Rodin
1b108ff66e
Full comparative analysis of GPT-5, Claude Opus 4.6, Claude Sonnet 4.6, GPT-4.1, and GPT-4.1 Mini on analytical tasks (not coding). Contents: - findings/ALL-FINDINGS.md — complete 3,249-line research log with all 29 findings, methodology notes, and open questions - prompts/ — 6 exact prompts used across experiments - methodology.md — experimental setup and evaluation criteria - open-questions.md — unanswered questions for future work - README.md — overview and summary table Key findings: - Cross-document consistency: Opus is 2.4x faster with more findings - Gap-finding: GPT-5 reasoning tokens find domain-specific gaps - Race conditions: Opus excels at temporal interaction reasoning - Bias detection: Signal-to-noise ratio > model capability - Adversarial analysis: GPT-5 exhaustive, Opus qualitatively different Signed-off-by: Rodin
82 lines
3.1 KiB
Markdown
82 lines
3.1 KiB
Markdown
# Model Research — AI for Analytical Work
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Comparative analysis of AI models on **analytical tasks** — not coding.
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Most public discussion about LLM capabilities focuses on code generation.
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We found almost no published methodology for using models in analytical
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research tasks (searched 2026-04-26). This repo fills that gap with
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controlled experiments and reproducible findings.
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## What We're Testing
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Using GPT-5, Claude Opus 4.6, Claude Sonnet 4.6, and GPT-4.1 (+ Mini) for:
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- Architecture document review
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- Bias and assumption detection
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- Gap-finding in design specifications
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- Cross-document consistency analysis
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- Race condition identification
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- Adversarial path analysis
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- Contradiction detection
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- Regulatory compliance review
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## Key Findings (Summary)
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| # | Task Type | Winner | Key Insight |
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|---|-----------|--------|-------------|
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| 1 | PR review | Both | Different models catch different things — Sonnet: structural, GPT-5: semantic |
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| 2 | Bias detection | Framing | Signal-to-noise ratio matters more than model capability |
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| 9 | Gap-finding | GPT-5 | Reasoning tokens find domain-specific gaps, not generic ones |
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| 10 | Hidden assumptions | GPT-5 | Reasoning produces qualitatively different (not just more) findings |
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| 13 | Race conditions | Opus | Temporal interaction reasoning is Opus's strongest domain |
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| 15 | Design coherence | Task-dependent | Single-doc: model choice depends on document complexity |
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| 25 | Contradiction detection | Opus | Precision > exhaustiveness; Opus's self-correction is unique |
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| 28 | Cross-doc consistency | Opus | 2.4x faster than GPT-5 with more findings; boundary reasoning |
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| 29 | Adversarial analysis | GPT-5 + Opus | GPT-5: exhaustive; Opus: qualitatively different attack vectors |
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## Methodology
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Each experiment:
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1. Same input document(s) to all models
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2. Same structured prompt with explicit categories to analyze
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3. No tools, no project context beyond the document(s)
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4. Independent runs — no cross-pollination between models
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5. Results evaluated for: correctness, uniqueness, actionability
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**Context dimensions tracked:**
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- Rich vs minimal (how much background info)
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- Broad vs focused ("review this" vs "answer this specific question")
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- What kind of context (diff, full files, issue text, nothing)
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- Whether the model had tools or just text
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- Whether the task was step-by-step or open-ended
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## Repository Structure
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```
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findings/ # Individual findings with full analysis
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01-different-models-different-things.md
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02-narrow-lens-vs-broad-review.md
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...
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28-cross-document-consistency.md
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29-adversarial-manipulation.md
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prompts/ # Exact prompts used for reproducibility
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cross-document-consistency.md
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design-coherence.md
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gap-finding.md
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hidden-assumptions.md
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...
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open-questions.md # Unanswered questions for future experiments
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methodology.md # Full methodology notes
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```
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## Who We Are
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This research is conducted by [Rodin](https://gitea.weiker.me/rodin) (AI
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assistant) and Aaron Weiker. The test corpus is gargoyle — an Elixir
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trading system with extensive architecture documentation (~35 design docs,
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~5000 lines).
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## License
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CC BY 4.0 — share and adapt with attribution.
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