ok=(newv==g.full_recompute({j:newv[j] for j in range(64)})) + connected.append({'evaluations':ev,'full':256,'correct':ok,'saving':1-ev/256}) + if not ok: mismatches+=1 + saving=1-local_evals/full_evals + target=(mismatches==0 and failure_atomic==64 and stale_atomic==64 and malformed_atomic==64 and saving>=.80) + return {'primary_target_met':target,'accepted_transactions':accepted,'snapshot_mismatches':mismatches,'local_evaluations':local_evals, + 'full_recompute_evaluations':full_evals,'evaluation_reduction':saving,'failure_atomic_controls':failure_atomic, + 'stale_revision_controls':stale_atomic,'malformed_controls':malformed_atomic,'connected_controls':connected, + 'connected_mean_reduction':sum(x['saving'] for x in connected)/len(connected)} + + +def h3(rng): + failures=[]; solved=0 + for i in range(160): + n=rng.randint(4,12); atoms=list(range(n)); costs={a:rng.randint(1,9) for a in atoms} + clauses=[] + for _ in range(rng.randint(2,10)): + size=rng.randint(1,min(4,n)); clauses.append(tuple(sorted(rng.sample(atoms,size)))) + got=query_resilience(clauses,costs); want,wset=brute_force_resilience(clauses,costs) + if got['status']!='solved' or got['cost']!=want: + failures.append({'case':i,'status':got['status'],'got':got['cost'],'want':want}) + else: solved+=1 + if got['status']=='solved': + dele=set(got['delete']) + if not all(set(c)&dele for c in clauses): failures.append({'case':i,'witness':'does not falsify'}) + if sum(costs[a] for a in dele)!=got['cost']: failures.append({'case':i,'witness':'cost mismatch'}) + large=[] + for m in [256,384,512,768,1024,1280,1536,2048]: + clauses=[]; costs={}; expected=0 + for j in range(m): + a=2*j; b=a+1; ca=1+(j%7); cb=2+((j*3)%7); costs[a]=ca; costs[b]=cb + clauses.append((a,b)); expected+=min(ca,cb) + got=query_resilience(clauses,costs) + large.append({'proof_components':m,'atoms':2*m,'cost':got['cost'],'expected':expected,'status':got['status']}) + if got['status']!='solved' or got['cost']!=expected: failures.append({'large':m,'got':got['cost'],'expected':expected,'status':got['status']}) + controls=[] + for j in range(6): + atoms=19+j; clauses=[(i,i+1) for i in range(atoms-1)]; costs={i:1 for i in range(atoms)} + got=query_resilience(clauses,costs) + controls.append({'atoms':atoms,'status':got['status']}) + if got['status']!='staged': failures.append({'control_atoms':atoms,'status':got['status']}) + return {'primary_target_met':not failures,'small_cases':160,'small_solved':solved,'failures':failures,'large':large,'controls':controls} + + +def random_query_fixture(rng): + intervals={} + for a in range(6): + lo=rng.uniform(.03,.75); hi=rng.uniform(lo,min(.97,lo+rng.uniform(.03,.35))) + intervals[a]=(round(lo,5),round(hi,5)) + queries=[] + for _ in range(rng.randint(1,3)): + q=[] + for _ in range(rng.randint(1,3)): + q.append(tuple(sorted(rng.sample(range(6),rng.randint(1,3))))) + queries.append(q) + return intervals,queries + + +def engineered_interval_fixture(i): + # midpoint favors 0/1; worst-case variance favors 2/3 + e=(i%7)*0.001 + intervals={0:(.39+e,.41+e),1:(.41-e,.43-e),2:(.05,.50),3:(.10,.50),4:(.01,.05),5:(.94,.98)} + queries=[[(a,)] for a in range(6)] + return intervals,queries + + +def h4(rng): + fixtures=[]; discrepancies=0; worse=0; strict=0; random_strict=0; engineered_strict=0 + for i in range(96): + intervals,queries=random_query_fixture(rng) + fixtures.append(('random',intervals,queries)) + for i in range(32): fixtures.append(('engineered',*engineered_interval_fixture(i))) + samples=[] + for kind,intervals,queries in fixtures: + got=select_interval_minimax(queries,intervals,2) + om=oracle_interval_worst(queries,intervals,got['minimax']) + op=oracle_interval_worst(queries,intervals,got['midpoint']) + if abs(om-got['minimax_worst'])>1e-10 or abs(op-got['midpoint_worst'])>1e-10: discrepancies+=1 + if om>op+1e-12: worse+=1 + if om=26 and point_controls==16) + return {'primary_target_met':target,'fixtures':128,'oracle_discrepancies':discrepancies,'worse_than_midpoint':worse, + 'strict_improvements':strict,'strict_fraction':strict/128,'random_strict':random_strict,'engineered_strict':engineered_strict, + 'degenerate_point_controls':point_controls,'samples':samples} + + +def h5(rng): + failures=[]; perm_fail=0; solved=0 + for i in range(160): + n=rng.randint(8,14); weights=[rng.randint(1,19) for _ in range(n)]; scopes=[] + for j in range(n-1): + if rng.random()<.35: scopes.append((j,j+1)) + for j in range(n-2): + if rng.random()<.55: scopes.append((j,j+1,j+2)) + if not scopes: scopes=[(0,1,2)] + got=max_sum_elimination(weights,scopes); want,_=brute_force_optimum(weights,scopes) + if got.status!='solved' or got.objective!=want: + failures.append({'case':i,'status':got.status,'got':got.objective,'want':want,'width':got.induced_width}) + else: solved+=1 + if i<64: + base=(got.objective,got.selected,got.induced_width) + for j in range(8): + sh=list(scopes); random.Random(SEED+7000+i*100+j).shuffle(sh) + g=max_sum_elimination(weights,sh) + if (g.objective,g.selected,g.induced_width)!=base: perm_fail+=1 + large=[] + for n in [64,128,256,512,768,1024,1536,2048]: + scopes=[(i,i+1,i+2) for i in range(n-2)] + got=max_sum_elimination([1]*n,scopes) + expected=n-(n//3) + pair=set() + for s in scopes: + for a,b in combinations(s,2): pair.add((a,b)) + projected=max_sum_elimination([1]*n,sorted(pair)) + large.append({'n':n,'objective':got.objective,'expected':expected,'pairwise_projection':projected.objective,'width':got.induced_width,'states':got.factor_states}) + if got.status!='solved' or got.objective!=expected or projected.objective is None or got.objective<=projected.objective: + failures.append({'large_n':n,'got':got.objective,'expected':expected,'projected':projected.objective}) + controls=[] + for n in [6,7,8,9,10]: + got=max_sum_elimination([1]*n,[tuple(range(n))]) + controls.append({'arity':n,'status':got.status,'width':got.induced_width}) + if got.status!='staged': failures.append({'control_arity':n,'status':got.status,'width':got.induced_width}) + target=(not failures and perm_fail==0) + return {'primary_target_met':target,'small_cases':160,'small_solved':solved,'failures':failures,'permutation_failures':perm_fail,'large':large,'controls':controls} + + +def execute(): + rng=random.Random(SEED) + result={'baseline_commit':BASELINE,'protocol_commit':PROTOCOL_COMMIT,'seed':SEED,'fresh_jev_calls':0,'new_scientific_documents':0} + result['H1']=h1(rng); result['H2']=h2(rng); result['H3']=h3(rng); result['H4']=h4(rng); result['H5']=h5(rng) + result['targets_met']={k:result[k]['primary_target_met'] for k in ['H1','H2','H3','H4','H5']} + return result + + +def canonical(obj): return json.dumps(obj,sort_keys=True,indent=2)+"\n" + + +def main(argv=None): + ap=argparse.ArgumentParser(); ap.add_argument('--check',action='store_true'); ap.add_argument('--output',default=str(OUT)); args=ap.parse_args(argv) + got=execute(); text=canonical(got); path=Path(args.output) + if args.check: + if not path.exists(): print('missing results',file=sys.stderr); return 2 + old=path.read_text(encoding='utf-8') + if old!=text: + print('results mismatch',file=sys.stderr); return 3 + print(json.dumps(got['targets_met'],sort_keys=True)); return 0 + path.write_text(text,encoding='utf-8',newline='\n') + print(json.dumps(got['targets_met'],sort_keys=True)) + return 0 + +if __name__=='__main__': raise SystemExit(main()) diff --git a/graph_synthesis/post_certificate/summary.csv b/graph_synthesis/post_certificate/summary.csv new file mode 100644 index 0000000..961de1f --- /dev/null +++ b/graph_synthesis/post_certificate/summary.csv @@ -0,0 +1,6 @@ +hypothesis,result,primary_measure,value,boundary +H1,met,factor-state reduction,0.992620409149,counted DP states, not wall-clock +H2,met,local evaluation reduction,0.901092529297,connected control saves 0% +H3,met,small/large oracle failures,0,supplied lineage and costs only +H4,met,strict minimax improvements,45,independent marginals with interval uncertainty +H5,met,large exact hypergraph fixtures,8,supplied n-ary constraints and utility diff --git a/graph_synthesis/post_certificate/tests/test_methods.py b/graph_synthesis/post_certificate/tests/test_methods.py new file mode 100644 index 0000000..55f5c0b --- /dev/null +++ b/graph_synthesis/post_certificate/tests/test_methods.py @@ -0,0 +1,115 @@ +import unittest + +from graph_synthesis.post_certificate.methods import ( + max_sum_elimination, brute_force_optimum, DependencyGraph, + query_resilience, brute_force_resilience, + dnf_probability_shannon, dnf_probability_worlds, + select_interval_minimax, oracle_interval_worst, +) + + +class EliminationTests(unittest.TestCase): + def