/skills/shared-references/evidence-precheck.md

Evidence Pre-check

ARIS's claim audits (/result-to-claim, /experiment-audit, /paper-claim-audit) spend a cross-model (codex/gemini) call to judge whether a claim is supported. The cheapest, most common integrity failure is hallucinated evidence: a claim cites a number + a source file, and the file doesn't exist or the number isn't in it. You should not need a model call to catch that.

Two stages — and verified ≠ correct

stage 1  tools/evidence_check.py   deterministic · no model · fail-closed
         catches HALLUCINATION — cited path missing, or cited value not in source.
stage 2  the cross-model jury      codex/gemini
         catches WRONG-BUT-REAL — the number IS in the file, but it doesn't
         support the claim.

A verified from stage 1 means only that the cited evidence exists — never that the claim holds. Existence is execution-completeness (deterministic / safe same-model); support is a quality verdict that stays with the cross-model jury (acceptance-gate.md: the pre-check DRIVES a gate, it cannot ACQUIT a claim). This is the reconcile pattern — a model's self-report cross-checked against mechanical ground-truth (adapted from Hermes's curator reconcile-classifier), made into a cheap pre-gate that catches hallucination before the jury runs and spares the codex call on fabricated evidence.

Conservative by design

The pre-check favors false-negative over false-positive: when in doubt it returns not-verified and lets the jury decide — it must never emit a false verified. A pure number is matched by numeric-token equality (so 73.2 matches 73.20 but 73 does NOT match 73.5); a non-numeric value by normalized substring.

Where ARIS uses it

• /result-to-claim Step 1.5: parse each claim's cited (value, source), run the batch pre-check, and before the codex judgment mark any claim whose evidence is path_missing / value_not_found as unsupported — evidence not found, and pass the per-claim pre-check status into the codex prompt so the jury sees which claims have verified vs hallucinated evidence.
• To extend: /experiment-audit (the "phantom results" check is exactly this) and /paper-claim-audit (every reported number → its result file).

API / CLI

from evidence_check import check_claim, check_batch
check_claim(value, source, root=".")   # -> {status: verified|path_missing|value_not_found, ...}
check_batch([{value, source, id?}, ...], root)  # -> {results:[...], summary:{status: n}}

python3 tools/evidence_check.py <root> --value 73.2 --source results/eval.json   # exit 0 verified
python3 tools/evidence_check.py <root> --batch claims.json   # exit 1 if any claim hallucinated

Cross-references

• acceptance-gate.md — the pre-check is the deterministic DRIVE; the jury is the ACQUIT. verified is existence (execution-completeness), not correctness.
• reviewer-independence.md — the jury still reads the artifacts itself; the pre-check only flags which claims have evidence to read, never pre-digests the verdict.
• experiment-integrity.md — fabricated/phantom results are exactly what stage 1 catches deterministically before stage 2.