Tested kill-switch.md + escalation-policy.md (same bounded context,
shared vocabulary). Key insight: shared vocabulary claims are the most
dangerous inconsistency — same words with opposite severity ordering.
Opus found the severity-ordering inversion (restrict/liquidate ladders
run in opposite directions). GPT-5 found the meta-issue (the 'same
vocabulary' claim is itself the problem). Sonnet fast but shallow.
Tightly coupled docs produce more Critical findings than loosely coupled
ones (Finding #28).
New experiment type: give models two related architecture documents and ask
them to identify assumptions each document makes about the other that could
be violated.
Results: GPT-5 (10 findings, 175s, operational/race-focused) and Opus (10
findings, 111s, structural/architectural) both found unique interface gaps.
Sonnet (7 findings, 29s) found nothing unique - all its findings were
simplified versions of GPT-5/Opus findings.
Key insight: Interface analysis requires holding two mental models simultaneously
and is harder than single-document analysis. Sonnet produced 0 unique findings
(vs 2-6 on single-doc tasks). Extended reasoning appears necessary for this
task type.
Tests GPT-5 → Opus critique+extend pipeline on dtbp-margin-call.md.
Key results:
- Ensemble produces 56 unique findings vs 43 (GPT-5) or 28 (Opus) alone
- Zero full disagreements — GPT-5's coverage is reliable signal
- Critique phase (severity calibration) more valuable than extension phase
- 28% more tokens for 30% more coverage + structured prioritization
- Answers open question about adversarial ensemble value
claw