claw
5b8f8caf8c
New analytical lens applied to signal-lifecycle.md (111 lines). All three models (GPT-5, Opus, Sonnet) found 7-9 findings each with 70% at Critical/High severity. Key insight: concurrency analysis rewards compositional temporal reasoning over enumeration breadth, narrowing the gap between models compared to other lenses. Unique finds: GPT-5 (stop-loss race, duplicate UUID), Opus (crash survival contradiction), Sonnet (Signal Risk audit gap after dispatch).
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.