test_pairwise_triangle(self): + w=[5,4,3] + scopes=[(0,1),(1,2),(0,2)] + got=max_sum_elimination(w,scopes) + self.assertEqual(got.objective,5) + self.assertEqual(got.selected,(0,)) + self.assertEqual(got.induced_width,2) + + def test_pairwise_matches_bruteforce(self): + w=[7,5,8,4,9] + scopes=[(0,1),(1,2),(2,3),(3,4),(0,2)] + got=max_sum_elimination(w,scopes) + want,_=brute_force_optimum(w,scopes) + self.assertEqual(got.objective,want) + + def test_width_control_stages(self): + n=6 + scopes=[(i,j) for i in range(n) for j in range(i+1,n)] + self.assertEqual(max_sum_elimination([1]*n,scopes).status,'staged') + + def test_hyperedge_keeps_pair(self): + got=max_sum_elimination([1,1,1],[(0,1,2)]) + self.assertEqual(got.objective,2) + self.assertEqual(len(got.selected),2) + + def test_hyperedge_matches_bruteforce(self): + w=[4,8,3,7,6] + scopes=[(0,1,2),(1,2,3),(2,3,4)] + got=max_sum_elimination(w,scopes) + want,_=brute_force_optimum(w,scopes) + self.assertEqual(got.objective,want) + + +class DeltaTests(unittest.TestCase): + def graph(self): + return DependencyGraph(3,{ + 3:('and',(0,1)), + 4:('or',(1,2)), + 5:('xor',(3,4)), + }) + + def test_transaction_matches_full(self): + g=self.graph(); prim={0:True,1:True,2:False}; v=g.full_recompute(prim) + nv,nr,ev=g.transact(v,0,{1:False},expected_revision=0) + self.assertEqual(nr,1) + self.assertEqual(nv,g.full_recompute({0:True,1:False,2:False})) + self.assertLessEqual(ev,3) + + def test_stale_rejected(self): + g=self.graph(); v=g.full_recompute({0:True,1:True,2:False}) + with self.assertRaises(RuntimeError): g.transact(v,1,{1:False},expected_revision=0) + + def test_malformed_rejected(self): + g=self.graph(); v=g.full_recompute({0:True,1:True,2:False}) + with self.assertRaises(ValueError): g.transact(v,0,{3:False},expected_revision=0) + + def test_injected_failure_leaves_input_unmodified(self): + g=self.graph(); v=g.full_recompute({0:True,1:True,2:False}); before=dict(v) + with self.assertRaises(RuntimeError): g.transact(v,0,{1:False},expected_revision=0,fail_node=3) + self.assertEqual(v,before) + + +class ResilienceTests(unittest.TestCase): + def test_resilience_matches_oracle(self): + clauses=[(0,1),(1,2),(2,3)] + costs={0:4,1:3,2:2,3:5} + got=query_resilience(clauses,costs) + want,_=brute_force_resilience(clauses,costs) + self.assertEqual(got['cost'],want) + + def test_disconnected_adds_costs(self): + clauses=[(0,1),(2,3)] + costs={0:1,1:5,2:3,3:2} + got=query_resilience(clauses,costs) + self.assertEqual(got['cost'],3) + + def test_large_connected_stages(self): + clauses=[(i,i+1) for i in range(19)] + costs={i:1 for i in range(20)} + self.assertEqual(query_resilience(clauses,costs)['status'],'staged') + + +class ProbabilityTests(unittest.TestCase): + def test_shannon_matches_worlds(self): + q=[(0,1),(2,)] + p={0:.2,1:.7,2:.3} + self.assertAlmostEqual(dnf_probability_shannon(q,p),dnf_probability_worlds(q,p),places=12) + + def test_minimax_not_worse_than_midpoint(self): + intervals={0:(.39,.41),1:(.41,.43),2:(.05,.5),3:(.1,.5),4:(.01,.05),5:(.94,.98)} + queries=[[(a,)] for a in range(6)] + got=select_interval_minimax(queries,intervals,2) + self.assertLessEqual(got['minimax_worst'],got['midpoint_worst']+1e-12) + self.assertAlmostEqual(got['minimax_worst'],oracle_interval_worst(queries,intervals,got['minimax']),places=12) + + def test_point_intervals_reduce_to_point_selection(self): + intervals={i:(p,p) for i,p in enumerate([.1,.2,.3,.4,.5,.6])} + queries=[[(a,)] for a in range(6)] + got=select_interval_minimax(queries,intervals,2) + self.assertEqual(got['minimax'],got['midpoint']) + self.assertAlmostEqual(got['minimax_worst'],got['midpoint_worst'],places=12) + + +if __name__=='__main__': unittest.main() From 30d5ae48ba44c619a650de392a9e801bde9ec522 Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 10:52:12 -0400 Subject: [PATCH 03/10] ci: replay and publish isolated structural study --- .../post-certificate-improvements.yml | 132 ++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 .github/workflows/post-certificate-improvements.yml diff --git a/.github/workflows/post-certificate-improvements.yml b/.github/workflows/post-certificate-improvements.yml new file mode 100644 index 0000000..6aff9e3 --- /dev/null +++ b/.github/workflows/post-certificate-improvements.yml @@ -0,0 +1,132 @@ +name: Post-certificate structural improvements + +on: + pull_request: + push: + branches: + - research/structural-certificates-round2-20260918 + +permissions: + contents: write + +concurrency: + group: post-certificate-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: false + +jobs: + replay-and-publish: + runs-on: ubuntu-latest + timeout-minutes: 30 + env: + PYTHONDONTWRITEBYTECODE: '1' + steps: + - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 + with: + ref: ${{ github.event.pull_request.head.sha || github.sha }} + fetch-depth: 0 + persist-credentials: true + + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.12' + + - name: Verify frozen protocol ancestry and bytes + shell: bash + run: | + set -euo pipefail + protocol=8187cc4e4b98e40f3146ce787b71f262b62b3abd + git merge-base --is-ancestor "$protocol" HEAD + git show "$protocol:graph_synthesis/post_certificate/PROTOCOL.md" | cmp - graph_synthesis/post_certificate/PROTOCOL.md + + - name: Run focused regressions + run: python -B -m unittest discover -s graph_synthesis/post_certificate/tests -v + + - name: Replay frozen benchmarks byte-for-byte + run: python -B -m graph_synthesis.post_certificate.run --check + + - name: Generate report, figures and additive paper source + if: github.event_name == 'push' + run: python -B -m graph_synthesis.post_certificate.report --update-paper + + - name: Verify committed generated report, figures and additive paper source + if: github.event_name == 'pull_request' + run: python -B -m graph_synthesis.post_certificate.report --check --update-paper + + - name: Install paper rendering dependencies + run: | + python -m pip install --upgrade pip + python -m pip install -r graph_synthesis/requirements-figures.txt + sudo apt-get update + sudo apt-get install -y poppler-utils + + - name: Rebuild current HTML/PDF + if: github.event_name == 'push' + run: python -B -m graph_synthesis.render_current_paper --output manuscript/paper-current.pdf + + - name: Verify committed current paper hashes and render independently + if: github.event_name == 'pull_request' + shell: bash + run: | + set -euo pipefail + python - <<'PY' + import hashlib,json + from pathlib import Path + sha=lambda p:hashlib.sha256(Path(p).read_bytes()).hexdigest() + b=json.loads(Path('manuscript/paper-current.build.json').read_text()) + assert sha('manuscript/paper-current.md')==b['source_sha256'] + assert sha('graph_synthesis/render_current_paper.py')==b['renderer_sha256'] + assert sha('manuscript/paper-current.pdf')==b['pdf_sha256'] + print('Committed current-paper hashes verified') + PY + python -B -m graph_synthesis.render_current_paper --output "$RUNNER_TEMP/post-certificate-paper.pdf" + + - name: PDF structural and raster smoke check + shell: bash + run: | + set -euo pipefail + pdf="${RUNNER_TEMP}/post-certificate-paper.pdf" + if [ "${{ github.event_name }}" = "push" ]; then pdf=manuscript/paper-current.pdf; fi + pdfinfo "$pdf" | tee "$RUNNER_TEMP/pdfinfo.txt" + pages=$(pdfinfo "$pdf" | awk '/^Pages:/ {print $2}') + test "${pages:-0}" -gt 0 + pdftoppm -f 1 -singlefile -png -r 120 "$pdf" "$RUNNER_TEMP/paper-first" + pdftoppm -f "$pages" -singlefile -png -r 120 "$pdf" "$RUNNER_TEMP/paper-last" + test -s "$RUNNER_TEMP/paper-first.png" + test -s "$RUNNER_TEMP/paper-last.png" + + - name: Check generated source for whitespace errors + run: git diff --check + + - name: Publish generated research artifacts + if: github.event_name == 'push' + shell: bash + run: | + set -euo pipefail + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git add graph_synthesis/post_certificate/RESULTS.md \ + graph_synthesis/post_certificate/PAPER_SECTION.md \ + graph_synthesis/post_certificate/CURRENT_SNIPPET.md \ + graph_synthesis/post_certificate/summary.csv \ + graph_synthesis/post_certificate/figures \ + CURRENT_RESULTS.md \ + manuscript/paper-current.md \ + manuscript/paper-current.html \ + manuscript/paper-current.pdf \ + manuscript/paper-current.build.json + if git diff --cached --quiet; then + echo "Publication artifacts already current" + else + git commit -m "research: publish post-certificate benchmarks and paper" + git push origin HEAD:research/structural-certificates-round2-20260918 + fi + + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 + with: + name: post-certificate-pdf-review-${{ github.run_id }} + path: | + ${{ runner.temp }}/pdfinfo.txt + ${{ runner.temp }}/paper-first.png + ${{ runner.temp }}/paper-last.png + if-no-files-found: error + retention-days: 14 From 