# 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:
1. Same input document(s) to all models
2. Same structured prompt with explicit categories to analyze
3. No tools, no project context beyond the document(s)
4. Independent runs — no cross-pollination between models
5. 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](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](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](https://gitea.weiker.me/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.