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research/README.md

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+# 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