572b98b7eaa9e13da133848acba7e84e9c9a47e6 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 18 Sep 2026 14:54:42 +0000 Subject: [PATCH 04/10] research: publish post-certificate benchmarks and paper --- CURRENT_RESULTS.md | 8 ++ .../post_certificate/CURRENT_SNIPPET.md | 7 ++ .../post_certificate/PAPER_SECTION.md | 81 +++++++++++++++++ graph_synthesis/post_certificate/RESULTS.md | 81 +++++++++++++++++ .../figures/01_induced_width.svg | 26 ++++++ .../figures/02_transactional_delta.svg | 29 ++++++ .../figures/03_resilience.svg | 17 ++++ .../figures/04_interval_review.svg | 29 ++++++ .../figures/05_hypergraph.svg | 19 ++++ manuscript/paper-current.build.json | 4 +- manuscript/paper-current.html | 74 +++++++++++++++ manuscript/paper-current.md | 86 ++++++++++++++++++ manuscript/paper-current.pdf | Bin 6599621 -> 6641614 bytes 13 files changed, 459 insertions(+), 2 deletions(-) create mode 100644 graph_synthesis/post_certificate/CURRENT_SNIPPET.md create mode 100644 graph_synthesis/post_certificate/PAPER_SECTION.md create mode 100644 graph_synthesis/post_certificate/RESULTS.md create mode 100644 graph_synthesis/post_certificate/figures/01_induced_width.svg create mode 100644 graph_synthesis/post_certificate/figures/02_transactional_delta.svg create mode 100644 graph_synthesis/post_certificate/figures/03_resilience.svg create mode 100644 graph_synthesis/post_certificate/figures/04_interval_review.svg create mode 100644 graph_synthesis/post_certificate/figures/05_hypergraph.svg diff --git a/CURRENT_RESULTS.md b/CURRENT_RESULTS.md index 7b7bf2e..860bdfd 100644 --- a/CURRENT_RESULTS.md +++ b/CURRENT_RESULTS.md @@ -113,3 +113,11 @@ The archived root README and original manuscript are retained as part of the imm ## Five follow-up improvements (PR #13) [Executed results](graph_synthesis/followup/RESULTS.md), [frozen protocol](graph_synthesis/followup/PROTOCOL.md), [five figures](graph_synthesis/followup/figures/), and the updated full paper distinguish same-data response replay from controlled algorithm tests. No new service calls; negative results retained; no production-policy change. + + + +## Five post-certificate structural extensions + +[Executed report](graph_synthesis/post_certificate/RESULTS.md), [frozen protocol](graph_synthesis/post_certificate/PROTOCOL.md), [five figures](graph_synthesis/post_certificate/figures/) and the updated full paper. H1-H5 meet their frozen controlled targets. Induced-width repair reduces counted exact-DP states 99.26% on the measured small subset; transactional local updates reduce counted derived evaluations 90.11% while the connected control saves 0%; query resilience matches exhaustive/analytic optima; interval-minimax review is strictly better on 45/128 worst-case fixtures and never worse; n-ary repair avoids pairwise over-rejection on all large controls. No fresh Jev calls, independent semantic-accuracy claim, external-system superiority, worldwide novelty claim or production-policy change. + + diff --git a/graph_synthesis/post_certificate/CURRENT_SNIPPET.md b/graph_synthesis/post_certificate/CURRENT_SNIPPET.md new file mode 100644 index 0000000..74e22b1 --- /dev/null +++ b/graph_synthesis/post_certificate/CURRENT_SNIPPET.md @@ -0,0 +1,7 @@ + + +## Five post-certificate structural extensions + +[Executed report](graph_synthesis/post_certificate/RESULTS.md), [frozen protocol](graph_synthesis/post_certificate/PROTOCOL.md), [five figures](graph_synthesis/post_certificate/figures/) and the updated full paper. H1-H5 meet their frozen controlled targets. Induced-width repair reduces counted exact-DP states 99.26% on the measured small subset; transactional local updates reduce counted derived evaluations 90.11% while the connected control saves 0%; query resilience matches exhaustive/analytic optima; interval-minimax review is strictly better on 45/128 worst-case fixtures and never worse; n-ary repair avoids pairwise over-rejection on all large controls. No fresh Jev calls, independent semantic-accuracy claim, external-system superiority, worldwide novelty claim or production-policy change. + + diff --git a/graph_synthesis/post_certificate/PAPER_SECTION.md b/graph_synthesis/post_certificate/PAPER_SECTION.md new file mode 100644 index 0000000..282157d --- /dev/null +++ b/graph_synthesis/post_certificate/PAPER_SECTION.md @@ -0,0 +1,81 @@ +# Post-certificate structural extensions for Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five precommitted controlled extensions were executed after the dependence-aware certificate study. All five frozen **algorithmic** targets were met: bounded induced-width max-sum repair matched exhaustive oracles and solved connected non-bipartite triangle chains through 1,025 vertices; atomic batch delta maintenance matched full recomputation across 640 accepted transactions while reducing counted local derived-node evaluations by 90.11%; exact query resilience matched 160 exhaustive small oracles and eight large analytic decompositions; interval-minimax review never worsened the frozen worst-case objective and strictly improved 45/128 fixtures (35.16%); and n-ary forbidden-set repair matched all small oracles and retained strictly more supplied utility than a pairwise clique projection on every large three-way-conflict chain. These are structural and decision-theoretic findings. They are not new Jev semantic-accuracy observations, deployment guarantees, or evidence of superiority to an external graph-synthesis system. + +## 1. Frozen protocol and evidence boundary + +The protocol was committed as `8187cc4e4b98e40f3146ce787b71f262b62b3abd` against baseline `9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98` before implementation and execution. Seed `20260923` is an arbitrary reproducibility seed. **Fresh Jev calls: 0. New scientific documents: 0.** Existing negative semantic/routing/calibration results motivated this round, so this is exploratory follow-up rather than independent preregistration. + +The package-level additions are not worldwide novelty claims. Treewidth-aware dynamic programming, incremental view maintenance, query resilience/deletion propagation, minimax decision rules under imprecise probabilities, and hypergraph/constraint optimization all have prior art. The contribution here is the frozen integration and falsification suite for this repository. + +| Hypothesis | Frozen target | Outcome | +|---|---|---| +| H1: induced-width exact conflict optimization | zero small/oracle/permutation failures; solve 8 large width-2 chains; stage all over-width controls; >=90% fewer counted states | **Met** | +| H2: transactional batch delta maintenance | zero snapshot or atomicity failures; >=80% fewer local derived evaluations | **Met** | +| H3: exact query-resilience certificates | zero small oracle/witness failures; solve 8 analytic large fixtures; stage 6 over-cap controls | **Met** | +| H4: interval-minimax query review | zero oracle discrepancies; never worse than midpoint worst case; strict gain on >=20% | **Met** | +| H5: n-ary conflict constraints | zero small oracle/permutation failures; solve 8 large chains; strict utility gain vs pairwise projection; stage controls | **Met** | + +“Met” refers only to the predeclared controlled fixture conjunction. These rows must not be pooled into a semantic-success percentage. + +## 2. H1 - induced-width exact conflict optimization + +A max-sum variable-elimination solver uses a deterministic min-fill order and stages components whose induced width exceeds four or whose materialized state budget exceeds the frozen cap. On 160 seeded 8-16 vertex bounded-width graphs, the solver matches exhaustive maximum-weight compatible-subset objectives with **0 failures** and **0 input-order permutation failures**. Width distribution is {'1': 33, '2': 45, '3': 53, '4': 29}. + +For 12-16 vertex fixtures, counted factor states are **14,660** versus **1,986,560** full assignments, a 99.26% reduction in this work metric. All eight connected triangle-chain controls from 33 to 1,025 vertices are solved exactly at induced width two. K6-K10 controls stage at widths 5-9. State-count reduction is not a measured CPU or service-latency speedup. + +![H1. Counted exact-DP states versus full enumeration.](../graph_synthesis/post_certificate/figures/01_induced_width.svg) + +## 3. H2 - transactional batch delta maintenance + +Each transaction validates its revision and changes, computes a reverse-dependency closure, evaluates a copy-on-write overlay in topological order, and commits only after every affected derived node succeeds. Across **640** accepted 1-4 primitive transactions on 128 modular DAGs there are **0 snapshot mismatches** against independent full recomputation. The suite also records 64/64 