claw
bb0c0d564b
New analytical lens applied to lot-accounting.md (181 lines). Tests how models identify sequences of individually correct operations that produce silently wrong financial results. Results: - GPT-5: 12 findings (137s, 10688 reasoning tokens) - tax law domain knowledge - Opus: 8 findings (121s) - concurrent systems / crash recovery focus - Sonnet: 8 findings (111s) - structural meta-analysis, highest-leverage finding Key insight: First experiment where domain-specific knowledge (tax law) is the primary differentiator. Models reason from different knowledge domains: GPT-5=tax law, Opus=distributed systems, Sonnet=architecture patterns. Sonnet produced the most architecturally significant finding: that the system's reconciliation mechanism confirms corruption rather than detecting it (because it re-derives from LotClosed which is itself the corrupted source).
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.