novelty-seeking/research

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