Closes the determinism gap observed in the 3-run baseline test: 1 of
8 findings (the "proven escalation ladder" block) was flipping across
identical-state audits. Root cause: cloud non-determinism at temp=0
is real in practice even though it shouldn't be in theory.
Fix: run the primary reviewer (gpt-oss:120b) N=3 times in PARALLEL
(Promise.all, wall-clock ≈ single call because they're independent
HTTP requests). Aggregate votes per claim_idx. Majority wins. On a
1-1-1 split, call a tie-breaker model with different architecture:
qwen3-coder:480b — newer coding specialist, 4x params of the primary,
distinct training lineage.
Every case where the 3 runs disagreed (even when majority resolved)
is logged to data/_kb/audit_discrepancies.jsonl with the vote counts
and resolution type. This is how we measure consensus drift over
time — a dashboard metric is literally `wc -l audit_discrepancies`
relative to audit count.
Verified: 2 back-to-back audits on unchanged PR #8 produced
identical 8 findings each (1 block + 7 warn). consensus=3/3 on every
claim, zero discrepancies logged. Cost: 3x primary tokens (7K per
audit vs 2K), wall-clock ~unchanged because calls are parallel.
New env vars:
LH_AUDITOR_CONSENSUS_N default 3
LH_AUDITOR_TIEBREAKER_MODEL default qwen3-coder:480b
Factored the cloud call into runCloudInference() helper so the
consensus loop is clean and the tie-breaker reuses the same prompt
shape as the primary.
2afad0f83f