injected-evaluation failures, 64/64 stale revisions and 64/64 malformed updates with no partial state/revision mutation. + +Local work falls from 163,840 full derived-node evaluations to 16,205, a 90.11% reduction. Every connected control evaluates all 256 derived nodes, yielding 0.00% saving and making the locality boundary explicit. This is in-memory atomic batch behavior, not crash durability or concurrent database isolation. + +![H2. Local delta work and connected worst-case control.](../graph_synthesis/post_certificate/figures/02_transactional_delta.svg) + +## 4. H3 - exact query-resilience certificates + +For monotone DNF lineage, the method computes a minimum-cost evidence deletion set that hits every active proof clause using incidence-component decomposition and bounded branch-and-bound. All **160** small fixtures match exhaustive deletion-subset oracles with valid witnesses. Eight analytic decomposable families from 256 to 2,048 proof components (up to 4,096 atoms) match their closed-form optimum; six connected 19-24 atom controls stage under the frozen component cap. + +The output is a robustness margin relative to the supplied proof lineage and removal costs. It does not say that those proofs are factually correct or that their costs are calibrated to real review effort. + +![H3. Certified resilience cost across large decomposable proof families.](../graph_synthesis/post_certificate/figures/03_resilience.svg) + +## 5. H4 - interval-minimax query review + +Point-risk review can be brittle when primitive probabilities are uncertain. The proposed selector chooses two reviews minimizing worst-case expected residual Bernoulli variance across all endpoint combinations of supplied marginal probability intervals. Production query probability uses memoized Shannon recursion; the independent checker enumerates Boolean worlds directly. + +Across 128 non-degenerate fixtures there are **0 oracle discrepancies** and **0 cases worse than the midpoint selector** under the same worst-case objective. The minimax selector is strictly better on **45 fixtures (35.16%)**, including 13 seeded random fixtures and all 32 engineered fragile-midpoint controls. All 16 point-interval controls reduce to the point solution. + +This remains conditional on independent primitives; interval robustness is not a dependence certificate or a semantic calibration guarantee. + +![H4. Minimax versus midpoint worst-case review outcomes.](../graph_synthesis/post_certificate/figures/04_interval_review.svg) + +## 6. H5 - n-ary conflict constraints + +Pairwise conflict edges cannot faithfully represent a rule such as “not all three assertions may coexist.” The factor solver is generalized to forbidden hyperedges: a factor rejects only the all-selected assignment for that scope. Pairwise conflict is the arity-two special case. + +All **160** weighted small hypergraphs match exhaustive subset optimization with **0 failures** and **0 permutation failures**. Eight three-consecutive-forbidden chains from 64 through 2,048 vertices match the analytic optimum `n - floor(n/3)`. Pairwise clique projection is deliberately over-conservative: at n=2,048 it retains 683 unit utility versus 1366 for the n-ary model. All arity-6 through arity-10 over-width controls stage. + +![H5. Exact n-ary repair versus pairwise projection.](../graph_synthesis/post_certificate/figures/05_hypergraph.svg) + +## 7. Interpretation + +This round strengthens a repeated pattern in the package: explicit structure can create large exactness/work-count gains under supplied assumptions, while those gains do not substitute for semantic validation. H1 and H5 broaden tractable repair structure; H2 makes local maintenance atomic across batches; H3 adds a quantitative fragility certificate; H4 replaces a point-risk review objective with a worst-case interval objective. None establishes that Jev extracted the right entities, relations, qualifiers, probabilities, priorities, costs or constraints. + +A decisive next semantic study still requires a new source-disjoint corpus, frozen policy before evaluation, independent adjudication, prospective service cost/latency and matched external baselines. No production graph policy changes in this extension. + +## 8. Reproducibility + +```bash +python -B -m unittest discover -s graph_synthesis/post_certificate/tests -v +python -B -m graph_synthesis.post_certificate.run --check +python -B -m graph_synthesis.post_certificate.report --check +``` + +`results.json` contains the frozen benchmark outcomes, controls and work counters; `summary.csv` provides a compact result table; the five SVG figures are deterministic renderings of those results. The frozen protocol remains unchanged. diff --git a/graph_synthesis/post_certificate/RESULTS.md b/graph_synthesis/post_certificate/RESULTS.md new file mode 100644 index 0000000..b83eb66 --- /dev/null +++ b/graph_synthesis/post_certificate/RESULTS.md @@ -0,0 +1,81 @@ +# Post-certificate structural extensions for Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five precommitted controlled extensions were executed after the dependence-aware certificate study. All five frozen **algorithmic** targets were met: bounded induced-width max-sum repair matched exhaustive oracles and solved connected non-bipartite triangle chains through 1,025 vertices; atomic batch delta maintenance matched full recomputation across 640 accepted transactions while reducing counted local derived-node evaluations by 90.11%; exact query resilience matched 160 exhaustive small oracles and eight large analytic decompositions; interval-minimax review never worsened the frozen worst-case objective and strictly improved 45/128 fixtures (35.16%); and n-ary forbidden-set repair matched all small oracles and retained strictly more supplied utility than a pairwise clique projection on every large three-way-conflict chain. These are structural and decision-theoretic findings. They are not new Jev semantic-accuracy observations, deployment guarantees, or evidence of superiority to an external graph-synthesis system. + +## 1. Frozen protocol and evidence boundary + +The protocol was committed as `8187cc4e4b98e40f3146ce787b71f262b62b3abd` against baseline `9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98` before implementation and execution. Seed `20260923` is an arbitrary reproducibility seed. **Fresh Jev calls: 0. New scientific documents: 0.** Existing negative semantic/routing/calibration results motivated this round, so this is exploratory follow-up rather than independent preregistration. + +The package-level additions are not worldwide novelty claims. Treewidth-aware dynamic programming, incremental view maintenance, query resilience/deletion propagation, minimax decision rules under imprecise probabilities, and hypergraph/constraint optimization all have prior art. The contribution here is the frozen integration and falsification suite for this repository. + +| Hypothesis | Frozen target | Outcome | +|---|---|---| +| H1: induced-width exact conflict optimization | zero small/oracle/permutation failures; solve 8 large width-2 chains; stage all over-width controls; >=90% fewer counted states | **Met** | +| H2: transactional batch delta maintenance | zero snapshot or atomicity failures; >=80% fewer local derived evaluations | **Met** | +| H3: exact query-resilience certificates | zero small oracle/witness failures; solve 8 analytic large fixtures; stage 6 over-cap controls | **Met** | +| H4: interval-minimax query review | zero oracle discrepancies; never worse than midpoint worst case; strict gain on >=20% | **Met** | +| H5: n-ary conflict constraints | zero small oracle/permutation failures; solve 8 large chains; strict utility gain vs pairwise projection; stage controls | **Met** | + +“Met” refers only to the predeclared controlled fixture conjunction. These rows must not be pooled into a semantic-success percentage. + +## 2. H1 - induced-width exact conflict optimization + +A max-sum variable-elimination solver uses a deterministic min-fill order and stages components whose induced width exceeds four or whose materialized state budget exceeds the frozen cap. On 160 seeded 8-16 vertex bounded-width graphs, the solver matches exhaustive maximum-weight compatible-subset objectives with **0 failures** and **0 input-order permutation failures**. Width distribution is {'1': 33, '2': 45, '3': 53, '4': 29}. + +For 12-16 vertex fixtures, counted factor states are **14,660** versus **1,986,560** full assignments, a 99.26% reduction in this work metric. All eight connected triangle-chain controls from 33 to 1,025 vertices are solved exactly at induced width two. K6-K10 controls stage at widths 5-9. State-count