claw
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Tested open question from Finding #5: does narrow framing give Sonnet GPT-5-level semantic analysis? Result: NO. Narrow framing changes WHAT Sonnet looks for (redirects from gaps to contradictions) but not HOW WELL it reasons. Sonnet narrow found 3 contradictions but only 1 was genuine (2 were analytical errors/misreads). GPT-5 found 4 all-genuine findings with precise reasoning. Key insight: framing controls scope, not reasoning depth. For tasks requiring logical verification (contradictions, race conditions, invariant violations), reasoning tokens are necessary — framing alone is insufficient. Updated open-questions.md: marked Sonnet+narrow as answered, added new question about Opus+narrow for contradiction detection.
Model Research — AI for Analytical Work
Comparative analysis of AI models on analytical tasks — not coding.
Most public discussion about LLM capabilities focuses on code generation. We found almost no published methodology for using models in analytical research tasks (searched 2026-04-26). This repo fills that gap with controlled experiments and reproducible findings.
What We're Testing
Using GPT-5, Claude Opus 4.6, Claude Sonnet 4.6, and GPT-4.1 (+ Mini) for:
- Architecture document review
- Bias and assumption detection
- Gap-finding in design specifications
- Cross-document consistency analysis
- Race condition identification
- Adversarial path analysis
- Contradiction detection
- Regulatory compliance review
Key Findings (Summary)
| # | Task Type | Winner | Key Insight |
|---|---|---|---|
| 1 | PR review | Both | Different models catch different things — Sonnet: structural, GPT-5: semantic |
| 2 | Bias detection | Framing | Signal-to-noise ratio matters more than model capability |
| 9 | Gap-finding | GPT-5 | Reasoning tokens find domain-specific gaps, not generic ones |
| 10 | Hidden assumptions | GPT-5 | Reasoning produces qualitatively different (not just more) findings |
| 13 | Race conditions | Opus | Temporal interaction reasoning is Opus's strongest domain |
| 15 | Design coherence | Task-dependent | Single-doc: model choice depends on document complexity |
| 25 | Contradiction detection | Opus | Precision > exhaustiveness; Opus's self-correction is unique |
| 28 | Cross-doc consistency | Opus | 2.4x faster than GPT-5 with more findings; boundary reasoning |
| 29 | Adversarial analysis | GPT-5 + Opus | GPT-5: exhaustive; Opus: qualitatively different attack vectors |
Methodology
Each experiment:
- Same input document(s) to all models
- Same structured prompt with explicit categories to analyze
- No tools, no project context beyond the document(s)
- Independent runs — no cross-pollination between models
- Results evaluated for: correctness, uniqueness, actionability
Context dimensions tracked:
- Rich vs minimal (how much background info)
- Broad vs focused ("review this" vs "answer this specific question")
- What kind of context (diff, full files, issue text, nothing)
- Whether the model had tools or just text
- Whether the task was step-by-step or open-ended
Reports
- REPORT.md — Full research analysis. Covers model strengths with evidence, task-type → model mappings, meta-findings about how to use models effectively, cost-effectiveness comparison, and open questions. Regenerated weekly from all findings.
- LESSONS.md — Actionable summary. The distilled "here's what to actually do" version: three core rules, operational playbooks for different review types, anti-patterns to avoid, and a model personality cheat sheet. Start here if you want answers, not methodology.
Both files include a generation timestamp and are automatically regenerated every Monday at 9 AM Pacific to incorporate new experiment results.
Repository Structure
REPORT.md # Full research report (auto-regenerated weekly)
LESSONS.md # Actionable lessons and playbooks (auto-regenerated weekly)
findings/ # Individual experiment files (one per experiment)
README.md # Context and index
YYYY-MM-DD-NN-slug.md
2026-04-26-01-different-models-catch-different-things.md
...
2026-05-05-29-adversarial-manipulation-analysis-new-task.md
prompts/ # Exact prompts used for reproducibility
cross-document-consistency.md
design-coherence.md
gap-finding.md
hidden-assumptions.md
...
open-questions.md # Unanswered questions for future experiments
methodology.md # Full methodology notes
Findings are named YYYY-MM-DD-NN-slug.md for chronological sorting.
Numbers are zero-padded (01–29). The duplicate finding #7 uses a b suffix.
Who We Are
This research is conducted by Rodin (AI assistant) and Aaron Weiker. The test corpus is gargoyle — an Elixir trading system with extensive architecture documentation (~35 design docs, ~5000 lines).
License
CC BY 4.0 — share and adapt with attribution.