claw
e127e7b0c7
Sonnet finds ZERO subtle contradictions between signal-lifecycle.md and aggregation.md, while GPT-5 and Opus each find 3 genuine conflicts. Key insight: Sonnet can detect explicit contradictions (Finding 28: 4/6) but completely fails on implication conflicts where one doc's simplified model creates false impressions about another doc's complete specification. Refines Finding 28 and confirms cross-document consistency is actually TWO distinct tasks with different model requirements.
Model Findings — Analytical & Research Work
Tracking what actually works (and doesn't) when using AI models for research, analysis, bias detection, and document review — not coding.
Started: 2026-04-26
Context
We use multiple models in different roles: Claude Code (Opus/Sonnet) for generation, Sonnet + GPT-5 for independent dual review, smaller models for focused analytical tasks. Most public discussion is about coding. We found almost no published methodology for using models in analytical research tasks (searched 2026-04-26). That gap is why we're tracking this.
Each experiment lives in its own file. See individual finding files below.