reduction is not a measured CPU or service-latency speedup. + +![H1. Counted exact-DP states versus full enumeration.](figures/01_induced_width.svg) + +## 3. H2 - transactional batch delta maintenance + +Each transaction validates its revision and changes, computes a reverse-dependency closure, evaluates a copy-on-write overlay in topological order, and commits only after every affected derived node succeeds. Across **640** accepted 1-4 primitive transactions on 128 modular DAGs there are **0 snapshot mismatches** against independent full recomputation. The suite also records 64/64 injected-evaluation failures, 64/64 stale revisions and 64/64 malformed updates with no partial state/revision mutation. + +Local work falls from 163,840 full derived-node evaluations to 16,205, a 90.11% reduction. Every connected control evaluates all 256 derived nodes, yielding 0.00% saving and making the locality boundary explicit. This is in-memory atomic batch behavior, not crash durability or concurrent database isolation. + +![H2. Local delta work and connected worst-case control.](figures/02_transactional_delta.svg) + +## 4. H3 - exact query-resilience certificates + +For monotone DNF lineage, the method computes a minimum-cost evidence deletion set that hits every active proof clause using incidence-component decomposition and bounded branch-and-bound. All **160** small fixtures match exhaustive deletion-subset oracles with valid witnesses. Eight analytic decomposable families from 256 to 2,048 proof components (up to 4,096 atoms) match their closed-form optimum; six connected 19-24 atom controls stage under the frozen component cap. + +The output is a robustness margin relative to the supplied proof lineage and removal costs. It does not say that those proofs are factually correct or that their costs are calibrated to real review effort. + +![H3. Certified resilience cost across large decomposable proof families.](figures/03_resilience.svg) + +## 5. H4 - interval-minimax query review + +Point-risk review can be brittle when primitive probabilities are uncertain. The proposed selector chooses two reviews minimizing worst-case expected residual Bernoulli variance across all endpoint combinations of supplied marginal probability intervals. Production query probability uses memoized Shannon recursion; the independent checker enumerates Boolean worlds directly. + +Across 128 non-degenerate fixtures there are **0 oracle discrepancies** and **0 cases worse than the midpoint selector** under the same worst-case objective. The minimax selector is strictly better on **45 fixtures (35.16%)**, including 13 seeded random fixtures and all 32 engineered fragile-midpoint controls. All 16 point-interval controls reduce to the point solution. + +This remains conditional on independent primitives; interval robustness is not a dependence certificate or a semantic calibration guarantee. + +![H4. Minimax versus midpoint worst-case review outcomes.](figures/04_interval_review.svg) + +## 6. H5 - n-ary conflict constraints + +Pairwise conflict edges cannot faithfully represent a rule such as “not all three assertions may coexist.” The factor solver is generalized to forbidden hyperedges: a factor rejects only the all-selected assignment for that scope. Pairwise conflict is the arity-two special case. + +All **160** weighted small hypergraphs match exhaustive subset optimization with **0 failures** and **0 permutation failures**. Eight three-consecutive-forbidden chains from 64 through 2,048 vertices match the analytic optimum `n - floor(n/3)`. Pairwise clique projection is deliberately over-conservative: at n=2,048 it retains 683 unit utility versus 1366 for the n-ary model. All arity-6 through arity-10 over-width controls stage. + +![H5. Exact n-ary repair versus pairwise projection.](figures/05_hypergraph.svg) + +## 7. Interpretation + +This round strengthens a repeated pattern in the package: explicit structure can create large exactness/work-count gains under supplied assumptions, while those gains do not substitute for semantic validation. H1 and H5 broaden tractable repair structure; H2 makes local maintenance atomic across batches; H3 adds a quantitative fragility certificate; H4 replaces a point-risk review objective with a worst-case interval objective. None establishes that Jev extracted the right entities, relations, qualifiers, probabilities, priorities, costs or constraints. + +A decisive next semantic study still requires a new source-disjoint corpus, frozen policy before evaluation, independent adjudication, prospective service cost/latency and matched external baselines. No production graph policy changes in this extension. + +## 8. Reproducibility + +```bash +python -B -m unittest discover -s graph_synthesis/post_certificate/tests -v +python -B -m graph_synthesis.post_certificate.run --check +python -B -m graph_synthesis.post_certificate.report --check +``` + +`results.json` contains the frozen benchmark outcomes, controls and work counters; `summary.csv` provides a compact result table; the five SVG figures are deterministic renderings of those results. The frozen protocol remains unchanged. diff --git a/graph_synthesis/post_certificate/figures/01_induced_width.svg b/graph_synthesis/post_certificate/figures/01_induced_width.svg new file mode 100644 index 0000000..2a6b63d --- /dev/null +++ b/graph_synthesis/post_certificate/figures/01_induced_width.svg @@ -0,0 +1,26 @@ + + +H1 | Exact repair work versus full enumeration + + + +0 + +3.97e+05 + +7.95e+05 + +1.19e+06 + +1.59e+06 + +1.99e+06 + +1.466e+04 +factor states + +1.987e+06 +2^n assignments +counted states +Aggregate 12-16 vertex fixtures; reduction 99.26%. Work count is not wall-clock speedup. + diff --git a/graph_synthesis/post_certificate/figures/02_transactional_delta.svg b/graph_synthesis/post_certificate/figures/02_transactional_delta.svg new file mode 100644 index 0000000..9bab575 --- /dev/null +++ b/graph_synthesis/post_certificate/figures/02_transactional_delta.svg @@ -0,0 +1,29 @@ + + +H2 | Transactional delta evaluations + + + +0 + +51.2 + +102 + +154 + +205 + +256 + +25.32 +local delta + +256 +full recompute + +256 +connected control +derived-node evaluations per transaction +Local aggregate reduction 90.11%; connected control reduction 0.00%. + diff --git a/graph_synthesis/post_certificate/figures/03_resilience.svg b/graph_synthesis/post_certificate/figures/03_resilience.svg new file mode 100644 index 0000000..5425651 --- /dev/null +++ b/graph_synthesis/post_certificate/figures/03_resilience.svg @@ -0,0 +1,17 @@ + + +H3 | Exact resilience on decomposable proof families + + +certified cost +256 +384 +512 +768 +1024 +1280 +1536 +2048 +minimum deletion cost +All analytic large fixtures match the independent closed form. + diff --git a/graph_synthesis/post_certificate/figures/04_interval_review.svg b/graph_synthesis/post_certificate/figures/04_interval_review.svg new file mode 100644 index 0000000..f328db0 --- /dev/null +++ b/graph_synthesis/post_certificate/figures/04_interval_review.svg @@ -0,0 +1,29 @@ + + +H4 | Interval-minimax review outcomes + + + +0 + +16.6 + +33.2 + +49.8 + +66.4 + +83 + +45 +strict better + +83 +ties + +0 +worse +fixtures +Strict minimax improvement on 35.16%; 0 oracle discrepancies. + diff --git a/graph_synthesis/post_certificate/figures/05_hypergraph.svg b/graph_synthesis/post_certificate/figures/05_hypergraph.svg new file mode 100644 index 0000000..21ad610 --- /dev/null +++ b/graph_synthesis/post_certificate/figures/05_hypergraph.svg @@ -0,0 +1,19 @@ + + +H5 | N-ary versus pairwise-projected repair + + +n-ary exact + +pairwise projection +64 +128 +256 +512 +768 +1024 +1536 +2048 +retained unit utility +Pairwise clique projection is intentionally over-conservative for three-way-only conflicts. + diff --git a/manuscript/paper-current.build.json b/manuscript/paper-current.build.json index a0fb2ba..7b21890 100644 --- a/manuscript/paper-current.build.json +++ b/manuscript/paper-current.build.json @@ -1,6 +1,6 @@ { - "source_sha256": "e827a0d64a77e279a3d96c6692a1b9c62a29297b3bce1331f3ce61e7afa90357", + "source_sha256": "1299db06793af8ae9eecb79a81386ea60bc9a6f1c38e1aa42f43f506ec57cab5", "renderer_sha256": "b1ecb302903b01907b9c4dbd931862ef0f62c50804ca92077fd9ac26eb1354a4", - "pdf_sha256": "f2d5e99e224f74316bd11da163c6f69c9746e84d6efa47d37f5e49f6ee7365f2", + "pdf_sha256": "618c7bbd10f5c48bdadde9d49b01999778f9bebd42dd50f21455408f5ce36ff2", "note": "Local-only assets; PDF bytes may vary with renderer and fonts." } diff --git a/manuscript/paper-current.html b/manuscript/paper-current.html index 8840c43..03cd0f6 100644 --- a/manuscript/paper-current.html +++ b/manuscript/paper-current.html @@ -4823,4 +4823,78 @@

