CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

# Research Integration: CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

## 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/coral-towards-autonomous-multi-agent-evolution-for
```

## The Research

**Paper**: [CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://arxiv.org/abs/2604.01658v1)
**PDF**: https://arxiv.org/pdf/2604.01658v1

**Core Achievement**:
21.4% improvement in discovery rate and 92% population diversity maintenance.

**Why This Matters for idea-transfer-bruteforce-cc-v1**:
Provides a framework for open-ended discovery and diversity maintenance through autonomous reflection cycles.

**Suggested Integration Approach**:
Adopt the CORAL 'Reflection Cycle' to analyze failed iterations and propose mutation strategies.

**Estimated Effort**: weekend project

## Goal Context

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

## Model Requirements

**Commercial APIs available**: Claude Code, Claude

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

*The CORAL framework is designed for autonomous agents using existing LLM runtimes (specifically mentioning Claude Code) to perform iterative discovery via API calls. It does not require weight modification or local fine-tuning.*

**Benchmarks used**: Kernel Engineering, Polyominoes, Circle Packing, Cloudcast, EPLB

## 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.*