FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration

# Research Integration: FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration

## Your Mission

Create a new branch and develop a detailed implementation plan for integrating this research idea into the codebase. Do NOT implement yet — focus on understanding, planning, and identifying risks.

## Branch Setup

```bash
git checkout -b experiment/flowpie-test-time-scientific-idea-evolution-with-f
```

## The Research

**Paper**: [FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration](https://arxiv.org/abs/2603.29557v1)
**PDF**: https://arxiv.org/pdf/2603.29557v1

**Core Achievement**:
15.6% improvement in novelty and 12.4% increase in high-quality idea success rate.

**Why This Matters for idea-transfer-bruteforce-cc-v1**:
Flow-guided MCTS and GFlowNet-inspired exploration are state-of-the-art for maintaining diversity in idea evolution.

**Suggested Integration Approach**:
Replace hill-climbing with Flow-guided MCTS using the ratchet metric as the reward signal.

**Estimated Effort**: weekend project

## Goal Context

This addresses the following project goal:
> Implement MAP-Elites or Quality-Diversity mechanisms

## Model Requirements

**Commercial APIs available**: GPT-4, GPT-4o-mini, Claude, Gemini, Anthropic API, OpenAI API
**Open-source (local GPU)**: Llama, Qwen, DeepSeek, Mistral, Phi...

✅ **You can implement this using API calls only** — no local GPU required.

*The paper demonstrates that the FlowPIE framework works effectively using GPT-4o-mini as a backbone and judge. A practitioner can implement the MCTS and evolutionary logic using only commercial API calls without local weight modification.*

## Your Task: Create the Integration Plan

### Phase 1: Understand the Codebase Context

1. **Identify the integration surface**: Which files/modules would this touch?
2. **Map dependencies**: What existing code would this interact with?
3. **Find similar patterns**: Is there existing code that does something similar we can learn from?

### Phase 2: Design the Integration

Create a detailed plan covering:

1. **Architecture**: How does this fit into the existing system?
2. **Data flow**: What inputs does it need? What outputs does it produce?
3. **Configuration**: What new settings/parameters are needed?
4. **Testing strategy**: How will we validate this works?

### Phase 3: Premortem — What Could Go Wrong?

**Think about this integration failing 2 weeks from now. Why did it fail?**

Consider:
- **Performance**: Could this slow down critical paths?
- **Complexity**: Are we adding too much complexity for the benefit?
- **Maintenance**: Will this be hard to maintain or debug?
- **Dependencies**: Are we adding risky dependencies?
- **Edge cases**: What inputs or states could break this?
- **Rollback**: If this doesn't work, how easily can we revert?

For each risk, note:
- Likelihood (low/medium/high)
- Impact (low/medium/high)
- Mitigation strategy

### Phase 4: Define Success Criteria

Before implementing, define:

1. **Minimum viable test**: What's the simplest way to prove this works?
2. **Quantitative metrics**: What numbers should improve? By how much?
3. **Qualitative checks**: What should "feel" better?
4. **Failure signals**: What would tell us to abandon this approach?

## Output Format

Create a `PLAN.md` file in the repo root with:

```markdown
# Experiment: [Title]

## Summary
[1-2 sentence summary of what we're trying]

## Integration Points
- [ ] File 1: description of changes
- [ ] File 2: description of changes

## Architecture Decision
[Explain the chosen approach and why]

## Risks & Mitigations
| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| ... | ... | ... | ... |

## Success Criteria
- [ ] Criterion 1
- [ ] Criterion 2

## Open Questions
- Question 1?
- Question 2?

## Next Steps
1. First implementation step
2. Second implementation step
```

## Important Guidelines

- **Read the paper first** — skim the abstract, intro, and methodology sections
- **Don't over-engineer** — start with the simplest version that could work
- **Preserve optionality** — design so we can easily extend or remove this later
- **Document decisions** — future you will thank present you
- **Ask questions** — if something is unclear, note it rather than assuming

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*This prompt was generated by DSI (Daily Session Intelligence) to help you systematically explore research ideas.*