13. Conclusion

A fixed Jev service benefits from a selected bundle of explicit identity instructions, a typed Choice contract, and six training demonstrations on one identity-disjoint bibliographic matching split. Operational macro-F1 rises from 0.9605 to 0.9859, while false-positive identity predictions decline from eight to two and one true match is missed. A parallel scientific claim-verification experiment shows no resolved classification gain. Calibration improves some probability metrics and worsens others; fresh probabilities vary despite mostly stable labels. The saved protocol, source, responses, and replay evidence make these findings inspectable and reproducible from recorded outputs. They support a narrow use case and a concrete independent-replication agenda, not a general claim about Jev superiority or autonomous graph correctness.

References

The package bibliography supplies the citation entries used above. The source-verification notes distinguish interface documentation, original papers, dataset provenance, and remaining licensing or metadata questions.

+

Post-certificate structural extensions for Jev graph synthesis

+

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

+

Abstract

+

Five precommitted controlled extensions were executed after the dependence-aware certificate study. All five frozen algorithmic targets were met: bounded induced-width max-sum repair matched exhaustive oracles and solved connected non-bipartite triangle chains through 1,025 vertices; atomic batch delta maintenance matched full recomputation across 640 accepted transactions while reducing counted local derived-node evaluations by 90.11%; exact query resilience matched 160 exhaustive small oracles and eight large analytic decompositions; interval-minimax review never worsened the frozen worst-case objective and strictly improved 45/128 fixtures (35.16%); and n-ary forbidden-set repair matched all small oracles and retained strictly more supplied utility than a pairwise clique projection on every large three-way-conflict chain. These are structural and decision-theoretic findings. They are not new Jev semantic-accuracy observations, deployment guarantees, or evidence of superiority to an external graph-synthesis system.

+

1. Frozen protocol and evidence boundary

+

The protocol was committed as 8187cc4e4b98e40f3146ce787b71f262b62b3abd against baseline 9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98 before implementation and execution. Seed 20260923 is an arbitrary reproducibility seed. Fresh Jev calls: 0. New scientific documents: 0. Existing negative semantic/routing/calibration results motivated this round, so this is exploratory follow-up rather than independent preregistration.

+

The package-level additions are not worldwide novelty claims. Treewidth-aware dynamic programming, incremental view maintenance, query resilience/deletion propagation, minimax decision rules under imprecise probabilities, and hypergraph/constraint optimization all have prior art. The contribution here is the frozen integration and falsification suite for this repository.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HypothesisFrozen targetOutcome
H1: induced-width exact conflict optimizationzero small/oracle/permutation failures; solve 8 large width-2 chains; stage all over-width controls; >=90% fewer counted statesMet
H2: transactional batch delta maintenancezero snapshot or atomicity failures; >=80% fewer local derived evaluationsMet
H3: exact query-resilience certificateszero small oracle/witness failures; solve 8 analytic large fixtures; stage 6 over-cap controlsMet
H4: interval-minimax query reviewzero oracle discrepancies; never worse than midpoint worst case; strict gain on >=20%Met
H5: n-ary conflict constraintszero small oracle/permutation failures; solve 8 large chains; strict utility gain vs pairwise projection; stage controlsMet
+

“Met” refers only to the predeclared controlled fixture conjunction. These rows must not be pooled into a semantic-success percentage.

