# Research: Expert-Augmented LLM Ideation

This folder contains research materials for the academic paper on the novelty-seeking system.

## Files

| File | Description |
|------|-------------|
| `literature_review.md` | Comprehensive literature review covering semantic distance theory, conceptual blending, design fixation, LLM limitations, and related work |
| `references.md` | 55+ academic references with links to papers |
| `theoretical_framework.md` | The "Semantic Gravity" theoretical model and testable hypotheses |
| `paper_outline.md` | Complete paper structure, experimental design, and target venues |

## Key Theoretical Contribution

**"Semantic Gravity"**: LLMs exhibit a tendency to generate outputs clustered around high-probability regions of their training distribution, limiting creative novelty. Expert perspectives provide "escape velocity" to break free from this gravity.

## Core Hypotheses

1. **H1**: Multi-expert generation → higher semantic diversity
2. **H2**: Multi-expert generation → lower patent overlap (higher novelty)
3. **H3**: Diversity increases with expert count (diminishing returns ~4-6)
4. **H4**: Expert source affects unconventionality of ideas

## Target Venues

- **CHI** (ACM Conference on Human Factors in Computing Systems)
- **CSCW** (ACM Conference on Computer-Supported Cooperative Work)
- **Creativity & Cognition** (ACM Conference)
- **IJHCS** (International Journal of Human-Computer Studies)

## Next Steps

1. Design concrete experiment protocol
2. Add measurement code to existing system
3. Collect experimental data
4. Conduct human evaluation
5. Write and submit paper
