Omnigraph
Graph engine for context assembly. Object-storage native. Agents work with graph data the way engineers work with code: branched, typed, reviewed, merged, audited, and governed.
Neurosymbolic verification for cross domain scientific claim comparison. Typed subgraph isomorphism. Four rung grading. Versioned testability. The judge is never a model.
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Verification and evaluation release of the Consilience engine: typed claims, a deterministic judge, locked registry
Taken from the repository README
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Read the full README ↗The verification and evaluation release of the Consilience engine (paper v1).
Typed-claim extraction + a deterministic rule-based judge that compares scientific claims across theories, with built-in verification (the judge re-expressed as a logic program, an SMT theory, and proof objects) and cross-field discovery (re-adjudication + held-out key recovery against explicit chance rates).
All headline numbers below render from one numbers-locked registry snapshot
(paper/NUMBERS_LOCK.json, sha e9c877812c8c6897…, 2026-10, instrument v2 / corpus v1) — the same number
appears in the paper and in every rendered table (numbers lock).
Verification (the judge is a transparent, checkable function of typed fields):
Discovery (reported with chance rates and honest negatives):
pipeline/ — the one LLM wrapper (llm.py), extraction/judge ports, task drivers, CI checks.logic/ — the verified-judge lane (minidatalog, judge.lp, Z3 congruence, proofs, integrity).bench/ — the alignment bench (AB0–AB7). paper/ — P1 + NUMBERS_LOCK.json + the arXiv build.docs/tables/ — per-task tables rendered from the locked registry; results/registry.json — the locked
metric store; docs/ — engine artifacts (instrument freeze, falsifiability audit, judge port, env report).Determinism rests on the llm.py cache (sha256 of the full request + replicate_tag), not on decoding.
The integrity gate is lint + holdout scan + read-only/quarantine/secret guards + registry validation:
.venv/bin/python pipeline/lint_claims.py
.venv/bin/python pipeline/holdout_check2.py
.venv/bin/python pipeline/check_readonly.py
.venv/bin/python pipeline/check_quarantine.py
.venv/bin/python pipeline/check_secrets.py
.venv/bin/python pipeline/validate_registry.py
(make ci runs the same spine.) The full pytest suite depends on held-out answer-key material that is not
part of this public release (see below).
This public mirror excludes the held-out answer keys and any held-out key text, the obfuscation maps, and
all corpus input PDFs, so some task drivers and tests cannot be re-run here without that material. Headline
numbers inject only from the locked registry snapshot. See docs/RELEASE.md.
Small corpus (95 claims, 6 theories); single-pass extraction noise floor (stability 0.25); near single-class polarity (92 asserts / 3 denies); reference labels are judge-after-audit, not ground truth; held-out keys control the prompt, not training data (the obfuscation control is the measured response); open-weight panel leg deferred (no key). Deeper arms (AB3/AB4/SS pilots, TD extensions, protocol game, B5) are paper v2.
Khatri, A. (2026). A deterministic, auditable engine for cross-theory claim comparison: cross-field discovery with built-in verification (paper v1). Code and locked registry: https://github.com/Anuja-Khatri/consilience-verification · Preregistration: DOI 10.17605/OSF.IO/5N24M.