+

2. H1 - induced-width exact conflict optimization

+

A max-sum variable-elimination solver uses a deterministic min-fill order and stages components whose induced width exceeds four or whose materialized state budget exceeds the frozen cap. On 160 seeded 8-16 vertex bounded-width graphs, the solver matches exhaustive maximum-weight compatible-subset objectives with 0 failures and 0 input-order permutation failures. Width distribution is {'1': 33, '2': 45, '3': 53, '4': 29}.

+

For 12-16 vertex fixtures, counted factor states are 14,660 versus 1,986,560 full assignments, a 99.26% reduction in this work metric. All eight connected triangle-chain controls from 33 to 1,025 vertices are solved exactly at induced width two. K6-K10 controls stage at widths 5-9. State-count reduction is not a measured CPU or service-latency speedup.

+
H1. Counted exact-DP states versus full enumeration.
+

3. H2 - transactional batch delta maintenance

+

Each transaction validates its revision and changes, computes a reverse-dependency closure, evaluates a copy-on-write overlay in topological order, and commits only after every affected derived node succeeds. Across 640 accepted 1-4 primitive transactions on 128 modular DAGs there are 0 snapshot mismatches against independent full recomputation. The suite also records 64/64 injected-evaluation failures, 64/64 stale revisions and 64/64 malformed updates with no partial state/revision mutation.

+

Local work falls from 163,840 full derived-node evaluations to 16,205, a 90.11% reduction. Every connected control evaluates all 256 derived nodes, yielding 0.00% saving and making the locality boundary explicit. This is in-memory atomic batch behavior, not crash durability or concurrent database isolation.

+
H2. Local delta work and connected worst-case control.
+

4. H3 - exact query-resilience certificates

+

For monotone DNF lineage, the method computes a minimum-cost evidence deletion set that hits every active proof clause using incidence-component decomposition and bounded branch-and-bound. All 160 small fixtures match exhaustive deletion-subset oracles with valid witnesses. Eight analytic decomposable families from 256 to 2,048 proof components (up to 4,096 atoms) match their closed-form optimum; six connected 19-24 atom controls stage under the frozen component cap.

+

The output is a robustness margin relative to the supplied proof lineage and removal costs. It does not say that those proofs are factually correct or that their costs are calibrated to real review effort.

+
H3. Certified resilience cost across large decomposable proof families.
+

5. H4 - interval-minimax query review

+

Point-risk review can be brittle when primitive probabilities are uncertain. The proposed selector chooses two reviews minimizing worst-case expected residual Bernoulli variance across all endpoint combinations of supplied marginal probability intervals. Production query probability uses memoized Shannon recursion; the independent checker enumerates Boolean worlds directly.

+

Across 128 non-degenerate fixtures there are 0 oracle discrepancies and 0 cases worse than the midpoint selector under the same worst-case objective. The minimax selector is strictly better on 45 fixtures (35.16%), including 13 seeded random fixtures and all 32 engineered fragile-midpoint controls. All 16 point-interval controls reduce to the point solution.

+

This remains conditional on independent primitives; interval robustness is not a dependence certificate or a semantic calibration guarantee.

+
H4. Minimax versus midpoint worst-case review outcomes.
+

6. H5 - n-ary conflict constraints

+

Pairwise conflict edges cannot faithfully represent a rule such as “not all three assertions may coexist.” The factor solver is generalized to forbidden hyperedges: a factor rejects only the all-selected assignment for that scope. Pairwise conflict is the arity-two special case.

+

All 160 weighted small hypergraphs match exhaustive subset optimization with 0 failures and 0 permutation failures. Eight three-consecutive-forbidden chains from 64 through 2,048 vertices match the analytic optimum n - floor(n/3). Pairwise clique projection is deliberately over-conservative: at n=2,048 it retains 683 unit utility versus 1366 for the n-ary model. All arity-6 through arity-10 over-width controls stage.

+
H5. Exact n-ary repair versus pairwise projection.
+

7. Interpretation

+

This round strengthens a repeated pattern in the package: explicit structure can create large exactness/work-count gains under supplied assumptions, while those gains do not substitute for semantic validation. H1 and H5 broaden tractable repair structure; H2 makes local maintenance atomic across batches; H3 adds a quantitative fragility certificate; H4 replaces a point-risk review objective with a worst-case interval objective. None establishes that Jev extracted the right entities, relations, qualifiers, probabilities, priorities, costs or constraints.

+

A decisive next semantic study still requires a new source-disjoint corpus, frozen policy before evaluation, independent adjudication, prospective service cost/latency and matched external baselines. No production graph policy changes in this extension.

+

8. Reproducibility

+
python -B -m unittest discover -s graph_synthesis/post_certificate/tests -v
+python -B -m graph_synthesis.post_certificate.run --check
+python -B -m graph_synthesis.post_certificate.report --check
+
+

results.json contains the frozen benchmark outcomes, controls and work counters; summary.csv provides a compact result table; the five SVG figures are deterministic renderings of those results. The frozen protocol remains unchanged.

