claw
7ca01f0cbf
New analytical lens (adversarial/offensive security) tested on gargoyle's signal audit log spec. GPT-5 most exhaustive (25), Opus deepest individual attack narratives (14), Sonnet most creative meta-attacks (11). Adversarial lens is ~2.5x more productive than defensive lenses on comparable docs. All three models converged on same root cause (trust model).
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.