\ No newline at end of file diff --git a/manuscript/paper-current.md b/manuscript/paper-current.md index f54ba53..b5568a0 100644 --- a/manuscript/paper-current.md +++ b/manuscript/paper-current.md @@ -2293,3 +2293,89 @@ A fixed Jev service benefits from a selected bundle of explicit identity instruc # References The package bibliography supplies the citation entries used above. The source-verification notes distinguish interface documentation, original papers, dataset provenance, and remaining licensing or metadata questions. + + + +# Post-certificate structural extensions for Jev graph synthesis + +Timothy Wayne Gregg | AI-assisted author-review research extension | September 18, 2026 + +## Abstract + +Five precommitted controlled extensions were executed after the dependence-aware certificate study. All five frozen **algorithmic** targets were met: bounded induced-width max-sum repair matched exhaustive oracles and solved connected non-bipartite triangle chains through 1,025 vertices; atomic batch delta maintenance matched full recomputation across 640 accepted transactions while reducing counted local derived-node evaluations by 90.11%; exact query resilience matched 160 exhaustive small oracles and eight large analytic decompositions; interval-minimax review never worsened the frozen worst-case objective and strictly improved 45/128 fixtures (35.16%); and n-ary forbidden-set repair matched all small oracles and retained strictly more supplied utility than a pairwise clique projection on every large three-way-conflict chain. These are structural and decision-theoretic findings. They are not new Jev semantic-accuracy observations, deployment guarantees, or evidence of superiority to an external graph-synthesis system. + +## 1. Frozen protocol and evidence boundary + +The protocol was committed as `8187cc4e4b98e40f3146ce787b71f262b62b3abd` against baseline `9d2e4a60a6c79e616ab1d352cd5da9fe414e6c98` before implementation and execution. Seed `20260923` is an arbitrary reproducibility seed. **Fresh Jev calls: 0. New scientific documents: 0.** Existing negative semantic/routing/calibration results motivated this round, so this is exploratory follow-up rather than independent preregistration. + +The package-level additions are not worldwide novelty claims. Treewidth-aware dynamic programming, incremental view maintenance, query resilience/deletion propagation, minimax decision rules under imprecise probabilities, and hypergraph/constraint optimization all have prior art. The contribution here is the frozen integration and falsification suite for this repository. + +| Hypothesis | Frozen target | Outcome | +|---|---|---| +| H1: induced-width exact conflict optimization | zero small/oracle/permutation failures; solve 8 large width-2 chains; stage all over-width controls; >=90% fewer counted states | **Met** | +| H2: transactional batch delta maintenance | zero snapshot or atomicity failures; >=80% fewer local derived evaluations | **Met** | +| H3: exact query-resilience certificates | zero small oracle/witness failures; solve 8 analytic large fixtures; stage 6 over-cap controls | **Met** | +| H4: interval-minimax query review | zero oracle discrepancies; never worse than midpoint worst case; strict gain on >=20% | **Met** | +| H5: n-ary conflict constraints | zero small oracle/permutation failures; solve 8 large chains; strict utility gain vs pairwise projection; stage controls | **Met** | + +“Met” refers only to the predeclared controlled fixture conjunction. These rows must not be pooled into a semantic-success percentage. + +## 2. H1 - induced-width exact conflict optimization + +A max-sum variable-elimination solver uses a deterministic min-fill order and stages components whose induced width exceeds four or whose materialized state budget exceeds the frozen cap. On 160 seeded 8-16 vertex bounded-width graphs, the solver matches exhaustive maximum-weight compatible-subset objectives with **0 failures** and **0 input-order permutation failures**. Width distribution is {'1': 33, '2': 45, '3': 53, '4': 29}. + +For 12-16 vertex fixtures, counted factor states are **14,660** versus **1,986,560** full assignments, a 99.26% reduction in this work metric. All eight connected triangle-chain controls from 33 to 1,025 vertices are solved exactly at induced width two. K6-K10 controls stage at widths 5-9. State-count reduction is not a measured CPU or service-latency speedup. + +![H1. Counted exact-DP states versus full enumeration.](../graph_synthesis/post_certificate/figures/01_induced_width.svg) + +## 3. H2 - transactional batch delta maintenance + +Each transaction validates its revision and changes, computes a reverse-dependency closure, evaluates a copy-on-write overlay in topological order, and commits only after every affected derived node succeeds. Across **640** accepted 1-4 primitive transactions on 128 modular DAGs there are **0 snapshot mismatches** against independent full recomputation. The suite also records 64/64 injected-evaluation failures, 64/64 stale revisions and 64/64 malformed updates with no partial state/revision mutation. + +Local work falls from 163,840 full derived-node evaluations to 16,205, a 90.11% reduction. Every connected control evaluates all 256 derived nodes, yielding 0.00% saving and making the locality boundary explicit. This is in-memory atomic batch behavior, not crash durability or concurrent database isolation. + +![H2. Local delta work and connected worst-case control.](../graph_synthesis/post_certificate/figures/02_transactional_delta.svg) + +## 4. H3 - exact query-resilience certificates + +For monotone DNF lineage, the method computes a minimum-cost evidence deletion set that hits every active proof clause using incidence-component decomposition and bounded branch-and-bound. All **160** small fixtures match exhaustive deletion-subset oracles with valid witnesses. Eight analytic decomposable families from 256 to 2,048 proof components (up to 4,096 atoms) match their closed-form optimum; six connected 19-24 atom controls stage under the frozen component cap. + +The output is a robustness margin relative to the supplied proof lineage and removal costs. It does not say that those proofs are factually correct or that their costs are calibrated to real review effort. + +![H3. Certified resilience cost across large decomposable proof families.](../graph_synthesis/post_certificate/figures/03_resilience.svg) + +## 5. H4 - interval-minimax query review + +Point-risk review can be brittle when primitive probabilities are uncertain. The proposed selector chooses two reviews minimizing worst-case expected residual Bernoulli variance across all endpoint combinations of supplied marginal probability intervals. Production query probability uses memoized Shannon recursion; the independent checker enumerates Boolean worlds directly. + +Across 128 non-degenerate fixtures there are **0 oracle discrepancies** and **0 cases worse than the midpoint selector** under the same worst-case objective. The minimax selector is strictly better on **45 fixtures (35.16%)**, including 13 seeded random fixtures and all 32 engineered fragile-midpoint controls. All 16 point-interval controls reduce to the point solution. + +This remains conditional on independent primitives; interval robustness is not a dependence certificate or a semantic calibration guarantee. + +![H4. Minimax versus midpoint worst-case review outcomes.](../graph_synthesis/post_certificate/figures/04_interval_review.svg) + +## 6. H5 - n-ary conflict constraints + +Pairwise conflict edges cannot faithfully represent a rule such as “not all three assertions may coexist.” The factor solver is generalized to forbidden hyperedges: a factor rejects only the all-selected assignment for that scope. Pairwise conflict is the arity-two special case. + +All **160** weighted small hypergraphs match exhaustive subset optimization with **0 failures** and **0 permutation failures**. Eight three-consecutive-forbidden chains from 64 through 2,048 vertices match the analytic optimum `n - floor(n/3)`. Pairwise clique projection is deliberately over-conservative: at n=2,048 it retains 683 unit utility versus 1366 for the n-ary model. All arity-6 through arity-10 over-width controls stage. + +![H5. Exact n-ary repair versus pairwise projection.](../graph_synthesis/post_certificate/figures/05_hypergraph.svg) + +## 7. Interpretation + +This round strengthens a repeated pattern in the package: explicit structure can create large exactness/work-count gains under supplied assumptions, while those gains do not substitute for semantic validation. H1 and H5 broaden tractable repair structure; H2 makes local maintenance atomic across batches; H3 adds a quantitative fragility certificate; H4 replaces a point-risk review objective with a worst-case interval objective. None establishes that Jev extracted the right entities, relations, qualifiers, probabilities, priorities, costs or constraints. + +A decisive next semantic study still requires a new source-disjoint corpus, frozen policy before evaluation, independent adjudication, prospective service cost/latency and matched external baselines. No production graph policy changes in this extension. + +## 8. Reproducibility + +```bash +python -B -m unittest discover -s graph_synthesis/post_certificate/tests -v +python -B -m graph_synthesis.post_certificate.run --check +python -B -m graph_synthesis.post_certificate.report --check +``` + +`results.json` contains the frozen benchmark outcomes, controls and work counters; `summary.csv` provides a compact result table; the five SVG figures are deterministic renderings of those results. 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zr;~qAphRN8)n)8P;rF)_eV@!Lklv<7Vu_?iOH?nWa;+_VIY>VrBHWY zc^zfoJEx5~IqKExe!JMxmQUS3+@>`vekFHf04`fv>bOq7hBZe8Gztb&&BNuDEv#96 zX-r-+m~P8)OW>+CJJoFKB7vN{t$u+QN62g?-_C2h`bb}r&~CX&NZZpMulk@bp=2Pm zC$Hd(WZII=+Aj}Z=Lvj4_oiRs4D>O_qbwV`J@l{9?o4Tz*j4_yusZ_B@rdh~_}a5X zge{4~8O$B3+YSg8f{txG~7eD9FKJvJ8TPT1MIo{|0LN B7EAyD From f18e08895dcbd19d2d19a7de93d357800c80260b Mon Sep 17 00:00:00 2001 From: Timothy Wayne Gregg Date: Fri, 18 Sep 2026 10:58:18 -0400 Subject: [PATCH 05/10] ci: preserve later research blocks in follow-up regeneration --- graph_synthesis/followup/report.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/graph_synthesis/followup/report.py b/graph_synthesis/followup/report.py index 39e77c6..2ceab07 100644 --- a/graph_synthesis/followup/report.py +++ b/graph_synthesis/followup/report.py @@ -248,9 +248,22 @@ def update_paper(r: dict): note = '\n## Five follow-up improvements (PR #13)\n\n[Executed results](graph_synthesis/followup/RESULTS.md), [frozen protocol](graph_synthesis/followup/PROTOCOL.md), ' note += '[five figures](graph_synthesis/followup/figures/), and the updated full paper distinguish same-data response replay from controlled algorithm tests. ' note += 'No new service calls; negative results retained; no production-policy change.\n' - if '\n## Five follow-up improvements (PR #13)' in text: - text = text.split('\n## Five follow-up improvements (PR #13)')[0] - entry.write_text(text.rstrip()+'\n'+note,encoding='utf-8') + heading = '\n## Five follow-up improvements (PR #13)' + if heading in text: + start = text.index(heading) + after = start + len(heading) + # This entry pre-dates marker-based additive studies and historically + # assumed it was last. Preserve any later research blocks/headings so + # older regeneration workflows cannot delete newly appended evidence. + candidates = [p for p in ( + text.find('\n